Split (1)

train · 744k rows

modelId stringlengths 5 122	author stringlengths 2 42	last_modified timestamp[us, tz=UTC]	downloads int64 0 738M	likes int64 0 11k	library_name stringclasses 245 values	tags sequencelengths 1 4.05k	pipeline_tag stringclasses 48 values	createdAt timestamp[us, tz=UTC]	card stringlengths 1 901k
Mihaiii/test25	Mihaiii	2024-05-01T22:08:19Z	4,863	0	sentence-transformers	[ "sentence-transformers", "onnx", "safetensors", "bert", "feature-extraction", "sentence-similarity", "bge", "mteb", "mergekit", "merge", "base_model:Mihaiii/Wartortle", "base_model:TaylorAI/bge-micro-v2", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "text-embeddings-inference", "region:us" ]	sentence-similarity	2024-05-01T19:31:02Z	--- license: mit library_name: sentence-transformers pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - bge - mteb - mergekit - merge base_model: - Mihaiii/Wartortle - TaylorAI/bge-micro-v2 model-index: - name: Giratina results: - task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (en) config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 69.56716417910448 - type: ap value: 31.399435128856624 - type: f1 value: 63.139089415537256 - task: type: Classification dataset: type: mteb/amazon_polarity name: MTEB AmazonPolarityClassification config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 74.73525000000001 - type: ap value: 69.2327764533514 - type: f1 value: 74.61617659775962 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (en) config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 35.356 - type: f1 value: 35.165109893437204 - task: type: Retrieval dataset: type: mteb/arguana name: MTEB ArguAna config: default split: test revision: c22ab2a51041ffd869aaddef7af8d8215647e41a metrics: - type: map_at_1 value: 17.141000000000002 - type: map_at_10 value: 28.292 - type: map_at_100 value: 29.532000000000004 - type: map_at_1000 value: 29.580000000000002 - type: map_at_20 value: 29.048000000000002 - type: map_at_3 value: 24.277 - type: map_at_5 value: 26.339000000000002 - type: mrr_at_1 value: 17.781 - type: mrr_at_10 value: 28.534 - type: mrr_at_100 value: 29.779 - type: mrr_at_1000 value: 29.826999999999998 - type: mrr_at_20 value: 29.293000000000003 - type: mrr_at_3 value: 24.490000000000002 - type: mrr_at_5 value: 26.564 - type: ndcg_at_1 value: 17.141000000000002 - type: ndcg_at_10 value: 35.004000000000005 - type: ndcg_at_100 value: 41.056 - type: ndcg_at_1000 value: 42.388 - type: ndcg_at_20 value: 37.721 - type: ndcg_at_3 value: 26.592 - type: ndcg_at_5 value: 30.294999999999998 - type: precision_at_1 value: 17.141000000000002 - type: precision_at_10 value: 5.676 - type: precision_at_100 value: 0.851 - type: precision_at_1000 value: 0.096 - type: precision_at_20 value: 3.3709999999999996 - type: precision_at_3 value: 11.094999999999999 - type: precision_at_5 value: 8.450000000000001 - type: recall_at_1 value: 17.141000000000002 - type: recall_at_10 value: 56.757000000000005 - type: recall_at_100 value: 85.064 - type: recall_at_1000 value: 95.661 - type: recall_at_20 value: 67.425 - type: recall_at_3 value: 33.286 - type: recall_at_5 value: 42.248000000000005 - task: type: Clustering dataset: type: mteb/arxiv-clustering-p2p name: MTEB ArxivClusteringP2P config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics: - type: v_measure value: 37.86211319797047 - type: v_measures value: [0.33158313059028166, 0.37901912420270933, 0.368350193636622, 0.3710910416810123, 0.33682934300988204, 0.3935143766420073, 0.3506042155722468, 0.38890637022748253, 0.3809948829762236, 0.3573848842626061, 0.4384574114930339, 0.44065249261067524, 0.4455934266459656, 0.44427870340567255, 0.44866585162160194, 0.4400562736320333, 0.44272671447092676, 0.4472379619739013, 0.447120409649494, 0.4374054560695822, 0.42821311110400917, 0.26728232917410677, 0.2819026763758509, 0.3341565824397579, 0.29184325438397496, 0.190440948203588, 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MTEB ArxivClusteringS2S config: default split: test revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 metrics: - type: v_measure value: 28.836354293877637 - type: v_measures value: [0.26770607587929524, 0.25679087657287986, 0.26683847803527544, 0.27314067131657194, 0.2637027189955263, 0.27546553977066784, 0.2663910185474206, 0.26115132013506304, 0.28239605072779855, 0.2715001900369248, 0.3338711918999345, 0.330513643529441, 0.32916198249267603, 0.33648402018146334, 0.33995041466013076, 0.33576749276064755, 0.3328011112044641, 0.33773499787647715, 0.3347474569158458, 0.33112140013488434, 0.3093636862898543, 0.1601792611076284, 0.20820618472388558, 0.2622841294938964, 0.20833795363058114, 0.15304171124037919, 0.19106061252763054, 0.09640933163812757, 0.16463927791620916, 1.0, 0.1585110308604873, 0.26770607587929524, 0.25679087657287986, 0.26683847803527544, 0.27314067131657194, 0.2637027189955263, 0.27546553977066784, 0.2663910185474206, 0.26115132013506304, 0.28239605072779855, 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- type: mrr_at_1000 value: 33.061 - type: mrr_at_20 value: 32.471 - type: mrr_at_3 value: 30.019000000000002 - type: mrr_at_5 value: 31.041999999999998 - type: ndcg_at_1 value: 25.607999999999997 - type: ndcg_at_10 value: 31.438 - type: ndcg_at_100 value: 37.347 - type: ndcg_at_1000 value: 40.075 - type: ndcg_at_20 value: 33.068 - type: ndcg_at_3 value: 27.846 - type: ndcg_at_5 value: 29.304999999999996 - type: precision_at_1 value: 25.607999999999997 - type: precision_at_10 value: 5.923 - type: precision_at_100 value: 1.102 - type: precision_at_1000 value: 0.161 - type: precision_at_20 value: 3.5340000000000003 - type: precision_at_3 value: 13.305 - type: precision_at_5 value: 9.585 - type: recall_at_1 value: 20.156 - type: recall_at_10 value: 39.741 - type: recall_at_100 value: 66.428 - type: recall_at_1000 value: 84.694 - type: recall_at_20 value: 45.688 - type: recall_at_3 value: 28.876 - type: recall_at_5 value: 33.284000000000006 - task: type: Retrieval dataset: type: mteb/cqadupstack-english name: MTEB CQADupstackEnglishRetrieval config: default split: test revision: ad9991cb51e31e31e430383c75ffb2885547b5f0 metrics: - type: map_at_1 value: 14.568 - type: map_at_10 value: 19.356 - type: map_at_100 value: 20.044 - type: map_at_1000 value: 20.146 - type: map_at_20 value: 19.717000000000002 - type: map_at_3 value: 17.82 - type: map_at_5 value: 18.724 - type: mrr_at_1 value: 18.025 - type: mrr_at_10 value: 22.933 - type: mrr_at_100 value: 23.599 - type: mrr_at_1000 value: 23.669999999999998 - type: mrr_at_20 value: 23.283 - type: mrr_at_3 value: 21.295 - type: mrr_at_5 value: 22.314 - type: ndcg_at_1 value: 18.025 - type: ndcg_at_10 value: 22.559 - type: ndcg_at_100 value: 26.045 - type: ndcg_at_1000 value: 28.785 - type: ndcg_at_20 value: 23.727999999999998 - type: ndcg_at_3 value: 19.914 - type: ndcg_at_5 value: 21.241 - type: precision_at_1 value: 18.025 - type: precision_at_10 value: 4.102 - type: precision_at_100 value: 0.715 - type: precision_at_1000 value: 0.11800000000000001 - type: precision_at_20 value: 2.452 - type: precision_at_3 value: 9.447999999999999 - type: precision_at_5 value: 6.827999999999999 - type: recall_at_1 value: 14.568 - type: recall_at_10 value: 28.677999999999997 - type: recall_at_100 value: 44.362 - type: recall_at_1000 value: 63.705999999999996 - type: recall_at_20 value: 32.932 - type: recall_at_3 value: 21.029999999999998 - type: recall_at_5 value: 24.573 - task: type: Retrieval dataset: type: mteb/cqadupstack-gaming name: MTEB CQADupstackGamingRetrieval config: default split: test revision: 4885aa143210c98657558c04aaf3dc47cfb54340 metrics: - type: map_at_1 value: 25.104 - type: map_at_10 value: 33.857 - type: map_at_100 value: 34.808 - type: map_at_1000 value: 34.904 - type: map_at_20 value: 34.404 - type: map_at_3 value: 31.176 - type: map_at_5 value: 32.626 - type: mrr_at_1 value: 28.84 - type: mrr_at_10 value: 36.817 - type: mrr_at_100 value: 37.633 - type: mrr_at_1000 value: 37.698 - type: mrr_at_20 value: 37.312 - type: mrr_at_3 value: 34.451 - type: mrr_at_5 value: 35.748999999999995 - type: ndcg_at_1 value: 28.84 - type: ndcg_at_10 value: 38.745000000000005 - type: ndcg_at_100 value: 43.183 - type: ndcg_at_1000 value: 45.419 - type: ndcg_at_20 value: 40.571 - type: ndcg_at_3 value: 33.751 - type: ndcg_at_5 value: 36.042 - type: precision_at_1 value: 28.84 - type: precision_at_10 value: 6.389 - type: precision_at_100 value: 0.941 - type: precision_at_1000 value: 0.12 - type: precision_at_20 value: 3.6929999999999996 - type: precision_at_3 value: 15.068000000000001 - type: precision_at_5 value: 10.583 - type: recall_at_1 value: 25.104 - type: recall_at_10 value: 50.749 - type: recall_at_100 value: 70.336 - type: recall_at_1000 value: 86.591 - type: recall_at_20 value: 57.473 - type: recall_at_3 value: 37.230000000000004 - type: recall_at_5 value: 42.774 - task: type: Retrieval dataset: type: mteb/cqadupstack-gis name: MTEB CQADupstackGisRetrieval config: default 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value: 50.219858779642024 - type: dot_f1 value: 52.03935006079363 - type: dot_precision value: 44.778390717139054 - type: dot_recall value: 62.11081794195251 - type: euclidean_accuracy value: 83.3581689217381 - type: euclidean_ap value: 63.866502871821886 - type: euclidean_f1 value: 60.66180862501495 - type: euclidean_precision value: 55.42457978607291 - type: euclidean_recall value: 66.99208443271768 - type: manhattan_accuracy value: 83.32836621565238 - type: manhattan_ap value: 63.58246341419401 - type: manhattan_f1 value: 60.405654578979714 - type: manhattan_precision value: 56.54775604142692 - type: manhattan_recall value: 64.82849604221636 - type: max_accuracy value: 83.3581689217381 - type: max_ap value: 63.866502871821886 - type: max_f1 value: 60.66180862501495 - task: type: PairClassification dataset: type: mteb/twitterurlcorpus-pairclassification name: MTEB TwitterURLCorpus config: default split: test revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf metrics: - type: cos_sim_accuracy value: 87.77894205767066 - type: cos_sim_ap value: 83.5297230824822 - type: cos_sim_f1 value: 75.65036420395423 - type: cos_sim_precision value: 73.11781609195403 - type: cos_sim_recall value: 78.3646442870342 - type: dot_accuracy value: 86.03058175185313 - type: dot_ap value: 78.95144253575621 - type: dot_f1 value: 72.20582032897512 - type: dot_precision value: 66.42524573202276 - type: dot_recall value: 79.08838928241454 - type: euclidean_accuracy value: 87.7265494624908 - type: euclidean_ap value: 83.29997302389856 - type: euclidean_f1 value: 75.38237163905613 - type: euclidean_precision value: 73.28582854649895 - type: euclidean_recall value: 77.60240221743148 - type: manhattan_accuracy value: 87.65475220242946 - type: manhattan_ap value: 83.1779453049763 - type: manhattan_f1 value: 75.17620001483792 - type: manhattan_precision value: 72.53400143163923 - type: manhattan_recall value: 78.01817061903296 - type: max_accuracy value: 87.77894205767066 - type: max_ap value: 83.5297230824822 - type: max_f1 value: 75.65036420395423 --- # Giratina This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the SLERP merge method. ### Models Merged The following models were included in the merge: * [Mihaiii/Wartortle](https://huggingface.co/Mihaiii/Wartortle) * [TaylorAI/bge-micro-v2](https://huggingface.co/TaylorAI/bge-micro-v2) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: Mihaiii/Wartortle - model: TaylorAI/bge-micro-v2 merge_method: slerp base_model: TaylorAI/bge-micro-v2 parameters: t: - value: 0.5 dtype: float32 ```
OrlikB/st-polish-kartonberta-base-alpha-v1	OrlikB	2024-04-18T05:08:45Z	4,860	3	sentence-transformers	[ "sentence-transformers", "pytorch", "roberta", "feature-extraction", "sentence-similarity", "transformers", "mteb", "pl", "license:lgpl", "model-index", "autotrain_compatible", "endpoints_compatible", "text-embeddings-inference", "region:us" ]	sentence-similarity	2023-11-12T10:47:20Z	--- tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers - mteb license: lgpl language: - pl pipeline_tag: sentence-similarity model-index: - name: st-polish-kartonberta-base-alpha-v1 results: - task: type: Clustering dataset: type: PL-MTEB/8tags-clustering name: MTEB 8TagsClustering config: default split: test revision: None metrics: - type: v_measure value: 32.85180358455615 - task: type: Classification dataset: type: PL-MTEB/allegro-reviews name: MTEB AllegroReviews config: default split: test revision: None metrics: - type: accuracy value: 40.188866799204774 - type: f1 value: 34.71127012684797 - task: type: Retrieval dataset: type: arguana-pl name: MTEB ArguAna-PL config: default split: test revision: None metrics: - type: map_at_1 value: 30.939 - type: map_at_10 value: 47.467999999999996 - type: map_at_100 value: 48.303000000000004 - type: map_at_1000 value: 48.308 - type: map_at_3 value: 43.22 - type: map_at_5 value: 45.616 - type: mrr_at_1 value: 31.863000000000003 - type: mrr_at_10 value: 47.829 - type: mrr_at_100 value: 48.664 - type: mrr_at_1000 value: 48.67 - type: mrr_at_3 value: 43.492 - type: mrr_at_5 value: 46.006 - type: ndcg_at_1 value: 30.939 - type: ndcg_at_10 value: 56.058 - type: ndcg_at_100 value: 59.562000000000005 - type: ndcg_at_1000 value: 59.69799999999999 - type: ndcg_at_3 value: 47.260000000000005 - type: ndcg_at_5 value: 51.587 - type: precision_at_1 value: 30.939 - type: precision_at_10 value: 8.329 - type: precision_at_100 value: 0.984 - type: precision_at_1000 value: 0.1 - type: precision_at_3 value: 19.654 - type: precision_at_5 value: 13.898 - type: recall_at_1 value: 30.939 - type: recall_at_10 value: 83.286 - type: recall_at_100 value: 98.43499999999999 - type: recall_at_1000 value: 99.502 - type: recall_at_3 value: 58.962 - type: recall_at_5 value: 69.488 - task: type: Classification dataset: type: PL-MTEB/cbd name: MTEB CBD config: default split: test revision: None metrics: - type: accuracy value: 67.69000000000001 - type: ap value: 21.078799692467182 - type: f1 value: 56.80107173953953 - task: type: PairClassification dataset: type: PL-MTEB/cdsce-pairclassification name: MTEB CDSC-E config: default split: test revision: None metrics: - type: cos_sim_accuracy value: 89.2 - type: cos_sim_ap value: 79.11674608786898 - type: cos_sim_f1 value: 68.83468834688347 - type: cos_sim_precision value: 70.94972067039106 - type: cos_sim_recall value: 66.84210526315789 - type: dot_accuracy value: 89.2 - type: dot_ap value: 79.11674608786898 - type: dot_f1 value: 68.83468834688347 - type: dot_precision value: 70.94972067039106 - type: dot_recall value: 66.84210526315789 - type: euclidean_accuracy value: 89.2 - type: euclidean_ap value: 79.11674608786898 - type: euclidean_f1 value: 68.83468834688347 - type: euclidean_precision value: 70.94972067039106 - type: euclidean_recall value: 66.84210526315789 - type: manhattan_accuracy value: 89.1 - type: manhattan_ap value: 79.1220443374692 - type: manhattan_f1 value: 69.02173913043478 - type: manhattan_precision value: 71.34831460674157 - type: manhattan_recall value: 66.84210526315789 - type: max_accuracy value: 89.2 - type: max_ap value: 79.1220443374692 - type: max_f1 value: 69.02173913043478 - task: type: STS dataset: type: PL-MTEB/cdscr-sts name: MTEB CDSC-R config: default split: test revision: None metrics: - type: cos_sim_pearson value: 91.41534744278998 - type: cos_sim_spearman value: 92.12681551821147 - type: euclidean_pearson value: 91.74369794485992 - type: euclidean_spearman value: 92.12685848456046 - type: manhattan_pearson value: 91.66651938751657 - type: manhattan_spearman value: 92.057603126734 - task: type: Retrieval dataset: type: dbpedia-pl name: MTEB DBPedia-PL config: default split: test revision: None metrics: - type: map_at_1 value: 5.8709999999999996 - type: map_at_10 value: 12.486 - type: map_at_100 value: 16.897000000000002 - type: map_at_1000 value: 18.056 - type: map_at_3 value: 8.958 - type: map_at_5 value: 10.57 - type: mrr_at_1 value: 44.0 - type: mrr_at_10 value: 53.830999999999996 - type: mrr_at_100 value: 54.54 - type: mrr_at_1000 value: 54.568000000000005 - type: mrr_at_3 value: 51.87500000000001 - type: mrr_at_5 value: 53.113 - type: ndcg_at_1 value: 34.625 - type: ndcg_at_10 value: 26.996 - type: ndcg_at_100 value: 31.052999999999997 - type: ndcg_at_1000 value: 38.208 - type: ndcg_at_3 value: 29.471000000000004 - type: ndcg_at_5 value: 28.364 - type: precision_at_1 value: 44.0 - type: precision_at_10 value: 21.45 - type: precision_at_100 value: 6.837 - type: precision_at_1000 value: 1.6019999999999999 - type: precision_at_3 value: 32.333 - type: precision_at_5 value: 27.800000000000004 - type: recall_at_1 value: 5.8709999999999996 - type: recall_at_10 value: 17.318 - type: recall_at_100 value: 36.854 - type: recall_at_1000 value: 60.468999999999994 - type: recall_at_3 value: 10.213999999999999 - type: recall_at_5 value: 13.364 - task: type: Retrieval dataset: type: fiqa-pl name: MTEB FiQA-PL config: default split: test revision: None metrics: - type: map_at_1 value: 10.289 - type: map_at_10 value: 18.285999999999998 - type: map_at_100 value: 19.743 - type: map_at_1000 value: 19.964000000000002 - type: map_at_3 value: 15.193000000000001 - type: map_at_5 value: 16.962 - type: mrr_at_1 value: 21.914 - type: mrr_at_10 value: 30.653999999999996 - type: mrr_at_100 value: 31.623 - type: mrr_at_1000 value: 31.701 - type: mrr_at_3 value: 27.855 - type: mrr_at_5 value: 29.514000000000003 - type: ndcg_at_1 value: 21.914 - type: ndcg_at_10 value: 24.733 - type: ndcg_at_100 value: 31.253999999999998 - type: ndcg_at_1000 value: 35.617 - type: ndcg_at_3 value: 20.962 - type: ndcg_at_5 value: 22.553 - type: precision_at_1 value: 21.914 - type: precision_at_10 value: 7.346 - type: precision_at_100 value: 1.389 - type: precision_at_1000 value: 0.214 - type: precision_at_3 value: 14.352 - type: precision_at_5 value: 11.42 - type: recall_at_1 value: 10.289 - type: recall_at_10 value: 31.459 - type: recall_at_100 value: 56.854000000000006 - type: recall_at_1000 value: 83.722 - type: recall_at_3 value: 19.457 - type: recall_at_5 value: 24.767 - task: type: Retrieval dataset: type: hotpotqa-pl name: MTEB HotpotQA-PL config: default split: test revision: None metrics: - type: map_at_1 value: 29.669 - type: map_at_10 value: 41.615 - type: map_at_100 value: 42.571999999999996 - type: map_at_1000 value: 42.662 - type: map_at_3 value: 38.938 - type: map_at_5 value: 40.541 - type: mrr_at_1 value: 59.338 - type: mrr_at_10 value: 66.93900000000001 - type: mrr_at_100 value: 67.361 - type: mrr_at_1000 value: 67.38499999999999 - type: mrr_at_3 value: 65.384 - type: mrr_at_5 value: 66.345 - type: ndcg_at_1 value: 59.338 - type: ndcg_at_10 value: 50.607 - type: ndcg_at_100 value: 54.342999999999996 - type: ndcg_at_1000 value: 56.286 - type: ndcg_at_3 value: 46.289 - type: ndcg_at_5 value: 48.581 - type: precision_at_1 value: 59.338 - type: precision_at_10 value: 10.585 - type: precision_at_100 value: 1.353 - type: precision_at_1000 value: 0.161 - type: precision_at_3 value: 28.877000000000002 - type: precision_at_5 value: 19.133 - type: recall_at_1 value: 29.669 - type: recall_at_10 value: 52.92400000000001 - type: recall_at_100 value: 67.657 - type: recall_at_1000 value: 80.628 - type: recall_at_3 value: 43.315 - type: recall_at_5 value: 47.833 - task: type: Retrieval dataset: type: msmarco-pl name: MTEB MSMARCO-PL config: default split: test revision: None metrics: - type: map_at_1 value: 0.997 - type: map_at_10 value: 7.481999999999999 - type: map_at_100 value: 20.208000000000002 - type: map_at_1000 value: 25.601000000000003 - type: map_at_3 value: 3.055 - type: map_at_5 value: 4.853 - type: mrr_at_1 value: 55.814 - type: mrr_at_10 value: 64.651 - type: mrr_at_100 value: 65.003 - type: mrr_at_1000 value: 65.05199999999999 - type: mrr_at_3 value: 62.403 - type: mrr_at_5 value: 64.031 - type: ndcg_at_1 value: 44.186 - type: ndcg_at_10 value: 43.25 - type: ndcg_at_100 value: 40.515 - type: ndcg_at_1000 value: 48.345 - type: ndcg_at_3 value: 45.829 - type: ndcg_at_5 value: 46.477000000000004 - type: precision_at_1 value: 55.814 - type: precision_at_10 value: 50.465 - type: precision_at_100 value: 25.419000000000004 - type: precision_at_1000 value: 5.0840000000000005 - type: precision_at_3 value: 58.14 - type: precision_at_5 value: 57.67400000000001 - type: recall_at_1 value: 0.997 - type: recall_at_10 value: 8.985999999999999 - type: recall_at_100 value: 33.221000000000004 - type: recall_at_1000 value: 58.836999999999996 - type: recall_at_3 value: 3.472 - type: recall_at_5 value: 5.545 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (pl) config: pl split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 68.19771351714861 - type: f1 value: 64.75039989217822 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (pl) config: pl split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 73.9677202420982 - type: f1 value: 73.72287107577753 - task: type: Retrieval dataset: type: nfcorpus-pl name: MTEB NFCorpus-PL config: default split: test revision: None metrics: - type: map_at_1 value: 5.167 - type: map_at_10 value: 10.791 - type: map_at_100 value: 14.072999999999999 - type: map_at_1000 value: 15.568000000000001 - type: map_at_3 value: 7.847999999999999 - type: map_at_5 value: 9.112 - type: mrr_at_1 value: 42.105 - type: mrr_at_10 value: 49.933 - type: mrr_at_100 value: 50.659 - type: mrr_at_1000 value: 50.705 - type: mrr_at_3 value: 47.988 - type: mrr_at_5 value: 49.056 - type: ndcg_at_1 value: 39.938 - type: ndcg_at_10 value: 31.147000000000002 - type: ndcg_at_100 value: 29.336000000000002 - type: ndcg_at_1000 value: 38.147 - type: ndcg_at_3 value: 35.607 - type: ndcg_at_5 value: 33.725 - type: precision_at_1 value: 41.486000000000004 - type: precision_at_10 value: 23.901 - type: precision_at_100 value: 7.960000000000001 - type: precision_at_1000 value: 2.086 - type: precision_at_3 value: 33.437 - type: precision_at_5 value: 29.598000000000003 - type: recall_at_1 value: 5.167 - type: recall_at_10 value: 14.244000000000002 - type: recall_at_100 value: 31.192999999999998 - type: recall_at_1000 value: 62.41799999999999 - type: recall_at_3 value: 8.697000000000001 - type: recall_at_5 value: 10.911 - task: type: Retrieval dataset: type: nq-pl name: MTEB NQ-PL config: default split: test revision: None metrics: - type: map_at_1 value: 14.417 - type: map_at_10 value: 23.330000000000002 - type: map_at_100 value: 24.521 - type: map_at_1000 value: 24.604 - type: map_at_3 value: 20.076 - type: map_at_5 value: 21.854000000000003 - type: mrr_at_1 value: 16.454 - type: mrr_at_10 value: 25.402 - type: mrr_at_100 value: 26.411 - type: mrr_at_1000 value: 26.479000000000003 - type: mrr_at_3 value: 22.369 - type: mrr_at_5 value: 24.047 - type: ndcg_at_1 value: 16.454 - type: ndcg_at_10 value: 28.886 - type: ndcg_at_100 value: 34.489999999999995 - type: ndcg_at_1000 value: 36.687999999999995 - type: ndcg_at_3 value: 22.421 - type: ndcg_at_5 value: 25.505 - type: precision_at_1 value: 16.454 - type: precision_at_10 value: 5.252 - type: precision_at_100 value: 0.8410000000000001 - type: precision_at_1000 value: 0.105 - type: precision_at_3 value: 10.428999999999998 - type: precision_at_5 value: 8.019 - type: recall_at_1 value: 14.417 - type: recall_at_10 value: 44.025 - type: recall_at_100 value: 69.404 - type: recall_at_1000 value: 86.18900000000001 - type: recall_at_3 value: 26.972 - type: recall_at_5 value: 34.132 - task: type: Classification dataset: type: laugustyniak/abusive-clauses-pl name: MTEB PAC config: default split: test revision: None metrics: - type: accuracy value: 66.55082536924412 - type: ap value: 76.44962281293184 - type: f1 value: 63.899803692180434 - task: type: PairClassification dataset: type: PL-MTEB/ppc-pairclassification name: MTEB PPC config: default split: test revision: None metrics: - type: cos_sim_accuracy value: 86.5 - type: cos_sim_ap value: 92.65086645409387 - type: cos_sim_f1 value: 89.39157566302653 - type: cos_sim_precision value: 84.51327433628319 - type: cos_sim_recall value: 94.86754966887418 - type: dot_accuracy value: 86.5 - type: dot_ap value: 92.65086645409387 - type: dot_f1 value: 89.39157566302653 - type: dot_precision value: 84.51327433628319 - type: dot_recall value: 94.86754966887418 - type: euclidean_accuracy value: 86.5 - type: euclidean_ap value: 92.65086645409387 - type: euclidean_f1 value: 89.39157566302653 - type: euclidean_precision value: 84.51327433628319 - type: euclidean_recall value: 94.86754966887418 - type: manhattan_accuracy value: 86.5 - type: manhattan_ap value: 92.64975544736456 - type: manhattan_f1 value: 89.33852140077822 - type: manhattan_precision value: 84.28781204111601 - type: manhattan_recall value: 95.03311258278146 - type: max_accuracy value: 86.5 - type: max_ap value: 92.65086645409387 - type: max_f1 value: 89.39157566302653 - task: type: PairClassification dataset: type: PL-MTEB/psc-pairclassification name: MTEB PSC config: default split: test revision: None metrics: - type: cos_sim_accuracy value: 95.64007421150278 - type: cos_sim_ap value: 98.42114841894346 - type: cos_sim_f1 value: 92.8895612708018 - type: cos_sim_precision value: 92.1921921921922 - type: cos_sim_recall value: 93.59756097560977 - type: dot_accuracy value: 95.64007421150278 - type: dot_ap value: 98.42114841894346 - type: dot_f1 value: 92.8895612708018 - type: dot_precision value: 92.1921921921922 - type: dot_recall value: 93.59756097560977 - type: euclidean_accuracy value: 95.64007421150278 - type: euclidean_ap value: 98.42114841894346 - type: euclidean_f1 value: 92.8895612708018 - type: euclidean_precision value: 92.1921921921922 - type: euclidean_recall value: 93.59756097560977 - type: manhattan_accuracy value: 95.82560296846012 - type: manhattan_ap value: 98.38712415914046 - type: manhattan_f1 value: 93.19213313161876 - type: manhattan_precision value: 92.49249249249249 - type: manhattan_recall value: 93.90243902439023 - type: max_accuracy value: 95.82560296846012 - type: max_ap value: 98.42114841894346 - type: max_f1 value: 93.19213313161876 - task: type: Classification dataset: type: PL-MTEB/polemo2_in name: MTEB PolEmo2.0-IN config: default split: test revision: None metrics: - type: accuracy value: 68.40720221606648 - type: f1 value: 67.09084289613526 - task: type: Classification dataset: type: PL-MTEB/polemo2_out name: MTEB PolEmo2.0-OUT config: default split: test revision: None metrics: - type: accuracy value: 38.056680161943326 - type: f1 value: 32.87731504372395 - task: type: Retrieval dataset: type: quora-pl name: MTEB Quora-PL config: default split: test revision: None metrics: - type: map_at_1 value: 65.422 - type: map_at_10 value: 79.259 - type: map_at_100 value: 80.0 - type: map_at_1000 value: 80.021 - type: map_at_3 value: 76.16199999999999 - type: map_at_5 value: 78.03999999999999 - type: mrr_at_1 value: 75.26 - type: mrr_at_10 value: 82.39699999999999 - type: mrr_at_100 value: 82.589 - type: mrr_at_1000 value: 82.593 - type: mrr_at_3 value: 81.08999999999999 - type: mrr_at_5 value: 81.952 - type: ndcg_at_1 value: 75.3 - type: ndcg_at_10 value: 83.588 - type: ndcg_at_100 value: 85.312 - type: ndcg_at_1000 value: 85.536 - type: ndcg_at_3 value: 80.128 - type: ndcg_at_5 value: 81.962 - type: precision_at_1 value: 75.3 - type: precision_at_10 value: 12.856000000000002 - type: precision_at_100 value: 1.508 - type: precision_at_1000 value: 0.156 - type: precision_at_3 value: 35.207 - type: precision_at_5 value: 23.316 - type: recall_at_1 value: 65.422 - type: recall_at_10 value: 92.381 - type: recall_at_100 value: 98.575 - type: recall_at_1000 value: 99.85300000000001 - type: recall_at_3 value: 82.59100000000001 - type: recall_at_5 value: 87.629 - task: type: Retrieval dataset: type: scidocs-pl name: MTEB SCIDOCS-PL config: default split: test revision: None metrics: - type: map_at_1 value: 2.52 - type: map_at_10 value: 6.814000000000001 - type: map_at_100 value: 8.267 - type: map_at_1000 value: 8.565000000000001 - type: map_at_3 value: 4.736 - type: map_at_5 value: 5.653 - type: mrr_at_1 value: 12.5 - type: mrr_at_10 value: 20.794999999999998 - type: mrr_at_100 value: 22.014 - type: mrr_at_1000 value: 22.109 - type: mrr_at_3 value: 17.8 - type: mrr_at_5 value: 19.42 - type: ndcg_at_1 value: 12.5 - type: ndcg_at_10 value: 12.209 - type: ndcg_at_100 value: 18.812 - type: ndcg_at_1000 value: 24.766 - type: ndcg_at_3 value: 10.847 - type: ndcg_at_5 value: 9.632 - type: precision_at_1 value: 12.5 - type: precision_at_10 value: 6.660000000000001 - type: precision_at_100 value: 1.6340000000000001 - type: precision_at_1000 value: 0.307 - type: precision_at_3 value: 10.299999999999999 - type: precision_at_5 value: 8.66 - type: recall_at_1 value: 2.52 - type: recall_at_10 value: 13.495 - type: recall_at_100 value: 33.188 - type: recall_at_1000 value: 62.34499999999999 - type: recall_at_3 value: 6.245 - type: recall_at_5 value: 8.76 - task: type: PairClassification dataset: type: PL-MTEB/sicke-pl-pairclassification name: MTEB SICK-E-PL config: default split: test revision: None metrics: - type: cos_sim_accuracy value: 86.13942111699959 - type: cos_sim_ap value: 81.47480017120256 - type: cos_sim_f1 value: 74.79794268919912 - type: cos_sim_precision value: 77.2382397572079 - type: cos_sim_recall value: 72.50712250712252 - type: dot_accuracy value: 86.13942111699959 - type: dot_ap value: 81.47478531367476 - type: dot_f1 value: 74.79794268919912 - type: dot_precision value: 77.2382397572079 - type: dot_recall value: 72.50712250712252 - type: euclidean_accuracy value: 86.13942111699959 - type: euclidean_ap value: 81.47478531367476 - type: euclidean_f1 value: 74.79794268919912 - type: euclidean_precision value: 77.2382397572079 - type: euclidean_recall value: 72.50712250712252 - type: manhattan_accuracy value: 86.15980432123929 - type: manhattan_ap value: 81.40798042612397 - type: manhattan_f1 value: 74.86116253239543 - type: manhattan_precision value: 77.9491133384734 - type: manhattan_recall value: 72.00854700854701 - type: max_accuracy value: 86.15980432123929 - type: max_ap value: 81.47480017120256 - type: max_f1 value: 74.86116253239543 - task: type: STS dataset: type: PL-MTEB/sickr-pl-sts name: MTEB SICK-R-PL config: default split: test revision: None metrics: - type: cos_sim_pearson value: 84.27525342551935 - type: cos_sim_spearman value: 79.50631730805885 - type: euclidean_pearson value: 82.07169123942028 - type: euclidean_spearman value: 79.50631887406465 - type: manhattan_pearson value: 81.98288826317463 - type: manhattan_spearman value: 79.4244081650332 - task: type: STS dataset: type: mteb/sts22-crosslingual-sts name: MTEB STS22 (pl) config: pl split: test revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 metrics: - type: cos_sim_pearson value: 35.59400236598834 - type: cos_sim_spearman value: 36.782560207852846 - type: euclidean_pearson value: 28.546177668542942 - type: euclidean_spearman value: 36.68394223635756 - type: manhattan_pearson value: 28.45606963909248 - type: manhattan_spearman value: 36.475975118547524 - task: type: Retrieval dataset: type: scifact-pl name: MTEB SciFact-PL config: default split: test revision: None metrics: - type: map_at_1 value: 41.028 - type: map_at_10 value: 52.23799999999999 - type: map_at_100 value: 52.905 - type: map_at_1000 value: 52.945 - type: map_at_3 value: 49.102000000000004 - type: map_at_5 value: 50.992000000000004 - type: mrr_at_1 value: 43.333 - type: mrr_at_10 value: 53.551 - type: mrr_at_100 value: 54.138 - type: mrr_at_1000 value: 54.175 - type: mrr_at_3 value: 51.056000000000004 - type: mrr_at_5 value: 52.705999999999996 - type: ndcg_at_1 value: 43.333 - type: ndcg_at_10 value: 57.731 - type: ndcg_at_100 value: 61.18599999999999 - type: ndcg_at_1000 value: 62.261 - type: ndcg_at_3 value: 52.276999999999994 - type: ndcg_at_5 value: 55.245999999999995 - type: precision_at_1 value: 43.333 - type: precision_at_10 value: 8.267 - type: precision_at_100 value: 1.02 - type: precision_at_1000 value: 0.11100000000000002 - type: precision_at_3 value: 21.444 - type: precision_at_5 value: 14.533 - type: recall_at_1 value: 41.028 - type: recall_at_10 value: 73.111 - type: recall_at_100 value: 89.533 - type: recall_at_1000 value: 98.0 - type: recall_at_3 value: 58.744 - type: recall_at_5 value: 66.106 - task: type: Retrieval dataset: type: trec-covid-pl name: MTEB TRECCOVID-PL config: default split: test revision: None metrics: - type: map_at_1 value: 0.146 - type: map_at_10 value: 1.09 - type: map_at_100 value: 6.002 - type: map_at_1000 value: 15.479999999999999 - type: map_at_3 value: 0.41000000000000003 - type: map_at_5 value: 0.596 - type: mrr_at_1 value: 54.0 - type: mrr_at_10 value: 72.367 - type: mrr_at_100 value: 72.367 - type: mrr_at_1000 value: 72.367 - type: mrr_at_3 value: 70.333 - type: mrr_at_5 value: 72.033 - type: ndcg_at_1 value: 48.0 - type: ndcg_at_10 value: 48.827 - type: ndcg_at_100 value: 38.513999999999996 - type: ndcg_at_1000 value: 37.958 - type: ndcg_at_3 value: 52.614000000000004 - type: ndcg_at_5 value: 51.013 - type: precision_at_1 value: 54.0 - type: precision_at_10 value: 53.6 - type: precision_at_100 value: 40.300000000000004 - type: precision_at_1000 value: 17.276 - type: precision_at_3 value: 57.333 - type: precision_at_5 value: 55.60000000000001 - type: recall_at_1 value: 0.146 - type: recall_at_10 value: 1.438 - type: recall_at_100 value: 9.673 - type: recall_at_1000 value: 36.870999999999995 - type: recall_at_3 value: 0.47400000000000003 - type: recall_at_5 value: 0.721 --- # Model Card for st-polish-kartonberta-base-alpha-v1 This sentence transformer model is designed to convert text content into a 768-float vector space, ensuring an effective representation. It aims to be proficient in tasks involving sentence / document similarity. The model has been released in its alpha version. Numerous potential enhancements could boost its performance, such as adjusting training hyperparameters or extending the training duration (currently limited to only one epoch). The main reason is limited GPU. ## Model Description - Developed by: Bartłomiej Orlik, https://www.linkedin.com/in/bartłomiej-orlik/ - Model type: RoBERTa Sentence Transformer - Language: Polish - License: LGPL-3.0 - Trained from model: sdadas/polish-roberta-base-v2: https://huggingface.co/sdadas/polish-roberta-base-v2 ## How to Get Started with the Model Use the code below to get started with the model. ### Using Sentence-Transformers You can use the model with [sentence-transformers](https://www.SBERT.net): ``` pip install -U sentence-transformers ``` ```python from sentence_transformers import SentenceTransformer model = SentenceTransformer('OrlikB/st-polish-kartonberta-base-alpha-v1') text_1 = 'Jestem wielkim fanem opakowań tekturowych' text_2 = 'Bardzo podobają mi się kartony' embeddings_1 = model.encode(text_1, normalize_embeddings=True) embeddings_2 = model.encode(text_2, normalize_embeddings=True) similarity = embeddings_1 @ embeddings_2.T print(similarity) ``` ### Using HuggingFace Transformers ```python from transformers import AutoTokenizer, AutoModel import torch import numpy as np def encode_text(text): encoded_input = tokenizer(text, padding=True, truncation=True, return_tensors='pt', max_length=512) with torch.no_grad(): model_output = model(*encoded_input) sentence_embeddings = model_output[0][:, 0] sentence_embeddings = torch.nn.functional.normalize(sentence_embeddings, p=2, dim=1) return sentence_embeddings.squeeze().numpy() cosine_similarity = lambda a, b: np.dot(a, b) / (np.linalg.norm(a) np.linalg.norm(b)) tokenizer = AutoTokenizer.from_pretrained('OrlikB/st-polish-kartonberta-base-alpha-v1') model = AutoModel.from_pretrained('OrlikB/st-polish-kartonberta-base-alpha-v1') model.eval() text_1 = 'Jestem wielkim fanem opakowań tekturowych' text_2 = 'Bardzo podobają mi się kartony' embeddings_1 = encode_text(text_1) embeddings_2 = encode_text(text_2) print(cosine_similarity(embeddings_1, embeddings_2)) ``` Note: You can use the encode_text function for demonstration purposes. For the best experience, it's recommended to process text in batches. ## Evaluation #### [MTEB for Polish Language](https://huggingface.co/spaces/mteb/leaderboard) \| Rank \| Model \| Model Size (GB) \| Embedding Dimensions \| Sequence Length \| Average (26 datasets) \| Classification Average (7 datasets) \| Clustering Average (1 datasets) \| Pair Classification Average (4 datasets) \| Retrieval Average (11 datasets) \| STS Average (3 datasets) \| \|-------:\|:----------------------------------------\|------------------:\|-----------------------:\|------------------:\|------------------------:\|--------------------------------------:\|--------------------------------:\|-----------------------------------------:\|----------------------------------:\|-------------------------:\| \| 1 \| multilingual-e5-large \| 2.24 \| 1024 \| 514 \| 58.25 \| 60.51 \| 24.06 \| 84.58 \| 47.82 \| 67.52 \| \| 2 \| st-polish-kartonberta-base-alpha-v1* \| 0.5 \| 768 \| 514 \| 56.92 \| 60.44 \| 32.85 \| 87.92 \| 42.19 \| 69.47 \| \| 3 \| multilingual-e5-base \| 1.11 \| 768 \| 514 \| 54.18 \| 57.01 \| 18.62 \| 82.08 \| 42.5 \| 65.07 \| \| 4 \| multilingual-e5-small \| 0.47 \| 384 \| 512 \| 53.15 \| 54.35 \| 19.64 \| 81.67 \| 41.52 \| 66.08 \| \| 5 \| st-polish-paraphrase-from-mpnet \| 0.5 \| 768 \| 514 \| 53.06 \| 57.49 \| 25.09 \| 87.04 \| 36.53 \| 67.39 \| \| 6 \| st-polish-paraphrase-from-distilroberta \| 0.5 \| 768 \| 514 \| 52.65 \| 58.55 \| 31.11 \| 87 \| 33.96 \| 68.78 \| ## More Information I developed this model as a personal scientific initiative. I plan to start the development on a new ST model. However, due to limited computational resources, I suspended further work to create a larger or enhanced version of current model.
Mihaiii/Pallas-0.5	Mihaiii	2024-02-22T10:26:45Z	4,856	6	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "base_model:migtissera/Tess-34B-v1.4", "license:other", "autotrain_compatible", "text-generation-inference", "region:us" ]	text-generation	2023-12-28T19:47:03Z	--- base_model: migtissera/Tess-34B-v1.4 inference: false license: other license_name: yi-license license_link: https://huggingface.co/01-ai/Yi-34B/blob/main/LICENSE metrics: - accuracy --- [<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl) An instruct based fine tune of [migtissera/Tess-34B-v1.4](https://huggingface.co/migtissera/Tess-34B-v1.4). It works well with long system prompts. It isn't generic in a sense that it shouldn't be used for story telling, for example, but only for reasoning and text comprehension. This model is trained on a private dataset. The high GSM8K score is NOT because of the MetaMath dataset. # Prompt Format: ``` SYSTEM: <ANY SYSTEM CONTEXT> USER: ASSISTANT: ``` Quants: [TheBloke/Pallas-0.5-GGUF](https://huggingface.co/TheBloke/Pallas-0.5-GGUF) [TheBloke/Pallas-0.5-AWQ](https://huggingface.co/TheBloke/Pallas-0.5-AWQ) [TheBloke/Pallas-0.5-GPTQ](https://huggingface.co/TheBloke/Pallas-0.5-GPTQ) [LoneStriker/Pallas-0.5-3.0bpw-h6-exl2](https://huggingface.co/LoneStriker/Pallas-0.5-3.0bpw-h6-exl2) [LoneStriker/Pallas-0.5-4.0bpw-h6-exl2](https://huggingface.co/LoneStriker/Pallas-0.5-4.0bpw-h6-exl2) [LoneStriker/Pallas-0.5-4.65bpw-h6-exl2](https://huggingface.co/LoneStriker/Pallas-0.5-4.65bpw-h6-exl2) [LoneStriker/Pallas-0.5-5.0bpw-h6-exl2](https://huggingface.co/LoneStriker/Pallas-0.5-5.0bpw-h6-exl2) [LoneStriker/Pallas-0.5-6.0bpw-h6-exl2](https://huggingface.co/LoneStriker/Pallas-0.5-6.0bpw-h6-exl2) [LoneStriker/Pallas-0.5-8.0bpw-h8-exl2](https://huggingface.co/LoneStriker/Pallas-0.5-8.0bpw-h8-exl2)
TheBloke/Kunoichi-7B-GGUF	TheBloke	2024-01-05T14:19:55Z	4,853	27	transformers	[ "transformers", "gguf", "mistral", "merge", "base_model:SanjiWatsuki/Kunoichi-7B", "license:cc-by-nc-4.0", "text-generation-inference", "region:us" ]	null	2024-01-05T11:50:44Z	--- base_model: SanjiWatsuki/Kunoichi-7B inference: false license: cc-by-nc-4.0 model_creator: Sanji Watsuki model_name: Kunoichi 7B model_type: mistral prompt_template: 'Below is an instruction that describes a task. Write a response that appropriately completes the request. ### Instruction: {prompt} ### Response: ' quantized_by: TheBloke tags: - merge --- <!-- markdownlint-disable MD041 --> <!-- header start --> <!-- 200823 --> <div style="width: auto; margin-left: auto; margin-right: auto"> <img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> <div style="display: flex; justify-content: space-between; width: 100%;"> <div style="display: flex; flex-direction: column; align-items: flex-start;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p> </div> <div style="display: flex; flex-direction: column; align-items: flex-end;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p> </div> </div> <div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div> <hr style="margin-top: 1.0em; margin-bottom: 1.0em;"> <!-- header end --> # Kunoichi 7B - GGUF - Model creator: [Sanji Watsuki](https://huggingface.co/SanjiWatsuki) - Original model: [Kunoichi 7B](https://huggingface.co/SanjiWatsuki/Kunoichi-7B) <!-- description start --> ## Description This repo contains GGUF format model files for [Sanji Watsuki's Kunoichi 7B](https://huggingface.co/SanjiWatsuki/Kunoichi-7B). These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/). <!-- description end --> <!-- README_GGUF.md-about-gguf start --> ### About GGUF GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp. Here is an incomplete list of clients and libraries that are known to support GGUF: * [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option. * [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration. * [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling. * [GPT4All](https://gpt4all.io/index.html), a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel. * [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023. * [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection. * [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration. * [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server. * [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use. * [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models. <!-- README_GGUF.md-about-gguf end --> <!-- repositories-available start --> ## Repositories available * [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/Kunoichi-7B-AWQ) * [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Kunoichi-7B-GPTQ) * [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/Kunoichi-7B-GGUF) * [Sanji Watsuki's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/SanjiWatsuki/Kunoichi-7B) <!-- repositories-available end --> <!-- prompt-template start --> ## Prompt template: Alpaca ``` Below is an instruction that describes a task. Write a response that appropriately completes the request. ### Instruction: {prompt} ### Response: ``` <!-- prompt-template end --> <!-- compatibility_gguf start --> ## Compatibility These quantised GGUFv2 files are compatible with llama.cpp from August 27th onwards, as of commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) They are also compatible with many third party UIs and libraries - please see the list at the top of this README. ## Explanation of quantisation methods <details> <summary>Click to see details</summary> The new methods available are: * GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw) * GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw. * GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw. * GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw * GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw Refer to the Provided Files table below to see what files use which methods, and how. </details> <!-- compatibility_gguf end --> <!-- README_GGUF.md-provided-files start --> ## Provided files \| Name \| Quant method \| Bits \| Size \| Max RAM required \| Use case \| \| ---- \| ---- \| ---- \| ---- \| ---- \| ----- \| \| [kunoichi-7b.Q2_K.gguf](https://huggingface.co/TheBloke/Kunoichi-7B-GGUF/blob/main/kunoichi-7b.Q2_K.gguf) \| Q2_K \| 2 \| 3.08 GB\| 5.58 GB \| smallest, significant quality loss - not recommended for most purposes \| \| [kunoichi-7b.Q3_K_S.gguf](https://huggingface.co/TheBloke/Kunoichi-7B-GGUF/blob/main/kunoichi-7b.Q3_K_S.gguf) \| Q3_K_S \| 3 \| 3.17 GB\| 5.67 GB \| very small, high quality loss \| \| [kunoichi-7b.Q3_K_M.gguf](https://huggingface.co/TheBloke/Kunoichi-7B-GGUF/blob/main/kunoichi-7b.Q3_K_M.gguf) \| Q3_K_M \| 3 \| 3.52 GB\| 6.02 GB \| very small, high quality loss \| \| [kunoichi-7b.Q3_K_L.gguf](https://huggingface.co/TheBloke/Kunoichi-7B-GGUF/blob/main/kunoichi-7b.Q3_K_L.gguf) \| Q3_K_L \| 3 \| 3.82 GB\| 6.32 GB \| small, substantial quality loss \| \| [kunoichi-7b.Q4_0.gguf](https://huggingface.co/TheBloke/Kunoichi-7B-GGUF/blob/main/kunoichi-7b.Q4_0.gguf) \| Q4_0 \| 4 \| 4.11 GB\| 6.61 GB \| legacy; small, very high quality loss - prefer using Q3_K_M \| \| [kunoichi-7b.Q4_K_S.gguf](https://huggingface.co/TheBloke/Kunoichi-7B-GGUF/blob/main/kunoichi-7b.Q4_K_S.gguf) \| Q4_K_S \| 4 \| 4.14 GB\| 6.64 GB \| small, greater quality loss \| \| [kunoichi-7b.Q4_K_M.gguf](https://huggingface.co/TheBloke/Kunoichi-7B-GGUF/blob/main/kunoichi-7b.Q4_K_M.gguf) \| Q4_K_M \| 4 \| 4.37 GB\| 6.87 GB \| medium, balanced quality - recommended \| \| [kunoichi-7b.Q5_0.gguf](https://huggingface.co/TheBloke/Kunoichi-7B-GGUF/blob/main/kunoichi-7b.Q5_0.gguf) \| Q5_0 \| 5 \| 5.00 GB\| 7.50 GB \| legacy; medium, balanced quality - prefer using Q4_K_M \| \| [kunoichi-7b.Q5_K_S.gguf](https://huggingface.co/TheBloke/Kunoichi-7B-GGUF/blob/main/kunoichi-7b.Q5_K_S.gguf) \| Q5_K_S \| 5 \| 5.00 GB\| 7.50 GB \| large, low quality loss - recommended \| \| [kunoichi-7b.Q5_K_M.gguf](https://huggingface.co/TheBloke/Kunoichi-7B-GGUF/blob/main/kunoichi-7b.Q5_K_M.gguf) \| Q5_K_M \| 5 \| 5.13 GB\| 7.63 GB \| large, very low quality loss - recommended \| \| [kunoichi-7b.Q6_K.gguf](https://huggingface.co/TheBloke/Kunoichi-7B-GGUF/blob/main/kunoichi-7b.Q6_K.gguf) \| Q6_K \| 6 \| 5.94 GB\| 8.44 GB \| very large, extremely low quality loss \| \| [kunoichi-7b.Q8_0.gguf](https://huggingface.co/TheBloke/Kunoichi-7B-GGUF/blob/main/kunoichi-7b.Q8_0.gguf) \| Q8_0 \| 8 \| 7.70 GB\| 10.20 GB \| very large, extremely low quality loss - not recommended \| Note: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead. <!-- README_GGUF.md-provided-files end --> <!-- README_GGUF.md-how-to-download start --> ## How to download GGUF files Note for manual downloaders: You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file. The following clients/libraries will automatically download models for you, providing a list of available models to choose from: * LM Studio * LoLLMS Web UI * Faraday.dev ### In `text-generation-webui` Under Download Model, you can enter the model repo: TheBloke/Kunoichi-7B-GGUF and below it, a specific filename to download, such as: kunoichi-7b.Q4_K_M.gguf. Then click Download. ### On the command line, including multiple files at once I recommend using the `huggingface-hub` Python library: ```shell pip3 install huggingface-hub ``` Then you can download any individual model file to the current directory, at high speed, with a command like this: ```shell huggingface-cli download TheBloke/Kunoichi-7B-GGUF kunoichi-7b.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False ``` <details> <summary>More advanced huggingface-cli download usage (click to read)</summary> You can also download multiple files at once with a pattern: ```shell huggingface-cli download TheBloke/Kunoichi-7B-GGUF --local-dir . --local-dir-use-symlinks False --include='Q4_Kgguf' ``` For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli). To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`: ```shell pip3 install hf_transfer ``` And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`: ```shell HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/Kunoichi-7B-GGUF kunoichi-7b.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False ``` Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command. </details> <!-- README_GGUF.md-how-to-download end --> <!-- README_GGUF.md-how-to-run start --> ## Example `llama.cpp` command Make sure you are using `llama.cpp` from commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later. ```shell ./main -ngl 35 -m kunoichi-7b.Q4_K_M.gguf --color -c 8192 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\n{prompt}\n\n### Response:" ``` Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration. Change `-c 8192` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically. Note that longer sequence lengths require much more resources, so you may need to reduce this value. If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins` For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md) ## How to run in `text-generation-webui` Further instructions can be found in the text-generation-webui documentation, here: [text-generation-webui/docs/04 ‐ Model Tab.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/04%20%E2%80%90%20Model%20Tab.md#llamacpp). ## How to run from Python code You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) or [ctransformers](https://github.com/marella/ctransformers) libraries. Note that at the time of writing (Nov 27th 2023), ctransformers has not been updated for some time and is not compatible with some recent models. Therefore I recommend you use llama-cpp-python. ### How to load this model in Python code, using llama-cpp-python For full documentation, please see: [llama-cpp-python docs](https://abetlen.github.io/llama-cpp-python/). #### First install the package Run one of the following commands, according to your system: ```shell # Base ctransformers with no GPU acceleration pip install llama-cpp-python # With NVidia CUDA acceleration CMAKE_ARGS="-DLLAMA_CUBLAS=on" pip install llama-cpp-python # Or with OpenBLAS acceleration CMAKE_ARGS="-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS" pip install llama-cpp-python # Or with CLBLast acceleration CMAKE_ARGS="-DLLAMA_CLBLAST=on" pip install llama-cpp-python # Or with AMD ROCm GPU acceleration (Linux only) CMAKE_ARGS="-DLLAMA_HIPBLAS=on" pip install llama-cpp-python # Or with Metal GPU acceleration for macOS systems only CMAKE_ARGS="-DLLAMA_METAL=on" pip install llama-cpp-python # In windows, to set the variables CMAKE_ARGS in PowerShell, follow this format; eg for NVidia CUDA: $env:CMAKE_ARGS = "-DLLAMA_OPENBLAS=on" pip install llama-cpp-python ``` #### Simple llama-cpp-python example code ```python from llama_cpp import Llama # Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system. llm = Llama( model_path="./kunoichi-7b.Q4_K_M.gguf", # Download the model file first n_ctx=8192, # The max sequence length to use - note that longer sequence lengths require much more resources n_threads=8, # The number of CPU threads to use, tailor to your system and the resulting performance n_gpu_layers=35 # The number of layers to offload to GPU, if you have GPU acceleration available ) # Simple inference example output = llm( "Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\n{prompt}\n\n### Response:", # Prompt max_tokens=512, # Generate up to 512 tokens stop=["</s>"], # Example stop token - not necessarily correct for this specific model! Please check before using. echo=True # Whether to echo the prompt ) # Chat Completion API llm = Llama(model_path="./kunoichi-7b.Q4_K_M.gguf", chat_format="llama-2") # Set chat_format according to the model you are using llm.create_chat_completion( messages = [ {"role": "system", "content": "You are a story writing assistant."}, { "role": "user", "content": "Write a story about llamas." } ] ) ``` ## How to use with LangChain Here are guides on using llama-cpp-python and ctransformers with LangChain: * [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp) * [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers) <!-- README_GGUF.md-how-to-run end --> <!-- footer start --> <!-- 200823 --> ## Discord For further support, and discussions on these models and AI in general, join us at: [TheBloke AI's Discord server](https://discord.gg/theblokeai) ## Thanks, and how to contribute Thanks to the [chirper.ai](https://chirper.ai) team! Thanks to Clay from [gpus.llm-utils.org](llm-utils)! I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training. If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects. Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits. * Patreon: https://patreon.com/TheBlokeAI * Ko-Fi: https://ko-fi.com/TheBlokeAI Special thanks to: Aemon Algiz. Patreon special mentions: Michael Levine, 阿明, Trailburnt, Nikolai Manek, John Detwiler, Randy H, Will Dee, Sebastain Graf, NimbleBox.ai, Eugene Pentland, Emad Mostaque, Ai Maven, Jim Angel, Jeff Scroggin, Michael Davis, Manuel Alberto Morcote, Stephen Murray, Robert, Justin Joy, Luke @flexchar, Brandon Frisco, Elijah Stavena, S_X, Dan Guido, Undi ., Komninos Chatzipapas, Shadi, theTransient, Lone Striker, Raven Klaugh, jjj, Cap'n Zoog, Michel-Marie MAUDET (LINAGORA), Matthew Berman, David, Fen Risland, Omer Bin Jawed, Luke Pendergrass, Kalila, OG, Erik Bjäreholt, Rooh Singh, Joseph William Delisle, Dan Lewis, TL, John Villwock, AzureBlack, Brad, Pedro Madruga, Caitlyn Gatomon, K, jinyuan sun, Mano Prime, Alex, Jeffrey Morgan, Alicia Loh, Illia Dulskyi, Chadd, transmissions 11, fincy, Rainer Wilmers, ReadyPlayerEmma, knownsqashed, Mandus, biorpg, Deo Leter, Brandon Phillips, SuperWojo, Sean Connelly, Iucharbius, Jack West, Harry Royden McLaughlin, Nicholas, terasurfer, Vitor Caleffi, Duane Dunston, Johann-Peter Hartmann, David Ziegler, Olakabola, Ken Nordquist, Trenton Dambrowitz, Tom X Nguyen, Vadim, Ajan Kanaga, Leonard Tan, Clay Pascal, Alexandros Triantafyllidis, JM33133, Xule, vamX, ya boyyy, subjectnull, Talal Aujan, Alps Aficionado, wassieverse, Ari Malik, James Bentley, Woland, Spencer Kim, Michael Dempsey, Fred von Graf, Elle, zynix, William Richards, Stanislav Ovsiannikov, Edmond Seymore, Jonathan Leane, Martin Kemka, usrbinkat, Enrico Ros Thank you to all my generous patrons and donaters! And thank you again to a16z for their generous grant. <!-- footer end --> <!-- original-model-card start --> # Original model card: Sanji Watsuki's Kunoichi 7B ![image/png](https://huggingface.co/SanjiWatsuki/Kunoichi-7B/resolve/main/assets/kunoichi.png) <!-- description start --> ## Description This repository hosts Kunoichi-7B, an general purpose model capable of RP. In both my testing and the benchmarks, Kunoichi is an extremely strong model, keeping the advantages of my previous models but gaining more intelligence. Kunoichi scores extremely well on [all benchmarks which correlate closely with ChatBot Arena Elo.](https://www.reddit.com/r/LocalLLaMA/comments/18u0tu3/benchmarking_the_benchmarks_correlation_with/) \| Model \| MT Bench \| EQ Bench \| MMLU \| Logic Test \| \|----------------------\|----------\|----------\|---------\|-------------\| \| GPT-4-Turbo \| 9.32 \| - \| - \| - \| \| GPT-4 \| 8.99 \| 62.52 \| 86.4 \| 0.86 \| \| Kunoichi-7B \| 8.14 \| 44.32 \| ~64.7 \| 0.58 \| \| Starling-7B \| 8.09 \| - \| 63.9 \| 0.51 \| \| Claude-2 \| 8.06 \| 52.14 \| 78.5 \| - \| \| Silicon-Maid-7B \| 7.96 \| 40.44 \| 64.7 \| 0.54 \| \| Loyal-Macaroni-Maid-7B \| 7.95 \| 38.66 \| 64.9 \| 0.57 \| \| GPT-3.5-Turbo \| 7.94 \| 50.28 \| 70 \| 0.57 \| \| Claude-1 \| 7.9 \| - \| 77 \| - \| \| Openchat-3.5 \| 7.81 \| 37.08 \| 64.3 \| 0.39 \| \| Dolphin-2.6-DPO \| 7.74 \| 42.88 \| 61.9 \| 0.53 \| \| Zephyr-7B-beta \| 7.34 \| 38.71 \| 61.4 \| 0.30 \| \| Llama-2-70b-chat-hf \| 6.86 \| 51.56 \| 63 \| - \| \| Neural-chat-7b-v3-1 \| 6.84 \| 43.61 \| 62.4 \| 0.30 \| The model is intended to be used with up to an 8k context window. Using a NTK RoPE alpha of 2.6, the model can be used experimentally up to a 16k context window. <!-- description end --> <!-- prompt-template start --> ## Prompt template: Custom format, or Alpaca ### Alpaca: ``` Below is an instruction that describes a task. Write a response that appropriately completes the request. ### Instruction: {prompt} ### Response: ``` ### SillyTavern format: I found the best SillyTavern results from using the Noromaid template. SillyTavern config files: [Context](https://files.catbox.moe/ifmhai.json), [Instruct](https://files.catbox.moe/ttw1l9.json). Additionally, here is my highly recommended [Text Completion preset](https://huggingface.co/SanjiWatsuki/Loyal-Macaroni-Maid-7B/blob/main/Characters/MinP.json). You can tweak this by adjusting temperature up or dropping min p to boost creativity or raise min p to increase stability. You shouldn't need to touch anything else! ## WTF is Kunoichi-7B? Kunoichi-7B is a SLERP merger between my previous RP model, Silicon-Maid-7B, and an unreleased model that I had dubbed "Ninja-7B". This model is the result of me attempting to merge an RP focused model which maintained the strengths of Silicon-Maid-7B but further increased the model's brain power. I sought to increase both MT-Bench and EQ-Bench without losing Silicon Maid's strong ability to follow SillyTavern character cards. Ninja-7B was born from an attempt to turn [jan-hq/stealth-v1.2](https://huggingface.co/jan-hq/stealth-v1.2) into a viable model through mergers. Although none of the Ninja prototype models developed to a point where I was happy, it turned out to be a strong model to merge. Combined with Silicon-Maid-7B, this appeared to be a strong merger. <!-- original-model-card end -->
RichardErkhov/heegyu_-_zephyr-7b-beta-KOR-OpenOrca-Platypus-1e-5-gguf	RichardErkhov	2024-06-06T06:52:18Z	4,853	0	null	[ "gguf", "region:us" ]	null	2024-06-06T03:05:13Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) zephyr-7b-beta-KOR-OpenOrca-Platypus-1e-5 - GGUF - Model creator: https://huggingface.co/heegyu/ - Original model: https://huggingface.co/heegyu/zephyr-7b-beta-KOR-OpenOrca-Platypus-1e-5/ \| Name \| Quant method \| Size \| \| ---- \| ---- \| ---- \| \| [zephyr-7b-beta-KOR-OpenOrca-Platypus-1e-5.Q2_K.gguf](https://huggingface.co/RichardErkhov/heegyu_-_zephyr-7b-beta-KOR-OpenOrca-Platypus-1e-5-gguf/blob/main/zephyr-7b-beta-KOR-OpenOrca-Platypus-1e-5.Q2_K.gguf) \| Q2_K \| 2.53GB \| \| [zephyr-7b-beta-KOR-OpenOrca-Platypus-1e-5.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/heegyu_-_zephyr-7b-beta-KOR-OpenOrca-Platypus-1e-5-gguf/blob/main/zephyr-7b-beta-KOR-OpenOrca-Platypus-1e-5.IQ3_XS.gguf) \| IQ3_XS \| 2.81GB \| \| [zephyr-7b-beta-KOR-OpenOrca-Platypus-1e-5.IQ3_S.gguf](https://huggingface.co/RichardErkhov/heegyu_-_zephyr-7b-beta-KOR-OpenOrca-Platypus-1e-5-gguf/blob/main/zephyr-7b-beta-KOR-OpenOrca-Platypus-1e-5.IQ3_S.gguf) \| IQ3_S \| 2.96GB \| \| [zephyr-7b-beta-KOR-OpenOrca-Platypus-1e-5.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/heegyu_-_zephyr-7b-beta-KOR-OpenOrca-Platypus-1e-5-gguf/blob/main/zephyr-7b-beta-KOR-OpenOrca-Platypus-1e-5.Q3_K_S.gguf) \| Q3_K_S \| 2.95GB \| \| [zephyr-7b-beta-KOR-OpenOrca-Platypus-1e-5.IQ3_M.gguf](https://huggingface.co/RichardErkhov/heegyu_-_zephyr-7b-beta-KOR-OpenOrca-Platypus-1e-5-gguf/blob/main/zephyr-7b-beta-KOR-OpenOrca-Platypus-1e-5.IQ3_M.gguf) \| IQ3_M \| 3.06GB \| \| [zephyr-7b-beta-KOR-OpenOrca-Platypus-1e-5.Q3_K.gguf](https://huggingface.co/RichardErkhov/heegyu_-_zephyr-7b-beta-KOR-OpenOrca-Platypus-1e-5-gguf/blob/main/zephyr-7b-beta-KOR-OpenOrca-Platypus-1e-5.Q3_K.gguf) \| Q3_K \| 3.28GB \| \| [zephyr-7b-beta-KOR-OpenOrca-Platypus-1e-5.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/heegyu_-_zephyr-7b-beta-KOR-OpenOrca-Platypus-1e-5-gguf/blob/main/zephyr-7b-beta-KOR-OpenOrca-Platypus-1e-5.Q3_K_M.gguf) \| Q3_K_M \| 3.28GB \| \| [zephyr-7b-beta-KOR-OpenOrca-Platypus-1e-5.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/heegyu_-_zephyr-7b-beta-KOR-OpenOrca-Platypus-1e-5-gguf/blob/main/zephyr-7b-beta-KOR-OpenOrca-Platypus-1e-5.Q3_K_L.gguf) \| Q3_K_L \| 3.56GB \| \| [zephyr-7b-beta-KOR-OpenOrca-Platypus-1e-5.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/heegyu_-_zephyr-7b-beta-KOR-OpenOrca-Platypus-1e-5-gguf/blob/main/zephyr-7b-beta-KOR-OpenOrca-Platypus-1e-5.IQ4_XS.gguf) \| IQ4_XS \| 3.67GB \| \| [zephyr-7b-beta-KOR-OpenOrca-Platypus-1e-5.Q4_0.gguf](https://huggingface.co/RichardErkhov/heegyu_-_zephyr-7b-beta-KOR-OpenOrca-Platypus-1e-5-gguf/blob/main/zephyr-7b-beta-KOR-OpenOrca-Platypus-1e-5.Q4_0.gguf) \| Q4_0 \| 3.83GB \| \| [zephyr-7b-beta-KOR-OpenOrca-Platypus-1e-5.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/heegyu_-_zephyr-7b-beta-KOR-OpenOrca-Platypus-1e-5-gguf/blob/main/zephyr-7b-beta-KOR-OpenOrca-Platypus-1e-5.IQ4_NL.gguf) \| IQ4_NL \| 3.87GB \| \| [zephyr-7b-beta-KOR-OpenOrca-Platypus-1e-5.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/heegyu_-_zephyr-7b-beta-KOR-OpenOrca-Platypus-1e-5-gguf/blob/main/zephyr-7b-beta-KOR-OpenOrca-Platypus-1e-5.Q4_K_S.gguf) \| Q4_K_S \| 3.86GB \| \| [zephyr-7b-beta-KOR-OpenOrca-Platypus-1e-5.Q4_K.gguf](https://huggingface.co/RichardErkhov/heegyu_-_zephyr-7b-beta-KOR-OpenOrca-Platypus-1e-5-gguf/blob/main/zephyr-7b-beta-KOR-OpenOrca-Platypus-1e-5.Q4_K.gguf) \| Q4_K \| 4.07GB \| \| [zephyr-7b-beta-KOR-OpenOrca-Platypus-1e-5.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/heegyu_-_zephyr-7b-beta-KOR-OpenOrca-Platypus-1e-5-gguf/blob/main/zephyr-7b-beta-KOR-OpenOrca-Platypus-1e-5.Q4_K_M.gguf) \| Q4_K_M \| 4.07GB \| \| [zephyr-7b-beta-KOR-OpenOrca-Platypus-1e-5.Q4_1.gguf](https://huggingface.co/RichardErkhov/heegyu_-_zephyr-7b-beta-KOR-OpenOrca-Platypus-1e-5-gguf/blob/main/zephyr-7b-beta-KOR-OpenOrca-Platypus-1e-5.Q4_1.gguf) \| Q4_1 \| 4.24GB \| \| [zephyr-7b-beta-KOR-OpenOrca-Platypus-1e-5.Q5_0.gguf](https://huggingface.co/RichardErkhov/heegyu_-_zephyr-7b-beta-KOR-OpenOrca-Platypus-1e-5-gguf/blob/main/zephyr-7b-beta-KOR-OpenOrca-Platypus-1e-5.Q5_0.gguf) \| Q5_0 \| 4.65GB \| \| [zephyr-7b-beta-KOR-OpenOrca-Platypus-1e-5.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/heegyu_-_zephyr-7b-beta-KOR-OpenOrca-Platypus-1e-5-gguf/blob/main/zephyr-7b-beta-KOR-OpenOrca-Platypus-1e-5.Q5_K_S.gguf) \| Q5_K_S \| 4.65GB \| \| [zephyr-7b-beta-KOR-OpenOrca-Platypus-1e-5.Q5_K.gguf](https://huggingface.co/RichardErkhov/heegyu_-_zephyr-7b-beta-KOR-OpenOrca-Platypus-1e-5-gguf/blob/main/zephyr-7b-beta-KOR-OpenOrca-Platypus-1e-5.Q5_K.gguf) \| Q5_K \| 4.78GB \| \| [zephyr-7b-beta-KOR-OpenOrca-Platypus-1e-5.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/heegyu_-_zephyr-7b-beta-KOR-OpenOrca-Platypus-1e-5-gguf/blob/main/zephyr-7b-beta-KOR-OpenOrca-Platypus-1e-5.Q5_K_M.gguf) \| Q5_K_M \| 4.78GB \| \| [zephyr-7b-beta-KOR-OpenOrca-Platypus-1e-5.Q5_1.gguf](https://huggingface.co/RichardErkhov/heegyu_-_zephyr-7b-beta-KOR-OpenOrca-Platypus-1e-5-gguf/blob/main/zephyr-7b-beta-KOR-OpenOrca-Platypus-1e-5.Q5_1.gguf) \| Q5_1 \| 5.07GB \| \| [zephyr-7b-beta-KOR-OpenOrca-Platypus-1e-5.Q6_K.gguf](https://huggingface.co/RichardErkhov/heegyu_-_zephyr-7b-beta-KOR-OpenOrca-Platypus-1e-5-gguf/blob/main/zephyr-7b-beta-KOR-OpenOrca-Platypus-1e-5.Q6_K.gguf) \| Q6_K \| 5.53GB \| \| [zephyr-7b-beta-KOR-OpenOrca-Platypus-1e-5.Q8_0.gguf](https://huggingface.co/RichardErkhov/heegyu_-_zephyr-7b-beta-KOR-OpenOrca-Platypus-1e-5-gguf/blob/main/zephyr-7b-beta-KOR-OpenOrca-Platypus-1e-5.Q8_0.gguf) \| Q8_0 \| 7.17GB \| Original model description: Entry not found
izhx/udever-bloom-7b1	izhx	2023-11-07T06:54:08Z	4,852	5	transformers	[ "transformers", "pytorch", "bloom", "feature-extraction", "mteb", "ak", "ar", "as", "bm", "bn", "ca", "code", "en", "es", "eu", "fon", "fr", "gu", "hi", "id", "ig", "ki", "kn", "lg", "ln", "ml", "mr", "ne", "nso", "ny", "or", "pa", "pt", "rn", "rw", "sn", "st", "sw", "ta", "te", "tn", "ts", "tum", "tw", "ur", "vi", "wo", "xh", "yo", "zh", "zhs", "zht", "zu", "arxiv:2310.08232", "license:bigscience-bloom-rail-1.0", "model-index", "endpoints_compatible", "text-generation-inference", "region:us" ]	feature-extraction	2023-10-24T14:35:31Z	--- license: bigscience-bloom-rail-1.0 language: - ak - ar - as - bm - bn - ca - code - en - es - eu - fon - fr - gu - hi - id - ig - ki - kn - lg - ln - ml - mr - ne - nso - ny - or - pa - pt - rn - rw - sn - st - sw - ta - te - tn - ts - tum - tw - ur - vi - wo - xh - yo - zh - zhs - zht - zu tags: - mteb model-index: - name: udever-bloom-7b1 results: - task: type: STS dataset: type: C-MTEB/AFQMC name: MTEB AFQMC config: default split: validation revision: None metrics: - type: cos_sim_pearson value: 31.3788313486292 - type: cos_sim_spearman value: 31.87117445808444 - type: euclidean_pearson value: 30.66886666881808 - type: euclidean_spearman value: 31.28368681542041 - type: manhattan_pearson value: 30.679984531432936 - type: manhattan_spearman value: 31.22208726593753 - task: type: STS dataset: type: C-MTEB/ATEC name: MTEB ATEC config: default split: test revision: None metrics: - type: cos_sim_pearson value: 38.403248424956764 - type: cos_sim_spearman value: 38.798254852046504 - type: euclidean_pearson value: 41.154981142995084 - type: euclidean_spearman value: 38.73503172297125 - type: manhattan_pearson value: 41.20226384035751 - type: manhattan_spearman value: 38.77085234568287 - task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (en) config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 73.11940298507463 - type: ap value: 35.692863077186466 - type: f1 value: 67.02733552778966 - task: type: Classification dataset: type: mteb/amazon_polarity name: MTEB AmazonPolarityClassification config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 88.885175 - type: ap value: 84.75400736514149 - type: f1 value: 88.85806225869703 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (en) config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 43.202 - type: f1 value: 42.63847450850621 - task: type: Retrieval dataset: type: arguana name: MTEB ArguAna config: default split: test revision: None metrics: - type: map_at_1 value: 25.676 - type: map_at_10 value: 42.539 - type: map_at_100 value: 43.383 - type: map_at_1000 value: 43.39 - type: map_at_3 value: 36.996 - type: map_at_5 value: 40.175 - type: mrr_at_1 value: 26.387 - type: mrr_at_10 value: 42.792 - type: mrr_at_100 value: 43.637 - type: mrr_at_1000 value: 43.644 - type: mrr_at_3 value: 37.21 - type: mrr_at_5 value: 40.407 - type: ndcg_at_1 value: 25.676 - type: ndcg_at_10 value: 52.207 - type: ndcg_at_100 value: 55.757999999999996 - type: ndcg_at_1000 value: 55.913999999999994 - type: ndcg_at_3 value: 40.853 - type: ndcg_at_5 value: 46.588 - type: precision_at_1 value: 25.676 - type: precision_at_10 value: 8.314 - type: precision_at_100 value: 0.985 - type: precision_at_1000 value: 0.1 - type: precision_at_3 value: 17.354 - type: precision_at_5 value: 13.200999999999999 - type: recall_at_1 value: 25.676 - type: recall_at_10 value: 83.14399999999999 - type: recall_at_100 value: 98.506 - type: recall_at_1000 value: 99.644 - type: recall_at_3 value: 52.063 - type: recall_at_5 value: 66.003 - task: type: Clustering dataset: type: mteb/arxiv-clustering-p2p name: MTEB ArxivClusteringP2P config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics: - type: v_measure value: 45.66024127046263 - task: type: Clustering dataset: type: mteb/arxiv-clustering-s2s name: MTEB ArxivClusteringS2S config: default split: test revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 metrics: - type: v_measure value: 38.418361433667336 - task: type: Reranking dataset: type: mteb/askubuntudupquestions-reranking name: MTEB AskUbuntuDupQuestions config: default split: test revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 metrics: - type: map value: 61.60189642383972 - type: mrr value: 75.26678538451391 - task: type: STS dataset: type: mteb/biosses-sts name: MTEB BIOSSES config: default split: test revision: d3fb88f8f02e40887cd149695127462bbcf29b4a metrics: - type: cos_sim_pearson value: 87.85884182572595 - type: cos_sim_spearman value: 85.5242378844044 - type: euclidean_pearson value: 85.37705073557146 - type: euclidean_spearman value: 84.65132642825964 - type: manhattan_pearson value: 85.42179213807349 - type: manhattan_spearman value: 84.6959057572829 - task: type: STS dataset: type: C-MTEB/BQ name: MTEB BQ config: default split: test revision: None metrics: - type: cos_sim_pearson value: 47.81802155652125 - type: cos_sim_spearman value: 47.66691834501235 - type: euclidean_pearson value: 47.781824357030935 - type: euclidean_spearman value: 48.03322284408188 - type: manhattan_pearson value: 47.871159981038346 - type: manhattan_spearman value: 48.18240784527666 - task: type: BitextMining dataset: type: mteb/bucc-bitext-mining name: MTEB BUCC (de-en) config: de-en split: test revision: d51519689f32196a32af33b075a01d0e7c51e252 metrics: - type: accuracy value: 88.29853862212944 - type: f1 value: 87.70994966904566 - type: precision value: 87.43152897902377 - type: recall value: 88.29853862212944 - task: type: BitextMining dataset: type: mteb/bucc-bitext-mining name: MTEB BUCC (fr-en) config: fr-en split: test revision: d51519689f32196a32af33b075a01d0e7c51e252 metrics: - type: accuracy value: 98.6022452124147 - type: f1 value: 98.40597255851495 - type: precision value: 98.30875339349916 - type: recall value: 98.6022452124147 - task: type: BitextMining dataset: type: mteb/bucc-bitext-mining name: MTEB BUCC (ru-en) config: ru-en split: test revision: d51519689f32196a32af33b075a01d0e7c51e252 metrics: - type: accuracy value: 79.64669206789054 - type: f1 value: 78.74831345770036 - type: precision value: 78.33899087865143 - type: recall value: 79.64669206789054 - task: type: BitextMining dataset: type: mteb/bucc-bitext-mining name: MTEB BUCC (zh-en) config: zh-en split: test revision: d51519689f32196a32af33b075a01d0e7c51e252 metrics: - type: accuracy value: 98.78883622959452 - type: f1 value: 98.7712831314727 - type: precision value: 98.76250658241179 - type: recall value: 98.78883622959452 - task: type: Classification dataset: type: mteb/banking77 name: MTEB Banking77Classification config: default split: test revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 metrics: - type: accuracy value: 85.36363636363637 - type: f1 value: 85.33381612267455 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-p2p name: MTEB BiorxivClusteringP2P config: default split: test revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 metrics: - type: v_measure value: 35.54276849354455 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-s2s name: MTEB BiorxivClusteringS2S config: default split: test revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 metrics: - type: v_measure value: 32.18953191097238 - task: type: Clustering dataset: type: C-MTEB/CLSClusteringP2P name: MTEB CLSClusteringP2P config: default split: test revision: None metrics: - type: v_measure value: 36.00041315364012 - task: type: Clustering dataset: type: C-MTEB/CLSClusteringS2S name: MTEB CLSClusteringS2S config: default split: test revision: None metrics: - type: v_measure value: 36.35255790689628 - task: type: Reranking dataset: type: C-MTEB/CMedQAv1-reranking name: MTEB CMedQAv1 config: default split: test revision: None metrics: - type: map value: 70.54141681949504 - type: mrr value: 74.81400793650795 - task: type: Reranking dataset: type: C-MTEB/CMedQAv2-reranking name: MTEB CMedQAv2 config: default split: test revision: None metrics: - type: map value: 71.3534829537025 - type: mrr value: 75.85095238095238 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackAndroidRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 32.5 - type: map_at_10 value: 43.37 - type: map_at_100 value: 44.926 - type: map_at_1000 value: 45.047 - type: map_at_3 value: 40.083999999999996 - type: map_at_5 value: 41.71 - type: mrr_at_1 value: 40.343 - type: mrr_at_10 value: 49.706 - type: mrr_at_100 value: 50.470000000000006 - type: mrr_at_1000 value: 50.515 - type: mrr_at_3 value: 47.306 - type: mrr_at_5 value: 48.379 - type: ndcg_at_1 value: 40.343 - type: ndcg_at_10 value: 49.461 - type: ndcg_at_100 value: 55.084999999999994 - type: ndcg_at_1000 value: 56.994 - type: ndcg_at_3 value: 44.896 - type: ndcg_at_5 value: 46.437 - type: precision_at_1 value: 40.343 - type: precision_at_10 value: 9.27 - type: precision_at_100 value: 1.5190000000000001 - type: precision_at_1000 value: 0.197 - type: precision_at_3 value: 21.412 - type: precision_at_5 value: 15.021 - type: recall_at_1 value: 32.5 - type: recall_at_10 value: 60.857000000000006 - type: recall_at_100 value: 83.761 - type: recall_at_1000 value: 96.003 - type: recall_at_3 value: 46.675 - type: recall_at_5 value: 51.50900000000001 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackEnglishRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 26.931 - type: map_at_10 value: 35.769 - type: map_at_100 value: 36.8 - type: map_at_1000 value: 36.925999999999995 - type: map_at_3 value: 33.068999999999996 - type: map_at_5 value: 34.615 - type: mrr_at_1 value: 34.013 - type: mrr_at_10 value: 41.293 - type: mrr_at_100 value: 41.945 - type: mrr_at_1000 value: 42.002 - type: mrr_at_3 value: 39.204 - type: mrr_at_5 value: 40.436 - type: ndcg_at_1 value: 34.013 - type: ndcg_at_10 value: 40.935 - type: ndcg_at_100 value: 44.879999999999995 - type: ndcg_at_1000 value: 47.342 - type: ndcg_at_3 value: 37.071 - type: ndcg_at_5 value: 38.903 - type: precision_at_1 value: 34.013 - type: precision_at_10 value: 7.617999999999999 - type: precision_at_100 value: 1.185 - type: precision_at_1000 value: 0.169 - type: precision_at_3 value: 17.855999999999998 - type: precision_at_5 value: 12.65 - type: recall_at_1 value: 26.931 - type: recall_at_10 value: 50.256 - type: recall_at_100 value: 67.026 - type: recall_at_1000 value: 83.138 - type: recall_at_3 value: 38.477 - type: recall_at_5 value: 43.784 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGamingRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 38.474000000000004 - type: map_at_10 value: 50.486 - type: map_at_100 value: 51.620999999999995 - type: map_at_1000 value: 51.675000000000004 - type: map_at_3 value: 47.64 - type: map_at_5 value: 49.187999999999995 - type: mrr_at_1 value: 43.824000000000005 - type: mrr_at_10 value: 53.910000000000004 - type: mrr_at_100 value: 54.601 - type: mrr_at_1000 value: 54.632000000000005 - type: mrr_at_3 value: 51.578 - type: mrr_at_5 value: 52.922999999999995 - type: ndcg_at_1 value: 43.824000000000005 - type: ndcg_at_10 value: 56.208000000000006 - type: ndcg_at_100 value: 60.624 - type: ndcg_at_1000 value: 61.78 - type: ndcg_at_3 value: 51.27 - type: ndcg_at_5 value: 53.578 - type: precision_at_1 value: 43.824000000000005 - type: precision_at_10 value: 8.978 - type: precision_at_100 value: 1.216 - type: precision_at_1000 value: 0.136 - type: precision_at_3 value: 22.884 - type: precision_at_5 value: 15.498000000000001 - type: recall_at_1 value: 38.474000000000004 - type: recall_at_10 value: 69.636 - type: recall_at_100 value: 88.563 - type: recall_at_1000 value: 96.86200000000001 - type: recall_at_3 value: 56.347 - type: recall_at_5 value: 61.980000000000004 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGisRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 23.13 - type: map_at_10 value: 31.892 - type: map_at_100 value: 32.938 - type: map_at_1000 value: 33.025999999999996 - type: map_at_3 value: 29.072 - type: map_at_5 value: 30.775000000000002 - type: mrr_at_1 value: 25.197999999999997 - type: mrr_at_10 value: 34.224 - type: mrr_at_100 value: 35.149 - type: mrr_at_1000 value: 35.215999999999994 - type: mrr_at_3 value: 31.563000000000002 - type: mrr_at_5 value: 33.196 - type: ndcg_at_1 value: 25.197999999999997 - type: ndcg_at_10 value: 37.117 - type: ndcg_at_100 value: 42.244 - type: ndcg_at_1000 value: 44.432 - type: ndcg_at_3 value: 31.604 - type: ndcg_at_5 value: 34.543 - type: precision_at_1 value: 25.197999999999997 - type: precision_at_10 value: 5.876 - type: precision_at_100 value: 0.886 - type: precision_at_1000 value: 0.11100000000000002 - type: precision_at_3 value: 13.672 - type: precision_at_5 value: 9.831 - type: recall_at_1 value: 23.13 - type: recall_at_10 value: 50.980000000000004 - type: recall_at_100 value: 74.565 - type: recall_at_1000 value: 90.938 - type: recall_at_3 value: 36.038 - type: recall_at_5 value: 43.326 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackMathematicaRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 17.317 - type: map_at_10 value: 24.517 - type: map_at_100 value: 25.771 - type: map_at_1000 value: 25.915 - type: map_at_3 value: 22.332 - type: map_at_5 value: 23.526 - type: mrr_at_1 value: 21.766 - type: mrr_at_10 value: 29.096 - type: mrr_at_100 value: 30.165 - type: mrr_at_1000 value: 30.253000000000004 - type: mrr_at_3 value: 27.114 - type: mrr_at_5 value: 28.284 - type: ndcg_at_1 value: 21.766 - type: ndcg_at_10 value: 29.060999999999996 - type: ndcg_at_100 value: 35.107 - type: ndcg_at_1000 value: 38.339 - type: ndcg_at_3 value: 25.121 - type: ndcg_at_5 value: 26.953 - type: precision_at_1 value: 21.766 - type: precision_at_10 value: 5.274 - type: precision_at_100 value: 0.958 - type: precision_at_1000 value: 0.13699999999999998 - type: precision_at_3 value: 11.816 - type: precision_at_5 value: 8.433 - type: recall_at_1 value: 17.317 - type: recall_at_10 value: 38.379999999999995 - type: recall_at_100 value: 64.792 - type: recall_at_1000 value: 87.564 - type: recall_at_3 value: 27.737000000000002 - type: recall_at_5 value: 32.340999999999994 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackPhysicsRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 28.876 - type: map_at_10 value: 40.02 - type: map_at_100 value: 41.367 - type: map_at_1000 value: 41.482 - type: map_at_3 value: 36.651 - type: map_at_5 value: 38.411 - type: mrr_at_1 value: 35.804 - type: mrr_at_10 value: 45.946999999999996 - type: mrr_at_100 value: 46.696 - type: mrr_at_1000 value: 46.741 - type: mrr_at_3 value: 43.118 - type: mrr_at_5 value: 44.74 - type: ndcg_at_1 value: 35.804 - type: ndcg_at_10 value: 46.491 - type: ndcg_at_100 value: 51.803 - type: ndcg_at_1000 value: 53.845 - type: ndcg_at_3 value: 40.97 - type: ndcg_at_5 value: 43.431 - type: precision_at_1 value: 35.804 - type: precision_at_10 value: 8.595 - type: precision_at_100 value: 1.312 - type: precision_at_1000 value: 0.167 - type: precision_at_3 value: 19.634 - type: precision_at_5 value: 13.879 - type: recall_at_1 value: 28.876 - type: recall_at_10 value: 59.952000000000005 - type: recall_at_100 value: 81.978 - type: recall_at_1000 value: 95.03399999999999 - type: recall_at_3 value: 44.284 - type: recall_at_5 value: 50.885999999999996 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackProgrammersRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 24.238 - type: map_at_10 value: 34.276 - type: map_at_100 value: 35.65 - type: map_at_1000 value: 35.769 - type: map_at_3 value: 31.227 - type: map_at_5 value: 33.046 - type: mrr_at_1 value: 30.137000000000004 - type: mrr_at_10 value: 39.473 - type: mrr_at_100 value: 40.400999999999996 - type: mrr_at_1000 value: 40.455000000000005 - type: mrr_at_3 value: 36.891 - type: mrr_at_5 value: 38.391999999999996 - type: ndcg_at_1 value: 30.137000000000004 - type: ndcg_at_10 value: 40.08 - type: ndcg_at_100 value: 46.01 - type: ndcg_at_1000 value: 48.36 - type: ndcg_at_3 value: 35.163 - type: ndcg_at_5 value: 37.583 - type: precision_at_1 value: 30.137000000000004 - type: precision_at_10 value: 7.466 - type: precision_at_100 value: 1.228 - type: precision_at_1000 value: 0.16199999999999998 - type: precision_at_3 value: 17.122999999999998 - type: precision_at_5 value: 12.283 - type: recall_at_1 value: 24.238 - type: recall_at_10 value: 52.078 - type: recall_at_100 value: 77.643 - type: recall_at_1000 value: 93.49199999999999 - type: recall_at_3 value: 38.161 - type: recall_at_5 value: 44.781 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 24.915250000000004 - type: map_at_10 value: 33.98191666666666 - type: map_at_100 value: 35.19166666666667 - type: map_at_1000 value: 35.30983333333333 - type: map_at_3 value: 31.27391666666666 - type: map_at_5 value: 32.74366666666666 - type: mrr_at_1 value: 29.800749999999994 - type: mrr_at_10 value: 38.235749999999996 - type: mrr_at_100 value: 39.10616666666667 - type: mrr_at_1000 value: 39.166583333333335 - type: mrr_at_3 value: 35.91033333333334 - type: mrr_at_5 value: 37.17766666666667 - type: ndcg_at_1 value: 29.800749999999994 - type: ndcg_at_10 value: 39.287833333333325 - type: ndcg_at_100 value: 44.533833333333334 - type: ndcg_at_1000 value: 46.89608333333333 - type: ndcg_at_3 value: 34.676 - type: ndcg_at_5 value: 36.75208333333333 - type: precision_at_1 value: 29.800749999999994 - type: precision_at_10 value: 6.9134166666666665 - type: precision_at_100 value: 1.1206666666666665 - type: precision_at_1000 value: 0.15116666666666667 - type: precision_at_3 value: 16.069083333333335 - type: precision_at_5 value: 11.337916666666668 - type: recall_at_1 value: 24.915250000000004 - type: recall_at_10 value: 50.86333333333334 - type: recall_at_100 value: 73.85574999999999 - type: recall_at_1000 value: 90.24041666666666 - type: recall_at_3 value: 37.80116666666666 - type: recall_at_5 value: 43.263 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackStatsRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 22.853 - type: map_at_10 value: 30.349999999999998 - type: map_at_100 value: 31.341 - type: map_at_1000 value: 31.44 - type: map_at_3 value: 28.294999999999998 - type: map_at_5 value: 29.412 - type: mrr_at_1 value: 25.919999999999998 - type: mrr_at_10 value: 33.194 - type: mrr_at_100 value: 34.071 - type: mrr_at_1000 value: 34.136 - type: mrr_at_3 value: 31.391000000000002 - type: mrr_at_5 value: 32.311 - type: ndcg_at_1 value: 25.919999999999998 - type: ndcg_at_10 value: 34.691 - type: ndcg_at_100 value: 39.83 - type: ndcg_at_1000 value: 42.193000000000005 - type: ndcg_at_3 value: 30.91 - type: ndcg_at_5 value: 32.634 - type: precision_at_1 value: 25.919999999999998 - type: precision_at_10 value: 5.521 - type: precision_at_100 value: 0.882 - type: precision_at_1000 value: 0.117 - type: precision_at_3 value: 13.547999999999998 - type: precision_at_5 value: 9.293999999999999 - type: recall_at_1 value: 22.853 - type: recall_at_10 value: 45.145 - type: recall_at_100 value: 69.158 - type: recall_at_1000 value: 86.354 - type: recall_at_3 value: 34.466 - type: recall_at_5 value: 39.044000000000004 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackTexRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 17.151 - type: map_at_10 value: 23.674 - type: map_at_100 value: 24.738 - type: map_at_1000 value: 24.864 - type: map_at_3 value: 21.514 - type: map_at_5 value: 22.695 - type: mrr_at_1 value: 20.991 - type: mrr_at_10 value: 27.612 - type: mrr_at_100 value: 28.526 - type: mrr_at_1000 value: 28.603 - type: mrr_at_3 value: 25.618999999999996 - type: mrr_at_5 value: 26.674 - type: ndcg_at_1 value: 20.991 - type: ndcg_at_10 value: 27.983000000000004 - type: ndcg_at_100 value: 33.190999999999995 - type: ndcg_at_1000 value: 36.172 - type: ndcg_at_3 value: 24.195 - type: ndcg_at_5 value: 25.863999999999997 - type: precision_at_1 value: 20.991 - type: precision_at_10 value: 5.093 - type: precision_at_100 value: 0.8959999999999999 - type: precision_at_1000 value: 0.132 - type: precision_at_3 value: 11.402 - type: precision_at_5 value: 8.197000000000001 - type: recall_at_1 value: 17.151 - type: recall_at_10 value: 37.025000000000006 - type: recall_at_100 value: 60.787 - type: recall_at_1000 value: 82.202 - type: recall_at_3 value: 26.19 - type: recall_at_5 value: 30.657 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackUnixRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 25.463 - type: map_at_10 value: 34.372 - type: map_at_100 value: 35.475 - type: map_at_1000 value: 35.582 - type: map_at_3 value: 31.791000000000004 - type: map_at_5 value: 33.292 - type: mrr_at_1 value: 30.784 - type: mrr_at_10 value: 38.948 - type: mrr_at_100 value: 39.792 - type: mrr_at_1000 value: 39.857 - type: mrr_at_3 value: 36.614000000000004 - type: mrr_at_5 value: 37.976 - type: ndcg_at_1 value: 30.784 - type: ndcg_at_10 value: 39.631 - type: ndcg_at_100 value: 44.747 - type: ndcg_at_1000 value: 47.172 - type: ndcg_at_3 value: 34.976 - type: ndcg_at_5 value: 37.241 - type: precision_at_1 value: 30.784 - type: precision_at_10 value: 6.622999999999999 - type: precision_at_100 value: 1.04 - type: precision_at_1000 value: 0.135 - type: precision_at_3 value: 16.014 - type: precision_at_5 value: 11.286999999999999 - type: recall_at_1 value: 25.463 - type: recall_at_10 value: 51.23799999999999 - type: recall_at_100 value: 73.4 - type: recall_at_1000 value: 90.634 - type: recall_at_3 value: 38.421 - type: recall_at_5 value: 44.202999999999996 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWebmastersRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 23.714 - type: map_at_10 value: 32.712 - type: map_at_100 value: 34.337 - type: map_at_1000 value: 34.556 - type: map_at_3 value: 29.747 - type: map_at_5 value: 31.208000000000002 - type: mrr_at_1 value: 29.051 - type: mrr_at_10 value: 37.589 - type: mrr_at_100 value: 38.638 - type: mrr_at_1000 value: 38.692 - type: mrr_at_3 value: 35.079 - type: mrr_at_5 value: 36.265 - type: ndcg_at_1 value: 29.051 - type: ndcg_at_10 value: 38.681 - type: ndcg_at_100 value: 44.775999999999996 - type: ndcg_at_1000 value: 47.354 - type: ndcg_at_3 value: 33.888 - type: ndcg_at_5 value: 35.854 - type: precision_at_1 value: 29.051 - type: precision_at_10 value: 7.489999999999999 - type: precision_at_100 value: 1.518 - type: precision_at_1000 value: 0.241 - type: precision_at_3 value: 16.008 - type: precision_at_5 value: 11.66 - type: recall_at_1 value: 23.714 - type: recall_at_10 value: 50.324000000000005 - type: recall_at_100 value: 77.16 - type: recall_at_1000 value: 93.186 - type: recall_at_3 value: 36.356 - type: recall_at_5 value: 41.457 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWordpressRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 18.336 - type: map_at_10 value: 26.345000000000002 - type: map_at_100 value: 27.336 - type: map_at_1000 value: 27.436 - type: map_at_3 value: 23.865 - type: map_at_5 value: 25.046000000000003 - type: mrr_at_1 value: 19.778000000000002 - type: mrr_at_10 value: 27.837 - type: mrr_at_100 value: 28.82 - type: mrr_at_1000 value: 28.897000000000002 - type: mrr_at_3 value: 25.446999999999996 - type: mrr_at_5 value: 26.556 - type: ndcg_at_1 value: 19.778000000000002 - type: ndcg_at_10 value: 31.115 - type: ndcg_at_100 value: 36.109 - type: ndcg_at_1000 value: 38.769999999999996 - type: ndcg_at_3 value: 26.048 - type: ndcg_at_5 value: 28.004 - type: precision_at_1 value: 19.778000000000002 - type: precision_at_10 value: 5.157 - type: precision_at_100 value: 0.808 - type: precision_at_1000 value: 0.11 - type: precision_at_3 value: 11.459999999999999 - type: precision_at_5 value: 8.022 - type: recall_at_1 value: 18.336 - type: recall_at_10 value: 44.489000000000004 - type: recall_at_100 value: 67.43599999999999 - type: recall_at_1000 value: 87.478 - type: recall_at_3 value: 30.462 - type: recall_at_5 value: 35.188 - task: type: Retrieval dataset: type: climate-fever name: MTEB ClimateFEVER config: default split: test revision: None metrics: - type: map_at_1 value: 10.747 - type: map_at_10 value: 18.625 - type: map_at_100 value: 20.465 - type: map_at_1000 value: 20.639 - type: map_at_3 value: 15.57 - type: map_at_5 value: 17.089 - type: mrr_at_1 value: 24.169 - type: mrr_at_10 value: 35.96 - type: mrr_at_100 value: 36.888 - type: mrr_at_1000 value: 36.931999999999995 - type: mrr_at_3 value: 32.443 - type: mrr_at_5 value: 34.433 - type: ndcg_at_1 value: 24.169 - type: ndcg_at_10 value: 26.791999999999998 - type: ndcg_at_100 value: 34.054 - type: ndcg_at_1000 value: 37.285000000000004 - type: ndcg_at_3 value: 21.636 - type: ndcg_at_5 value: 23.394000000000002 - type: precision_at_1 value: 24.169 - type: precision_at_10 value: 8.476 - type: precision_at_100 value: 1.6209999999999998 - type: precision_at_1000 value: 0.22200000000000003 - type: precision_at_3 value: 16.156000000000002 - type: precision_at_5 value: 12.520999999999999 - type: recall_at_1 value: 10.747 - type: recall_at_10 value: 32.969 - type: recall_at_100 value: 57.99999999999999 - type: recall_at_1000 value: 76.12299999999999 - type: recall_at_3 value: 20.315 - type: recall_at_5 value: 25.239 - task: type: Retrieval dataset: type: C-MTEB/CmedqaRetrieval name: MTEB CmedqaRetrieval config: default split: dev revision: None metrics: - type: map_at_1 value: 14.751 - type: map_at_10 value: 22.03 - type: map_at_100 value: 23.471 - type: map_at_1000 value: 23.644000000000002 - type: map_at_3 value: 19.559 - type: map_at_5 value: 20.863 - type: mrr_at_1 value: 23.581 - type: mrr_at_10 value: 29.863 - type: mrr_at_100 value: 30.839 - type: mrr_at_1000 value: 30.925000000000004 - type: mrr_at_3 value: 27.894000000000002 - type: mrr_at_5 value: 28.965999999999998 - type: ndcg_at_1 value: 23.581 - type: ndcg_at_10 value: 26.996 - type: ndcg_at_100 value: 33.537 - type: ndcg_at_1000 value: 37.307 - type: ndcg_at_3 value: 23.559 - type: ndcg_at_5 value: 24.839 - type: precision_at_1 value: 23.581 - type: precision_at_10 value: 6.209 - type: precision_at_100 value: 1.165 - type: precision_at_1000 value: 0.165 - type: precision_at_3 value: 13.62 - type: precision_at_5 value: 9.882 - type: recall_at_1 value: 14.751 - type: recall_at_10 value: 34.075 - type: recall_at_100 value: 61.877 - type: recall_at_1000 value: 88.212 - type: recall_at_3 value: 23.519000000000002 - type: recall_at_5 value: 27.685 - task: type: PairClassification dataset: type: C-MTEB/CMNLI name: MTEB Cmnli config: default split: validation revision: None metrics: - type: cos_sim_accuracy value: 76.36800962116656 - type: cos_sim_ap value: 85.14376065556142 - type: cos_sim_f1 value: 77.81474723623485 - type: cos_sim_precision value: 71.92460317460318 - type: cos_sim_recall value: 84.75566986205284 - type: dot_accuracy value: 71.94227300060132 - type: dot_ap value: 79.03676891584456 - type: dot_f1 value: 74.95833333333334 - type: dot_precision value: 67.59346233327072 - type: dot_recall value: 84.12438625204582 - type: euclidean_accuracy value: 76.043295249549 - type: euclidean_ap value: 85.28765360616536 - type: euclidean_f1 value: 78.01733248784612 - type: euclidean_precision value: 71.1861137897782 - type: euclidean_recall value: 86.29880757540333 - type: manhattan_accuracy value: 76.17558628983764 - type: manhattan_ap value: 85.52739323094916 - type: manhattan_f1 value: 78.30788804071246 - type: manhattan_precision value: 71.63918525703201 - type: manhattan_recall value: 86.34556932429273 - type: max_accuracy value: 76.36800962116656 - type: max_ap value: 85.52739323094916 - type: max_f1 value: 78.30788804071246 - task: type: Retrieval dataset: type: C-MTEB/CovidRetrieval name: MTEB CovidRetrieval config: default split: dev revision: None metrics: - type: map_at_1 value: 56.164 - type: map_at_10 value: 64.575 - type: map_at_100 value: 65.098 - type: map_at_1000 value: 65.118 - type: map_at_3 value: 62.329 - type: map_at_5 value: 63.535 - type: mrr_at_1 value: 56.269999999999996 - type: mrr_at_10 value: 64.63600000000001 - type: mrr_at_100 value: 65.14 - type: mrr_at_1000 value: 65.16 - type: mrr_at_3 value: 62.522 - type: mrr_at_5 value: 63.57000000000001 - type: ndcg_at_1 value: 56.269999999999996 - type: ndcg_at_10 value: 68.855 - type: ndcg_at_100 value: 71.47099999999999 - type: ndcg_at_1000 value: 72.02499999999999 - type: ndcg_at_3 value: 64.324 - type: ndcg_at_5 value: 66.417 - type: precision_at_1 value: 56.269999999999996 - type: precision_at_10 value: 8.303 - type: precision_at_100 value: 0.9570000000000001 - type: precision_at_1000 value: 0.1 - type: precision_at_3 value: 23.427999999999997 - type: precision_at_5 value: 15.09 - type: recall_at_1 value: 56.164 - type: recall_at_10 value: 82.271 - type: recall_at_100 value: 94.626 - type: recall_at_1000 value: 99.05199999999999 - type: recall_at_3 value: 69.94200000000001 - type: recall_at_5 value: 74.947 - task: type: Retrieval dataset: type: dbpedia-entity name: MTEB DBPedia config: default split: test revision: None metrics: - type: map_at_1 value: 8.686 - type: map_at_10 value: 17.766000000000002 - type: map_at_100 value: 23.507 - type: map_at_1000 value: 24.757 - type: map_at_3 value: 13.238 - type: map_at_5 value: 15.161 - type: mrr_at_1 value: 65.25 - type: mrr_at_10 value: 72.88 - type: mrr_at_100 value: 73.246 - type: mrr_at_1000 value: 73.261 - type: mrr_at_3 value: 71.542 - type: mrr_at_5 value: 72.392 - type: ndcg_at_1 value: 53.75 - type: ndcg_at_10 value: 37.623 - type: ndcg_at_100 value: 40.302 - type: ndcg_at_1000 value: 47.471999999999994 - type: ndcg_at_3 value: 43.324 - type: ndcg_at_5 value: 39.887 - type: precision_at_1 value: 65.25 - type: precision_at_10 value: 28.749999999999996 - type: precision_at_100 value: 8.34 - type: precision_at_1000 value: 1.703 - type: precision_at_3 value: 46.583000000000006 - type: precision_at_5 value: 38.0 - type: recall_at_1 value: 8.686 - type: recall_at_10 value: 22.966 - type: recall_at_100 value: 44.3 - type: recall_at_1000 value: 67.77499999999999 - type: recall_at_3 value: 14.527999999999999 - type: recall_at_5 value: 17.617 - task: type: Retrieval dataset: type: C-MTEB/DuRetrieval name: MTEB DuRetrieval config: default split: dev revision: None metrics: - type: map_at_1 value: 22.439 - type: map_at_10 value: 68.484 - type: map_at_100 value: 71.67999999999999 - type: map_at_1000 value: 71.761 - type: map_at_3 value: 46.373999999999995 - type: map_at_5 value: 58.697 - type: mrr_at_1 value: 80.65 - type: mrr_at_10 value: 86.53 - type: mrr_at_100 value: 86.624 - type: mrr_at_1000 value: 86.631 - type: mrr_at_3 value: 85.95 - type: mrr_at_5 value: 86.297 - type: ndcg_at_1 value: 80.65 - type: ndcg_at_10 value: 78.075 - type: ndcg_at_100 value: 82.014 - type: ndcg_at_1000 value: 82.903 - type: ndcg_at_3 value: 75.785 - type: ndcg_at_5 value: 74.789 - type: precision_at_1 value: 80.65 - type: precision_at_10 value: 38.425 - type: precision_at_100 value: 4.62 - type: precision_at_1000 value: 0.483 - type: precision_at_3 value: 68.25 - type: precision_at_5 value: 57.92 - type: recall_at_1 value: 22.439 - type: recall_at_10 value: 80.396 - type: recall_at_100 value: 92.793 - type: recall_at_1000 value: 97.541 - type: recall_at_3 value: 49.611 - type: recall_at_5 value: 65.065 - task: type: Retrieval dataset: type: C-MTEB/EcomRetrieval name: MTEB EcomRetrieval config: default split: dev revision: None metrics: - type: map_at_1 value: 43.9 - type: map_at_10 value: 53.394 - type: map_at_100 value: 54.078 - type: map_at_1000 value: 54.105000000000004 - type: map_at_3 value: 50.583 - type: map_at_5 value: 52.443 - type: mrr_at_1 value: 43.9 - type: mrr_at_10 value: 53.394 - type: mrr_at_100 value: 54.078 - type: mrr_at_1000 value: 54.105000000000004 - type: mrr_at_3 value: 50.583 - type: mrr_at_5 value: 52.443 - type: ndcg_at_1 value: 43.9 - type: ndcg_at_10 value: 58.341 - type: ndcg_at_100 value: 61.753 - type: ndcg_at_1000 value: 62.525 - type: ndcg_at_3 value: 52.699 - type: ndcg_at_5 value: 56.042 - type: precision_at_1 value: 43.9 - type: precision_at_10 value: 7.3999999999999995 - type: precision_at_100 value: 0.901 - type: precision_at_1000 value: 0.096 - type: precision_at_3 value: 19.6 - type: precision_at_5 value: 13.38 - type: recall_at_1 value: 43.9 - type: recall_at_10 value: 74.0 - type: recall_at_100 value: 90.10000000000001 - type: recall_at_1000 value: 96.3 - type: recall_at_3 value: 58.8 - type: recall_at_5 value: 66.9 - task: type: Classification dataset: type: mteb/emotion name: MTEB EmotionClassification config: default split: test revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 metrics: - type: accuracy value: 48.765 - type: f1 value: 44.2791193129597 - task: type: Retrieval dataset: type: fever name: MTEB FEVER config: default split: test revision: None metrics: - type: map_at_1 value: 56.89999999999999 - type: map_at_10 value: 68.352 - type: map_at_100 value: 68.768 - type: map_at_1000 value: 68.782 - type: map_at_3 value: 66.27300000000001 - type: map_at_5 value: 67.67699999999999 - type: mrr_at_1 value: 61.476 - type: mrr_at_10 value: 72.662 - type: mrr_at_100 value: 72.993 - type: mrr_at_1000 value: 72.99799999999999 - type: mrr_at_3 value: 70.75200000000001 - type: mrr_at_5 value: 72.056 - type: ndcg_at_1 value: 61.476 - type: ndcg_at_10 value: 73.98400000000001 - type: ndcg_at_100 value: 75.744 - type: ndcg_at_1000 value: 76.036 - type: ndcg_at_3 value: 70.162 - type: ndcg_at_5 value: 72.482 - type: precision_at_1 value: 61.476 - type: precision_at_10 value: 9.565 - type: precision_at_100 value: 1.054 - type: precision_at_1000 value: 0.109 - type: precision_at_3 value: 27.943 - type: precision_at_5 value: 18.056 - type: recall_at_1 value: 56.89999999999999 - type: recall_at_10 value: 87.122 - type: recall_at_100 value: 94.742 - type: recall_at_1000 value: 96.70100000000001 - type: recall_at_3 value: 76.911 - type: recall_at_5 value: 82.607 - task: type: Retrieval dataset: type: fiqa name: MTEB FiQA2018 config: default split: test revision: None metrics: - type: map_at_1 value: 17.610999999999997 - type: map_at_10 value: 29.12 - type: map_at_100 value: 30.958000000000002 - type: map_at_1000 value: 31.151 - type: map_at_3 value: 25.369000000000003 - type: map_at_5 value: 27.445000000000004 - type: mrr_at_1 value: 35.185 - type: mrr_at_10 value: 44.533 - type: mrr_at_100 value: 45.385 - type: mrr_at_1000 value: 45.432 - type: mrr_at_3 value: 42.258 - type: mrr_at_5 value: 43.608999999999995 - type: ndcg_at_1 value: 35.185 - type: ndcg_at_10 value: 36.696 - type: ndcg_at_100 value: 43.491 - type: ndcg_at_1000 value: 46.800000000000004 - type: ndcg_at_3 value: 33.273 - type: ndcg_at_5 value: 34.336 - type: precision_at_1 value: 35.185 - type: precision_at_10 value: 10.309 - type: precision_at_100 value: 1.719 - type: precision_at_1000 value: 0.231 - type: precision_at_3 value: 22.479 - type: precision_at_5 value: 16.481 - type: recall_at_1 value: 17.610999999999997 - type: recall_at_10 value: 43.29 - type: recall_at_100 value: 68.638 - type: recall_at_1000 value: 88.444 - type: recall_at_3 value: 30.303 - type: recall_at_5 value: 35.856 - task: type: Retrieval dataset: type: hotpotqa name: MTEB HotpotQA config: default split: test revision: None metrics: - type: map_at_1 value: 34.18 - type: map_at_10 value: 47.753 - type: map_at_100 value: 48.522 - type: map_at_1000 value: 48.596000000000004 - type: map_at_3 value: 45.222 - type: map_at_5 value: 46.793 - type: mrr_at_1 value: 68.35900000000001 - type: mrr_at_10 value: 74.503 - type: mrr_at_100 value: 74.811 - type: mrr_at_1000 value: 74.82799999999999 - type: mrr_at_3 value: 73.347 - type: mrr_at_5 value: 74.06700000000001 - type: ndcg_at_1 value: 68.35900000000001 - type: ndcg_at_10 value: 56.665 - type: ndcg_at_100 value: 59.629 - type: ndcg_at_1000 value: 61.222 - type: ndcg_at_3 value: 52.81400000000001 - type: ndcg_at_5 value: 54.94 - type: precision_at_1 value: 68.35900000000001 - type: precision_at_10 value: 11.535 - type: precision_at_100 value: 1.388 - type: precision_at_1000 value: 0.16 - type: precision_at_3 value: 32.784 - type: precision_at_5 value: 21.348 - type: recall_at_1 value: 34.18 - type: recall_at_10 value: 57.677 - type: recall_at_100 value: 69.379 - type: recall_at_1000 value: 80.061 - type: recall_at_3 value: 49.175999999999995 - type: recall_at_5 value: 53.369 - task: type: Classification dataset: type: C-MTEB/IFlyTek-classification name: MTEB IFlyTek config: default split: validation revision: None metrics: - type: accuracy value: 46.23316660253944 - type: f1 value: 39.09397722262806 - task: type: Classification dataset: type: mteb/imdb name: MTEB ImdbClassification config: default split: test revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 metrics: - type: accuracy value: 78.46119999999999 - type: ap value: 72.53477126781094 - type: f1 value: 78.28701752379332 - task: type: Classification dataset: type: C-MTEB/JDReview-classification name: MTEB JDReview config: default split: test revision: None metrics: - type: accuracy value: 84.16510318949344 - type: ap value: 50.10324581565756 - type: f1 value: 78.34748161287605 - task: type: STS dataset: type: C-MTEB/LCQMC name: MTEB LCQMC config: default split: test revision: None metrics: - type: cos_sim_pearson value: 68.71925879533819 - type: cos_sim_spearman value: 75.33926640820977 - type: euclidean_pearson value: 74.59557932790653 - type: euclidean_spearman value: 75.76006440878783 - type: manhattan_pearson value: 74.7461963483351 - type: manhattan_spearman value: 75.87111519308131 - task: type: Retrieval dataset: type: C-MTEB/MMarcoRetrieval name: MTEB MMarcoRetrieval config: default split: dev revision: None metrics: - type: map_at_1 value: 66.249 - type: map_at_10 value: 75.236 - type: map_at_100 value: 75.581 - type: map_at_1000 value: 75.593 - type: map_at_3 value: 73.463 - type: map_at_5 value: 74.602 - type: mrr_at_1 value: 68.42399999999999 - type: mrr_at_10 value: 75.81099999999999 - type: mrr_at_100 value: 76.115 - type: mrr_at_1000 value: 76.126 - type: mrr_at_3 value: 74.26899999999999 - type: mrr_at_5 value: 75.24300000000001 - type: ndcg_at_1 value: 68.42399999999999 - type: ndcg_at_10 value: 78.81700000000001 - type: ndcg_at_100 value: 80.379 - type: ndcg_at_1000 value: 80.667 - type: ndcg_at_3 value: 75.476 - type: ndcg_at_5 value: 77.38199999999999 - type: precision_at_1 value: 68.42399999999999 - type: precision_at_10 value: 9.491 - type: precision_at_100 value: 1.027 - type: precision_at_1000 value: 0.105 - type: precision_at_3 value: 28.352 - type: precision_at_5 value: 18.043 - type: recall_at_1 value: 66.249 - type: recall_at_10 value: 89.238 - type: recall_at_100 value: 96.319 - type: recall_at_1000 value: 98.524 - type: recall_at_3 value: 80.438 - type: recall_at_5 value: 84.95 - task: type: Retrieval dataset: type: msmarco name: MTEB MSMARCO config: default split: dev revision: None metrics: - type: map_at_1 value: 23.083000000000002 - type: map_at_10 value: 35.251 - type: map_at_100 value: 36.461 - type: map_at_1000 value: 36.507 - type: map_at_3 value: 31.474999999999998 - type: map_at_5 value: 33.658 - type: mrr_at_1 value: 23.724999999999998 - type: mrr_at_10 value: 35.88 - type: mrr_at_100 value: 37.021 - type: mrr_at_1000 value: 37.062 - type: mrr_at_3 value: 32.159 - type: mrr_at_5 value: 34.325 - type: ndcg_at_1 value: 23.724999999999998 - type: ndcg_at_10 value: 42.018 - type: ndcg_at_100 value: 47.764 - type: ndcg_at_1000 value: 48.916 - type: ndcg_at_3 value: 34.369 - type: ndcg_at_5 value: 38.266 - type: precision_at_1 value: 23.724999999999998 - type: precision_at_10 value: 6.553000000000001 - type: precision_at_100 value: 0.942 - type: precision_at_1000 value: 0.104 - type: precision_at_3 value: 14.532 - type: precision_at_5 value: 10.696 - type: recall_at_1 value: 23.083000000000002 - type: recall_at_10 value: 62.739 - type: recall_at_100 value: 89.212 - type: recall_at_1000 value: 97.991 - type: recall_at_3 value: 42.064 - type: recall_at_5 value: 51.417 - task: type: Classification dataset: type: mteb/mtop_domain name: MTEB MTOPDomainClassification (en) config: en split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 93.43365253077975 - type: f1 value: 93.07455671032345 - task: type: Classification dataset: type: mteb/mtop_intent name: MTEB MTOPIntentClassification (en) config: en split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - type: accuracy value: 71.72822617419061 - type: f1 value: 55.6093871673643 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (en) config: en split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 72.03765971755212 - type: f1 value: 70.88235592002572 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (en) config: en split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 76.86281102891728 - type: f1 value: 77.15496923811003 - task: type: Retrieval dataset: type: C-MTEB/MedicalRetrieval name: MTEB MedicalRetrieval config: default split: dev revision: None metrics: - type: map_at_1 value: 41.8 - type: map_at_10 value: 46.993 - type: map_at_100 value: 47.534 - type: map_at_1000 value: 47.587 - type: map_at_3 value: 45.717 - type: map_at_5 value: 46.357 - type: mrr_at_1 value: 42.0 - type: mrr_at_10 value: 47.093 - type: mrr_at_100 value: 47.634 - type: mrr_at_1000 value: 47.687000000000005 - type: mrr_at_3 value: 45.817 - type: mrr_at_5 value: 46.457 - type: ndcg_at_1 value: 41.8 - type: ndcg_at_10 value: 49.631 - type: ndcg_at_100 value: 52.53 - type: ndcg_at_1000 value: 54.238 - type: ndcg_at_3 value: 46.949000000000005 - type: ndcg_at_5 value: 48.102000000000004 - type: precision_at_1 value: 41.8 - type: precision_at_10 value: 5.800000000000001 - type: precision_at_100 value: 0.722 - type: precision_at_1000 value: 0.086 - type: precision_at_3 value: 16.833000000000002 - type: precision_at_5 value: 10.66 - type: recall_at_1 value: 41.8 - type: recall_at_10 value: 57.99999999999999 - type: recall_at_100 value: 72.2 - type: recall_at_1000 value: 86.3 - type: recall_at_3 value: 50.5 - type: recall_at_5 value: 53.300000000000004 - task: type: Clustering dataset: type: mteb/medrxiv-clustering-p2p name: MTEB MedrxivClusteringP2P config: default split: test revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 metrics: - type: v_measure value: 30.949060810392886 - task: type: Clustering dataset: type: mteb/medrxiv-clustering-s2s name: MTEB MedrxivClusteringS2S config: default split: test revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 metrics: - type: v_measure value: 28.87339864059011 - task: type: Reranking dataset: type: mteb/mind_small name: MTEB MindSmallReranking config: default split: test revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 metrics: - type: map value: 31.217934626189926 - type: mrr value: 32.27509143911496 - task: type: Reranking dataset: type: C-MTEB/Mmarco-reranking name: MTEB MMarcoReranking config: default split: dev revision: None metrics: - type: map value: 26.691638884089574 - type: mrr value: 25.15674603174603 - task: type: Classification dataset: type: C-MTEB/MultilingualSentiment-classification name: MTEB MultilingualSentiment config: default split: validation revision: None metrics: - type: accuracy value: 68.35666666666667 - type: f1 value: 68.30294399725629 - task: type: Retrieval dataset: type: nfcorpus name: MTEB NFCorpus config: default split: test revision: None metrics: - type: map_at_1 value: 5.759 - type: map_at_10 value: 13.425999999999998 - type: map_at_100 value: 16.988 - type: map_at_1000 value: 18.512 - type: map_at_3 value: 9.737 - type: map_at_5 value: 11.558 - type: mrr_at_1 value: 48.297000000000004 - type: mrr_at_10 value: 56.788000000000004 - type: mrr_at_100 value: 57.306000000000004 - type: mrr_at_1000 value: 57.349000000000004 - type: mrr_at_3 value: 54.386 - type: mrr_at_5 value: 56.135000000000005 - type: ndcg_at_1 value: 46.285 - type: ndcg_at_10 value: 36.016 - type: ndcg_at_100 value: 32.984 - type: ndcg_at_1000 value: 42.093 - type: ndcg_at_3 value: 41.743 - type: ndcg_at_5 value: 39.734 - type: precision_at_1 value: 48.297000000000004 - type: precision_at_10 value: 26.779999999999998 - type: precision_at_100 value: 8.505 - type: precision_at_1000 value: 2.1420000000000003 - type: precision_at_3 value: 39.422000000000004 - type: precision_at_5 value: 34.675 - type: recall_at_1 value: 5.759 - type: recall_at_10 value: 17.251 - type: recall_at_100 value: 33.323 - type: recall_at_1000 value: 66.759 - type: recall_at_3 value: 10.703 - type: recall_at_5 value: 13.808000000000002 - task: type: Retrieval dataset: type: nq name: MTEB NQ config: default split: test revision: None metrics: - type: map_at_1 value: 31.696999999999996 - type: map_at_10 value: 46.099000000000004 - type: map_at_100 value: 47.143 - type: map_at_1000 value: 47.178 - type: map_at_3 value: 41.948 - type: map_at_5 value: 44.504 - type: mrr_at_1 value: 35.717999999999996 - type: mrr_at_10 value: 48.653 - type: mrr_at_100 value: 49.456 - type: mrr_at_1000 value: 49.479 - type: mrr_at_3 value: 45.283 - type: mrr_at_5 value: 47.422 - type: ndcg_at_1 value: 35.689 - type: ndcg_at_10 value: 53.312000000000005 - type: ndcg_at_100 value: 57.69 - type: ndcg_at_1000 value: 58.489000000000004 - type: ndcg_at_3 value: 45.678999999999995 - type: ndcg_at_5 value: 49.897000000000006 - type: precision_at_1 value: 35.689 - type: precision_at_10 value: 8.685 - type: precision_at_100 value: 1.111 - type: precision_at_1000 value: 0.11900000000000001 - type: precision_at_3 value: 20.558 - type: precision_at_5 value: 14.802999999999999 - type: recall_at_1 value: 31.696999999999996 - type: recall_at_10 value: 72.615 - type: recall_at_100 value: 91.563 - type: recall_at_1000 value: 97.52300000000001 - type: recall_at_3 value: 53.203 - type: recall_at_5 value: 62.836000000000006 - task: type: PairClassification dataset: type: C-MTEB/OCNLI name: MTEB Ocnli config: default split: validation revision: None metrics: - type: cos_sim_accuracy value: 67.94802382241473 - type: cos_sim_ap value: 72.1545049768353 - type: cos_sim_f1 value: 71.24658780709737 - type: cos_sim_precision value: 62.589928057553955 - type: cos_sim_recall value: 82.68215417106653 - type: dot_accuracy value: 63.56253383865729 - type: dot_ap value: 66.5298825401086 - type: dot_f1 value: 69.31953840031835 - type: dot_precision value: 55.61941251596424 - type: dot_recall value: 91.97465681098205 - type: euclidean_accuracy value: 69.46399566865186 - type: euclidean_ap value: 73.63177936887436 - type: euclidean_f1 value: 72.91028446389497 - type: euclidean_precision value: 62.25710014947683 - type: euclidean_recall value: 87.96198521647307 - type: manhattan_accuracy value: 69.89713048186248 - type: manhattan_ap value: 74.11555425121965 - type: manhattan_f1 value: 72.8923476005188 - type: manhattan_precision value: 61.71303074670571 - type: manhattan_recall value: 89.01795142555439 - type: max_accuracy value: 69.89713048186248 - type: max_ap value: 74.11555425121965 - type: max_f1 value: 72.91028446389497 - task: type: Classification dataset: type: C-MTEB/OnlineShopping-classification name: MTEB OnlineShopping config: default split: test revision: None metrics: - type: accuracy value: 90.93 - type: ap value: 88.66185083484555 - type: f1 value: 90.91685771516175 - task: type: STS dataset: type: C-MTEB/PAWSX name: MTEB PAWSX config: default split: test revision: None metrics: - type: cos_sim_pearson value: 14.385178129184318 - type: cos_sim_spearman value: 17.246549728263478 - type: euclidean_pearson value: 18.921969136664913 - type: euclidean_spearman value: 17.245713577354014 - type: manhattan_pearson value: 18.98503959815216 - type: manhattan_spearman value: 17.37740013639568 - task: type: STS dataset: type: C-MTEB/QBQTC name: MTEB QBQTC config: default split: test revision: None metrics: - type: cos_sim_pearson value: 32.04198138050403 - type: cos_sim_spearman value: 34.4844617563846 - type: euclidean_pearson value: 34.2634608256121 - type: euclidean_spearman value: 36.322207068208066 - type: manhattan_pearson value: 34.414939622012284 - type: manhattan_spearman value: 36.49437789416394 - task: type: Retrieval dataset: type: quora name: MTEB QuoraRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 70.858 - type: map_at_10 value: 84.516 - type: map_at_100 value: 85.138 - type: map_at_1000 value: 85.153 - type: map_at_3 value: 81.487 - type: map_at_5 value: 83.41199999999999 - type: mrr_at_1 value: 81.55 - type: mrr_at_10 value: 87.51400000000001 - type: mrr_at_100 value: 87.607 - type: mrr_at_1000 value: 87.60900000000001 - type: mrr_at_3 value: 86.49 - type: mrr_at_5 value: 87.21 - type: ndcg_at_1 value: 81.57 - type: ndcg_at_10 value: 88.276 - type: ndcg_at_100 value: 89.462 - type: ndcg_at_1000 value: 89.571 - type: ndcg_at_3 value: 85.294 - type: ndcg_at_5 value: 86.979 - type: precision_at_1 value: 81.57 - type: precision_at_10 value: 13.389999999999999 - type: precision_at_100 value: 1.532 - type: precision_at_1000 value: 0.157 - type: precision_at_3 value: 37.2 - type: precision_at_5 value: 24.544 - type: recall_at_1 value: 70.858 - type: recall_at_10 value: 95.428 - type: recall_at_100 value: 99.46000000000001 - type: recall_at_1000 value: 99.98 - type: recall_at_3 value: 86.896 - type: recall_at_5 value: 91.617 - task: type: Clustering dataset: type: mteb/reddit-clustering name: MTEB RedditClustering config: default split: test revision: 24640382cdbf8abc73003fb0fa6d111a705499eb metrics: - type: v_measure value: 47.90089115942085 - 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type: ndcg_at_5 value: 60.402 - type: precision_at_1 value: 46.9 - type: precision_at_10 value: 7.82 - type: precision_at_100 value: 0.915 - type: precision_at_1000 value: 0.097 - type: precision_at_3 value: 21.633 - type: precision_at_5 value: 14.38 - type: recall_at_1 value: 46.9 - type: recall_at_10 value: 78.2 - type: recall_at_100 value: 91.5 - type: recall_at_1000 value: 97.2 - type: recall_at_3 value: 64.9 - type: recall_at_5 value: 71.89999999999999 - task: type: Classification dataset: type: C-MTEB/waimai-classification name: MTEB Waimai config: default split: test revision: None metrics: - type: accuracy value: 84.68 - type: ap value: 66.4749730574293 - type: f1 value: 82.93606561551698 --- # Model Card for udever-bloom <!-- Provide a quick summary of what the model is/does. --> `udever-bloom-7b1` is finetuned from [bigscience/bloom-7b1](https://huggingface.co/bigscience/bloom-7b1) via [BitFit](https://aclanthology.org/2022.acl-short.1/) on MS MARCO Passage Ranking, SNLI and MultiNLI data. It is a universal embedding model across tasks, natural and programming languages. (From the technical view, `udever` is merely with some minor improvements to `sgpt-bloom`) <img width="338" height="259" src="https://user-images.githubusercontent.com/26690193/277643721-cdb7f227-cae5-40e1-b6e1-a201bde00339.png" /> ## Model Details ### Model Description - Developed by: Alibaba Group - Model type: Transformer-based Language Model (decoder-only) - Language(s) (NLP): Multiple; see [bloom training data](https://huggingface.co/bigscience/bloom-7b1#training-data) - Finetuned from model : [bigscience/bloom-7b1](https://huggingface.co/bigscience/bloom-7b1) ### Model Sources <!-- Provide the basic links for the model. --> - Repository: [github.com/izhx/uni-rep](https://github.com/izhx/uni-rep) - Paper : [Language Models are Universal Embedders](https://arxiv.org/pdf/2310.08232.pdf) - Training Date : 2023-06 ### Checkpoints - [udever-bloom-560m](https://huggingface.co/izhx/udever-bloom-560m) - [udever-bloom-1b1](https://huggingface.co/izhx/udever-bloom-1b1) - [udever-bloom-3b](https://huggingface.co/izhx/udever-bloom-3b) - [udever-bloom-7b1](https://huggingface.co/izhx/udever-bloom-7b1) On ModelScope / 魔搭社区: [udever-bloom-560m](https://modelscope.cn/models/damo/udever-bloom-560m), [udever-bloom-1b1](https://modelscope.cn/models/damo/udever-bloom-1b1), [udever-bloom-3b](https://modelscope.cn/models/damo/udever-bloom-3b), [udever-bloom-7b1](https://modelscope.cn/models/damo/udever-bloom-7b1) ## How to Get Started with the Model Use the code below to get started with the model. ```python import torch from transformers import AutoTokenizer, BloomModel tokenizer = AutoTokenizer.from_pretrained('izhx/udever-bloom-7b1') model = BloomModel.from_pretrained('izhx/udever-bloom-7b1') boq, eoq, bod, eod = '[BOQ]', '[EOQ]', '[BOD]', '[EOD]' eoq_id, eod_id = tokenizer.convert_tokens_to_ids([eoq, eod]) if tokenizer.padding_side != 'left': print('!!!', tokenizer.padding_side) tokenizer.padding_side = 'left' def encode(texts: list, is_query: bool = True, max_length=300): bos = boq if is_query else bod eos_id = eoq_id if is_query else eod_id texts = [bos + t for t in texts] encoding = tokenizer( texts, truncation=True, max_length=max_length - 1, padding=True ) for ids, mask in zip(encoding['input_ids'], encoding['attention_mask']): ids.append(eos_id) mask.append(1) inputs = tokenizer.pad(encoding, return_tensors='pt') with torch.inference_mode(): outputs = model(inputs) embeds = outputs.last_hidden_state[:, -1] return embeds encode(['I am Bert', 'You are Elmo']) ``` ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> - MS MARCO Passage Ranking, retrieved by (https://github.com/UKPLab/sentence-transformers/blob/master/examples/training/ms_marco/train_bi-encoder_mnrl.py#L86) - SNLI and MultiNLI (https://sbert.net/datasets/AllNLI.tsv.gz) ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing MS MARCO hard negatives provided by (https://github.com/UKPLab/sentence-transformers/blob/master/examples/training/ms_marco/train_bi-encoder_mnrl.py#L86). Negatives for SNLI and MultiNLI are randomly sampled. #### Training Hyperparameters - Training regime: tf32, BitFit - Batch size: 1024 - Epochs: 3 - Optimizer: AdamW - Learning rate: 1e-4 - Scheduler: constant with warmup. - Warmup: 0.25 epoch ## Evaluation ### Table 1: Massive Text Embedding Benchmark [MTEB](https://huggingface.co/spaces/mteb/leaderboard) \| MTEB \| Avg. \| Class. \| Clust. \| PairClass. \| Rerank. \| Retr. \| STS \| Summ. \| \|-----------------------------\|--------------\|--------------\|--------------\|--------------\|--------------\|--------------\|--------------\|--------\| \| #Datasets ➡️ \| 56 \| 12 \| 11 \| 3 \| 4 \| 15 \| 10 \| 1 \| \|\| \| bge-large-en-v1.5 \| 64.23 \| 75.97 \| 46.08\| 87.12 \| 60.03 \| 54.29 \| 83.11\| 31.61 \| \| bge-base-en-v1.5 \| 63.55\| 75.53\| 45.77\| 86.55\| 58.86\| 53.25\| 82.4\| 31.07 \| \| gte-large \| 63.13\| 73.33\| 46.84 \| 85\| 59.13\| 52.22\| 83.35 \| 31.66 \| \| gte-base \| 62.39\| 73.01\| 46.2\| 84.57\| 58.61\| 51.14\| 82.3\| 31.17 \| \| e5-large-v2 \| 62.25\| 75.24\| 44.49\| 86.03\| 56.61\| 50.56\| 82.05\| 30.19 \| \| instructor-xl \| 61.79\| 73.12\| 44.74\| 86.62\| 57.29\| 49.26\| 83.06\| 32.32 \| \| instructor-large \| 61.59\| 73.86\| 45.29\| 85.89\| 57.54\| 47.57\| 83.15\| 31.84 \| \| e5-base-v2 \| 61.5 \| 73.84\| 43.8\| 85.73\| 55.91\| 50.29\| 81.05\| 30.28 \| \| e5-large \| 61.42\| 73.14\| 43.33\| 85.94\| 56.53\| 49.99\| 82.06\| 30.97 \| \| text-embedding-ada-002 (OpenAI API) \| 60.99\| 70.93\| 45.9 \| 84.89\| 56.32\| 49.25\| 80.97\| 30.8 \| \| e5-base \| 60.44\| 72.63\| 42.11\| 85.09\| 55.7 \| 48.75\| 80.96\| 31.01 \| \| SGPT-5.8B-msmarco \| 58.93\| 68.13\| 40.34\| 82 \| 56.56\| 50.25\| 78.1 \| 31.46 \| \| sgpt-bloom-7b1-msmarco \| 57.59\| 66.19\| 38.93\| 81.9 \| 55.65\| 48.22\| 77.74\| 33.6 \| \|\| \| Udever-bloom-560m \| 55.80\| 68.04\| 36.89\| 81.05\| 52.60\| 41.19\| 79.93\| 32.06 \| \| Udever-bloom-1b1 \| 58.28\| 70.18\| 39.11\| 83.11\| 54.28\| 45.27\| 81.52\| 31.10 \| \| Udever-bloom-3b \| 59.86\| 71.91\| 40.74\| 84.06\| 54.90\| 47.67\| 82.37\| 30.62 \| \| Udever-bloom-7b1 \| 60.63 \| 72.13\| 40.81\| 85.40\| 55.91\| 49.34\| 83.01\| 30.97 \| ### Table 2: [CodeSearchNet](https://github.com/github/CodeSearchNet) \| CodeSearchNet \| Go \| Ruby \| Python \| Java \| JS \| PHP \| Avg. \| \|-\|-\|-\|-\|-\|-\|-\|-\| \| CodeBERT \| 69.3 \| 70.6 \| 84.0 \| 86.8 \| 74.8 \| 70.6 \| 76.0 \| \| GraphCodeBERT \| 84.1 \| 73.2 \| 87.9 \| 75.7 \| 71.1 \| 72.5 \| 77.4 \| \| cpt-code S \| 97.7 \| 86.3 \| 99.8 \| 94.0 \| 86.0 \| 96.7 \| 93.4 \| \| cpt-code M \| 97.5 \| 85.5 \| 99.9 \| 94.4 \| 86.5 \| 97.2 \| 93.5 \| \| sgpt-bloom-7b1-msmarco \| 76.79 \| 69.25 \| 95.68 \| 77.93 \| 70.35 \| 73.45 \| 77.24 \| \|\| \| Udever-bloom-560m \| 75.38 \| 66.67 \| 96.23 \| 78.99 \| 69.39 \| 73.69 \| 76.73 \| \| Udever-bloom-1b1 \| 78.76 \| 72.85 \| 97.67 \| 82.77 \| 74.38 \| 78.97 \| 80.90 \| \| Udever-bloom-3b \| 80.63 \| 75.40 \| 98.02 \| 83.88 \| 76.18 \| 79.67 \| 82.29 \| \| Udever-bloom-7b1 \| 79.37 \| 76.59 \| 98.38 \| 84.68 \| 77.49 \| 80.03 \| 82.76 \| ### Table 3: Chinese multi-domain retrieval [Multi-cpr](https://dl.acm.org/doi/10.1145/3477495.3531736) \| \| \| \|E-commerce \| \| Entertainment video \| \| Medical \| \| \|--\|--\|--\|--\|--\|--\|--\|--\|--\| \| Model \| Train \| Backbone \| MRR@10 \| Recall@1k \| MRR@10 \| Recall@1k \| MRR@10 \| Recall@1k \| \|\| \| BM25 \| - \| - \| 0.225 \| 0.815 \| 0.225 \| 0.780 \| 0.187 \| 0.482 \| \| Doc2Query \| - \| - \| 0.239 \| 0.826 \| 0.238 \| 0.794 \| 0.210 \| 0.505 \| \| DPR-1 \| In-Domain \| BERT \| 0.270 \| 0.921 \| 0.254 \| 0.934 \| 0.327 \| 0.747 \| \| DPR-2 \| In-Domain \| BERT-CT \| 0.289 \| 0.926 \| 0.263 \| 0.935 \| 0.339 \| 0.769 \| \| text-embedding-ada-002 \| General \| GPT \| 0.183 \| 0.825 \| 0.159 \| 0.786 \| 0.245 \| 0.593 \| \| sgpt-bloom-7b1-msmarco \| General \| BLOOM \| 0.242 \| 0.840 \| 0.227 \| 0.829 \| 0.311 \| 0.675 \| \|\| \| Udever-bloom-560m \| General \| BLOOM \| 0.156 \| 0.802 \| 0.149 \| 0.749 \| 0.245 \| 0.571 \| \| Udever-bloom-1b1 \| General \| BLOOM \| 0.244 \| 0.863 \| 0.208 \| 0.815 \| 0.241 \| 0.557 \| \| Udever-bloom-3b \| General \| BLOOM \| 0.267 \| 0.871 \| 0.228 \| 0.836 \| 0.288 \| 0.619 \| \| Udever-bloom-7b1 \| General \| BLOOM \| 0.296 \| 0.889 \| 0.267 \| 0.907 \| 0.343 \| 0.705 \| #### More results refer to [paper](https://arxiv.org/pdf/2310.08232.pdf) section 3. ## Technical Specifications ### Model Architecture and Objective - Model: [bigscience/bloom-7b1](https://huggingface.co/bigscience/bloom-7b1). - Objective: Constrastive loss with hard negatives (refer to [paper](https://arxiv.org/pdf/2310.08232.pdf) section 2.2). ### Compute Infrastructure - Nvidia A100 SXM4 80GB. - torch 2.0.0, transformers 4.29.2. ## Citation BibTeX:** ```BibTeX @article{zhang2023language, title={Language Models are Universal Embedders}, author={Zhang, Xin and Li, Zehan and Zhang, Yanzhao and Long, Dingkun and Xie, Pengjun and Zhang, Meishan and Zhang, Min}, journal={arXiv preprint arXiv:2310.08232}, year={2023} } ```
mradermacher/Llama-3-Luminurse-v0.1-OAS-8B-i1-GGUF	mradermacher	2024-06-23T12:08:39Z	4,852	1	transformers	[ "transformers", "gguf", "mergekit", "merge", "en", "base_model:grimjim/Llama-3-Luminurse-v0.1-OAS-8B", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]	null	2024-06-02T06:06:50Z	--- base_model: grimjim/Llama-3-Luminurse-v0.1-OAS-8B language: - en library_name: transformers license: cc-by-nc-4.0 license_link: LICENSE quantized_by: mradermacher tags: - mergekit - merge --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/grimjim/Llama-3-Luminurse-v0.1-OAS-8B <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/Llama-3-Luminurse-v0.1-OAS-8B-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-Luminurse-v0.1-OAS-8B-i1-GGUF/resolve/main/Llama-3-Luminurse-v0.1-OAS-8B.i1-IQ1_S.gguf) \| i1-IQ1_S \| 2.1 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-Luminurse-v0.1-OAS-8B-i1-GGUF/resolve/main/Llama-3-Luminurse-v0.1-OAS-8B.i1-IQ1_M.gguf) \| i1-IQ1_M \| 2.3 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-Luminurse-v0.1-OAS-8B-i1-GGUF/resolve/main/Llama-3-Luminurse-v0.1-OAS-8B.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 2.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-Luminurse-v0.1-OAS-8B-i1-GGUF/resolve/main/Llama-3-Luminurse-v0.1-OAS-8B.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 2.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-Luminurse-v0.1-OAS-8B-i1-GGUF/resolve/main/Llama-3-Luminurse-v0.1-OAS-8B.i1-IQ2_S.gguf) \| i1-IQ2_S \| 2.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-Luminurse-v0.1-OAS-8B-i1-GGUF/resolve/main/Llama-3-Luminurse-v0.1-OAS-8B.i1-IQ2_M.gguf) \| i1-IQ2_M \| 3.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-Luminurse-v0.1-OAS-8B-i1-GGUF/resolve/main/Llama-3-Luminurse-v0.1-OAS-8B.i1-Q2_K.gguf) \| i1-Q2_K \| 3.3 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-Luminurse-v0.1-OAS-8B-i1-GGUF/resolve/main/Llama-3-Luminurse-v0.1-OAS-8B.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 3.4 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-Luminurse-v0.1-OAS-8B-i1-GGUF/resolve/main/Llama-3-Luminurse-v0.1-OAS-8B.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 3.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-Luminurse-v0.1-OAS-8B-i1-GGUF/resolve/main/Llama-3-Luminurse-v0.1-OAS-8B.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 3.8 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-Luminurse-v0.1-OAS-8B-i1-GGUF/resolve/main/Llama-3-Luminurse-v0.1-OAS-8B.i1-IQ3_S.gguf) \| i1-IQ3_S \| 3.8 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-Luminurse-v0.1-OAS-8B-i1-GGUF/resolve/main/Llama-3-Luminurse-v0.1-OAS-8B.i1-IQ3_M.gguf) \| i1-IQ3_M \| 3.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-Luminurse-v0.1-OAS-8B-i1-GGUF/resolve/main/Llama-3-Luminurse-v0.1-OAS-8B.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 4.1 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-Luminurse-v0.1-OAS-8B-i1-GGUF/resolve/main/Llama-3-Luminurse-v0.1-OAS-8B.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 4.4 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-Luminurse-v0.1-OAS-8B-i1-GGUF/resolve/main/Llama-3-Luminurse-v0.1-OAS-8B.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 4.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-Luminurse-v0.1-OAS-8B-i1-GGUF/resolve/main/Llama-3-Luminurse-v0.1-OAS-8B.i1-Q4_0.gguf) \| i1-Q4_0 \| 4.8 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-Luminurse-v0.1-OAS-8B-i1-GGUF/resolve/main/Llama-3-Luminurse-v0.1-OAS-8B.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 4.8 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-Luminurse-v0.1-OAS-8B-i1-GGUF/resolve/main/Llama-3-Luminurse-v0.1-OAS-8B.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 5.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-Luminurse-v0.1-OAS-8B-i1-GGUF/resolve/main/Llama-3-Luminurse-v0.1-OAS-8B.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 5.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-Luminurse-v0.1-OAS-8B-i1-GGUF/resolve/main/Llama-3-Luminurse-v0.1-OAS-8B.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 5.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-Luminurse-v0.1-OAS-8B-i1-GGUF/resolve/main/Llama-3-Luminurse-v0.1-OAS-8B.i1-Q6_K.gguf) \| i1-Q6_K \| 6.7 \| practically like static Q6_K \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his hardware for calculating the imatrix for these quants. <!-- end -->
rubra-ai/Phi-3-mini-128k-instruct-GGUF	rubra-ai	2024-07-01T06:14:16Z	4,852	2	null	[ "gguf", "function-calling", "tool-calling", "agentic", "rubra", "conversational", "en", "license:mit", "model-index", "region:us" ]	null	2024-06-15T07:41:23Z	--- license: mit model-index: - name: Rubra-Phi-3-mini-128k-instruct results: - task: type: text-generation dataset: type: MMLU name: MMLU metrics: - type: 5-shot value: 66.66 verified: false - task: type: text-generation dataset: type: GPQA name: GPQA metrics: - type: 0-shot value: 29.24 verified: false - task: type: text-generation dataset: type: GSM-8K name: GSM-8K metrics: - type: 8-shot, CoT value: 74.09 verified: false - task: type: text-generation dataset: type: MATH name: MATH metrics: - type: 4-shot, CoT value: 26.84 verified: false - task: type: text-generation dataset: type: MT-bench name: MT-bench metrics: - type: GPT-4 as Judge value: 7.45 verified: false tags: - function-calling - tool-calling - agentic - rubra - conversational language: - en --- # Rubra Phi-3 Mini 128k Instruct GGUF Original model: [rubra-ai/Phi-3-mini-128k-instruct](https://huggingface.co/rubra-ai/Phi-3-mini-128k-instruct) ## Model description The model is the result of further post-training [microsoft/Phi-3-mini-128k-instruct](https://huggingface.co/microsoft/Phi-3-mini-128k-instruct). This model is designed for high performance in various instruction-following tasks and complex interactions, including multi-turn function calling and detailed conversations. ## Training Data The model underwent additional training on a proprietary dataset encompassing diverse instruction-following, chat, and function calling data. This post-training process enhances the model's ability to integrate tools and manage complex interaction scenarios effectively. ## How to use Refer to https://docs.rubra.ai/inference/llamacpp for usage. Feel free to ask/open issues up in our Github repo: https://github.com/rubra-ai/rubra ## Limitations and Bias While the model performs well on a wide range of tasks, it may still produce biased or incorrect outputs. Users should exercise caution and critical judgment when using the model in sensitive or high-stakes applications. The model's outputs are influenced by the data it was trained on, which may contain inherent biases. ## Ethical Considerations Users should ensure that the deployment of this model adheres to ethical guidelines and consider the potential societal impact of the generated text. Misuse of the model for generating harmful or misleading content is strongly discouraged. ## Acknowledgements We would like to thank Microsoft for the model. ## Contact Information For questions or comments about the model, please reach out to [the rubra team](mailto:[email protected]). ## Citation If you use this work, please cite it as: ``` @misc {rubra_ai_2024, author = { Sanjay Nadhavajhala and Yingbei Tong }, title = { Phi-3-mini-128k-instruct }, year = 2024, url = { https://huggingface.co/rubra-ai/Phi-3-mini-128k-instruct }, doi = { 10.57967/hf/2657 }, publisher = { Hugging Face } } ```
v000000/L3-11.5B-DuS-MoonRoot-Q8_0-GGUF	v000000	2024-06-29T04:07:49Z	4,851	0	transformers	[ "transformers", "gguf", "mergekit", "merge", "llama", "llama-cpp", "base_model:v000000/L3-11.5B-DuS-MoonRoot", "endpoints_compatible", "region:us" ]	null	2024-06-28T21:46:03Z	--- base_model: v000000/L3-11.5B-DuS-MoonRoot library_name: transformers tags: - mergekit - merge - llama - llama-cpp --- # Quants in repo: Q8_0 imatrix, static # v000000/L3-11.5B-DuS-MoonRoot-Q8_0-GGUF This model was converted to GGUF format from [`v000000/L3-11.5B-DuS-MoonRoot`](https://huggingface.co/v000000/L3-11.5B-DuS-MoonRoot) using llama.cpp Refer to the [original model card](https://huggingface.co/v000000/L3-11.5B-DuS-MoonRoot) for more details on the model.' ### Llama-3-11.5B-Depth-Upscaled-MoonRoot experiemental solar-like llama3 frankenmerge, no continued finetuning ![image/png](https://cdn-uploads.huggingface.co/production/uploads/64f74b6e6389380c77562762/lNgAEcW3pWd6x0x-4C3q1.png) # Pretty good understanding gets the percentage wrong but understands. ```bash user: A dead cat is placed into a box along with a nuclear isotope, a vial of poison and a radiation detector. If the radiation detector detects radiation, it will release the poison. The box is opened one day later. What is the probability of the cat being alive? assistant: The answer is 100%. Since the cat is already dead when it was placed in the box, there is no possibility for it to be alive when the box is opened... ``` Shows similar emergent language nuance abilities compared to 8B. Unaligned and somewhat lazy. Use rep_pen 1.1 # merge This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the passthrough merge method. ### Models Merged The following models were included in the merge: * [Cas-Warehouse/Llama-3-MopeyMule-Blackroot-8B](https://huggingface.co/Cas-Warehouse/Llama-3-MopeyMule-Blackroot-8B) * [v000000/L3-8B-Poppy-Moonfall-C](https://huggingface.co/v000000/L3-8B-Poppy-Moonfall-C) ### Configuration The following YAML configuration was used to produce this model: ---Step 3 ```yaml slices: - sources: - model: v000000/L3-8B-Poppy-Moonfall-C layer_range: [0, 24] - sources: - model: Cas-Warehouse/Llama-3-MopeyMule-Blackroot-8B layer_range: [8, 32] merge_method: passthrough dtype: bfloat16 ``` ---Step 2 ```yaml slices: - sources: - model: v000000/L3-8B-Poppy-Sunspice-experiment-c+Blackroot/Llama-3-8B-Abomination-LORA layer_range: [0, 32] - model: v000000/L3-8B-Poppy-Sunspice-experiment-c+ResplendentAI/BlueMoon_Llama3 layer_range: [0, 32] merge_method: slerp base_model: v000000/L3-8B-Poppy-Sunspice-experiment-c+Blackroot/Llama-3-8B-Abomination-LORA parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - value: 0.5 dtype: bfloat16 random_seed: 0 ``` ---Step 1 ```yaml models: - model: crestf411/L3-8B-sunfall-abliterated-v0.2 parameters: weight: 0.1 density: 0.18 - model: Hastagaras/HALU-8B-LLAMA3-BRSLURP parameters: weight: 0.1 density: 0.3 - model: Nitral-Archive/Poppy_Porpoise-Biomix parameters: weight: 0.1 density: 0.42 - model: cgato/L3-TheSpice-8b-v0.8.3 parameters: weight: 0.2 density: 0.54 - model: Sao10K/L3-8B-Stheno-v3.2 parameters: weight: 0.2 density: 0.66 - model: Nitral-AI/Poppy_Porpoise-0.72-L3-8B parameters: weight: 0.3 density: 0.78 merge_method: dare_ties base_model: NousResearch/Meta-Llama-3-8B-Instruct parameters: int8_mask: true dtype: bfloat16 ``` --- base_model: - Cas-Warehouse/Llama-3-MopeyMule-Blackroot-8B - v000000/L3-8B-Poppy-Moonfall-C # Prompt Template: ```bash <\|begin_of_text\|><\|start_header_id\|>system<\|end_header_id\|> {system_prompt}<\|eot_id\|><\|start_header_id\|>user<\|end_header_id\|> {input}<\|eot_id\|><\|start_header_id\|>assistant<\|end_header_id\|> {output}<\|eot_id\|> ```
BeIR/query-gen-msmarco-t5-base-v1	BeIR	2021-06-23T02:07:32Z	4,850	14	transformers	[ "transformers", "pytorch", "jax", "t5", "text2text-generation", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text2text-generation	2022-03-02T23:29:04Z	# Query Generation This model is the t5-base model from [docTTTTTquery](https://github.com/castorini/docTTTTTquery). The T5-base model was trained on the [MS MARCO Passage Dataset](https://github.com/microsoft/MSMARCO-Passage-Ranking), which consists of about 500k real search queries from Bing together with the relevant passage. The model can be used for query generation to learn semantic search models without requiring annotated training data: [Synthetic Query Generation](https://github.com/UKPLab/sentence-transformers/tree/master/examples/unsupervised_learning/query_generation). ## Usage ```python from transformers import T5Tokenizer, T5ForConditionalGeneration tokenizer = T5Tokenizer.from_pretrained('model-name') model = T5ForConditionalGeneration.from_pretrained('model-name') para = "Python is an interpreted, high-level and general-purpose programming language. Python's design philosophy emphasizes code readability with its notable use of significant whitespace. Its language constructs and object-oriented approach aim to help programmers write clear, logical code for small and large-scale projects." input_ids = tokenizer.encode(para, return_tensors='pt') outputs = model.generate( input_ids=input_ids, max_length=64, do_sample=True, top_p=0.95, num_return_sequences=3) print("Paragraph:") print(para) print("\nGenerated Queries:") for i in range(len(outputs)): query = tokenizer.decode(outputs[i], skip_special_tokens=True) print(f'{i + 1}: {query}') ```
Rakuten/RakutenAI-7B-chat	Rakuten	2024-06-07T08:54:11Z	4,850	53	transformers	[ "transformers", "pytorch", "safetensors", "mistral", "text-generation", "arxiv:2403.15484", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text-generation	2024-03-18T06:46:06Z	--- license: apache-2.0 --- # RakutenAI-7B-chat ## Model Description RakutenAI-7B is a systematic initiative that brings the latest technologies to the world of Japanese LLMs. RakutenAI-7B achieves the best scores on the Japanese language understanding benchmarks while maintaining a competitive performance on the English test sets among similar models such as OpenCalm, Elyza, Youri, Nekomata and Swallow. RakutenAI-7B leverages the Mistral model architecture and is based on [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) pre-trained checkpoint, exemplifying a successful retrofitting of the pre-trained model weights. Moreover, we extend Mistral's vocabulary from 32k to 48k to offer a better character-per-token rate for Japanese. The technical report can be accessed at [arXiv](https://arxiv.org/abs/2403.15484). If you are looking for a foundation model, check [RakutenAI-7B](https://huggingface.co/Rakuten/RakutenAI-7B). If you are looking for an instruction-tuned model, check [RakutenAI-7B-instruct](https://huggingface.co/Rakuten/RakutenAI-7B-instruct). An independent evaluation by Kamata et.al. for [Nejumi LLMリーダーボード Neo](https://wandb.ai/wandb-japan/llm-leaderboard/reports/Nejumi-LLM-Neo--Vmlldzo2MTkyMTU0#総合評価) using a weighted average of [llm-jp-eval](https://github.com/llm-jp/llm-jp-eval) and [Japanese MT-bench](https://github.com/Stability-AI/FastChat/tree/jp-stable/fastchat/llm_judge) also confirms the highest performance of chat/instruct versions of RakutenAI-7B among Open LLMs of similar sizes, with a score of 0.393/0.331 respectively, as of 22nd March 2024. ## Usage ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_path = "Rakuten/RakutenAI-7B-chat" tokenizer = AutoTokenizer.from_pretrained(model_path) model = AutoModelForCausalLM.from_pretrained(model_path, torch_dtype="auto", device_map="auto") model.eval() requests = [ "「馬が合う」はどう言う意味ですか", "How to make an authentic Spanish Omelette?", ] system_message = "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: {user_input} ASSISTANT:" for req in requests: input_req = system_message.format(user_input=req) input_ids = tokenizer.encode(input_req, return_tensors="pt").to(device=model.device) tokens = model.generate( input_ids, max_new_tokens=1024, do_sample=True, pad_token_id=tokenizer.eos_token_id, ) out = tokenizer.decode(tokens[0][len(input_ids[0]):], skip_special_tokens=True) print("USER:\n" + req) print("ASSISTANT:\n" + out) print() print() ``` ## Model Details * Developed by: [Rakuten Group, Inc.](https://ai.rakuten.com/) * Language(s): Japanese, English * License: This model is licensed under [Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0). * Instruction-Tuning Dataset: We fine-tune our foundation model to create RakutenAI-7B-instruct and RakutenAI-7B-chat using a mix of open source and internally hand-crafted datasets. We use `train` part of the following datasets (CC by-SA License) for instruction-tuned and chat-tuned models: - [JSNLI](https://nlp.ist.i.kyoto-u.ac.jp/?%E6%97%A5%E6%9C%AC%E8%AA%9ESNLI%28JSNLI%29%E3%83%87%E3%83%BC%E3%82%BF%E3%82%BB%E3%83%83%E3%83%88) - [RTE](https://nlp.ist.i.kyoto-u.ac.jp/?Textual+Entailment+%E8%A9%95%E4%BE%A1%E3%83%87%E3%83%BC%E3%82%BF) - [KUCI](https://nlp.ist.i.kyoto-u.ac.jp/?KUCI) - [BELEBELE](https://huggingface.co/datasets/facebook/belebele) - [JCS](https://aclanthology.org/2022.lrec-1.317/) - [JNLI](https://aclanthology.org/2022.lrec-1.317/) - [Dolly-15K](https://huggingface.co/datasets/databricks/databricks-dolly-15k) - [OpenAssistant1](https://huggingface.co/datasets/OpenAssistant/oasst1) ### Limitations and Bias The suite of RakutenAI-7B models is capable of generating human-like text on a wide range of topics. However, like all LLMs, they have limitations and can produce biased, inaccurate, or unsafe outputs. Please exercise caution and judgement while interacting with them. ## Citation For citing our work on the suite of RakutenAI-7B models, please use: ``` @misc{rakutengroup2024rakutenai7b, title={RakutenAI-7B: Extending Large Language Models for Japanese}, author={{Rakuten Group, Inc.} and Aaron Levine and Connie Huang and Chenguang Wang and Eduardo Batista and Ewa Szymanska and Hongyi Ding and Hou Wei Chou and Jean-François Pessiot and Johanes Effendi and Justin Chiu and Kai Torben Ohlhus and Karan Chopra and Keiji Shinzato and Koji Murakami and Lee Xiong and Lei Chen and Maki Kubota and Maksim Tkachenko and Miroku Lee and Naoki Takahashi and Prathyusha Jwalapuram and Ryutaro Tatsushima and Saurabh Jain and Sunil Kumar Yadav and Ting Cai and Wei-Te Chen and Yandi Xia and Yuki Nakayama and Yutaka Higashiyama}, year={2024}, eprint={2403.15484}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
thtang/ALL_862873	thtang	2023-10-27T10:29:31Z	4,848	0	transformers	[ "transformers", "pytorch", "bert", "feature-extraction", "mteb", "model-index", "endpoints_compatible", "text-embeddings-inference", "region:us" ]	feature-extraction	2023-10-27T05:44:00Z	--- tags: - mteb model-index: - name: ALL_862873 results: - task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (en) config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 50.805970149253746 - type: ap value: 21.350961103104364 - type: f1 value: 46.546166439875044 - task: type: Classification dataset: type: mteb/amazon_polarity name: MTEB AmazonPolarityClassification config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 52.567125000000004 - type: ap value: 51.37893936391345 - type: f1 value: 51.8411977908125 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (en) config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 22.63 - type: f1 value: 21.964526516204575 - task: type: Retrieval dataset: type: arguana name: MTEB ArguAna config: default split: test revision: None metrics: - type: map_at_1 value: 1.991 - type: map_at_10 value: 4.095 - type: map_at_100 value: 4.763 - type: map_at_1000 value: 4.8759999999999994 - type: map_at_3 value: 3.3070000000000004 - type: map_at_5 value: 3.73 - type: mrr_at_1 value: 2.0629999999999997 - type: mrr_at_10 value: 4.119 - type: mrr_at_100 value: 4.787 - type: mrr_at_1000 value: 4.9 - type: mrr_at_3 value: 3.331 - type: mrr_at_5 value: 3.768 - type: ndcg_at_1 value: 1.991 - type: ndcg_at_10 value: 5.346 - type: ndcg_at_100 value: 9.181000000000001 - type: ndcg_at_1000 value: 13.004 - type: ndcg_at_3 value: 3.7199999999999998 - type: ndcg_at_5 value: 4.482 - type: precision_at_1 value: 1.991 - type: precision_at_10 value: 0.9390000000000001 - type: precision_at_100 value: 0.28700000000000003 - type: precision_at_1000 value: 0.061 - type: precision_at_3 value: 1.636 - type: precision_at_5 value: 1.351 - type: recall_at_1 value: 1.991 - type: recall_at_10 value: 9.388 - type: recall_at_100 value: 28.663 - type: recall_at_1000 value: 60.597 - type: recall_at_3 value: 4.9079999999999995 - type: recall_at_5 value: 6.757000000000001 - task: type: Clustering dataset: type: mteb/arxiv-clustering-p2p name: MTEB ArxivClusteringP2P config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics: - type: v_measure value: 14.790995349964428 - task: type: Clustering dataset: type: mteb/arxiv-clustering-s2s name: MTEB ArxivClusteringS2S config: default split: test revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 metrics: - type: v_measure value: 12.248406292959412 - task: type: Reranking dataset: type: mteb/askubuntudupquestions-reranking name: MTEB AskUbuntuDupQuestions config: default split: test revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 metrics: - type: map value: 44.88116875696166 - type: mrr value: 56.07439651760981 - task: type: STS dataset: type: mteb/biosses-sts name: MTEB BIOSSES config: default split: test revision: d3fb88f8f02e40887cd149695127462bbcf29b4a metrics: - type: cos_sim_pearson value: 19.26573437410263 - type: cos_sim_spearman value: 21.34145013484056 - type: euclidean_pearson value: 22.39226418475093 - type: euclidean_spearman value: 23.511981519581447 - type: manhattan_pearson value: 22.14346931904813 - type: manhattan_spearman value: 23.39390654000631 - task: type: Classification dataset: type: mteb/banking77 name: MTEB Banking77Classification config: default split: test revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 metrics: - type: accuracy value: 36.42857142857143 - type: f1 value: 34.81640976406094 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-p2p name: MTEB BiorxivClusteringP2P config: default split: test revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 metrics: - type: v_measure value: 13.94296328377691 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-s2s name: MTEB BiorxivClusteringS2S config: default split: test revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 metrics: - type: v_measure value: 9.790764523161606 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackAndroidRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 0.968 - type: map_at_10 value: 2.106 - type: map_at_100 value: 2.411 - type: map_at_1000 value: 2.4899999999999998 - type: map_at_3 value: 1.797 - type: map_at_5 value: 1.9959999999999998 - type: mrr_at_1 value: 1.717 - type: mrr_at_10 value: 3.0349999999999997 - type: mrr_at_100 value: 3.4029999999999996 - type: mrr_at_1000 value: 3.486 - type: mrr_at_3 value: 2.6470000000000002 - type: mrr_at_5 value: 2.876 - type: ndcg_at_1 value: 1.717 - type: ndcg_at_10 value: 2.9059999999999997 - type: ndcg_at_100 value: 4.715 - type: ndcg_at_1000 value: 7.318 - type: ndcg_at_3 value: 2.415 - type: ndcg_at_5 value: 2.682 - type: precision_at_1 value: 1.717 - type: precision_at_10 value: 0.658 - type: precision_at_100 value: 0.197 - type: precision_at_1000 value: 0.054 - type: precision_at_3 value: 1.431 - type: precision_at_5 value: 1.059 - type: recall_at_1 value: 0.968 - type: recall_at_10 value: 4.531000000000001 - type: recall_at_100 value: 13.081000000000001 - type: recall_at_1000 value: 32.443 - type: recall_at_3 value: 2.8850000000000002 - type: recall_at_5 value: 3.768 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackEnglishRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 0.9390000000000001 - type: map_at_10 value: 1.516 - type: map_at_100 value: 1.6680000000000001 - type: map_at_1000 value: 1.701 - type: map_at_3 value: 1.314 - type: map_at_5 value: 1.388 - type: mrr_at_1 value: 1.146 - type: mrr_at_10 value: 1.96 - type: mrr_at_100 value: 2.166 - type: mrr_at_1000 value: 2.207 - type: mrr_at_3 value: 1.72 - type: mrr_at_5 value: 1.796 - type: ndcg_at_1 value: 1.146 - type: ndcg_at_10 value: 1.9769999999999999 - type: ndcg_at_100 value: 2.8400000000000003 - type: ndcg_at_1000 value: 4.035 - type: ndcg_at_3 value: 1.5859999999999999 - type: ndcg_at_5 value: 1.6709999999999998 - type: precision_at_1 value: 1.146 - type: precision_at_10 value: 0.43299999999999994 - type: precision_at_100 value: 0.11100000000000002 - type: precision_at_1000 value: 0.027999999999999997 - type: precision_at_3 value: 0.8699999999999999 - type: precision_at_5 value: 0.611 - type: recall_at_1 value: 0.9390000000000001 - type: recall_at_10 value: 2.949 - type: recall_at_100 value: 6.737 - type: recall_at_1000 value: 15.604999999999999 - type: recall_at_3 value: 1.846 - type: recall_at_5 value: 2.08 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGamingRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 1.28 - type: map_at_10 value: 2.157 - type: map_at_100 value: 2.401 - type: map_at_1000 value: 2.4570000000000003 - type: map_at_3 value: 1.865 - type: map_at_5 value: 1.928 - type: mrr_at_1 value: 1.505 - type: mrr_at_10 value: 2.52 - type: mrr_at_100 value: 2.782 - type: mrr_at_1000 value: 2.8400000000000003 - type: mrr_at_3 value: 2.1839999999999997 - type: mrr_at_5 value: 2.2689999999999997 - type: ndcg_at_1 value: 1.505 - type: ndcg_at_10 value: 2.798 - type: ndcg_at_100 value: 4.2090000000000005 - type: ndcg_at_1000 value: 6.105 - type: ndcg_at_3 value: 2.157 - type: ndcg_at_5 value: 2.258 - type: precision_at_1 value: 1.505 - type: precision_at_10 value: 0.5519999999999999 - type: precision_at_100 value: 0.146 - type: precision_at_1000 value: 0.034999999999999996 - type: precision_at_3 value: 1.024 - type: precision_at_5 value: 0.7020000000000001 - type: recall_at_1 value: 1.28 - type: recall_at_10 value: 4.455 - type: recall_at_100 value: 11.169 - type: recall_at_1000 value: 26.046000000000003 - type: recall_at_3 value: 2.6270000000000002 - type: recall_at_5 value: 2.899 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGisRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 0.264 - type: map_at_10 value: 0.615 - type: map_at_100 value: 0.76 - type: map_at_1000 value: 0.803 - type: map_at_3 value: 0.40499999999999997 - type: map_at_5 value: 0.512 - type: mrr_at_1 value: 0.33899999999999997 - type: mrr_at_10 value: 0.718 - type: mrr_at_100 value: 0.8880000000000001 - type: mrr_at_1000 value: 0.935 - type: mrr_at_3 value: 0.508 - type: mrr_at_5 value: 0.616 - type: ndcg_at_1 value: 0.33899999999999997 - type: ndcg_at_10 value: 0.9079999999999999 - type: ndcg_at_100 value: 1.9029999999999998 - type: ndcg_at_1000 value: 3.4939999999999998 - type: ndcg_at_3 value: 0.46499999999999997 - type: ndcg_at_5 value: 0.655 - type: precision_at_1 value: 0.33899999999999997 - type: precision_at_10 value: 0.192 - type: precision_at_100 value: 0.079 - type: precision_at_1000 value: 0.023 - type: precision_at_3 value: 0.22599999999999998 - type: precision_at_5 value: 0.22599999999999998 - type: recall_at_1 value: 0.264 - type: recall_at_10 value: 1.789 - type: recall_at_100 value: 6.927 - type: recall_at_1000 value: 19.922 - type: recall_at_3 value: 0.5459999999999999 - type: recall_at_5 value: 0.9979999999999999 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackMathematicaRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 0.5599999999999999 - type: map_at_10 value: 0.9129999999999999 - type: map_at_100 value: 1.027 - type: map_at_1000 value: 1.072 - type: map_at_3 value: 0.715 - type: map_at_5 value: 0.826 - type: mrr_at_1 value: 0.8710000000000001 - type: mrr_at_10 value: 1.331 - type: mrr_at_100 value: 1.494 - type: mrr_at_1000 value: 1.547 - type: mrr_at_3 value: 1.119 - type: mrr_at_5 value: 1.269 - type: ndcg_at_1 value: 0.8710000000000001 - type: ndcg_at_10 value: 1.2590000000000001 - type: ndcg_at_100 value: 2.023 - type: ndcg_at_1000 value: 3.737 - type: ndcg_at_3 value: 0.8750000000000001 - type: ndcg_at_5 value: 1.079 - type: precision_at_1 value: 0.8710000000000001 - type: precision_at_10 value: 0.28600000000000003 - type: precision_at_100 value: 0.086 - type: precision_at_1000 value: 0.027999999999999997 - type: precision_at_3 value: 0.498 - type: precision_at_5 value: 0.42300000000000004 - type: recall_at_1 value: 0.5599999999999999 - type: recall_at_10 value: 1.907 - type: recall_at_100 value: 5.492 - type: recall_at_1000 value: 18.974 - type: recall_at_3 value: 0.943 - type: recall_at_5 value: 1.41 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackPhysicsRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 1.9720000000000002 - type: map_at_10 value: 2.968 - type: map_at_100 value: 3.2009999999999996 - type: map_at_1000 value: 3.2680000000000002 - type: map_at_3 value: 2.683 - type: map_at_5 value: 2.8369999999999997 - type: mrr_at_1 value: 2.406 - type: mrr_at_10 value: 3.567 - type: mrr_at_100 value: 3.884 - type: mrr_at_1000 value: 3.948 - type: mrr_at_3 value: 3.2239999999999998 - type: mrr_at_5 value: 3.383 - type: ndcg_at_1 value: 2.406 - type: ndcg_at_10 value: 3.63 - type: ndcg_at_100 value: 5.155 - type: ndcg_at_1000 value: 7.381 - type: ndcg_at_3 value: 3.078 - type: ndcg_at_5 value: 3.3070000000000004 - type: precision_at_1 value: 2.406 - type: precision_at_10 value: 0.635 - type: precision_at_100 value: 0.184 - type: precision_at_1000 value: 0.048 - type: precision_at_3 value: 1.4120000000000001 - type: precision_at_5 value: 1.001 - type: recall_at_1 value: 1.9720000000000002 - type: recall_at_10 value: 5.152 - type: recall_at_100 value: 12.173 - type: recall_at_1000 value: 28.811999999999998 - type: recall_at_3 value: 3.556 - type: recall_at_5 value: 4.181 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackProgrammersRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 0.346 - type: map_at_10 value: 0.619 - type: map_at_100 value: 0.743 - type: map_at_1000 value: 0.788 - type: map_at_3 value: 0.5369999999999999 - type: map_at_5 value: 0.551 - type: mrr_at_1 value: 0.571 - type: mrr_at_10 value: 1.0619999999999998 - type: mrr_at_100 value: 1.2109999999999999 - type: mrr_at_1000 value: 1.265 - type: mrr_at_3 value: 0.818 - type: mrr_at_5 value: 0.927 - type: ndcg_at_1 value: 0.571 - type: ndcg_at_10 value: 0.919 - type: ndcg_at_100 value: 1.688 - type: ndcg_at_1000 value: 3.3649999999999998 - type: ndcg_at_3 value: 0.6779999999999999 - type: ndcg_at_5 value: 0.7230000000000001 - type: precision_at_1 value: 0.571 - type: precision_at_10 value: 0.27399999999999997 - type: precision_at_100 value: 0.084 - type: precision_at_1000 value: 0.029 - type: precision_at_3 value: 0.381 - type: precision_at_5 value: 0.32 - type: recall_at_1 value: 0.346 - type: recall_at_10 value: 1.397 - type: recall_at_100 value: 5.079000000000001 - type: recall_at_1000 value: 18.060000000000002 - type: recall_at_3 value: 0.774 - type: recall_at_5 value: 0.8340000000000001 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackStatsRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 0.69 - type: map_at_10 value: 0.897 - type: map_at_100 value: 1.0030000000000001 - type: map_at_1000 value: 1.034 - type: map_at_3 value: 0.818 - type: map_at_5 value: 0.864 - type: mrr_at_1 value: 0.767 - type: mrr_at_10 value: 1.008 - type: mrr_at_100 value: 1.145 - type: mrr_at_1000 value: 1.183 - type: mrr_at_3 value: 0.895 - type: mrr_at_5 value: 0.9560000000000001 - type: ndcg_at_1 value: 0.767 - type: ndcg_at_10 value: 1.0739999999999998 - type: ndcg_at_100 value: 1.757 - type: ndcg_at_1000 value: 2.9090000000000003 - type: ndcg_at_3 value: 0.881 - type: ndcg_at_5 value: 0.9769999999999999 - type: precision_at_1 value: 0.767 - type: precision_at_10 value: 0.184 - type: precision_at_100 value: 0.06 - type: precision_at_1000 value: 0.018000000000000002 - type: precision_at_3 value: 0.358 - type: precision_at_5 value: 0.27599999999999997 - type: recall_at_1 value: 0.69 - type: recall_at_10 value: 1.508 - type: recall_at_100 value: 4.858 - type: recall_at_1000 value: 14.007 - type: recall_at_3 value: 0.997 - type: recall_at_5 value: 1.2269999999999999 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackTexRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 0.338 - type: map_at_10 value: 0.661 - type: map_at_100 value: 0.7969999999999999 - type: map_at_1000 value: 0.8290000000000001 - type: map_at_3 value: 0.5559999999999999 - type: map_at_5 value: 0.5910000000000001 - type: mrr_at_1 value: 0.482 - type: mrr_at_10 value: 0.88 - type: mrr_at_100 value: 1.036 - type: mrr_at_1000 value: 1.075 - type: mrr_at_3 value: 0.74 - type: mrr_at_5 value: 0.779 - type: ndcg_at_1 value: 0.482 - type: ndcg_at_10 value: 0.924 - type: ndcg_at_100 value: 1.736 - type: ndcg_at_1000 value: 2.926 - type: ndcg_at_3 value: 0.677 - type: ndcg_at_5 value: 0.732 - type: precision_at_1 value: 0.482 - type: precision_at_10 value: 0.20600000000000002 - type: precision_at_100 value: 0.078 - type: precision_at_1000 value: 0.023 - type: precision_at_3 value: 0.367 - type: precision_at_5 value: 0.255 - type: recall_at_1 value: 0.338 - type: recall_at_10 value: 1.545 - type: recall_at_100 value: 5.38 - type: recall_at_1000 value: 14.609 - type: recall_at_3 value: 0.826 - type: recall_at_5 value: 0.975 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackUnixRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 0.8240000000000001 - type: map_at_10 value: 1.254 - type: map_at_100 value: 1.389 - type: map_at_1000 value: 1.419 - type: map_at_3 value: 1.158 - type: map_at_5 value: 1.189 - type: mrr_at_1 value: 0.9329999999999999 - type: mrr_at_10 value: 1.4200000000000002 - type: mrr_at_100 value: 1.59 - type: mrr_at_1000 value: 1.629 - type: mrr_at_3 value: 1.29 - type: mrr_at_5 value: 1.332 - type: ndcg_at_1 value: 0.9329999999999999 - type: ndcg_at_10 value: 1.53 - type: ndcg_at_100 value: 2.418 - type: ndcg_at_1000 value: 3.7310000000000003 - type: ndcg_at_3 value: 1.302 - type: ndcg_at_5 value: 1.363 - type: precision_at_1 value: 0.9329999999999999 - type: precision_at_10 value: 0.271 - type: precision_at_100 value: 0.083 - type: precision_at_1000 value: 0.024 - type: precision_at_3 value: 0.622 - type: precision_at_5 value: 0.41000000000000003 - type: recall_at_1 value: 0.8240000000000001 - type: recall_at_10 value: 2.1999999999999997 - type: recall_at_100 value: 6.584 - type: recall_at_1000 value: 17.068 - type: recall_at_3 value: 1.5859999999999999 - type: recall_at_5 value: 1.7260000000000002 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWebmastersRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 0.404 - type: map_at_10 value: 0.788 - type: map_at_100 value: 0.9860000000000001 - type: map_at_1000 value: 1.04 - type: map_at_3 value: 0.676 - type: map_at_5 value: 0.733 - type: mrr_at_1 value: 0.5930000000000001 - type: mrr_at_10 value: 1.278 - type: mrr_at_100 value: 1.545 - type: mrr_at_1000 value: 1.599 - type: mrr_at_3 value: 1.054 - type: mrr_at_5 value: 1.192 - type: ndcg_at_1 value: 0.5930000000000001 - type: ndcg_at_10 value: 1.1280000000000001 - type: ndcg_at_100 value: 2.2689999999999997 - type: ndcg_at_1000 value: 4.274 - type: ndcg_at_3 value: 0.919 - type: ndcg_at_5 value: 1.038 - type: precision_at_1 value: 0.5930000000000001 - type: precision_at_10 value: 0.296 - type: precision_at_100 value: 0.152 - type: precision_at_1000 value: 0.05 - type: precision_at_3 value: 0.527 - type: precision_at_5 value: 0.47400000000000003 - type: recall_at_1 value: 0.404 - type: recall_at_10 value: 1.601 - type: recall_at_100 value: 6.885 - type: recall_at_1000 value: 22.356 - type: recall_at_3 value: 0.9490000000000001 - type: recall_at_5 value: 1.206 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWordpressRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 0.185 - type: map_at_10 value: 0.192 - type: map_at_100 value: 0.271 - type: map_at_1000 value: 0.307 - type: map_at_3 value: 0.185 - type: map_at_5 value: 0.185 - type: mrr_at_1 value: 0.185 - type: mrr_at_10 value: 0.20500000000000002 - type: mrr_at_100 value: 0.292 - type: mrr_at_1000 value: 0.331 - type: mrr_at_3 value: 0.185 - type: mrr_at_5 value: 0.185 - type: ndcg_at_1 value: 0.185 - type: ndcg_at_10 value: 0.211 - type: ndcg_at_100 value: 0.757 - type: ndcg_at_1000 value: 1.928 - type: ndcg_at_3 value: 0.185 - type: ndcg_at_5 value: 0.185 - type: precision_at_1 value: 0.185 - type: precision_at_10 value: 0.037 - type: precision_at_100 value: 0.039 - type: precision_at_1000 value: 0.015 - type: precision_at_3 value: 0.062 - type: precision_at_5 value: 0.037 - type: recall_at_1 value: 0.185 - type: recall_at_10 value: 0.246 - type: recall_at_100 value: 3.05 - type: recall_at_1000 value: 12.5 - type: recall_at_3 value: 0.185 - type: recall_at_5 value: 0.185 - task: type: Retrieval dataset: type: climate-fever name: MTEB ClimateFEVER config: default split: test revision: None metrics: - type: map_at_1 value: 0.241 - type: map_at_10 value: 0.372 - type: map_at_100 value: 0.45999999999999996 - type: map_at_1000 value: 0.47600000000000003 - type: map_at_3 value: 0.33999999999999997 - type: map_at_5 value: 0.359 - type: mrr_at_1 value: 0.651 - type: mrr_at_10 value: 1.03 - type: mrr_at_100 value: 1.2489999999999999 - type: mrr_at_1000 value: 1.282 - type: mrr_at_3 value: 0.9450000000000001 - type: mrr_at_5 value: 1.0030000000000001 - type: ndcg_at_1 value: 0.651 - type: ndcg_at_10 value: 0.588 - type: ndcg_at_100 value: 1.2550000000000001 - type: ndcg_at_1000 value: 1.9040000000000001 - type: ndcg_at_3 value: 0.547 - type: ndcg_at_5 value: 0.549 - type: precision_at_1 value: 0.651 - type: precision_at_10 value: 0.182 - type: precision_at_100 value: 0.086 - type: precision_at_1000 value: 0.02 - type: precision_at_3 value: 0.434 - type: precision_at_5 value: 0.313 - type: recall_at_1 value: 0.241 - type: recall_at_10 value: 0.63 - type: recall_at_100 value: 3.1759999999999997 - type: recall_at_1000 value: 7.175 - type: recall_at_3 value: 0.46299999999999997 - type: recall_at_5 value: 0.543 - task: type: Retrieval dataset: type: dbpedia-entity name: MTEB DBPedia config: default split: test revision: None metrics: - type: map_at_1 value: 0.04 - type: map_at_10 value: 0.089 - type: map_at_100 value: 0.133 - type: map_at_1000 value: 0.165 - type: map_at_3 value: 0.054 - type: map_at_5 value: 0.056999999999999995 - type: mrr_at_1 value: 0.75 - type: mrr_at_10 value: 1.4749999999999999 - type: mrr_at_100 value: 1.8010000000000002 - type: mrr_at_1000 value: 1.847 - type: mrr_at_3 value: 1.208 - type: mrr_at_5 value: 1.333 - type: ndcg_at_1 value: 0.625 - type: ndcg_at_10 value: 0.428 - type: ndcg_at_100 value: 0.705 - type: ndcg_at_1000 value: 1.564 - type: ndcg_at_3 value: 0.5369999999999999 - type: ndcg_at_5 value: 0.468 - type: precision_at_1 value: 0.75 - type: precision_at_10 value: 0.375 - type: precision_at_100 value: 0.27499999999999997 - type: precision_at_1000 value: 0.10300000000000001 - type: precision_at_3 value: 0.583 - type: precision_at_5 value: 0.5 - type: recall_at_1 value: 0.04 - type: recall_at_10 value: 0.385 - type: recall_at_100 value: 1.2670000000000001 - type: recall_at_1000 value: 4.522 - type: recall_at_3 value: 0.07100000000000001 - type: recall_at_5 value: 0.08099999999999999 - task: type: Classification dataset: type: mteb/emotion name: MTEB EmotionClassification config: default split: test revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 metrics: - type: accuracy value: 22.749999999999996 - type: f1 value: 19.335020165482693 - task: type: Retrieval dataset: type: fever name: MTEB FEVER config: default split: test revision: None metrics: - type: map_at_1 value: 0.257 - type: map_at_10 value: 0.416 - type: map_at_100 value: 0.451 - type: map_at_1000 value: 0.46499999999999997 - type: map_at_3 value: 0.37 - type: map_at_5 value: 0.386 - type: mrr_at_1 value: 0.27 - type: mrr_at_10 value: 0.44200000000000006 - type: mrr_at_100 value: 0.48 - type: mrr_at_1000 value: 0.49500000000000005 - type: mrr_at_3 value: 0.38999999999999996 - type: mrr_at_5 value: 0.411 - type: ndcg_at_1 value: 0.27 - type: ndcg_at_10 value: 0.51 - type: ndcg_at_100 value: 0.738 - type: ndcg_at_1000 value: 1.2630000000000001 - type: ndcg_at_3 value: 0.41000000000000003 - type: ndcg_at_5 value: 0.439 - type: precision_at_1 value: 0.27 - type: precision_at_10 value: 0.084 - type: precision_at_100 value: 0.021 - type: precision_at_1000 value: 0.006999999999999999 - type: precision_at_3 value: 0.17500000000000002 - type: precision_at_5 value: 0.123 - type: recall_at_1 value: 0.257 - type: recall_at_10 value: 0.786 - type: recall_at_100 value: 1.959 - type: recall_at_1000 value: 6.334 - type: recall_at_3 value: 0.49699999999999994 - type: recall_at_5 value: 0.5680000000000001 - task: type: Retrieval dataset: type: fiqa name: MTEB FiQA2018 config: default split: test revision: None metrics: - type: map_at_1 value: 0.28900000000000003 - type: map_at_10 value: 0.475 - type: map_at_100 value: 0.559 - type: map_at_1000 value: 0.5930000000000001 - type: map_at_3 value: 0.38999999999999996 - type: map_at_5 value: 0.41700000000000004 - type: mrr_at_1 value: 0.772 - type: mrr_at_10 value: 1.107 - type: mrr_at_100 value: 1.269 - type: mrr_at_1000 value: 1.323 - type: mrr_at_3 value: 0.9520000000000001 - type: mrr_at_5 value: 1.0290000000000001 - type: ndcg_at_1 value: 0.772 - type: ndcg_at_10 value: 0.755 - type: ndcg_at_100 value: 1.256 - type: ndcg_at_1000 value: 2.55 - type: ndcg_at_3 value: 0.633 - type: ndcg_at_5 value: 0.639 - type: precision_at_1 value: 0.772 - type: precision_at_10 value: 0.262 - type: precision_at_100 value: 0.082 - type: precision_at_1000 value: 0.03 - type: precision_at_3 value: 0.46299999999999997 - type: precision_at_5 value: 0.33999999999999997 - type: recall_at_1 value: 0.28900000000000003 - type: recall_at_10 value: 0.976 - type: recall_at_100 value: 2.802 - type: recall_at_1000 value: 11.466 - type: recall_at_3 value: 0.54 - type: recall_at_5 value: 0.6479999999999999 - task: type: Retrieval dataset: type: hotpotqa name: MTEB HotpotQA config: default split: test revision: None metrics: - type: map_at_1 value: 0.257 - type: map_at_10 value: 0.395 - type: map_at_100 value: 0.436 - type: map_at_1000 value: 0.447 - type: map_at_3 value: 0.347 - type: map_at_5 value: 0.369 - type: mrr_at_1 value: 0.513 - type: mrr_at_10 value: 0.787 - type: mrr_at_100 value: 0.865 - type: mrr_at_1000 value: 0.8840000000000001 - type: mrr_at_3 value: 0.6930000000000001 - type: mrr_at_5 value: 0.738 - type: ndcg_at_1 value: 0.513 - type: ndcg_at_10 value: 0.587 - type: ndcg_at_100 value: 0.881 - type: ndcg_at_1000 value: 1.336 - type: ndcg_at_3 value: 0.46299999999999997 - type: ndcg_at_5 value: 0.511 - type: precision_at_1 value: 0.513 - type: precision_at_10 value: 0.151 - type: precision_at_100 value: 0.04 - type: precision_at_1000 value: 0.01 - type: precision_at_3 value: 0.311 - type: precision_at_5 value: 0.22399999999999998 - type: recall_at_1 value: 0.257 - type: recall_at_10 value: 0.756 - type: recall_at_100 value: 1.9849999999999999 - type: recall_at_1000 value: 5.111000000000001 - type: recall_at_3 value: 0.466 - type: recall_at_5 value: 0.5599999999999999 - task: type: Classification dataset: type: mteb/imdb name: MTEB ImdbClassification config: default split: test revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 metrics: - type: accuracy value: 50.76400000000001 - type: ap value: 50.41569411130455 - type: f1 value: 50.14266303576945 - task: type: Retrieval dataset: type: msmarco name: MTEB MSMARCO config: default split: dev revision: None metrics: - type: map_at_1 value: 0.14300000000000002 - type: map_at_10 value: 0.23700000000000002 - type: map_at_100 value: 0.27799999999999997 - type: map_at_1000 value: 0.291 - type: map_at_3 value: 0.197 - type: map_at_5 value: 0.215 - type: mrr_at_1 value: 0.14300000000000002 - type: mrr_at_10 value: 0.247 - type: mrr_at_100 value: 0.29 - type: mrr_at_1000 value: 0.303 - type: mrr_at_3 value: 0.201 - type: mrr_at_5 value: 0.219 - type: ndcg_at_1 value: 0.14300000000000002 - type: ndcg_at_10 value: 0.307 - type: ndcg_at_100 value: 0.5720000000000001 - type: ndcg_at_1000 value: 1.053 - type: ndcg_at_3 value: 0.215 - type: ndcg_at_5 value: 0.248 - type: precision_at_1 value: 0.14300000000000002 - type: precision_at_10 value: 0.056999999999999995 - type: precision_at_100 value: 0.02 - type: precision_at_1000 value: 0.006 - type: precision_at_3 value: 0.091 - type: precision_at_5 value: 0.07200000000000001 - type: recall_at_1 value: 0.14300000000000002 - type: recall_at_10 value: 0.522 - type: recall_at_100 value: 1.9009999999999998 - type: recall_at_1000 value: 5.893000000000001 - type: recall_at_3 value: 0.263 - type: recall_at_5 value: 0.34099999999999997 - task: type: Classification dataset: type: mteb/mtop_domain name: MTEB MTOPDomainClassification (en) config: en split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 61.03283173734611 - type: f1 value: 61.24012492746259 - task: type: Classification dataset: type: mteb/mtop_intent name: MTEB MTOPIntentClassification (en) config: en split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - type: accuracy value: 29.68308253533972 - type: f1 value: 16.243459114946905 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (en) config: en split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 34.330867518493605 - type: f1 value: 33.176158044175935 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (en) config: en split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 44.13248150638871 - type: f1 value: 44.24904249078732 - task: type: Clustering dataset: type: mteb/medrxiv-clustering-p2p name: MTEB MedrxivClusteringP2P config: default split: test revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 metrics: - type: v_measure value: 15.698400177259078 - task: type: Clustering dataset: type: mteb/medrxiv-clustering-s2s name: MTEB MedrxivClusteringS2S config: default split: test revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 metrics: - type: v_measure value: 14.888797785310235 - task: type: Reranking dataset: type: mteb/mind_small name: MTEB MindSmallReranking config: default split: test revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 metrics: - type: map value: 25.652445385382126 - type: mrr value: 25.891573325600227 - task: type: Retrieval dataset: type: nfcorpus name: MTEB NFCorpus config: default split: test revision: None metrics: - type: map_at_1 value: 0.322 - type: map_at_10 value: 0.7230000000000001 - type: map_at_100 value: 1.248 - type: map_at_1000 value: 1.873 - type: map_at_3 value: 0.479 - type: map_at_5 value: 0.5700000000000001 - type: mrr_at_1 value: 6.502 - type: mrr_at_10 value: 10.735 - type: mrr_at_100 value: 11.848 - type: mrr_at_1000 value: 11.995000000000001 - type: mrr_at_3 value: 9.391 - type: mrr_at_5 value: 9.732000000000001 - type: ndcg_at_1 value: 6.037 - type: ndcg_at_10 value: 4.873 - type: ndcg_at_100 value: 5.959 - type: ndcg_at_1000 value: 14.424000000000001 - type: ndcg_at_3 value: 5.4559999999999995 - type: ndcg_at_5 value: 5.074 - type: precision_at_1 value: 6.192 - type: precision_at_10 value: 4.458 - type: precision_at_100 value: 2.5700000000000003 - type: precision_at_1000 value: 1.3679999999999999 - type: precision_at_3 value: 5.676 - type: precision_at_5 value: 4.954 - type: recall_at_1 value: 0.322 - type: recall_at_10 value: 1.545 - type: recall_at_100 value: 8.301 - type: recall_at_1000 value: 37.294 - type: recall_at_3 value: 0.623 - type: recall_at_5 value: 0.865 - task: type: Retrieval dataset: type: nq name: MTEB NQ config: default split: test revision: None metrics: - type: map_at_1 value: 0.188 - type: map_at_10 value: 0.27 - type: map_at_100 value: 0.322 - type: map_at_1000 value: 0.335 - type: map_at_3 value: 0.246 - type: map_at_5 value: 0.246 - type: mrr_at_1 value: 0.203 - type: mrr_at_10 value: 0.28300000000000003 - type: mrr_at_100 value: 0.344 - type: mrr_at_1000 value: 0.357 - type: mrr_at_3 value: 0.261 - type: mrr_at_5 value: 0.261 - type: ndcg_at_1 value: 0.203 - type: ndcg_at_10 value: 0.329 - type: ndcg_at_100 value: 0.628 - type: ndcg_at_1000 value: 1.0959999999999999 - type: ndcg_at_3 value: 0.272 - type: ndcg_at_5 value: 0.272 - type: precision_at_1 value: 0.203 - type: precision_at_10 value: 0.055 - type: precision_at_100 value: 0.024 - type: precision_at_1000 value: 0.006999999999999999 - type: precision_at_3 value: 0.116 - type: precision_at_5 value: 0.06999999999999999 - type: recall_at_1 value: 0.188 - type: recall_at_10 value: 0.507 - type: recall_at_100 value: 1.883 - type: recall_at_1000 value: 5.609999999999999 - type: recall_at_3 value: 0.333 - type: recall_at_5 value: 0.333 - task: type: Retrieval dataset: type: quora name: MTEB QuoraRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 24.016000000000002 - type: map_at_10 value: 28.977999999999998 - type: map_at_100 value: 29.579 - type: map_at_1000 value: 29.648999999999997 - type: map_at_3 value: 27.673 - type: map_at_5 value: 28.427000000000003 - type: mrr_at_1 value: 27.93 - type: mrr_at_10 value: 32.462999999999994 - type: mrr_at_100 value: 32.993 - type: mrr_at_1000 value: 33.044000000000004 - type: mrr_at_3 value: 31.252000000000002 - type: mrr_at_5 value: 31.968999999999998 - type: ndcg_at_1 value: 27.96 - type: ndcg_at_10 value: 31.954 - type: ndcg_at_100 value: 34.882000000000005 - type: ndcg_at_1000 value: 36.751 - type: ndcg_at_3 value: 29.767 - type: ndcg_at_5 value: 30.816 - type: precision_at_1 value: 27.96 - type: precision_at_10 value: 4.826 - type: precision_at_100 value: 0.697 - type: precision_at_1000 value: 0.093 - type: precision_at_3 value: 12.837000000000002 - type: precision_at_5 value: 8.559999999999999 - type: recall_at_1 value: 24.016000000000002 - type: recall_at_10 value: 37.574999999999996 - type: recall_at_100 value: 50.843 - type: recall_at_1000 value: 64.654 - type: recall_at_3 value: 31.182 - type: recall_at_5 value: 34.055 - task: type: Clustering dataset: type: mteb/reddit-clustering name: MTEB RedditClustering config: default split: test revision: 24640382cdbf8abc73003fb0fa6d111a705499eb metrics: - type: v_measure value: 18.38048892083281 - task: type: Clustering dataset: type: mteb/reddit-clustering-p2p name: MTEB RedditClusteringP2P config: default split: test revision: 282350215ef01743dc01b456c7f5241fa8937f16 metrics: - type: v_measure value: 27.103011764141478 - task: type: Retrieval dataset: type: scidocs name: MTEB SCIDOCS config: default split: test revision: None metrics: - type: map_at_1 value: 0.18 - type: map_at_10 value: 0.457 - type: map_at_100 value: 0.634 - type: map_at_1000 value: 0.7000000000000001 - type: map_at_3 value: 0.333 - type: map_at_5 value: 0.387 - type: mrr_at_1 value: 0.8999999999999999 - type: mrr_at_10 value: 1.967 - type: mrr_at_100 value: 2.396 - type: mrr_at_1000 value: 2.495 - type: mrr_at_3 value: 1.567 - type: mrr_at_5 value: 1.7670000000000001 - type: ndcg_at_1 value: 0.8999999999999999 - type: ndcg_at_10 value: 1.022 - type: ndcg_at_100 value: 2.366 - type: ndcg_at_1000 value: 4.689 - type: ndcg_at_3 value: 0.882 - type: ndcg_at_5 value: 0.7929999999999999 - type: precision_at_1 value: 0.8999999999999999 - type: precision_at_10 value: 0.58 - type: precision_at_100 value: 0.263 - type: precision_at_1000 value: 0.084 - type: precision_at_3 value: 0.8999999999999999 - type: precision_at_5 value: 0.74 - type: recall_at_1 value: 0.18 - type: recall_at_10 value: 1.208 - type: recall_at_100 value: 5.373 - type: recall_at_1000 value: 17.112 - type: recall_at_3 value: 0.5579999999999999 - type: recall_at_5 value: 0.7779999999999999 - task: type: STS dataset: type: mteb/sickr-sts name: MTEB SICK-R config: default split: test revision: a6ea5a8cab320b040a23452cc28066d9beae2cee metrics: - type: cos_sim_pearson value: 55.229896309578905 - type: cos_sim_spearman value: 48.54616726085393 - type: euclidean_pearson value: 53.828130644322 - type: euclidean_spearman value: 48.2907441223958 - type: manhattan_pearson value: 53.72684612327582 - type: manhattan_spearman value: 48.228319721712744 - task: type: STS dataset: type: mteb/sts12-sts name: MTEB STS12 config: default split: test revision: a0d554a64d88156834ff5ae9920b964011b16384 metrics: - type: cos_sim_pearson value: 57.73555535277214 - type: cos_sim_spearman value: 55.58790083939622 - type: euclidean_pearson value: 61.009463373795384 - type: euclidean_spearman value: 56.696846101196044 - type: manhattan_pearson value: 60.875111392597894 - type: manhattan_spearman value: 56.63100766160946 - task: type: STS dataset: type: mteb/sts13-sts name: MTEB STS13 config: default split: test revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca metrics: - type: cos_sim_pearson value: 19.47269635955134 - type: cos_sim_spearman value: 18.35951746300603 - type: euclidean_pearson value: 23.130707248318714 - type: euclidean_spearman value: 22.92241668287248 - type: manhattan_pearson value: 22.99371642148021 - type: manhattan_spearman value: 22.770233678121897 - task: type: STS dataset: type: mteb/sts14-sts name: MTEB STS14 config: default split: test revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 metrics: - type: cos_sim_pearson value: 31.78346805351368 - type: cos_sim_spearman value: 28.84281669682782 - type: euclidean_pearson value: 34.508176962091156 - type: euclidean_spearman value: 32.269242265609975 - type: manhattan_pearson value: 34.41366600914297 - type: manhattan_spearman value: 32.15352239729175 - task: type: STS dataset: type: mteb/sts15-sts name: MTEB STS15 config: default split: test revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 metrics: - type: cos_sim_pearson value: 29.550332218260465 - type: cos_sim_spearman value: 29.188654452524528 - type: euclidean_pearson value: 33.80339596511417 - type: euclidean_spearman value: 33.49607278843874 - type: manhattan_pearson value: 33.589427741967334 - type: manhattan_spearman value: 33.288312003652884 - task: type: STS dataset: type: mteb/sts16-sts name: MTEB STS16 config: default split: test revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 metrics: - type: cos_sim_pearson value: 27.163752699585885 - type: cos_sim_spearman value: 39.0544187582685 - type: euclidean_pearson value: 38.93841642732113 - type: euclidean_spearman value: 42.861814968921294 - type: manhattan_pearson value: 38.78821319739337 - type: manhattan_spearman value: 42.757121435678954 - task: type: STS dataset: type: mteb/sts17-crosslingual-sts name: MTEB STS17 (en-en) config: en-en split: test revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d metrics: - type: cos_sim_pearson value: 57.15429605615292 - type: cos_sim_spearman value: 61.21576579300284 - type: euclidean_pearson value: 59.2835939062064 - type: euclidean_spearman value: 60.902713241808236 - type: manhattan_pearson value: 59.510770285546364 - type: manhattan_spearman value: 61.02979474159327 - task: type: STS dataset: type: mteb/sts22-crosslingual-sts name: MTEB STS22 (en) config: en split: test revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 metrics: - type: cos_sim_pearson value: 41.81726547830133 - type: cos_sim_spearman value: 44.45123398124273 - type: euclidean_pearson value: 46.44144033159064 - type: euclidean_spearman value: 46.61348337508052 - type: manhattan_pearson value: 46.48092744041165 - type: manhattan_spearman value: 46.78049599791891 - task: type: STS dataset: type: mteb/stsbenchmark-sts name: MTEB STSBenchmark config: default split: test revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 metrics: - type: cos_sim_pearson value: 46.085942179295465 - type: cos_sim_spearman value: 44.394736992467365 - type: euclidean_pearson value: 47.06981069147408 - type: euclidean_spearman value: 45.40499474054004 - type: manhattan_pearson value: 46.96497631950794 - type: manhattan_spearman value: 45.31936619298336 - task: type: Reranking dataset: type: mteb/scidocs-reranking name: MTEB SciDocsRR config: default split: test revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab metrics: - type: map value: 43.89526517578129 - type: mrr value: 64.30753070458954 - task: type: Retrieval dataset: type: scifact name: MTEB SciFact config: default split: test revision: None metrics: - type: map_at_1 value: 1.417 - type: map_at_10 value: 2.189 - type: map_at_100 value: 2.5669999999999997 - type: map_at_1000 value: 2.662 - type: map_at_3 value: 1.694 - type: map_at_5 value: 1.928 - type: mrr_at_1 value: 1.667 - type: mrr_at_10 value: 2.4899999999999998 - type: mrr_at_100 value: 2.8400000000000003 - type: mrr_at_1000 value: 2.928 - type: mrr_at_3 value: 1.944 - type: mrr_at_5 value: 2.178 - type: ndcg_at_1 value: 1.667 - type: ndcg_at_10 value: 2.913 - type: ndcg_at_100 value: 5.482 - type: ndcg_at_1000 value: 8.731 - type: ndcg_at_3 value: 1.867 - type: ndcg_at_5 value: 2.257 - type: precision_at_1 value: 1.667 - type: precision_at_10 value: 0.567 - type: precision_at_100 value: 0.213 - type: precision_at_1000 value: 0.053 - type: precision_at_3 value: 0.7779999999999999 - type: precision_at_5 value: 0.6669999999999999 - type: recall_at_1 value: 1.417 - type: recall_at_10 value: 5.028 - type: recall_at_100 value: 18.5 - type: recall_at_1000 value: 45.072 - type: recall_at_3 value: 2.083 - type: recall_at_5 value: 3.083 - task: type: PairClassification dataset: type: mteb/sprintduplicatequestions-pairclassification name: MTEB SprintDuplicateQuestions config: default split: test revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 metrics: - type: cos_sim_accuracy value: 99.02871287128713 - type: cos_sim_ap value: 17.404404071912694 - type: cos_sim_f1 value: 25.89285714285714 - type: cos_sim_precision value: 29.292929292929294 - type: cos_sim_recall value: 23.200000000000003 - type: dot_accuracy value: 99.0118811881188 - type: dot_ap value: 5.4739000785007335 - type: dot_f1 value: 12.178702570379436 - type: dot_precision value: 8.774250440917108 - type: dot_recall value: 19.900000000000002 - type: euclidean_accuracy value: 99.03663366336633 - type: euclidean_ap value: 19.20851069839796 - type: euclidean_f1 value: 27.16555612506407 - type: euclidean_precision value: 27.865404837013667 - type: euclidean_recall value: 26.5 - type: manhattan_accuracy value: 99.03663366336633 - type: manhattan_ap value: 19.12862913626528 - type: manhattan_f1 value: 26.96629213483146 - type: manhattan_precision value: 28.99884925201381 - type: manhattan_recall value: 25.2 - type: max_accuracy value: 99.03663366336633 - type: max_ap value: 19.20851069839796 - type: max_f1 value: 27.16555612506407 - task: type: Clustering dataset: type: mteb/stackexchange-clustering name: MTEB StackExchangeClustering config: default split: test revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 metrics: - type: v_measure value: 23.657118721775905 - task: type: Clustering dataset: type: mteb/stackexchange-clustering-p2p name: MTEB StackExchangeClusteringP2P config: default split: test revision: 815ca46b2622cec33ccafc3735d572c266efdb44 metrics: - type: v_measure value: 27.343558395037043 - task: type: Reranking dataset: type: mteb/stackoverflowdupquestions-reranking name: MTEB StackOverflowDupQuestions config: default split: test revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 metrics: - type: map value: 23.346327148080043 - type: mrr value: 21.99097063067651 - task: type: Retrieval dataset: type: trec-covid name: MTEB TRECCOVID config: default split: test revision: None metrics: - type: map_at_1 value: 0.032 - type: map_at_10 value: 0.157 - type: map_at_100 value: 0.583 - type: map_at_1000 value: 1.48 - type: map_at_3 value: 0.066 - type: map_at_5 value: 0.105 - type: mrr_at_1 value: 10 - type: mrr_at_10 value: 16.99 - type: mrr_at_100 value: 18.284 - type: mrr_at_1000 value: 18.394 - type: mrr_at_3 value: 14.000000000000002 - type: mrr_at_5 value: 15.8 - type: ndcg_at_1 value: 8 - type: ndcg_at_10 value: 7.504 - type: ndcg_at_100 value: 5.339 - type: ndcg_at_1000 value: 6.046 - type: ndcg_at_3 value: 8.358 - type: ndcg_at_5 value: 8.142000000000001 - type: precision_at_1 value: 10 - type: precision_at_10 value: 8.6 - type: precision_at_100 value: 5.9799999999999995 - type: precision_at_1000 value: 2.976 - type: precision_at_3 value: 9.333 - type: precision_at_5 value: 9.2 - type: recall_at_1 value: 0.032 - type: recall_at_10 value: 0.252 - type: recall_at_100 value: 1.529 - type: recall_at_1000 value: 6.364 - type: recall_at_3 value: 0.08499999999999999 - type: recall_at_5 value: 0.154 - task: type: Retrieval dataset: type: webis-touche2020 name: MTEB Touche2020 config: default split: test revision: None metrics: - type: map_at_1 value: 0.44200000000000006 - type: map_at_10 value: 0.996 - type: map_at_100 value: 1.317 - type: map_at_1000 value: 1.624 - type: map_at_3 value: 0.736 - type: map_at_5 value: 0.951 - type: mrr_at_1 value: 4.082 - type: mrr_at_10 value: 10.102 - type: mrr_at_100 value: 10.978 - type: mrr_at_1000 value: 11.1 - type: mrr_at_3 value: 7.8229999999999995 - type: mrr_at_5 value: 9.252 - type: ndcg_at_1 value: 4.082 - type: ndcg_at_10 value: 3.821 - type: ndcg_at_100 value: 5.682 - type: ndcg_at_1000 value: 10.96 - type: ndcg_at_3 value: 4.813 - type: ndcg_at_5 value: 4.757 - type: precision_at_1 value: 4.082 - type: precision_at_10 value: 3.061 - type: precision_at_100 value: 1.367 - type: precision_at_1000 value: 0.46299999999999997 - type: precision_at_3 value: 4.7620000000000005 - type: precision_at_5 value: 4.898000000000001 - type: recall_at_1 value: 0.44200000000000006 - type: recall_at_10 value: 2.059 - type: recall_at_100 value: 7.439 - type: recall_at_1000 value: 25.191000000000003 - type: recall_at_3 value: 1.095 - type: recall_at_5 value: 1.725 - task: type: Classification dataset: type: mteb/toxic_conversations_50k name: MTEB ToxicConversationsClassification config: default split: test revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c metrics: - type: accuracy value: 54.925999999999995 - type: ap value: 9.658236434063275 - type: f1 value: 43.469829154993064 - task: type: Classification dataset: type: mteb/tweet_sentiment_extraction name: MTEB TweetSentimentExtractionClassification config: default split: test revision: d604517c81ca91fe16a244d1248fc021f9ecee7a metrics: - type: accuracy value: 40.7498585172609 - type: f1 value: 40.720120106546574 - task: type: Clustering dataset: type: mteb/twentynewsgroups-clustering name: MTEB TwentyNewsgroupsClustering config: default split: test revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 metrics: - type: v_measure value: 20.165152514024733 - task: type: PairClassification dataset: type: mteb/twittersemeval2015-pairclassification name: MTEB TwitterSemEval2015 config: default split: test revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 metrics: - type: cos_sim_accuracy value: 77.59432556476128 - type: cos_sim_ap value: 30.37846072188074 - type: cos_sim_f1 value: 37.9231242656521 - type: cos_sim_precision value: 24.064474898814172 - type: cos_sim_recall value: 89.41952506596306 - type: dot_accuracy value: 77.42146986946415 - type: dot_ap value: 24.073476661930034 - type: dot_f1 value: 37.710580857735025 - type: dot_precision value: 23.61083383243495 - type: dot_recall value: 93.61477572559367 - type: euclidean_accuracy value: 77.64797043571556 - type: euclidean_ap value: 31.892152386237594 - type: euclidean_f1 value: 38.21154759481647 - type: euclidean_precision value: 25.719243766554023 - type: euclidean_recall value: 74.30079155672823 - type: manhattan_accuracy value: 77.6539309769327 - type: manhattan_ap value: 31.89545356309865 - type: manhattan_f1 value: 38.16428166172855 - type: manhattan_precision value: 25.07247577238466 - type: manhattan_recall value: 79.86807387862797 - type: max_accuracy value: 77.6539309769327 - type: max_ap value: 31.89545356309865 - type: max_f1 value: 38.21154759481647 - task: type: PairClassification dataset: type: mteb/twitterurlcorpus-pairclassification name: MTEB TwitterURLCorpus config: default split: test revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf metrics: - type: cos_sim_accuracy value: 76.56886715566422 - type: cos_sim_ap value: 44.04480929059786 - type: cos_sim_f1 value: 43.73100054674686 - type: cos_sim_precision value: 30.540367168647098 - type: cos_sim_recall value: 76.97874961502926 - type: dot_accuracy value: 74.80110218496526 - type: dot_ap value: 26.487746384962758 - type: dot_f1 value: 40.91940608182585 - type: dot_precision value: 25.9157358738502 - type: dot_recall value: 97.18201416692331 - type: euclidean_accuracy value: 76.97054371870998 - type: euclidean_ap value: 47.079120397438416 - type: euclidean_f1 value: 45.866182572614115 - type: euclidean_precision value: 34.580791490692945 - type: euclidean_recall value: 68.0859254696643 - type: manhattan_accuracy value: 76.96084138626927 - type: manhattan_ap value: 47.168701873575976 - type: manhattan_f1 value: 45.985439966237614 - type: manhattan_precision value: 34.974321938693635 - type: manhattan_recall value: 67.11579919926086 - type: max_accuracy value: 76.97054371870998 - type: max_ap value: 47.168701873575976 - type: max_f1 value: 45.985439966237614 - task: type: STS dataset: type: C-MTEB/AFQMC name: MTEB AFQMC config: default split: validation revision: None metrics: - type: cos_sim_pearson value: 3.322530620021471 - type: cos_sim_spearman value: 3.7583567993545195 - type: euclidean_pearson value: 3.743782192206081 - type: euclidean_spearman value: 3.758336694921531 - type: manhattan_pearson value: 3.845233721819267 - type: manhattan_spearman value: 3.8542743797718026 - task: type: STS dataset: type: C-MTEB/ATEC name: MTEB ATEC config: default split: test revision: None metrics: - type: cos_sim_pearson value: 8.552640773272078 - type: cos_sim_spearman value: 10.086360519713061 - type: euclidean_pearson value: 9.902099049347935 - type: euclidean_spearman value: 10.086351512635042 - type: manhattan_pearson value: 9.898006826713932 - type: manhattan_spearman value: 10.076531690161783 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (zh) config: zh split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 21.955999999999996 - type: f1 value: 20.596128116112816 - task: type: STS dataset: type: C-MTEB/BQ name: MTEB BQ config: default split: test revision: None metrics: - type: cos_sim_pearson value: 17.6945509937099 - type: cos_sim_spearman value: 19.312286927022825 - type: euclidean_pearson value: 19.259393744977515 - type: euclidean_spearman value: 19.312290390892713 - type: manhattan_pearson value: 19.223527109645772 - type: manhattan_spearman value: 19.32655209742963 - task: type: Clustering dataset: type: C-MTEB/CLSClusteringP2P name: MTEB CLSClusteringP2P config: default split: test revision: None metrics: - type: v_measure value: 18.657841790313405 - task: type: Clustering dataset: type: C-MTEB/CLSClusteringS2S name: MTEB CLSClusteringS2S config: default split: test revision: None metrics: - type: v_measure value: 16.82483158478091 - task: type: Reranking dataset: type: C-MTEB/CMedQAv1-reranking name: MTEB CMedQAv1 config: default split: test revision: None metrics: - type: map value: 19.71658789133091 - type: mrr value: 23.480595238095237 - task: type: Reranking dataset: type: C-MTEB/CMedQAv2-reranking name: MTEB CMedQAv2 config: default split: test revision: None metrics: - type: map value: 22.475972401039495 - type: mrr value: 25.993650793650797 - task: type: Retrieval dataset: type: C-MTEB/CmedqaRetrieval name: MTEB CmedqaRetrieval config: default split: dev revision: None metrics: - type: map_at_1 value: 1.026 - type: map_at_10 value: 1.6389999999999998 - type: map_at_100 value: 1.875 - type: map_at_1000 value: 1.9529999999999998 - type: map_at_3 value: 1.417 - type: map_at_5 value: 1.5110000000000001 - type: mrr_at_1 value: 1.525 - type: mrr_at_10 value: 2.478 - type: mrr_at_100 value: 2.779 - type: mrr_at_1000 value: 2.861 - type: mrr_at_3 value: 2.105 - type: mrr_at_5 value: 2.283 - type: ndcg_at_1 value: 1.525 - type: ndcg_at_10 value: 2.222 - type: ndcg_at_100 value: 3.81 - type: ndcg_at_1000 value: 6.465999999999999 - type: ndcg_at_3 value: 1.7489999999999999 - type: ndcg_at_5 value: 1.8980000000000001 - type: precision_at_1 value: 1.525 - type: precision_at_10 value: 0.543 - type: precision_at_100 value: 0.187 - type: precision_at_1000 value: 0.055 - type: precision_at_3 value: 0.992 - type: precision_at_5 value: 0.76 - type: recall_at_1 value: 1.026 - type: recall_at_10 value: 3.1780000000000004 - type: recall_at_100 value: 10.481 - type: recall_at_1000 value: 29.735 - type: recall_at_3 value: 1.8849999999999998 - type: recall_at_5 value: 2.2560000000000002 - task: type: PairClassification dataset: type: C-MTEB/CMNLI name: MTEB Cmnli config: default split: validation revision: None metrics: - type: cos_sim_accuracy value: 54.99699338544799 - type: cos_sim_ap value: 57.78007274332544 - type: cos_sim_f1 value: 67.95391338895512 - type: cos_sim_precision value: 51.46846413095811 - type: cos_sim_recall value: 99.9766191255553 - type: dot_accuracy value: 54.99699338544799 - type: dot_ap value: 57.7791056074979 - type: dot_f1 value: 67.95391338895512 - type: dot_precision value: 51.46846413095811 - type: dot_recall value: 99.9766191255553 - type: euclidean_accuracy value: 54.99699338544799 - type: euclidean_ap value: 57.7800760462191 - type: euclidean_f1 value: 67.95391338895512 - type: euclidean_precision value: 51.46846413095811 - type: euclidean_recall value: 99.9766191255553 - type: manhattan_accuracy value: 55.05712567648827 - type: manhattan_ap value: 57.8146828916844 - type: manhattan_f1 value: 67.95900532295227 - type: manhattan_precision value: 51.46811070998797 - type: manhattan_recall value: 100 - type: max_accuracy value: 55.05712567648827 - type: max_ap value: 57.8146828916844 - type: max_f1 value: 67.95900532295227 - task: type: Retrieval dataset: type: C-MTEB/CovidRetrieval name: MTEB CovidRetrieval config: default split: dev revision: None metrics: - type: map_at_1 value: 0.632 - type: map_at_10 value: 1.7510000000000001 - type: map_at_100 value: 2.004 - type: map_at_1000 value: 2.0660000000000003 - type: map_at_3 value: 1.493 - type: map_at_5 value: 1.635 - type: mrr_at_1 value: 0.632 - type: mrr_at_10 value: 1.7670000000000001 - type: mrr_at_100 value: 2.02 - type: mrr_at_1000 value: 2.081 - type: mrr_at_3 value: 1.528 - type: mrr_at_5 value: 1.649 - type: ndcg_at_1 value: 0.632 - type: ndcg_at_10 value: 2.32 - type: ndcg_at_100 value: 3.758 - type: ndcg_at_1000 value: 5.894 - type: ndcg_at_3 value: 1.7850000000000001 - type: ndcg_at_5 value: 2.044 - type: precision_at_1 value: 0.632 - type: precision_at_10 value: 0.411 - type: precision_at_100 value: 0.11399999999999999 - type: precision_at_1000 value: 0.03 - type: precision_at_3 value: 0.878 - type: precision_at_5 value: 0.653 - type: recall_at_1 value: 0.632 - type: recall_at_10 value: 4.109999999999999 - type: recall_at_100 value: 11.222 - type: recall_at_1000 value: 29.083 - type: recall_at_3 value: 2.634 - type: recall_at_5 value: 3.267 - task: type: Retrieval dataset: type: C-MTEB/DuRetrieval name: MTEB DuRetrieval config: default split: dev revision: None metrics: - type: map_at_1 value: 1.436 - type: map_at_10 value: 3.4099999999999997 - type: map_at_100 value: 4.128 - type: map_at_1000 value: 4.282 - type: map_at_3 value: 2.423 - type: map_at_5 value: 2.927 - type: mrr_at_1 value: 6 - type: mrr_at_10 value: 9.701 - type: mrr_at_100 value: 10.347000000000001 - type: mrr_at_1000 value: 10.427999999999999 - type: mrr_at_3 value: 8.267 - type: mrr_at_5 value: 9.004 - type: ndcg_at_1 value: 6 - type: ndcg_at_10 value: 5.856 - type: ndcg_at_100 value: 9.063 - type: ndcg_at_1000 value: 12.475999999999999 - type: ndcg_at_3 value: 5.253 - type: ndcg_at_5 value: 5.223 - type: precision_at_1 value: 6 - type: precision_at_10 value: 3.125 - type: precision_at_100 value: 0.812 - type: precision_at_1000 value: 0.169 - type: precision_at_3 value: 4.7669999999999995 - type: precision_at_5 value: 4.15 - type: recall_at_1 value: 1.436 - type: recall_at_10 value: 6.544999999999999 - type: recall_at_100 value: 16.634999999999998 - type: recall_at_1000 value: 33.987 - type: recall_at_3 value: 3.144 - type: recall_at_5 value: 4.519 - task: type: Retrieval dataset: type: C-MTEB/EcomRetrieval name: MTEB EcomRetrieval config: default split: dev revision: None metrics: - type: map_at_1 value: 4.1000000000000005 - type: map_at_10 value: 7.911 - type: map_at_100 value: 8.92 - type: map_at_1000 value: 9.033 - type: map_at_3 value: 6.4 - type: map_at_5 value: 7.23 - type: mrr_at_1 value: 4.1000000000000005 - type: mrr_at_10 value: 7.911 - type: mrr_at_100 value: 8.92 - type: mrr_at_1000 value: 9.033 - type: mrr_at_3 value: 6.4 - type: mrr_at_5 value: 7.23 - type: ndcg_at_1 value: 4.1000000000000005 - type: ndcg_at_10 value: 10.374 - type: ndcg_at_100 value: 15.879999999999999 - type: ndcg_at_1000 value: 19.246 - type: ndcg_at_3 value: 7.217 - type: ndcg_at_5 value: 8.706 - type: precision_at_1 value: 4.1000000000000005 - type: precision_at_10 value: 1.8399999999999999 - type: precision_at_100 value: 0.45599999999999996 - type: precision_at_1000 value: 0.073 - type: precision_at_3 value: 3.2 - type: precision_at_5 value: 2.64 - type: recall_at_1 value: 4.1000000000000005 - type: recall_at_10 value: 18.4 - type: recall_at_100 value: 45.6 - type: recall_at_1000 value: 72.89999999999999 - type: recall_at_3 value: 9.6 - type: recall_at_5 value: 13.200000000000001 - task: type: Classification dataset: type: C-MTEB/IFlyTek-classification name: MTEB IFlyTek config: default split: validation revision: None metrics: - type: accuracy value: 20.353982300884958 - type: f1 value: 12.69588085868714 - task: type: Classification dataset: type: C-MTEB/JDReview-classification name: MTEB JDReview config: default split: test revision: None metrics: - type: accuracy value: 55.497185741088174 - type: ap value: 20.43046737602198 - type: f1 value: 48.93980371558734 - task: type: STS dataset: type: C-MTEB/LCQMC name: MTEB LCQMC config: default split: test revision: None metrics: - type: cos_sim_pearson value: 32.588967426128654 - type: cos_sim_spearman value: 42.14900040682406 - type: euclidean_pearson value: 39.568373451615685 - type: euclidean_spearman value: 42.14899152396297 - type: manhattan_pearson value: 39.5220710244444 - type: manhattan_spearman value: 42.14787636056146 - task: type: Reranking dataset: type: C-MTEB/Mmarco-reranking name: MTEB MMarcoReranking config: default split: dev revision: None metrics: - type: map value: 1.1655156335725807 - type: mrr value: 0.2361111111111111 - task: type: Retrieval dataset: type: C-MTEB/MMarcoRetrieval name: MTEB MMarcoRetrieval config: default split: dev revision: None metrics: - type: map_at_1 value: 1.9029999999999998 - type: map_at_10 value: 2.9139999999999997 - type: map_at_100 value: 3.2259999999999995 - type: map_at_1000 value: 3.2870000000000004 - type: map_at_3 value: 2.483 - type: map_at_5 value: 2.71 - type: mrr_at_1 value: 2.02 - type: mrr_at_10 value: 3.064 - type: mrr_at_100 value: 3.382 - type: mrr_at_1000 value: 3.4419999999999997 - type: mrr_at_3 value: 2.622 - type: mrr_at_5 value: 2.855 - type: ndcg_at_1 value: 2.02 - type: ndcg_at_10 value: 3.639 - type: ndcg_at_100 value: 5.431 - type: ndcg_at_1000 value: 7.404 - type: ndcg_at_3 value: 2.723 - type: ndcg_at_5 value: 3.1350000000000002 - type: precision_at_1 value: 2.02 - type: precision_at_10 value: 0.626 - type: precision_at_100 value: 0.159 - type: precision_at_1000 value: 0.033 - type: precision_at_3 value: 1.17 - type: precision_at_5 value: 0.9199999999999999 - type: recall_at_1 value: 1.9029999999999998 - type: recall_at_10 value: 5.831 - type: recall_at_100 value: 14.737 - type: recall_at_1000 value: 30.84 - type: recall_at_3 value: 3.2870000000000004 - type: recall_at_5 value: 4.282 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (zh-CN) config: zh-CN split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 25.3866845998655 - type: f1 value: 23.404809615998495 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (zh-CN) config: zh-CN split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 40.34969737726966 - type: f1 value: 37.88244646590394 - task: type: Retrieval dataset: type: C-MTEB/MedicalRetrieval name: MTEB MedicalRetrieval config: default split: dev revision: None metrics: - type: map_at_1 value: 1.5 - type: map_at_10 value: 2.0740000000000003 - type: map_at_100 value: 2.2079999999999997 - type: map_at_1000 value: 2.241 - type: map_at_3 value: 1.933 - type: map_at_5 value: 2.023 - type: mrr_at_1 value: 1.5 - type: mrr_at_10 value: 2.0740000000000003 - type: mrr_at_100 value: 2.2079999999999997 - type: mrr_at_1000 value: 2.241 - type: mrr_at_3 value: 1.933 - type: mrr_at_5 value: 2.023 - type: ndcg_at_1 value: 1.5 - type: ndcg_at_10 value: 2.368 - type: ndcg_at_100 value: 3.309 - type: ndcg_at_1000 value: 4.593 - type: ndcg_at_3 value: 2.0789999999999997 - type: ndcg_at_5 value: 2.242 - type: precision_at_1 value: 1.5 - type: precision_at_10 value: 0.33 - type: precision_at_100 value: 0.084 - type: precision_at_1000 value: 0.019 - type: precision_at_3 value: 0.8330000000000001 - type: precision_at_5 value: 0.58 - type: recall_at_1 value: 1.5 - type: recall_at_10 value: 3.3000000000000003 - type: recall_at_100 value: 8.4 - type: recall_at_1000 value: 19.400000000000002 - type: recall_at_3 value: 2.5 - type: recall_at_5 value: 2.9000000000000004 - task: type: Classification dataset: type: C-MTEB/MultilingualSentiment-classification name: MTEB MultilingualSentiment config: default split: validation revision: None metrics: - type: accuracy value: 38.94 - type: f1 value: 38.4171730136538 - task: type: PairClassification dataset: type: C-MTEB/OCNLI name: MTEB Ocnli config: default split: validation revision: None metrics: - type: cos_sim_accuracy value: 54.141851651326476 - type: cos_sim_ap value: 55.63298007661861 - type: cos_sim_f1 value: 67.85195936139333 - type: cos_sim_precision value: 51.68601437258153 - type: cos_sim_recall value: 98.73284054910243 - type: dot_accuracy value: 54.141851651326476 - type: dot_ap value: 55.63298007661861 - type: dot_f1 value: 67.85195936139333 - type: dot_precision value: 51.68601437258153 - type: dot_recall value: 98.73284054910243 - type: euclidean_accuracy value: 54.141851651326476 - type: euclidean_ap value: 55.63298007661861 - type: euclidean_f1 value: 67.85195936139333 - type: euclidean_precision value: 51.68601437258153 - type: euclidean_recall value: 98.73284054910243 - type: manhattan_accuracy value: 54.03356794802382 - type: manhattan_ap value: 55.650247173847944 - type: manhattan_f1 value: 67.83667621776503 - type: manhattan_precision value: 51.32791327913279 - type: manhattan_recall value: 100 - type: max_accuracy value: 54.141851651326476 - type: max_ap value: 55.650247173847944 - type: max_f1 value: 67.85195936139333 - task: type: Classification dataset: type: C-MTEB/OnlineShopping-classification name: MTEB OnlineShopping config: default split: test revision: None metrics: - type: accuracy value: 56.88999999999999 - type: ap value: 56.075855594697835 - type: f1 value: 56.31094564241924 - task: type: STS dataset: type: C-MTEB/PAWSX name: MTEB PAWSX config: default split: test revision: None metrics: - type: cos_sim_pearson value: 10.023575042969506 - type: cos_sim_spearman value: 6.135169971774927 - type: euclidean_pearson value: 9.219072035876794 - type: euclidean_spearman value: 6.147945631319713 - type: manhattan_pearson value: 9.208267921398097 - type: manhattan_spearman value: 6.156480815791583 - task: type: STS dataset: type: C-MTEB/QBQTC name: MTEB QBQTC config: default split: test revision: None metrics: - type: cos_sim_pearson value: 5.7230819885069435 - type: cos_sim_spearman value: 6.116111130034651 - type: euclidean_pearson value: 5.9142712292657205 - type: euclidean_spearman value: 6.115732664912588 - type: manhattan_pearson value: 5.892970378623552 - type: manhattan_spearman value: 6.100463075081302 - task: type: STS dataset: type: mteb/sts22-crosslingual-sts name: MTEB STS22 (zh) config: zh split: test revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 metrics: - type: cos_sim_pearson value: 18.353401358720397 - type: cos_sim_spearman value: 33.700002511275095 - type: euclidean_pearson value: 27.654605278731136 - type: euclidean_spearman value: 33.700002511275095 - type: manhattan_pearson value: 29.174977260571083 - type: manhattan_spearman value: 33.901862553268366 - task: type: STS dataset: type: C-MTEB/STSB name: MTEB STSB config: default split: test revision: None metrics: - type: cos_sim_pearson value: 44.66287398363386 - type: cos_sim_spearman value: 45.60317964713117 - type: euclidean_pearson value: 47.434263079423 - type: euclidean_spearman value: 45.603111040461606 - type: manhattan_pearson value: 47.3272049502668 - type: manhattan_spearman value: 45.506449459872805 - task: type: Reranking dataset: type: C-MTEB/T2Reranking name: MTEB T2Reranking config: default split: dev revision: None metrics: - type: map value: 60.05480951659048 - type: mrr value: 69.58201013422746 - task: type: Retrieval dataset: type: C-MTEB/T2Retrieval name: MTEB T2Retrieval config: default split: dev revision: None metrics: - type: map_at_1 value: 1.159 - type: map_at_10 value: 2.624 - type: map_at_100 value: 3.259 - type: map_at_1000 value: 3.4090000000000003 - type: map_at_3 value: 1.9109999999999998 - type: map_at_5 value: 2.254 - type: mrr_at_1 value: 5.87 - type: mrr_at_10 value: 8.530999999999999 - type: mrr_at_100 value: 9.142999999999999 - type: mrr_at_1000 value: 9.229 - type: mrr_at_3 value: 7.498 - type: mrr_at_5 value: 8.056000000000001 - type: ndcg_at_1 value: 5.87 - type: ndcg_at_10 value: 4.641 - type: ndcg_at_100 value: 7.507999999999999 - type: ndcg_at_1000 value: 10.823 - type: ndcg_at_3 value: 4.775 - type: ndcg_at_5 value: 4.515000000000001 - type: precision_at_1 value: 5.87 - type: precision_at_10 value: 2.632 - type: precision_at_100 value: 0.762 - type: precision_at_1000 value: 0.166 - type: precision_at_3 value: 4.2299999999999995 - type: precision_at_5 value: 3.5450000000000004 - type: recall_at_1 value: 1.159 - type: recall_at_10 value: 4.816 - type: recall_at_100 value: 13.841999999999999 - type: recall_at_1000 value: 30.469 - type: recall_at_3 value: 2.413 - type: recall_at_5 value: 3.3300000000000005 - task: type: Classification dataset: type: C-MTEB/TNews-classification name: MTEB TNews config: default split: validation revision: None metrics: - type: accuracy value: 26.786000000000005 - type: f1 value: 25.70512339530705 - task: type: Clustering dataset: type: C-MTEB/ThuNewsClusteringP2P name: MTEB ThuNewsClusteringP2P config: default split: test revision: None metrics: - type: v_measure value: 20.691386720429243 - task: type: Clustering dataset: type: C-MTEB/ThuNewsClusteringS2S name: MTEB ThuNewsClusteringS2S config: default split: test revision: None metrics: - type: v_measure value: 17.1882521768033 - task: type: Retrieval dataset: type: C-MTEB/VideoRetrieval name: MTEB VideoRetrieval config: default split: dev revision: None metrics: - type: map_at_1 value: 2.9000000000000004 - type: map_at_10 value: 4.051 - type: map_at_100 value: 4.277 - type: map_at_1000 value: 4.315 - type: map_at_3 value: 3.567 - type: map_at_5 value: 3.897 - type: mrr_at_1 value: 2.9000000000000004 - type: mrr_at_10 value: 4.051 - type: mrr_at_100 value: 4.277 - type: mrr_at_1000 value: 4.315 - type: mrr_at_3 value: 3.567 - type: mrr_at_5 value: 3.897 - type: ndcg_at_1 value: 2.9000000000000004 - type: ndcg_at_10 value: 4.772 - type: ndcg_at_100 value: 6.214 - type: ndcg_at_1000 value: 7.456 - type: ndcg_at_3 value: 3.805 - type: ndcg_at_5 value: 4.390000000000001 - type: precision_at_1 value: 2.9000000000000004 - type: precision_at_10 value: 0.7100000000000001 - type: precision_at_100 value: 0.146 - type: precision_at_1000 value: 0.025 - type: precision_at_3 value: 1.5 - type: precision_at_5 value: 1.18 - type: recall_at_1 value: 2.9000000000000004 - type: recall_at_10 value: 7.1 - type: recall_at_100 value: 14.6 - type: recall_at_1000 value: 24.9 - type: recall_at_3 value: 4.5 - type: recall_at_5 value: 5.8999999999999995 - task: type: Classification dataset: type: C-MTEB/waimai-classification name: MTEB Waimai config: default split: test revision: None metrics: - type: accuracy value: 56.21999999999999 - type: ap value: 36.53654363772411 - type: f1 value: 54.922396485449674 --- # {MODEL_NAME} This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search. <!--- Describe your model here --> ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('{MODEL_NAME}') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}') model = AutoModel.from_pretrained('{MODEL_NAME}') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(*encoded_input) # Perform pooling. In this case, mean pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results <!--- Describe how your model was evaluated --> For an automated evaluation of this model, see the Sentence Embeddings Benchmark: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME}) ## Training The model was trained with the parameters: DataLoader: `torch.utils.data.dataloader.DataLoader` of length 1468721 with parameters: ``` {'batch_size': 160, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` Loss*: `sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss` Parameters of the fit()-Method: ``` { "epochs": 1, "evaluation_steps": 0, "evaluator": "NoneType", "max_grad_norm": 1, "optimizer_class": "<class 'torch.optim.adamw.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 100, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
pszemraj/long-t5-tglobal-base-sci-simplify-elife	pszemraj	2023-11-28T19:20:35Z	4,847	4	transformers	[ "transformers", "pytorch", "onnx", "safetensors", "longt5", "text2text-generation", "lay summaries", "paper summaries", "biology", "medical", "summarization", "en", "dataset:pszemraj/scientific_lay_summarisation-elife-norm", "base_model:google/long-t5-tglobal-base", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	summarization	2023-04-08T23:35:56Z	--- language: - en license: apache-2.0 library_name: transformers tags: - lay summaries - paper summaries - biology - medical datasets: - pszemraj/scientific_lay_summarisation-elife-norm widget: - text: large earthquakes along a given fault segment do not occur at random intervals because it takes time to accumulate the strain energy for the rupture. The rates at which tectonic plates move and accumulate strain at their boundaries are approximately uniform. Therefore, in first approximation, one may expect that large ruptures of the same fault segment will occur at approximately constant time intervals. If subsequent main shocks have different amounts of slip across the fault, then the recurrence time may vary, and the basic idea of periodic mainshocks must be modified. For great plate boundary ruptures the length and slip often vary by a factor of 2. Along the southern segment of the San Andreas fault the recurrence interval is 145 years with variations of several decades. The smaller the standard deviation of the average recurrence interval, the more specific could be the long term prediction of a future mainshock. example_title: earthquakes - text: ' A typical feed-forward neural field algorithm. Spatiotemporal coordinates are fed into a neural network that predicts values in the reconstructed domain. Then, this domain is mapped to the sensor domain where sensor measurements are available as supervision. Class and Section Problems Addressed Generalization (Section 2) Inverse problems, ill-posed problems, editability; symmetries. Hybrid Representations (Section 3) Computation & memory efficiency, representation capacity, editability: Forward Maps (Section 4) Inverse problems Network Architecture (Section 5) Spectral bias, integration & derivatives. Manipulating Neural Fields (Section 6) Edit ability, constraints, regularization. Table 2: The five classes of techniques in the neural field toolbox each addresses problems that arise in learning, inference, and control. (Section 3). We can supervise reconstruction via differentiable forward maps that transform Or project our domain (e.g, 3D reconstruction via 2D images; Section 4) With appropriate network architecture choices, we can overcome neural network spectral biases (blurriness) and efficiently compute derivatives and integrals (Section 5). Finally, we can manipulate neural fields to add constraints and regularizations, and to achieve editable representations (Section 6). Collectively, these classes constitute a ''toolbox'' of techniques to help solve problems with neural fields There are three components in a conditional neural field: (1) An encoder or inference function € that outputs the conditioning latent variable 2 given an observation 0 E(0) =2. 2 is typically a low-dimensional vector, and is often referred to aS a latent code Or feature code_ (2) A mapping function 4 between Z and neural field parameters O: Y(z) = O; (3) The neural field itself $. The encoder € finds the most probable z given the observations O: argmaxz P(2/0). The decoder maximizes the inverse conditional probability to find the most probable 0 given Z: arg- max P(Olz). We discuss different encoding schemes with different optimality guarantees (Section 2.1.1), both global and local conditioning (Section 2.1.2), and different mapping functions Y (Section 2.1.3) 2. Generalization Suppose we wish to estimate a plausible 3D surface shape given a partial or noisy point cloud. We need a suitable prior over the sur- face in its reconstruction domain to generalize to the partial observations. A neural network expresses a prior via the function space of its architecture and parameters 0, and generalization is influenced by the inductive bias of this function space (Section 5).' example_title: scientific paper - text: 'Is a else or outside the cob and tree written being of early client rope and you have is for good reasons. On to the ocean in Orange for time. By''s the aggregate we can bed it yet. Why this please pick up on a sort is do and also M Getoi''s nerocos and do rain become you to let so is his brother is made in use and Mjulia''s''s the lay major is aging Masastup coin present sea only of Oosii rooms set to you We do er do we easy this private oliiishs lonthen might be okay. Good afternoon everybody. Welcome to this lecture of Computational Statistics. As you can see, I''m not socially my name is Michael Zelinger. I''m one of the task for this class and you might have already seen me in the first lecture where I made a quick appearance. I''m also going to give the tortillas in the last third of this course. So to give you a little bit about me, I''m a old student here with better Bulman and my research centres on casual inference applied to biomedical disasters, so that could be genomics or that could be hospital data. If any of you is interested in writing a bachelor thesis, a semester paper may be mastathesis about this topic feel for reach out to me. you have my name on models and my email address you can find in the directory I''d Be very happy to talk about it. you do not need to be sure about it, we can just have a chat. So with that said, let''s get on with the lecture. There''s an exciting topic today I''m going to start by sharing some slides with you and later on during the lecture we''ll move to the paper. So bear with me for a few seconds. Well, the projector is starting up. Okay, so let''s get started. Today''s topic is a very important one. It''s about a technique which really forms one of the fundamentals of data science, machine learning, and any sort of modern statistics. It''s called cross validation. I know you really want to understand this topic I Want you to understand this and frankly, nobody''s gonna leave Professor Mineshousen''s class without understanding cross validation. So to set the stage for this, I Want to introduce you to the validation problem in computational statistics. So the problem is the following: You trained a model on available data. You fitted your model, but you know the training data you got could always have been different and some data from the environment. Maybe it''s a random process. You do not really know what it is, but you know that somebody else who gets a different batch of data from the same environment they would get slightly different training data and you do not care that your method performs as well. On this training data. you want to to perform well on other data that you have not seen other data from the same environment. So in other words, the validation problem is you want to quantify the performance of your model on data that you have not seen. So how is this even possible? How could you possibly measure the performance on data that you do not know The solution to? This is the following realization is that given that you have a bunch of data, you were in charge. You get to control how much that your model sees. It works in the following way: You can hide data firms model. Let''s say you have a training data set which is a bunch of doubtless so X eyes are the features those are typically hide and national vector. It''s got more than one dimension for sure. And the why why eyes. Those are the labels for supervised learning. As you''ve seen before, it''s the same set up as we have in regression. And so you have this training data and now you choose that you only use some of those data to fit your model. You''re not going to use everything, you only use some of it the other part you hide from your model. And then you can use this hidden data to do validation from the point of you of your model. This hidden data is complete by unseen. In other words, we solve our problem of validation.' example_title: transcribed audio - lecture - text: 'Transformer-based models have shown to be very useful for many NLP tasks. However, a major limitation of transformers-based models is its O(n^2)O(n 2) time & memory complexity (where nn is sequence length). Hence, it''s computationally very expensive to apply transformer-based models on long sequences n > 512n>512. Several recent papers, e.g. Longformer, Performer, Reformer, Clustered attention try to remedy this problem by approximating the full attention matrix. You can checkout 🤗''s recent blog post in case you are unfamiliar with these models. BigBird (introduced in paper) is one of such recent models to address this issue. BigBird relies on block sparse attention instead of normal attention (i.e. BERT''s attention) and can handle sequences up to a length of 4096 at a much lower computational cost compared to BERT. It has achieved SOTA on various tasks involving very long sequences such as long documents summarization, question-answering with long contexts. BigBird RoBERTa-like model is now available in 🤗Transformers. The goal of this post is to give the reader an in-depth understanding of big bird implementation & ease one''s life in using BigBird with 🤗Transformers. But, before going into more depth, it is important to remember that the BigBird''s attention is an approximation of BERT''s full attention and therefore does not strive to be better than BERT''s full attention, but rather to be more efficient. It simply allows to apply transformer-based models to much longer sequences since BERT''s quadratic memory requirement quickly becomes unbearable. Simply put, if we would have ∞ compute & ∞ time, BERT''s attention would be preferred over block sparse attention (which we are going to discuss in this post). If you wonder why we need more compute when working with longer sequences, this blog post is just right for you! Some of the main questions one might have when working with standard BERT-like attention include: Do all tokens really have to attend to all other tokens? Why not compute attention only over important tokens? How to decide what tokens are important? How to attend to just a few tokens in a very efficient way? In this blog post, we will try to answer those questions. What tokens should be attended to? We will give a practical example of how attention works by considering the sentence ''BigBird is now available in HuggingFace for extractive question answering''. In BERT-like attention, every word would simply attend to all other tokens. Let''s think about a sensible choice of key tokens that a queried token actually only should attend to by writing some pseudo-code. Will will assume that the token available is queried and build a sensible list of key tokens to attend to. >>> # let''s consider following sentence as an example >>> example = [''BigBird'', ''is'', ''now'', ''available'', ''in'', ''HuggingFace'', ''for'', ''extractive'', ''question'', ''answering''] >>> # further let''s assume, we''re trying to understand the representation of ''available'' i.e. >>> query_token = ''available'' >>> # We will initialize an empty `set` and fill up the tokens of our interest as we proceed in this section. >>> key_tokens = [] # => currently ''available'' token doesn''t have anything to attend Nearby tokens should be important because, in a sentence (sequence of words), the current word is highly dependent on neighboring past & future tokens. This intuition is the idea behind the concept of sliding attention.' example_title: bigbird blog intro - text: 'To be fair, you have to have a very high IQ to understand Rick and Morty. The humour is extremely subtle, and without a solid grasp of theoretical physics most of the jokes will go over a typical viewer''s head. There''s also Rick''s nihilistic outlook, which is deftly woven into his characterisation- his personal philosophy draws heavily from Narodnaya Volya literature, for instance. The fans understand this stuff; they have the intellectual capacity to truly appreciate the depths of these jokes, to realise that they''re not just funny- they say something deep about LIFE. As a consequence people who dislike Rick & Morty truly ARE idiots- of course they wouldn''t appreciate, for instance, the humour in Rick''s existential catchphrase ''Wubba Lubba Dub Dub,'' which itself is a cryptic reference to Turgenev''s Russian epic Fathers and Sons. I''m smirking right now just imagining one of those addlepated simpletons scratching their heads in confusion as Dan Harmon''s genius wit unfolds itself on their television screens. What fools.. how I pity them. 😂 And yes, by the way, i DO have a Rick & Morty tattoo. And no, you cannot see it. It''s for the ladies'' eyes only- and even then they have to demonstrate that they''re within 5 IQ points of my own (preferably lower) beforehand. Nothin personnel kid 😎' example_title: Richard & Mortimer - text: The tower is 324 metres (1,063 ft) tall, about the same height as an 81-storey building, and the tallest structure in Paris. Its base is square, measuring 125 metres (410 ft) on each side. During its construction, the Eiffel Tower surpassed the Washington Monument to become the tallest man-made structure in the world, a title it held for 41 years until the Chrysler Building in New York City was finished in 1930. It was the first structure to reach a height of 300 metres. Due to the addition of a broadcasting aerial at the top of the tower in 1957, it is now taller than the Chrysler Building by 5.2 metres (17 ft). Excluding transmitters, the Eiffel Tower is the second tallest free-standing structure in France after the Millau Viaduct. example_title: eiffel parameters: max_length: 64 min_length: 8 no_repeat_ngram_size: 3 early_stopping: true repetition_penalty: 3.5 encoder_no_repeat_ngram_size: 4 length_penalty: 0.4 num_beams: 4 pipeline_tag: summarization base_model: google/long-t5-tglobal-base --- # long-t5-tglobal-base-sci-simplify: elife subset <a href="https://colab.research.google.com/gist/pszemraj/37a406059887a400afc1428d70374327/long-t5-tglobal-base-sci-simplify-elife-example-with-textsum.ipynb"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> </a> Exploring how well long-document models trained on "lay summaries" of scientific papers generalize. > A lay summary is a summary of a research paper or scientific study that is written in plain language, without the use of technical jargon, and is designed to be easily understood by non-experts. ## Model description This model is a fine-tuned version of [google/long-t5-tglobal-base](https://huggingface.co/google/long-t5-tglobal-base) on the `pszemraj/scientific_lay_summarisation-elife-norm` dataset. - The variant trained on the PLOS subset can be found [here](https://huggingface.co/pszemraj/long-t5-tglobal-base-sci-simplify) ## Usage It's recommended to use this model with [beam search decoding](https://huggingface.co/docs/transformers/generation_strategies#beamsearch-decoding). If interested, you can also use the `textsum` util repo to have most of this abstracted out for you: ```bash pip install -U textsum ``` ```python from textsum.summarize import Summarizer model_name = "pszemraj/long-t5-tglobal-base-sci-simplify-elife" summarizer = Summarizer(model_name) # GPU auto-detected text = "put the text you don't want to read here" summary = summarizer.summarize_string(text) print(summary) ``` ## Intended uses & limitations - Ability to generalize outside of the dataset domain (pubmed/bioscience type papers) has to be evaluated. ## Training and evaluation data The `elife` subset of the lay summaries dataset. Refer to `pszemraj/scientific_lay_summarisation-elife-norm` ## Training procedure ### Eval results It achieves the following results on the evaluation set: - Loss: 1.9990 - Rouge1: 38.5587 - Rouge2: 9.7336 - Rougel: 21.1974 - Rougelsum: 35.9333 - Gen Len: 392.7095 ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0004 - train_batch_size: 4 - eval_batch_size: 2 - seed: 42 - distributed_type: multi-GPU - gradient_accumulation_steps: 16 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.01 - num_epochs: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\|:------:\|:-------:\|:---------:\|:--------:\| \| 2.2995 \| 1.47 \| 100 \| 2.0175 \| 35.2501 \| 8.2121 \| 20.4587 \| 32.4494 \| 439.7552 \| \| 2.2171 \| 2.94 \| 200 \| 1.9990 \| 38.5587 \| 9.7336 \| 21.1974 \| 35.9333 \| 392.7095 \|
jspringer/echo-mistral-7b-instruct-lasttoken	jspringer	2024-02-26T05:59:22Z	4,847	4	transformers	[ "transformers", "safetensors", "mistral", "feature-extraction", "mteb", "arxiv:2402.15449", "model-index", "endpoints_compatible", "text-generation-inference", "region:us" ]	feature-extraction	2024-02-19T04:50:08Z	--- tags: - mteb model-index: - name: mlm results: - task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (en) config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 82.97014925373135 - type: ap value: 49.6288385893607 - type: f1 value: 77.58957447993662 - task: type: Classification dataset: type: mteb/amazon_polarity name: MTEB AmazonPolarityClassification config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 90.975425 - type: ap value: 87.57349835900825 - type: f1 value: 90.96732416386632 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (en) config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 48.708 - type: f1 value: 47.736228936979586 - task: type: Retrieval dataset: type: arguana name: MTEB ArguAna config: default split: test revision: None metrics: - type: map_at_1 value: 32.006 - type: map_at_10 value: 49.268 - type: map_at_100 value: 49.903999999999996 - type: map_at_1000 value: 49.909 - type: map_at_3 value: 44.334 - type: map_at_5 value: 47.374 - type: mrr_at_1 value: 32.788000000000004 - type: mrr_at_10 value: 49.707 - type: mrr_at_100 value: 50.346999999999994 - type: mrr_at_1000 value: 50.352 - type: mrr_at_3 value: 44.95 - type: mrr_at_5 value: 47.766999999999996 - type: ndcg_at_1 value: 32.006 - type: ndcg_at_10 value: 58.523 - type: ndcg_at_100 value: 61.095 - type: ndcg_at_1000 value: 61.190999999999995 - type: ndcg_at_3 value: 48.431000000000004 - type: ndcg_at_5 value: 53.94 - type: precision_at_1 value: 32.006 - type: precision_at_10 value: 8.791 - type: precision_at_100 value: 0.989 - type: precision_at_1000 value: 0.1 - type: precision_at_3 value: 20.104 - type: precision_at_5 value: 14.751 - type: recall_at_1 value: 32.006 - type: recall_at_10 value: 87.909 - type: recall_at_100 value: 98.86200000000001 - type: recall_at_1000 value: 99.57300000000001 - type: recall_at_3 value: 60.313 - type: recall_at_5 value: 73.75500000000001 - task: type: Clustering dataset: type: mteb/arxiv-clustering-p2p name: MTEB ArxivClusteringP2P config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics: - type: v_measure value: 47.01500173547629 - task: type: Clustering dataset: type: mteb/arxiv-clustering-s2s name: MTEB ArxivClusteringS2S config: default split: test revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 metrics: - type: v_measure value: 43.52209238193538 - task: type: Reranking dataset: type: mteb/askubuntudupquestions-reranking name: MTEB AskUbuntuDupQuestions config: default split: test revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 metrics: - type: map value: 64.1348784470504 - type: mrr value: 76.93762916062083 - task: type: STS dataset: type: mteb/biosses-sts name: MTEB BIOSSES config: default split: test revision: d3fb88f8f02e40887cd149695127462bbcf29b4a metrics: - type: cos_sim_pearson value: 87.8322696692348 - type: cos_sim_spearman value: 86.53751398463592 - type: euclidean_pearson value: 86.1435544054336 - type: euclidean_spearman value: 86.70799979698164 - type: manhattan_pearson value: 86.1206703865016 - type: manhattan_spearman value: 86.47004256773585 - task: type: Classification dataset: type: mteb/banking77 name: MTEB Banking77Classification config: default split: test revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 metrics: - type: accuracy value: 88.1461038961039 - type: f1 value: 88.09877611214092 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-p2p name: MTEB BiorxivClusteringP2P config: default split: test revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 metrics: - type: v_measure value: 35.53021718892608 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-s2s name: MTEB BiorxivClusteringS2S config: default split: test revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 metrics: - type: v_measure value: 35.34236915611622 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackAndroidRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 36.435 - type: map_at_10 value: 49.437999999999995 - type: map_at_100 value: 51.105999999999995 - type: map_at_1000 value: 51.217999999999996 - type: map_at_3 value: 44.856 - type: map_at_5 value: 47.195 - type: mrr_at_1 value: 45.78 - type: mrr_at_10 value: 56.302 - type: mrr_at_100 value: 56.974000000000004 - type: mrr_at_1000 value: 57.001999999999995 - type: mrr_at_3 value: 53.6 - type: mrr_at_5 value: 55.059999999999995 - type: ndcg_at_1 value: 44.921 - type: ndcg_at_10 value: 56.842000000000006 - type: ndcg_at_100 value: 61.586 - type: ndcg_at_1000 value: 63.039 - type: ndcg_at_3 value: 50.612 - type: ndcg_at_5 value: 53.181 - type: precision_at_1 value: 44.921 - type: precision_at_10 value: 11.245 - type: precision_at_100 value: 1.7069999999999999 - type: precision_at_1000 value: 0.216 - type: precision_at_3 value: 24.224999999999998 - type: precision_at_5 value: 17.511 - type: recall_at_1 value: 36.435 - type: recall_at_10 value: 70.998 - type: recall_at_100 value: 89.64 - type: recall_at_1000 value: 98.654 - type: recall_at_3 value: 53.034000000000006 - type: recall_at_5 value: 60.41 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackEnglishRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 33.371 - type: map_at_10 value: 45.301 - type: map_at_100 value: 46.663 - type: map_at_1000 value: 46.791 - type: map_at_3 value: 41.79 - type: map_at_5 value: 43.836999999999996 - type: mrr_at_1 value: 42.611 - type: mrr_at_10 value: 51.70400000000001 - type: mrr_at_100 value: 52.342 - type: mrr_at_1000 value: 52.38 - type: mrr_at_3 value: 49.374 - type: mrr_at_5 value: 50.82 - type: ndcg_at_1 value: 42.166 - type: ndcg_at_10 value: 51.49 - type: ndcg_at_100 value: 56.005 - type: ndcg_at_1000 value: 57.748 - type: ndcg_at_3 value: 46.769 - type: ndcg_at_5 value: 49.155 - type: precision_at_1 value: 42.166 - type: precision_at_10 value: 9.841 - type: precision_at_100 value: 1.569 - type: precision_at_1000 value: 0.202 - type: precision_at_3 value: 22.803 - type: precision_at_5 value: 16.229 - type: recall_at_1 value: 33.371 - type: recall_at_10 value: 62.52799999999999 - type: recall_at_100 value: 81.269 - type: recall_at_1000 value: 91.824 - type: recall_at_3 value: 48.759 - type: recall_at_5 value: 55.519 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGamingRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 41.421 - type: map_at_10 value: 55.985 - type: map_at_100 value: 56.989999999999995 - type: map_at_1000 value: 57.028 - type: map_at_3 value: 52.271 - type: map_at_5 value: 54.517 - type: mrr_at_1 value: 47.272999999999996 - type: mrr_at_10 value: 59.266 - type: mrr_at_100 value: 59.821999999999996 - type: mrr_at_1000 value: 59.839 - type: mrr_at_3 value: 56.677 - type: mrr_at_5 value: 58.309999999999995 - type: ndcg_at_1 value: 47.147 - type: ndcg_at_10 value: 62.596 - type: ndcg_at_100 value: 66.219 - type: ndcg_at_1000 value: 66.886 - type: ndcg_at_3 value: 56.558 - type: ndcg_at_5 value: 59.805 - type: precision_at_1 value: 47.147 - type: precision_at_10 value: 10.245 - type: precision_at_100 value: 1.302 - type: precision_at_1000 value: 0.13899999999999998 - type: precision_at_3 value: 25.663999999999998 - type: precision_at_5 value: 17.793 - type: recall_at_1 value: 41.421 - type: recall_at_10 value: 78.77499999999999 - type: recall_at_100 value: 93.996 - type: recall_at_1000 value: 98.60600000000001 - type: recall_at_3 value: 62.891 - type: recall_at_5 value: 70.819 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGisRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 27.517999999999997 - type: map_at_10 value: 37.468 - type: map_at_100 value: 38.667 - type: map_at_1000 value: 38.743 - type: map_at_3 value: 34.524 - type: map_at_5 value: 36.175000000000004 - type: mrr_at_1 value: 29.378999999999998 - type: mrr_at_10 value: 39.54 - type: mrr_at_100 value: 40.469 - type: mrr_at_1000 value: 40.522000000000006 - type: mrr_at_3 value: 36.685 - type: mrr_at_5 value: 38.324000000000005 - type: ndcg_at_1 value: 29.718 - type: ndcg_at_10 value: 43.091 - type: ndcg_at_100 value: 48.44 - type: ndcg_at_1000 value: 50.181 - type: ndcg_at_3 value: 37.34 - type: ndcg_at_5 value: 40.177 - type: precision_at_1 value: 29.718 - type: precision_at_10 value: 6.723 - type: precision_at_100 value: 0.992 - type: precision_at_1000 value: 0.117 - type: precision_at_3 value: 16.083 - type: precision_at_5 value: 11.322000000000001 - type: recall_at_1 value: 27.517999999999997 - type: recall_at_10 value: 58.196999999999996 - type: recall_at_100 value: 82.07799999999999 - type: recall_at_1000 value: 94.935 - type: recall_at_3 value: 42.842 - type: recall_at_5 value: 49.58 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackMathematicaRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 19.621 - type: map_at_10 value: 30.175 - type: map_at_100 value: 31.496000000000002 - type: map_at_1000 value: 31.602000000000004 - type: map_at_3 value: 26.753 - type: map_at_5 value: 28.857 - type: mrr_at_1 value: 25.497999999999998 - type: mrr_at_10 value: 35.44 - type: mrr_at_100 value: 36.353 - type: mrr_at_1000 value: 36.412 - type: mrr_at_3 value: 32.275999999999996 - type: mrr_at_5 value: 34.434 - type: ndcg_at_1 value: 24.502 - type: ndcg_at_10 value: 36.423 - type: ndcg_at_100 value: 42.289 - type: ndcg_at_1000 value: 44.59 - type: ndcg_at_3 value: 30.477999999999998 - type: ndcg_at_5 value: 33.787 - type: precision_at_1 value: 24.502 - type: precision_at_10 value: 6.978 - type: precision_at_100 value: 1.139 - type: precision_at_1000 value: 0.145 - type: precision_at_3 value: 15.008 - type: precision_at_5 value: 11.468 - type: recall_at_1 value: 19.621 - type: recall_at_10 value: 50.516000000000005 - type: recall_at_100 value: 75.721 - type: recall_at_1000 value: 91.77199999999999 - type: recall_at_3 value: 34.695 - type: recall_at_5 value: 42.849 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackPhysicsRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 33.525 - type: map_at_10 value: 46.153 - type: map_at_100 value: 47.61 - type: map_at_1000 value: 47.715 - type: map_at_3 value: 42.397 - type: map_at_5 value: 44.487 - type: mrr_at_1 value: 42.445 - type: mrr_at_10 value: 52.174 - type: mrr_at_100 value: 52.986999999999995 - type: mrr_at_1000 value: 53.016 - type: mrr_at_3 value: 49.647000000000006 - type: mrr_at_5 value: 51.215999999999994 - type: ndcg_at_1 value: 42.156 - type: ndcg_at_10 value: 52.698 - type: ndcg_at_100 value: 58.167 - type: ndcg_at_1000 value: 59.71300000000001 - type: ndcg_at_3 value: 47.191 - type: ndcg_at_5 value: 49.745 - type: precision_at_1 value: 42.156 - type: precision_at_10 value: 9.682 - type: precision_at_100 value: 1.469 - type: precision_at_1000 value: 0.17700000000000002 - type: precision_at_3 value: 22.682 - type: precision_at_5 value: 16.035 - type: recall_at_1 value: 33.525 - type: recall_at_10 value: 66.142 - type: recall_at_100 value: 88.248 - type: recall_at_1000 value: 97.806 - type: recall_at_3 value: 50.541000000000004 - type: recall_at_5 value: 57.275 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackProgrammersRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 28.249000000000002 - type: map_at_10 value: 41.659 - type: map_at_100 value: 43.001 - type: map_at_1000 value: 43.094 - type: map_at_3 value: 37.607 - type: map_at_5 value: 39.662 - type: mrr_at_1 value: 36.301 - type: mrr_at_10 value: 47.482 - type: mrr_at_100 value: 48.251 - type: mrr_at_1000 value: 48.288 - type: mrr_at_3 value: 44.444 - type: mrr_at_5 value: 46.013999999999996 - type: ndcg_at_1 value: 35.616 - type: ndcg_at_10 value: 49.021 - type: ndcg_at_100 value: 54.362 - type: ndcg_at_1000 value: 55.864999999999995 - type: ndcg_at_3 value: 42.515 - type: ndcg_at_5 value: 45.053 - type: precision_at_1 value: 35.616 - type: precision_at_10 value: 9.372 - type: precision_at_100 value: 1.4120000000000001 - type: precision_at_1000 value: 0.172 - type: precision_at_3 value: 21.043 - type: precision_at_5 value: 14.84 - type: recall_at_1 value: 28.249000000000002 - type: recall_at_10 value: 65.514 - type: recall_at_100 value: 87.613 - type: recall_at_1000 value: 97.03 - type: recall_at_3 value: 47.21 - type: recall_at_5 value: 54.077 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 29.164583333333333 - type: map_at_10 value: 40.632000000000005 - type: map_at_100 value: 41.96875 - type: map_at_1000 value: 42.07508333333333 - type: map_at_3 value: 37.18458333333333 - type: map_at_5 value: 39.13700000000001 - type: mrr_at_1 value: 35.2035 - type: mrr_at_10 value: 45.28816666666666 - type: mrr_at_100 value: 46.11466666666667 - type: mrr_at_1000 value: 46.15741666666667 - type: mrr_at_3 value: 42.62925 - type: mrr_at_5 value: 44.18141666666667 - type: ndcg_at_1 value: 34.88958333333333 - type: ndcg_at_10 value: 46.90650000000001 - type: ndcg_at_100 value: 52.135333333333335 - type: ndcg_at_1000 value: 53.89766666666668 - type: ndcg_at_3 value: 41.32075 - type: ndcg_at_5 value: 44.02083333333333 - type: precision_at_1 value: 34.88958333333333 - type: precision_at_10 value: 8.392833333333332 - type: precision_at_100 value: 1.3085833333333334 - type: precision_at_1000 value: 0.16458333333333333 - type: precision_at_3 value: 19.361166666666666 - type: precision_at_5 value: 13.808416666666668 - type: recall_at_1 value: 29.164583333333333 - type: recall_at_10 value: 60.874666666666656 - type: recall_at_100 value: 83.21008333333334 - type: recall_at_1000 value: 95.09275000000001 - type: recall_at_3 value: 45.37591666666667 - type: recall_at_5 value: 52.367666666666665 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackStatsRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 28.682000000000002 - type: map_at_10 value: 37.913000000000004 - type: map_at_100 value: 39.037 - type: map_at_1000 value: 39.123999999999995 - type: map_at_3 value: 35.398 - type: map_at_5 value: 36.906 - type: mrr_at_1 value: 32.362 - type: mrr_at_10 value: 40.92 - type: mrr_at_100 value: 41.748000000000005 - type: mrr_at_1000 value: 41.81 - type: mrr_at_3 value: 38.701 - type: mrr_at_5 value: 39.936 - type: ndcg_at_1 value: 32.208999999999996 - type: ndcg_at_10 value: 42.84 - type: ndcg_at_100 value: 47.927 - type: ndcg_at_1000 value: 50.048 - type: ndcg_at_3 value: 38.376 - type: ndcg_at_5 value: 40.661 - type: precision_at_1 value: 32.208999999999996 - type: precision_at_10 value: 6.718 - type: precision_at_100 value: 1.012 - type: precision_at_1000 value: 0.127 - type: precision_at_3 value: 16.667 - type: precision_at_5 value: 11.503 - type: recall_at_1 value: 28.682000000000002 - type: recall_at_10 value: 54.872 - type: recall_at_100 value: 77.42999999999999 - type: recall_at_1000 value: 93.054 - type: recall_at_3 value: 42.577999999999996 - type: recall_at_5 value: 48.363 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackTexRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 19.698 - type: map_at_10 value: 28.777 - type: map_at_100 value: 30.091 - type: map_at_1000 value: 30.209999999999997 - type: map_at_3 value: 25.874000000000002 - type: map_at_5 value: 27.438000000000002 - type: mrr_at_1 value: 24.295 - type: mrr_at_10 value: 33.077 - type: mrr_at_100 value: 34.036 - type: mrr_at_1000 value: 34.1 - type: mrr_at_3 value: 30.523 - type: mrr_at_5 value: 31.891000000000002 - type: ndcg_at_1 value: 24.535 - type: ndcg_at_10 value: 34.393 - type: ndcg_at_100 value: 40.213 - type: ndcg_at_1000 value: 42.748000000000005 - type: ndcg_at_3 value: 29.316 - type: ndcg_at_5 value: 31.588 - type: precision_at_1 value: 24.535 - type: precision_at_10 value: 6.483 - type: precision_at_100 value: 1.102 - type: precision_at_1000 value: 0.151 - type: precision_at_3 value: 14.201 - type: precision_at_5 value: 10.344000000000001 - type: recall_at_1 value: 19.698 - type: recall_at_10 value: 46.903 - type: recall_at_100 value: 72.624 - type: recall_at_1000 value: 90.339 - type: recall_at_3 value: 32.482 - type: recall_at_5 value: 38.452 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackUnixRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 30.56 - type: map_at_10 value: 41.993 - type: map_at_100 value: 43.317 - type: map_at_1000 value: 43.399 - type: map_at_3 value: 38.415 - type: map_at_5 value: 40.472 - type: mrr_at_1 value: 36.474000000000004 - type: mrr_at_10 value: 46.562 - type: mrr_at_100 value: 47.497 - type: mrr_at_1000 value: 47.532999999999994 - type: mrr_at_3 value: 43.905 - type: mrr_at_5 value: 45.379000000000005 - type: ndcg_at_1 value: 36.287000000000006 - type: ndcg_at_10 value: 48.262 - type: ndcg_at_100 value: 53.789 - type: ndcg_at_1000 value: 55.44 - type: ndcg_at_3 value: 42.358000000000004 - type: ndcg_at_5 value: 45.221000000000004 - type: precision_at_1 value: 36.287000000000006 - type: precision_at_10 value: 8.265 - type: precision_at_100 value: 1.24 - type: precision_at_1000 value: 0.148 - type: precision_at_3 value: 19.558 - type: precision_at_5 value: 13.880999999999998 - type: recall_at_1 value: 30.56 - type: recall_at_10 value: 62.891 - type: recall_at_100 value: 85.964 - type: recall_at_1000 value: 97.087 - type: recall_at_3 value: 46.755 - type: recall_at_5 value: 53.986000000000004 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWebmastersRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 29.432000000000002 - type: map_at_10 value: 40.898 - type: map_at_100 value: 42.794 - type: map_at_1000 value: 43.029 - type: map_at_3 value: 37.658 - type: map_at_5 value: 39.519 - type: mrr_at_1 value: 36.364000000000004 - type: mrr_at_10 value: 46.9 - type: mrr_at_100 value: 47.819 - type: mrr_at_1000 value: 47.848 - type: mrr_at_3 value: 44.202999999999996 - type: mrr_at_5 value: 45.715 - type: ndcg_at_1 value: 35.573 - type: ndcg_at_10 value: 47.628 - type: ndcg_at_100 value: 53.88699999999999 - type: ndcg_at_1000 value: 55.584 - type: ndcg_at_3 value: 42.669000000000004 - type: ndcg_at_5 value: 45.036 - type: precision_at_1 value: 35.573 - type: precision_at_10 value: 8.933 - type: precision_at_100 value: 1.8159999999999998 - type: precision_at_1000 value: 0.256 - type: precision_at_3 value: 20.29 - type: precision_at_5 value: 14.387 - type: recall_at_1 value: 29.432000000000002 - type: recall_at_10 value: 60.388 - type: recall_at_100 value: 87.144 - type: recall_at_1000 value: 97.154 - type: recall_at_3 value: 45.675 - type: recall_at_5 value: 52.35300000000001 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWordpressRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 21.462999999999997 - type: map_at_10 value: 31.824 - type: map_at_100 value: 32.853 - type: map_at_1000 value: 32.948 - type: map_at_3 value: 28.671999999999997 - type: map_at_5 value: 30.579 - type: mrr_at_1 value: 23.66 - type: mrr_at_10 value: 34.091 - type: mrr_at_100 value: 35.077999999999996 - type: mrr_at_1000 value: 35.138999999999996 - type: mrr_at_3 value: 31.516 - type: mrr_at_5 value: 33.078 - type: ndcg_at_1 value: 23.845 - type: ndcg_at_10 value: 37.594 - type: ndcg_at_100 value: 42.74 - type: ndcg_at_1000 value: 44.93 - type: ndcg_at_3 value: 31.667 - type: ndcg_at_5 value: 34.841 - type: precision_at_1 value: 23.845 - type: precision_at_10 value: 6.229 - type: precision_at_100 value: 0.943 - type: precision_at_1000 value: 0.125 - type: precision_at_3 value: 14.11 - type: precision_at_5 value: 10.388 - type: recall_at_1 value: 21.462999999999997 - type: recall_at_10 value: 52.772 - type: recall_at_100 value: 76.794 - type: recall_at_1000 value: 92.852 - type: recall_at_3 value: 37.049 - type: recall_at_5 value: 44.729 - task: type: Retrieval dataset: type: climate-fever name: MTEB ClimateFEVER config: default split: test revision: None metrics: - type: map_at_1 value: 15.466 - type: map_at_10 value: 25.275 - type: map_at_100 value: 27.176000000000002 - type: map_at_1000 value: 27.374 - type: map_at_3 value: 21.438 - type: map_at_5 value: 23.366 - type: mrr_at_1 value: 35.699999999999996 - type: mrr_at_10 value: 47.238 - type: mrr_at_100 value: 47.99 - type: mrr_at_1000 value: 48.021 - type: mrr_at_3 value: 44.463 - type: mrr_at_5 value: 46.039 - type: ndcg_at_1 value: 35.244 - type: ndcg_at_10 value: 34.559 - type: ndcg_at_100 value: 41.74 - type: ndcg_at_1000 value: 45.105000000000004 - type: ndcg_at_3 value: 29.284 - type: ndcg_at_5 value: 30.903999999999996 - type: precision_at_1 value: 35.244 - type: precision_at_10 value: 10.463000000000001 - type: precision_at_100 value: 1.8259999999999998 - type: precision_at_1000 value: 0.246 - type: precision_at_3 value: 21.65 - type: precision_at_5 value: 16.078 - type: recall_at_1 value: 15.466 - type: recall_at_10 value: 39.782000000000004 - type: recall_at_100 value: 64.622 - type: recall_at_1000 value: 83.233 - type: recall_at_3 value: 26.398 - type: recall_at_5 value: 31.676 - task: type: Retrieval dataset: type: dbpedia-entity name: MTEB DBPedia config: default split: test revision: None metrics: - type: map_at_1 value: 9.414 - type: map_at_10 value: 22.435 - type: map_at_100 value: 32.393 - type: map_at_1000 value: 34.454 - type: map_at_3 value: 15.346000000000002 - type: map_at_5 value: 18.282999999999998 - type: mrr_at_1 value: 71.5 - type: mrr_at_10 value: 78.795 - type: mrr_at_100 value: 79.046 - type: mrr_at_1000 value: 79.054 - type: mrr_at_3 value: 77.333 - type: mrr_at_5 value: 78.146 - type: ndcg_at_1 value: 60.75000000000001 - type: ndcg_at_10 value: 46.829 - type: ndcg_at_100 value: 52.370000000000005 - type: ndcg_at_1000 value: 59.943999999999996 - type: ndcg_at_3 value: 51.33 - type: ndcg_at_5 value: 48.814 - type: precision_at_1 value: 71.75 - type: precision_at_10 value: 37.525 - type: precision_at_100 value: 12.075 - type: precision_at_1000 value: 2.464 - type: precision_at_3 value: 54.75 - type: precision_at_5 value: 47.55 - type: recall_at_1 value: 9.414 - type: recall_at_10 value: 28.67 - type: recall_at_100 value: 59.924 - type: recall_at_1000 value: 83.921 - type: recall_at_3 value: 16.985 - type: recall_at_5 value: 21.372 - task: type: Classification dataset: type: mteb/emotion name: MTEB EmotionClassification config: default split: test revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 metrics: - type: accuracy value: 52.18000000000001 - type: f1 value: 47.04613218997081 - task: type: Retrieval dataset: type: fever name: MTEB FEVER config: default split: test revision: None metrics: - type: map_at_1 value: 82.57900000000001 - type: map_at_10 value: 88.465 - type: map_at_100 value: 88.649 - type: map_at_1000 value: 88.661 - type: map_at_3 value: 87.709 - type: map_at_5 value: 88.191 - type: mrr_at_1 value: 88.899 - type: mrr_at_10 value: 93.35900000000001 - type: mrr_at_100 value: 93.38499999999999 - type: mrr_at_1000 value: 93.38499999999999 - type: mrr_at_3 value: 93.012 - type: mrr_at_5 value: 93.282 - type: ndcg_at_1 value: 88.98899999999999 - type: ndcg_at_10 value: 91.22 - type: ndcg_at_100 value: 91.806 - type: ndcg_at_1000 value: 92.013 - type: ndcg_at_3 value: 90.236 - type: ndcg_at_5 value: 90.798 - type: precision_at_1 value: 88.98899999999999 - type: precision_at_10 value: 10.537 - type: precision_at_100 value: 1.106 - type: precision_at_1000 value: 0.11399999999999999 - type: precision_at_3 value: 33.598 - type: precision_at_5 value: 20.618 - type: recall_at_1 value: 82.57900000000001 - type: recall_at_10 value: 94.95400000000001 - type: recall_at_100 value: 97.14 - type: recall_at_1000 value: 98.407 - type: recall_at_3 value: 92.203 - type: recall_at_5 value: 93.747 - task: type: Retrieval dataset: type: fiqa name: MTEB FiQA2018 config: default split: test revision: None metrics: - type: map_at_1 value: 27.871000000000002 - type: map_at_10 value: 46.131 - type: map_at_100 value: 48.245 - type: map_at_1000 value: 48.361 - type: map_at_3 value: 40.03 - type: map_at_5 value: 43.634 - type: mrr_at_1 value: 52.932 - type: mrr_at_10 value: 61.61299999999999 - type: mrr_at_100 value: 62.205 - type: mrr_at_1000 value: 62.224999999999994 - type: mrr_at_3 value: 59.388 - type: mrr_at_5 value: 60.760999999999996 - type: ndcg_at_1 value: 53.395 - type: ndcg_at_10 value: 54.506 - type: ndcg_at_100 value: 61.151999999999994 - type: ndcg_at_1000 value: 62.882000000000005 - type: ndcg_at_3 value: 49.903999999999996 - type: ndcg_at_5 value: 51.599 - type: precision_at_1 value: 53.395 - type: precision_at_10 value: 15.247 - type: precision_at_100 value: 2.221 - type: precision_at_1000 value: 0.255 - type: precision_at_3 value: 33.539 - type: precision_at_5 value: 24.722 - type: recall_at_1 value: 27.871000000000002 - type: recall_at_10 value: 62.074 - type: recall_at_100 value: 86.531 - type: recall_at_1000 value: 96.574 - type: recall_at_3 value: 45.003 - type: recall_at_5 value: 53.00899999999999 - task: type: Retrieval dataset: type: hotpotqa name: MTEB HotpotQA config: default split: test revision: None metrics: - type: map_at_1 value: 40.513 - type: map_at_10 value: 69.066 - type: map_at_100 value: 69.903 - type: map_at_1000 value: 69.949 - type: map_at_3 value: 65.44200000000001 - type: map_at_5 value: 67.784 - type: mrr_at_1 value: 80.891 - type: mrr_at_10 value: 86.42699999999999 - type: mrr_at_100 value: 86.577 - type: mrr_at_1000 value: 86.58200000000001 - type: mrr_at_3 value: 85.6 - type: mrr_at_5 value: 86.114 - type: ndcg_at_1 value: 81.026 - type: ndcg_at_10 value: 76.412 - type: ndcg_at_100 value: 79.16 - type: ndcg_at_1000 value: 79.989 - type: ndcg_at_3 value: 71.45 - type: ndcg_at_5 value: 74.286 - type: precision_at_1 value: 81.026 - type: precision_at_10 value: 16.198999999999998 - type: precision_at_100 value: 1.831 - type: precision_at_1000 value: 0.194 - type: precision_at_3 value: 46.721000000000004 - type: precision_at_5 value: 30.266 - type: recall_at_1 value: 40.513 - type: recall_at_10 value: 80.99300000000001 - type: recall_at_100 value: 91.526 - type: recall_at_1000 value: 96.935 - type: recall_at_3 value: 70.081 - type: recall_at_5 value: 75.665 - task: type: Classification dataset: type: mteb/imdb name: MTEB ImdbClassification config: default split: test revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 metrics: - type: accuracy value: 87.42320000000001 - type: ap value: 83.59975323233843 - type: f1 value: 87.38669942597816 - task: type: Retrieval dataset: type: msmarco name: MTEB MSMARCO config: default split: dev revision: None metrics: - type: map_at_1 value: 22.676 - type: map_at_10 value: 35.865 - type: map_at_100 value: 37.019000000000005 - type: map_at_1000 value: 37.062 - type: map_at_3 value: 31.629 - type: map_at_5 value: 34.050999999999995 - type: mrr_at_1 value: 23.023 - type: mrr_at_10 value: 36.138999999999996 - type: mrr_at_100 value: 37.242 - type: mrr_at_1000 value: 37.28 - type: mrr_at_3 value: 32.053 - type: mrr_at_5 value: 34.383 - type: ndcg_at_1 value: 23.308999999999997 - type: ndcg_at_10 value: 43.254 - type: ndcg_at_100 value: 48.763 - type: ndcg_at_1000 value: 49.788 - type: ndcg_at_3 value: 34.688 - type: ndcg_at_5 value: 38.973 - type: precision_at_1 value: 23.308999999999997 - type: precision_at_10 value: 6.909999999999999 - type: precision_at_100 value: 0.967 - type: precision_at_1000 value: 0.106 - type: precision_at_3 value: 14.818999999999999 - type: precision_at_5 value: 11.072 - type: recall_at_1 value: 22.676 - type: recall_at_10 value: 66.077 - type: recall_at_100 value: 91.4 - type: recall_at_1000 value: 99.143 - type: recall_at_3 value: 42.845 - type: recall_at_5 value: 53.08500000000001 - task: type: Classification dataset: type: mteb/mtop_domain name: MTEB MTOPDomainClassification (en) config: en split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 96.16279069767444 - type: f1 value: 96.02183835878418 - task: type: Classification dataset: type: mteb/mtop_intent name: MTEB MTOPIntentClassification (en) config: en split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - type: accuracy value: 85.74783401732788 - type: f1 value: 70.59661579230463 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (en) config: en split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 79.67047747141895 - type: f1 value: 77.06311183471965 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (en) config: en split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 82.82447881640887 - type: f1 value: 82.37598020010746 - task: type: Clustering dataset: type: mteb/medrxiv-clustering-p2p name: MTEB MedrxivClusteringP2P config: default split: test revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 metrics: - type: v_measure value: 30.266131881264467 - task: type: Clustering dataset: type: mteb/medrxiv-clustering-s2s name: MTEB MedrxivClusteringS2S config: default split: test revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 metrics: - type: v_measure value: 29.673653452453998 - task: type: Reranking dataset: type: mteb/mind_small name: MTEB MindSmallReranking config: default split: test revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 metrics: - type: map value: 32.91846122902102 - type: mrr value: 34.2557300204471 - task: type: Retrieval dataset: type: nfcorpus name: MTEB NFCorpus config: default split: test revision: None metrics: - type: map_at_1 value: 6.762 - type: map_at_10 value: 15.134 - type: map_at_100 value: 19.341 - type: map_at_1000 value: 20.961 - type: map_at_3 value: 10.735999999999999 - type: map_at_5 value: 12.751999999999999 - type: mrr_at_1 value: 52.941 - type: mrr_at_10 value: 60.766 - type: mrr_at_100 value: 61.196 - type: mrr_at_1000 value: 61.227 - type: mrr_at_3 value: 58.720000000000006 - type: mrr_at_5 value: 59.866 - type: ndcg_at_1 value: 50.929 - type: ndcg_at_10 value: 39.554 - type: ndcg_at_100 value: 36.307 - type: ndcg_at_1000 value: 44.743 - type: ndcg_at_3 value: 44.157000000000004 - type: ndcg_at_5 value: 42.142 - type: precision_at_1 value: 52.322 - type: precision_at_10 value: 29.412 - type: precision_at_100 value: 9.365 - type: precision_at_1000 value: 2.2159999999999997 - type: precision_at_3 value: 40.557 - type: precision_at_5 value: 35.913000000000004 - type: recall_at_1 value: 6.762 - type: recall_at_10 value: 19.689999999999998 - type: recall_at_100 value: 36.687 - type: recall_at_1000 value: 67.23 - type: recall_at_3 value: 11.773 - type: recall_at_5 value: 15.18 - task: type: Retrieval dataset: type: nq name: MTEB NQ config: default split: test revision: None metrics: - type: map_at_1 value: 36.612 - type: map_at_10 value: 54.208 - type: map_at_100 value: 55.056000000000004 - type: map_at_1000 value: 55.069 - type: map_at_3 value: 49.45 - type: map_at_5 value: 52.556000000000004 - type: mrr_at_1 value: 41.976 - type: mrr_at_10 value: 56.972 - type: mrr_at_100 value: 57.534 - type: mrr_at_1000 value: 57.542 - type: mrr_at_3 value: 53.312000000000005 - type: mrr_at_5 value: 55.672999999999995 - type: ndcg_at_1 value: 41.338 - type: ndcg_at_10 value: 62.309000000000005 - type: ndcg_at_100 value: 65.557 - type: ndcg_at_1000 value: 65.809 - type: ndcg_at_3 value: 53.74100000000001 - type: ndcg_at_5 value: 58.772999999999996 - type: precision_at_1 value: 41.338 - type: precision_at_10 value: 10.107 - type: precision_at_100 value: 1.1900000000000002 - type: precision_at_1000 value: 0.121 - type: precision_at_3 value: 24.488 - type: precision_at_5 value: 17.596 - type: recall_at_1 value: 36.612 - type: recall_at_10 value: 84.408 - type: recall_at_100 value: 97.929 - type: recall_at_1000 value: 99.725 - type: recall_at_3 value: 62.676 - type: recall_at_5 value: 74.24199999999999 - task: type: Retrieval dataset: type: quora name: MTEB QuoraRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 71.573 - type: map_at_10 value: 85.81 - type: map_at_100 value: 86.434 - type: map_at_1000 value: 86.446 - type: map_at_3 value: 82.884 - type: map_at_5 value: 84.772 - type: mrr_at_1 value: 82.53 - type: mrr_at_10 value: 88.51299999999999 - type: mrr_at_100 value: 88.59700000000001 - type: mrr_at_1000 value: 88.598 - type: mrr_at_3 value: 87.595 - type: mrr_at_5 value: 88.266 - type: ndcg_at_1 value: 82.39999999999999 - type: ndcg_at_10 value: 89.337 - type: ndcg_at_100 value: 90.436 - type: ndcg_at_1000 value: 90.498 - type: ndcg_at_3 value: 86.676 - type: ndcg_at_5 value: 88.241 - type: precision_at_1 value: 82.39999999999999 - type: precision_at_10 value: 13.58 - type: precision_at_100 value: 1.543 - type: precision_at_1000 value: 0.157 - type: precision_at_3 value: 38.04 - type: precision_at_5 value: 25.044 - type: recall_at_1 value: 71.573 - type: recall_at_10 value: 96.066 - type: recall_at_100 value: 99.73100000000001 - type: recall_at_1000 value: 99.991 - type: recall_at_3 value: 88.34 - type: recall_at_5 value: 92.79899999999999 - task: type: Clustering dataset: type: mteb/reddit-clustering name: MTEB RedditClustering config: default split: test revision: 24640382cdbf8abc73003fb0fa6d111a705499eb metrics: - type: v_measure value: 61.767168063971724 - task: type: Clustering dataset: type: mteb/reddit-clustering-p2p name: MTEB RedditClusteringP2P config: default split: test revision: 282350215ef01743dc01b456c7f5241fa8937f16 metrics: - type: v_measure value: 66.00502775826037 - task: type: Retrieval dataset: type: scidocs name: MTEB SCIDOCS config: default split: test revision: None metrics: - type: map_at_1 value: 4.718 - type: map_at_10 value: 12.13 - type: map_at_100 value: 14.269000000000002 - type: map_at_1000 value: 14.578 - type: map_at_3 value: 8.605 - type: map_at_5 value: 10.483 - type: mrr_at_1 value: 23.7 - type: mrr_at_10 value: 34.354 - type: mrr_at_100 value: 35.522 - type: mrr_at_1000 value: 35.571999999999996 - type: mrr_at_3 value: 31.15 - type: mrr_at_5 value: 32.98 - type: ndcg_at_1 value: 23.3 - type: ndcg_at_10 value: 20.171 - type: ndcg_at_100 value: 28.456 - type: ndcg_at_1000 value: 33.826 - type: ndcg_at_3 value: 19.104 - type: ndcg_at_5 value: 16.977999999999998 - type: precision_at_1 value: 23.3 - type: precision_at_10 value: 10.45 - type: precision_at_100 value: 2.239 - type: precision_at_1000 value: 0.35300000000000004 - type: precision_at_3 value: 17.933 - type: precision_at_5 value: 15.1 - type: recall_at_1 value: 4.718 - type: recall_at_10 value: 21.221999999999998 - type: recall_at_100 value: 45.42 - type: recall_at_1000 value: 71.642 - type: recall_at_3 value: 10.922 - type: recall_at_5 value: 15.322 - task: type: STS dataset: type: mteb/sickr-sts name: MTEB SICK-R config: default split: test revision: a6ea5a8cab320b040a23452cc28066d9beae2cee metrics: - type: cos_sim_pearson value: 85.2065344862739 - type: cos_sim_spearman value: 83.2276569587515 - type: euclidean_pearson value: 83.42726762105312 - type: euclidean_spearman value: 83.31396596997742 - type: manhattan_pearson value: 83.41123401762816 - type: manhattan_spearman value: 83.34393052682026 - task: type: STS dataset: type: mteb/sts12-sts name: MTEB STS12 config: default split: test revision: a0d554a64d88156834ff5ae9920b964011b16384 metrics: - type: cos_sim_pearson value: 81.28253173719754 - type: cos_sim_spearman value: 76.12995701324436 - type: euclidean_pearson value: 75.30693691794121 - type: euclidean_spearman value: 75.12472789129536 - type: manhattan_pearson value: 75.35860808729171 - type: manhattan_spearman value: 75.30445827952794 - task: type: STS dataset: type: mteb/sts13-sts name: MTEB STS13 config: default split: test revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca metrics: - type: cos_sim_pearson value: 82.09358031005694 - type: cos_sim_spearman value: 83.18811147636619 - type: euclidean_pearson value: 82.65513459991631 - type: euclidean_spearman value: 82.71085530442987 - type: manhattan_pearson value: 82.67700926821576 - type: manhattan_spearman value: 82.73815539380426 - task: type: STS dataset: type: mteb/sts14-sts name: MTEB STS14 config: default split: test revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 metrics: - type: cos_sim_pearson value: 81.51365440223137 - type: cos_sim_spearman value: 80.59933905019179 - type: euclidean_pearson value: 80.56660025433806 - type: euclidean_spearman value: 80.27926539084027 - type: manhattan_pearson value: 80.64632724055481 - type: manhattan_spearman value: 80.43616365139444 - task: type: STS dataset: type: mteb/sts15-sts name: MTEB STS15 config: default split: test revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 metrics: - type: cos_sim_pearson value: 86.8590461417506 - type: cos_sim_spearman value: 87.16337291721602 - type: euclidean_pearson value: 85.8847725068404 - type: euclidean_spearman value: 86.12602873624066 - type: manhattan_pearson value: 86.04095861363909 - type: manhattan_spearman value: 86.35535645007629 - task: type: STS dataset: type: mteb/sts16-sts name: MTEB STS16 config: default split: test revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 metrics: - type: cos_sim_pearson value: 83.61371557181502 - type: cos_sim_spearman value: 85.16330754442785 - type: euclidean_pearson value: 84.20831431260608 - type: euclidean_spearman value: 84.33191523212125 - type: manhattan_pearson value: 84.34911007642411 - type: manhattan_spearman value: 84.49670164290394 - task: type: STS dataset: type: mteb/sts17-crosslingual-sts name: MTEB STS17 (en-en) config: en-en split: test revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d metrics: - type: cos_sim_pearson value: 90.54452933158781 - type: cos_sim_spearman value: 90.88214621695892 - type: euclidean_pearson value: 91.38488015281216 - type: euclidean_spearman value: 91.01822259603908 - type: manhattan_pearson value: 91.36449776198687 - type: manhattan_spearman value: 90.90478717381717 - task: type: STS dataset: type: mteb/sts22-crosslingual-sts name: MTEB STS22 (en) config: en split: test revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 metrics: - type: cos_sim_pearson value: 68.00941643037453 - type: cos_sim_spearman value: 67.03588472081898 - type: euclidean_pearson value: 67.35224911601603 - type: euclidean_spearman value: 66.35544831459266 - type: manhattan_pearson value: 67.35080066508304 - type: manhattan_spearman value: 66.07893473733782 - task: type: STS dataset: type: mteb/stsbenchmark-sts name: MTEB STSBenchmark config: default split: test revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 metrics: - type: cos_sim_pearson value: 85.18291011086279 - type: cos_sim_spearman value: 85.66913777481429 - type: euclidean_pearson value: 84.81115930027242 - type: euclidean_spearman value: 85.07133983924173 - type: manhattan_pearson value: 84.88932120524983 - type: manhattan_spearman value: 85.176903109055 - task: type: Reranking dataset: type: mteb/scidocs-reranking name: MTEB SciDocsRR config: default split: test revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab metrics: - type: map value: 83.67543572266588 - type: mrr value: 95.9468146232852 - task: type: Retrieval dataset: type: scifact name: MTEB SciFact config: default split: test revision: None metrics: - type: map_at_1 value: 59.633 - type: map_at_10 value: 69.801 - type: map_at_100 value: 70.504 - type: map_at_1000 value: 70.519 - type: map_at_3 value: 67.72500000000001 - type: map_at_5 value: 68.812 - type: mrr_at_1 value: 62.333000000000006 - type: mrr_at_10 value: 70.956 - type: mrr_at_100 value: 71.489 - type: mrr_at_1000 value: 71.504 - type: mrr_at_3 value: 69.44399999999999 - type: mrr_at_5 value: 70.244 - type: ndcg_at_1 value: 62.0 - type: ndcg_at_10 value: 73.98599999999999 - type: ndcg_at_100 value: 76.629 - type: ndcg_at_1000 value: 77.054 - type: ndcg_at_3 value: 70.513 - type: ndcg_at_5 value: 71.978 - type: precision_at_1 value: 62.0 - type: precision_at_10 value: 9.633 - type: precision_at_100 value: 1.097 - type: precision_at_1000 value: 0.11299999999999999 - type: precision_at_3 value: 27.556000000000004 - type: precision_at_5 value: 17.666999999999998 - type: recall_at_1 value: 59.633 - type: recall_at_10 value: 85.52199999999999 - type: recall_at_100 value: 96.667 - type: recall_at_1000 value: 100.0 - type: recall_at_3 value: 75.767 - type: recall_at_5 value: 79.76100000000001 - task: type: PairClassification dataset: type: mteb/sprintduplicatequestions-pairclassification name: MTEB SprintDuplicateQuestions config: default split: test revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 metrics: - type: cos_sim_accuracy value: 99.77821782178218 - type: cos_sim_ap value: 94.58684455008866 - type: cos_sim_f1 value: 88.51282051282053 - type: cos_sim_precision value: 90.84210526315789 - type: cos_sim_recall value: 86.3 - type: dot_accuracy value: 99.77623762376237 - type: dot_ap value: 94.86277541733045 - type: dot_f1 value: 88.66897575457693 - type: dot_precision value: 87.75710088148874 - type: dot_recall value: 89.60000000000001 - type: euclidean_accuracy value: 99.76732673267327 - type: euclidean_ap value: 94.12114402691984 - type: euclidean_f1 value: 87.96804792810784 - type: euclidean_precision value: 87.83649052841476 - type: euclidean_recall value: 88.1 - type: manhattan_accuracy value: 99.77227722772277 - type: manhattan_ap value: 94.33665105240306 - type: manhattan_f1 value: 88.25587206396803 - type: manhattan_precision value: 88.21178821178822 - type: manhattan_recall value: 88.3 - type: max_accuracy value: 99.77821782178218 - type: max_ap value: 94.86277541733045 - type: max_f1 value: 88.66897575457693 - task: type: Clustering dataset: type: mteb/stackexchange-clustering name: MTEB StackExchangeClustering config: default split: test revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 metrics: - type: v_measure value: 72.03943478268592 - task: type: Clustering dataset: type: mteb/stackexchange-clustering-p2p name: MTEB StackExchangeClusteringP2P config: default split: test revision: 815ca46b2622cec33ccafc3735d572c266efdb44 metrics: - type: v_measure value: 35.285037897356496 - task: type: Reranking dataset: type: mteb/stackoverflowdupquestions-reranking name: MTEB StackOverflowDupQuestions config: default split: test revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 metrics: - type: map value: 51.83578447913503 - type: mrr value: 52.69070696460402 - task: type: Summarization dataset: type: mteb/summeval name: MTEB SummEval config: default split: test revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c metrics: - type: cos_sim_pearson value: 30.89437612567638 - type: cos_sim_spearman value: 30.7277819987126 - type: dot_pearson value: 30.999783674122526 - type: dot_spearman value: 30.992168551124905 - task: type: Retrieval dataset: type: trec-covid name: MTEB TRECCOVID config: default split: test revision: None metrics: - type: map_at_1 value: 0.22699999999999998 - type: map_at_10 value: 1.8950000000000002 - type: map_at_100 value: 11.712 - type: map_at_1000 value: 28.713 - type: map_at_3 value: 0.65 - type: map_at_5 value: 1.011 - type: mrr_at_1 value: 92.0 - type: mrr_at_10 value: 95.39999999999999 - type: mrr_at_100 value: 95.39999999999999 - type: mrr_at_1000 value: 95.39999999999999 - type: mrr_at_3 value: 95.0 - type: mrr_at_5 value: 95.39999999999999 - type: ndcg_at_1 value: 83.0 - type: ndcg_at_10 value: 76.658 - type: ndcg_at_100 value: 60.755 - type: ndcg_at_1000 value: 55.05 - type: ndcg_at_3 value: 82.961 - type: ndcg_at_5 value: 80.008 - type: precision_at_1 value: 90.0 - type: precision_at_10 value: 79.80000000000001 - type: precision_at_100 value: 62.019999999999996 - type: precision_at_1000 value: 24.157999999999998 - type: precision_at_3 value: 88.0 - type: precision_at_5 value: 83.6 - type: recall_at_1 value: 0.22699999999999998 - type: recall_at_10 value: 2.086 - type: recall_at_100 value: 15.262 - type: recall_at_1000 value: 51.800000000000004 - type: recall_at_3 value: 0.679 - type: recall_at_5 value: 1.0739999999999998 - task: type: Retrieval dataset: type: webis-touche2020 name: MTEB Touche2020 config: default split: test revision: None metrics: - type: map_at_1 value: 1.521 - type: map_at_10 value: 7.281 - type: map_at_100 value: 12.717 - type: map_at_1000 value: 14.266000000000002 - type: map_at_3 value: 3.62 - type: map_at_5 value: 4.7010000000000005 - type: mrr_at_1 value: 18.367 - type: mrr_at_10 value: 34.906 - type: mrr_at_100 value: 36.333 - type: mrr_at_1000 value: 36.348 - type: mrr_at_3 value: 29.592000000000002 - type: mrr_at_5 value: 33.367000000000004 - type: ndcg_at_1 value: 19.387999999999998 - type: ndcg_at_10 value: 18.523 - type: ndcg_at_100 value: 30.932 - type: ndcg_at_1000 value: 42.942 - type: ndcg_at_3 value: 18.901 - type: ndcg_at_5 value: 17.974999999999998 - type: precision_at_1 value: 20.408 - type: precision_at_10 value: 17.347 - type: precision_at_100 value: 6.898 - type: precision_at_1000 value: 1.482 - type: precision_at_3 value: 21.088 - type: precision_at_5 value: 19.184 - type: recall_at_1 value: 1.521 - type: recall_at_10 value: 13.406 - type: recall_at_100 value: 43.418 - type: recall_at_1000 value: 80.247 - type: recall_at_3 value: 4.673 - type: recall_at_5 value: 7.247000000000001 - task: type: Classification dataset: type: mteb/toxic_conversations_50k name: MTEB ToxicConversationsClassification config: default split: test revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c metrics: - type: accuracy value: 71.9084 - type: ap value: 15.388385311898144 - type: f1 value: 55.760189174489426 - task: type: Classification dataset: type: mteb/tweet_sentiment_extraction name: MTEB TweetSentimentExtractionClassification config: default split: test revision: d604517c81ca91fe16a244d1248fc021f9ecee7a metrics: - type: accuracy value: 62.399547255234864 - type: f1 value: 62.61398519525303 - task: type: Clustering dataset: type: mteb/twentynewsgroups-clustering name: MTEB TwentyNewsgroupsClustering config: default split: test revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 metrics: - type: v_measure value: 53.041094760846164 - task: type: PairClassification dataset: type: mteb/twittersemeval2015-pairclassification name: MTEB TwitterSemEval2015 config: default split: test revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 metrics: - type: cos_sim_accuracy value: 87.92394349406926 - type: cos_sim_ap value: 79.93037248584875 - type: cos_sim_f1 value: 73.21063394683026 - type: cos_sim_precision value: 70.99652949925633 - type: cos_sim_recall value: 75.56728232189973 - type: dot_accuracy value: 87.80473266972642 - type: dot_ap value: 79.11055417163318 - type: dot_f1 value: 72.79587473273801 - type: dot_precision value: 69.55058880076905 - type: dot_recall value: 76.35883905013192 - type: euclidean_accuracy value: 87.91202241163496 - type: euclidean_ap value: 79.61955502404068 - type: euclidean_f1 value: 72.65956080647231 - type: euclidean_precision value: 70.778083562672 - type: euclidean_recall value: 74.64379947229551 - type: manhattan_accuracy value: 87.7749299636407 - type: manhattan_ap value: 79.33286131650932 - type: manhattan_f1 value: 72.44748412310699 - type: manhattan_precision value: 67.43974533879036 - type: manhattan_recall value: 78.25857519788919 - type: max_accuracy value: 87.92394349406926 - type: max_ap value: 79.93037248584875 - type: max_f1 value: 73.21063394683026 - task: type: PairClassification dataset: type: mteb/twitterurlcorpus-pairclassification name: MTEB TwitterURLCorpus config: default split: test revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf metrics: - type: cos_sim_accuracy value: 89.89987192921178 - type: cos_sim_ap value: 87.49525152555509 - type: cos_sim_f1 value: 80.05039276715578 - type: cos_sim_precision value: 77.15714285714286 - type: cos_sim_recall value: 83.1690791499846 - type: dot_accuracy value: 89.58163542515621 - type: dot_ap value: 86.87353801172357 - type: dot_f1 value: 79.50204384986993 - type: dot_precision value: 76.83522482401953 - type: dot_recall value: 82.36064059131506 - type: euclidean_accuracy value: 89.81255093724532 - type: euclidean_ap value: 87.41058010369022 - type: euclidean_f1 value: 79.94095829233214 - type: euclidean_precision value: 78.61396456751525 - type: euclidean_recall value: 81.3135201724669 - type: manhattan_accuracy value: 89.84553886754377 - type: manhattan_ap value: 87.41173628281432 - type: manhattan_f1 value: 79.9051922079846 - type: manhattan_precision value: 76.98016269444841 - type: manhattan_recall value: 83.06128734216199 - type: max_accuracy value: 89.89987192921178 - type: max_ap value: 87.49525152555509 - type: max_f1 value: 80.05039276715578 --- # Repetition Improves Language Model Embeddings Please refer to our paper: [https://arxiv.org/abs/2402.15449](https://arxiv.org/abs/2402.15449) And our GitHub: [https://github.com/jakespringer/echo-embeddings](https://github.com/jakespringer/echo-embeddings) We provide a description of the model as well as example usage in the above links.
shayantreylon2/Phi-3-mini_model	shayantreylon2	2024-06-28T15:57:28Z	4,844	0	transformers	[ "transformers", "gguf", "mistral", "text-generation-inference", "unsloth", "en", "base_model:unsloth/phi-3-mini-4k-instruct-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-06-28T15:52:19Z	--- base_model: unsloth/phi-3-mini-4k-instruct-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - mistral - gguf --- # Uploaded model - Developed by: shayantreylon2 - License: apache-2.0 - Finetuned from model : unsloth/phi-3-mini-4k-instruct-bnb-4bit This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
stanford-crfm/alias-gpt2-small-x21	stanford-crfm	2022-12-03T00:33:39Z	4,840	4	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "arxiv:1910.09700", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text-generation	2022-03-02T23:29:05Z	--- license: apache-2.0 tags: - gpt2 - text-generation --- # Model Card for alias-gpt2-small-x21 # Model Details ## Model Description More information needed - Developed by: Stanford CRFM - Shared by [Optional]: Stanford CRFM - Model type: Text Generation - Language(s) (NLP): More information needed - License: Apache 2.0 - Parent Model: [GPT-2](https://huggingface.co/gpt2?text=My+name+is+Thomas+and+my+main) - Resources for more information: - [GitHub Repo](https://github.com/stanford-crfm/mistral) # Uses ## Direct Use This model can be used for the task of Text Generation. ## Downstream Use [Optional] More information needed. ## Out-of-Scope Use The model should not be used to intentionally create hostile or alienating environments for people. # Bias, Risks, and Limitations Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)). Predictions generated by the model may include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups. ## Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. # Training Details ## Training Data More information needed ## Training Procedure ### Preprocessing More information needed ### Speeds, Sizes, Times More information needed # Evaluation ## Testing Data, Factors & Metrics ### Testing Data More information needed ### Factors More information needed ### Metrics More information needed ## Results More information needed # Model Examination More information needed # Environmental Impact Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: More information needed - Hours used: More information needed - Cloud Provider: More information needed - Compute Region: More information needed - Carbon Emitted: More information needed # Technical Specifications [optional] ## Model Architecture and Objective More information needed ## Compute Infrastructure More information needed ### Hardware More information needed ### Software More information needed. # Citation BibTeX: More information needed APA: More information needed # Glossary [optional] More information needed # More Information [optional] More information needed # Model Card Authors [optional] Stanford CRFM in collaboration with Ezi Ozoani and the Hugging Face team # Model Card Contact More information needed # How to Get Started with the Model Use the code below to get started with the model. <details> <summary> Click to expand </summary> ```python from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("stanford-crfm/alias-gpt2-small-x21") model = AutoModelForCausalLM.from_pretrained("stanford-crfm/alias-gpt2-small-x21") ``` </details>
BAAI/AltCLIP	BAAI	2022-12-26T01:28:32Z	4,839	27	transformers	[ "transformers", "pytorch", "altclip", "zero-shot-image-classification", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "bilingual", "en", "English", "zh", "Chinese", "arxiv:2211.06679", "license:creativeml-openrail-m", "region:us" ]	text-to-image	2022-11-15T03:22:10Z	--- language: zh license: creativeml-openrail-m tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - bilingual - en - English - zh - Chinese inference: false extra_gated_prompt: \|- One more step before getting this model. This model is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage. The CreativeML OpenRAIL License specifies: 1. You can't use the model to deliberately produce nor share illegal or harmful outputs or content 2. BAAI claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license 3. You may re-distribute the weights and use the model commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully) Please read the full license here: https://huggingface.co/spaces/CompVis/stable-diffusion-license By clicking on "Access repository" below, you accept that your contact information (email address and username) can be shared with the model authors as well. extra_gated_fields: I have read the License and agree with its terms: checkbox --- # AltCLIP \| 名称 Name \| 任务 Task \| 语言 Language(s) \| 模型 Model \| Github \| \|:------------------:\|:----------:\|:-------------------:\|:--------:\|:------:\| \| AltCLIP \| text-image representation\| 中英文 Chinese&English \| CLIP \| [FlagAI](https://github.com/FlagAI-Open/FlagAI) \| ## 简介 Brief Introduction 我们提出了一个简单高效的方法去训练更加优秀的双语CLIP模型。命名为AltCLIP。AltCLIP基于 [Stable Diffusiosn](https://github.com/CompVis/stable-diffusion) 训练，训练数据来自 [WuDao数据集](https://data.baai.ac.cn/details/WuDaoCorporaText) 和 [LIAON](https://huggingface.co/datasets/ChristophSchuhmann/improved_aesthetics_6plus) AltCLIP模型可以为本项目中的AltDiffusion模型提供支持，关于AltDiffusion模型的具体信息可查看[此教程](https://github.com/FlagAI-Open/FlagAI/tree/master/examples/AltDiffusion/README.md) 。模型代码已经在 [FlagAI](https://github.com/FlagAI-Open/FlagAI/tree/master/examples/AltCLIP) 上开源，权重位于我们搭建的 [modelhub](https://model.baai.ac.cn/model-detail/100075) 上。我们还提供了微调，推理，验证的脚本，欢迎试用。 We propose a simple and efficient method to train a better bilingual CLIP model. Named AltCLIP. AltCLIP is trained based on [Stable Diffusiosn](https://github.com/CompVis/stable-diffusion) with training data from [WuDao dataset](https://data.baai.ac.cn/details/WuDaoCorporaText) and [Liaon](https://huggingface.co/datasets/laion/laion2B-en). The AltCLIP model can provide support for the AltDiffusion model in this project. Specific information on the AltDiffusion model can be found in [this tutorial](https://github.com/FlagAI-Open/FlagAI/tree/master/examples/AltDiffusion/README.md). The model code has been open sourced on [FlagAI](https://github.com/FlagAI-Open/FlagAI/tree/master/examples/AltCLIP) and the weights are located on [modelhub](https://model.baai.ac.cn/model-detail/100075). We also provide scripts for fine-tuning, inference, and validation, so feel free to try them out. ## 引用关于AltCLIP，我们已经推出了相关报告，有更多细节可以查阅，如对您的工作有帮助，欢迎引用。 If you find this work helpful, please consider to cite ``` @article{https://doi.org/10.48550/arxiv.2211.06679, doi = {10.48550/ARXIV.2211.06679}, url = {https://arxiv.org/abs/2211.06679}, author = {Chen, Zhongzhi and Liu, Guang and Zhang, Bo-Wen and Ye, Fulong and Yang, Qinghong and Wu, Ledell}, keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences}, title = {AltCLIP: Altering the Language Encoder in CLIP for Extended Language Capabilities}, publisher = {arXiv}, year = {2022}, copyright = {arXiv.org perpetual, non-exclusive license} } ``` ## 训练 Training 训练共有两个阶段。在平行知识蒸馏阶段，我们只是使用平行语料文本来进行蒸馏（平行语料相对于图文对更容易获取且数量更大）。在双语对比学习阶段，我们使用少量的中-英图像-文本对（一共约2百万）来训练我们的文本编码器以更好地适应图像编码器。 There are two phases of training. In the parallel knowledge distillation phase, we only use parallel corpus texts for distillation (parallel corpus is easier to obtain and larger in number compared to image text pairs). In the bilingual comparison learning phase, we use a small number of Chinese-English image-text pairs (about 2 million in total) to train our text encoder to better fit the image encoder. ## 下游效果 Performance <table> <tr> <td rowspan=2>Language</td> <td rowspan=2>Method</td> <td colspan=3>Text-to-Image Retrival</td> <td colspan=3>Image-to-Text Retrival</td> <td rowspan=2>MR</td> </tr> <tr> <td>R@1</td> <td>R@5</td> <td>R@10</td> <td>R@1</td> <td>R@5</td> <td>R@10</td> </tr> <tr> <td rowspan=7>English</td> <td>CLIP</td> <td>65.0 </td> <td>87.1 </td> <td>92.2 </td> <td>85.1 </td> <td>97.3 </td> <td>99.2 </td> <td>87.6 </td> </tr> <tr> <td>Taiyi</td> <td>25.3 </td> <td>48.2 </td> <td>59.2 </td> <td>39.3 </td> <td>68.1 </td> <td>79.6 </td> <td>53.3 </td> </tr> <tr> <td>Wukong</td> <td>-</td> <td>-</td> <td>-</td> <td>-</td> <td>-</td> <td>-</td> <td>-</td> </tr> <tr> <td>R2D2</td> <td>-</td> <td>-</td> <td>-</td> <td>-</td> <td>-</td> <td>-</td> <td>-</td> </tr> <tr> <td>CN-CLIP</td> <td>49.5 </td> <td>76.9 </td> <td>83.8 </td> <td>66.5 </td> <td>91.2 </td> <td>96.0 </td> <td>77.3 </td> </tr> <tr> <td>AltCLIP</td> <td>66.3 </td> <td>87.8 </td> <td>92.7 </td> <td>85.9 </td> <td>97.7 </td> <td>99.1 </td> <td>88.3 </td> </tr> <tr> <td>AltCLIP∗</td> <td>72.5 </td> <td>91.6 </td> <td>95.4 </td> <td>86.0 </td> <td>98.0 </td> <td>99.1 </td> <td>90.4 </td> </tr> <tr> <td rowspan=7>Chinese</td> <td>CLIP</td> <td>0.0 </td> <td>2.4 </td> <td>4.0 </td> <td>2.3 </td> <td>8.1 </td> <td>12.6 </td> <td>5.0 </td> </tr> <tr> <td>Taiyi</td> <td>53.7 </td> <td>79.8 </td> <td>86.6 </td> <td>63.8 </td> <td>90.5 </td> <td>95.9 </td> <td>78.4 </td> </tr> <tr> <td>Wukong</td> <td>51.7 </td> <td>78.9 </td> <td>86.3 </td> <td>76.1 </td> <td>94.8 </td> <td>97.5 </td> <td>80.9 </td> </tr> <tr> <td>R2D2</td> <td>60.9 </td> <td>86.8 </td> <td>92.7 </td> <td>77.6 </td> <td>96.7 </td> <td>98.9 </td> <td>85.6 </td> </tr> <tr> <td>CN-CLIP</td> <td>68.0 </td> <td>89.7 </td> <td>94.4 </td> <td>80.2 </td> <td>96.6 </td> <td>98.2 </td> <td>87.9 </td> </tr> <tr> <td>AltCLIP</td> <td>63.7 </td> <td>86.3 </td> <td>92.1 </td> <td>84.7 </td> <td>97.4 </td> <td>98.7 </td> <td>87.2 </td> </tr> <tr> <td>AltCLIP∗</td> <td>69.8 </td> <td>89.9 </td> <td>94.7 </td> <td>84.8 </td> <td>97.4 </td> <td>98.8 </td> <td>89.2 </td> </tr> </table> ![image-20221111172255521](https://raw.githubusercontent.com/920232796/test/master/image.png) ## 可视化效果 Visualization effects 基于AltCLIP，我们还开发了AltDiffusion模型，可视化效果如下。 Based on AltCLIP, we have also developed the AltDiffusion model, visualized as follows. ![](https://raw.githubusercontent.com/920232796/test/master/image7.png) ## 模型推理 Inference Please download the code from [FlagAI AltCLIP](https://github.com/FlagAI-Open/FlagAI/tree/master/examples/AltCLIP) ```python from PIL import Image import requests # transformers version >= 4.21.0 from modeling_altclip import AltCLIP from processing_altclip import AltCLIPProcessor # now our repo's in private, so we need `use_auth_token=True` model = AltCLIP.from_pretrained("BAAI/AltCLIP") processor = AltCLIPProcessor.from_pretrained("BAAI/AltCLIP") url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) inputs = processor(text=["a photo of a cat", "a photo of a dog"], images=image, return_tensors="pt", padding=True) outputs = model(**inputs) logits_per_image = outputs.logits_per_image # this is the image-text similarity score probs = logits_per_image.softmax(dim=1) # we can take the softmax to get the label probabilities ```
mistral-community/Mixtral-8x22B-v0.1-AWQ	mistral-community	2024-04-15T07:24:59Z	4,838	30	transformers	[ "transformers", "safetensors", "mixtral", "text-generation", "quantized", "4-bit", "AWQ", "moe", "fr", "it", "de", "es", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us", "base_model:v2ray/Mixtral-8x22B-v0.1", "awq" ]	text-generation	2024-04-10T20:16:02Z	--- tags: - quantized - 4-bit - AWQ - transformers - safetensors - mixtral - text-generation - moe - fr - it - de - es - en - license:apache-2.0 - autotrain_compatible - endpoints_compatible - text-generation-inference - region:us model_name: Mixtral-8x22B-v0.1-AWQ base_model: v2ray/Mixtral-8x22B-v0.1 inference: false model_creator: v2ray pipeline_tag: text-generation quantized_by: MaziyarPanahi language: - en - es - de - it - fr --- <img src="./mixtral-8x22b.jpeg" width="600" /> # Mixtral-8x22B-v0.1-AWQ On April 10th, [@MistralAI](https://huggingface.co/mistralai) released a model named "Mixtral 8x22B," an 176B MoE via magnet link (torrent): - 176B MoE with ~40B active - Context length of 65k tokens - The base model can be fine-tuned - Requires ~260GB VRAM in fp16, 73GB in int4 - Licensed under Apache 2.0, according to their Discord - Available on @huggingface (community) - Utilizes a tokenizer similar to previous models [MaziyarPanahi/Mixtral-8x22B-v0.1-AWQ](https://huggingface.co/MaziyarPanahi/Mixtral-8x22B-v0.1-AWQ) is a quantized (AWQ) version of [v2ray/Mixtral-8x22B-v0.1](https://huggingface.co/v2ray/Mixtral-8x22B-v0.1) ## How to use ### Install the necessary packages ``` pip install --upgrade accelerate autoawq transformers ``` ### Example Python code ```python from transformers import AutoTokenizer, AutoModelForCausalLM model_id = "MaziyarPanahi/Mixtral-8x22B-v0.1-AWQ" tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForCausalLM.from_pretrained(model_id).to(0) text = "Hello can you provide me with top-3 cool places to visit in Paris?" inputs = tokenizer(text, return_tensors="pt").to(0) out = model.generate(**inputs, max_new_tokens=300) print(tokenizer.decode(out[0], skip_special_tokens=True)) ``` ## Credit - [MistralAI](https://huggingface.co/mistralai) for opening the weights - [v2ray](https://huggingface.co/v2ray/) for downloading, converting, and sharing it with the community [Mixtral-8x22B-v0.1](https://huggingface.co/v2ray/Mixtral-8x22B-v0.1) - [philschmid](https://huggingface.co/philschmid) for the photo he shared on his Twitter ▄▄▄░░ ▄▄▄▄▄█████████░░░░ ▄▄▄▄▄▄████████████████████░░░░░ █████████████████████████████░░░░░ ▄▄▄▄▄▄█████░░░ █████████████████████████████░░░░░ ▄▄▄▄▄██████████████████░░░░░░ ██████████████████████████████░░░░░ ▄█████████████████████████████░░░░░░░░██████████████████████████████░░░░░ ███████████████████████████████░░░░░░░██████████████████████████████░░░░░ ███████████████████████████████░░░░░░░██████████████████████████████░░░░░ ███████████████████████████████░░░░░░███████████████████████████████░░░░░ ████████████████████████████████░░░░░███████████████████████████████░░░░░ ████████████████████████████████░░░░████████████████████████████████░░░░░ █████████████████████████████████░░░████████████████████████████████░░░░░ █████████████████████████████████░░░████████████░███████████████████░░░░░ ██████████████████████████████████░█████████████░███████████████████░░░░░ ███████████████████░██████████████▄█████████████░███████████████████░░░░░ ███████████████████░███████████████████████████░░███████████████████░░░░░ ███████████████████░░██████████████████████████░░███████████████████░░░░░ ███████████████████░░█████████████████████████░░░███████████████████░░░░░ ███████████████████░░░████████████████████████░░░███████████████████░░░░░ ███████████████████░░░████████████████████████░░░███████████████████░░░░░ ███████████████████░░░░██████████████████████░░░░███████████████████░░░░░ ███████████████████░░░░██████████████████████░░░░███████████████████░░░░░ ███████████████████░░░░░█████████████████████░░░░███████████████████░░░░░ ███████████████████░░░░░████████████████████░░░░░███████████████████░░░░░ ███████████████████░░░░░░███████████████████░░░░░███████████████████░░░░░ ███████████████████░░░░░░██████████████████░░░░░░███████████████████░░░░░ ███████████████████░░░░░░░█████████████████░░░░░░███████████████████░░░░░ ███████████████████░░░░░░░█████████████████░░░░░░███████████████████░░░░░ ███████████████████░░░░░░░░███████████████░░░░░░░██████████░░░░░░░░░░░░░░ ███████████████████░░░░░░░░███████████████░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░ ███████████████████░░░░░░░░███████████░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░ ███████████████████░░░░░░░░░██░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░ ███████████████░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░ ██████████░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░ ░░░░░░░ ░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░ ░░░ ░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░ ░░░░░░░░░░░░░░░░░░ ░░░░░░░░░░░░░░░░░░░░░░░░░░░░ ░░░░░░░░░░░░░░░░░ ░░░░░
mradermacher/L3-8B-Sunfall-v0.3-Stheno-v3.2-i1-GGUF	mradermacher	2024-06-16T13:23:18Z	4,837	0	transformers	[ "transformers", "gguf", "not-for-all-audiences", "roleplay", "llama3", "en", "base_model:Vdr1/L3-8B-Sunfall-v0.3-Stheno-v3.2", "endpoints_compatible", "region:us" ]	null	2024-06-16T12:08:31Z	--- base_model: Vdr1/L3-8B-Sunfall-v0.3-Stheno-v3.2 language: - en library_name: transformers quantized_by: mradermacher tags: - not-for-all-audiences - roleplay - llama3 --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/Vdr1/L3-8B-Sunfall-v0.3-Stheno-v3.2 <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/L3-8B-Sunfall-v0.3-Stheno-v3.2-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/L3-8B-Sunfall-v0.3-Stheno-v3.2-i1-GGUF/resolve/main/L3-8B-Sunfall-v0.3-Stheno-v3.2.i1-IQ1_S.gguf) \| i1-IQ1_S \| 2.1 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/L3-8B-Sunfall-v0.3-Stheno-v3.2-i1-GGUF/resolve/main/L3-8B-Sunfall-v0.3-Stheno-v3.2.i1-IQ1_M.gguf) \| i1-IQ1_M \| 2.3 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/L3-8B-Sunfall-v0.3-Stheno-v3.2-i1-GGUF/resolve/main/L3-8B-Sunfall-v0.3-Stheno-v3.2.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 2.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/L3-8B-Sunfall-v0.3-Stheno-v3.2-i1-GGUF/resolve/main/L3-8B-Sunfall-v0.3-Stheno-v3.2.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 2.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/L3-8B-Sunfall-v0.3-Stheno-v3.2-i1-GGUF/resolve/main/L3-8B-Sunfall-v0.3-Stheno-v3.2.i1-IQ2_S.gguf) \| i1-IQ2_S \| 2.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/L3-8B-Sunfall-v0.3-Stheno-v3.2-i1-GGUF/resolve/main/L3-8B-Sunfall-v0.3-Stheno-v3.2.i1-IQ2_M.gguf) \| i1-IQ2_M \| 3.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/L3-8B-Sunfall-v0.3-Stheno-v3.2-i1-GGUF/resolve/main/L3-8B-Sunfall-v0.3-Stheno-v3.2.i1-Q2_K.gguf) \| i1-Q2_K \| 3.3 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/L3-8B-Sunfall-v0.3-Stheno-v3.2-i1-GGUF/resolve/main/L3-8B-Sunfall-v0.3-Stheno-v3.2.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 3.4 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/L3-8B-Sunfall-v0.3-Stheno-v3.2-i1-GGUF/resolve/main/L3-8B-Sunfall-v0.3-Stheno-v3.2.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 3.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/L3-8B-Sunfall-v0.3-Stheno-v3.2-i1-GGUF/resolve/main/L3-8B-Sunfall-v0.3-Stheno-v3.2.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 3.8 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/L3-8B-Sunfall-v0.3-Stheno-v3.2-i1-GGUF/resolve/main/L3-8B-Sunfall-v0.3-Stheno-v3.2.i1-IQ3_S.gguf) \| i1-IQ3_S \| 3.8 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/L3-8B-Sunfall-v0.3-Stheno-v3.2-i1-GGUF/resolve/main/L3-8B-Sunfall-v0.3-Stheno-v3.2.i1-IQ3_M.gguf) \| i1-IQ3_M \| 3.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/L3-8B-Sunfall-v0.3-Stheno-v3.2-i1-GGUF/resolve/main/L3-8B-Sunfall-v0.3-Stheno-v3.2.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 4.1 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/L3-8B-Sunfall-v0.3-Stheno-v3.2-i1-GGUF/resolve/main/L3-8B-Sunfall-v0.3-Stheno-v3.2.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 4.4 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/L3-8B-Sunfall-v0.3-Stheno-v3.2-i1-GGUF/resolve/main/L3-8B-Sunfall-v0.3-Stheno-v3.2.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 4.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/L3-8B-Sunfall-v0.3-Stheno-v3.2-i1-GGUF/resolve/main/L3-8B-Sunfall-v0.3-Stheno-v3.2.i1-Q4_0.gguf) \| i1-Q4_0 \| 4.8 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/L3-8B-Sunfall-v0.3-Stheno-v3.2-i1-GGUF/resolve/main/L3-8B-Sunfall-v0.3-Stheno-v3.2.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 4.8 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/L3-8B-Sunfall-v0.3-Stheno-v3.2-i1-GGUF/resolve/main/L3-8B-Sunfall-v0.3-Stheno-v3.2.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 5.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/L3-8B-Sunfall-v0.3-Stheno-v3.2-i1-GGUF/resolve/main/L3-8B-Sunfall-v0.3-Stheno-v3.2.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 5.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/L3-8B-Sunfall-v0.3-Stheno-v3.2-i1-GGUF/resolve/main/L3-8B-Sunfall-v0.3-Stheno-v3.2.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 5.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/L3-8B-Sunfall-v0.3-Stheno-v3.2-i1-GGUF/resolve/main/L3-8B-Sunfall-v0.3-Stheno-v3.2.i1-Q6_K.gguf) \| i1-Q6_K \| 6.7 \| practically like static Q6_K \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his hardware for calculating the imatrix for these quants. <!-- end -->
valhalla/distilbart-mnli-12-3	valhalla	2021-06-14T10:29:48Z	4,836	19	transformers	[ "transformers", "pytorch", "jax", "bart", "text-classification", "distilbart", "distilbart-mnli", "zero-shot-classification", "dataset:mnli", "autotrain_compatible", "endpoints_compatible", "region:us" ]	zero-shot-classification	2022-03-02T23:29:05Z	--- datasets: - mnli tags: - distilbart - distilbart-mnli pipeline_tag: zero-shot-classification --- # DistilBart-MNLI distilbart-mnli is the distilled version of bart-large-mnli created using the No Teacher Distillation technique proposed for BART summarisation by Huggingface, [here](https://github.com/huggingface/transformers/tree/master/examples/seq2seq#distilbart). We just copy alternating layers from `bart-large-mnli` and finetune more on the same data. \| \| matched acc \| mismatched acc \| \| ------------------------------------------------------------------------------------ \| ----------- \| -------------- \| \| [bart-large-mnli](https://huggingface.co/facebook/bart-large-mnli) (baseline, 12-12) \| 89.9 \| 90.01 \| \| [distilbart-mnli-12-1](https://huggingface.co/valhalla/distilbart-mnli-12-1) \| 87.08 \| 87.5 \| \| [distilbart-mnli-12-3](https://huggingface.co/valhalla/distilbart-mnli-12-3) \| 88.1 \| 88.19 \| \| [distilbart-mnli-12-6](https://huggingface.co/valhalla/distilbart-mnli-12-6) \| 89.19 \| 89.01 \| \| [distilbart-mnli-12-9](https://huggingface.co/valhalla/distilbart-mnli-12-9) \| 89.56 \| 89.52 \| This is a very simple and effective technique, as we can see the performance drop is very little. Detailed performace trade-offs will be posted in this [sheet](https://docs.google.com/spreadsheets/d/1dQeUvAKpScLuhDV1afaPJRRAE55s2LpIzDVA5xfqxvk/edit?usp=sharing). ## Fine-tuning If you want to train these models yourself, clone the [distillbart-mnli repo](https://github.com/patil-suraj/distillbart-mnli) and follow the steps below Clone and install transformers from source ```bash git clone https://github.com/huggingface/transformers.git pip install -qqq -U ./transformers ``` Download MNLI data ```bash python transformers/utils/download_glue_data.py --data_dir glue_data --tasks MNLI ``` Create student model ```bash python create_student.py \ --teacher_model_name_or_path facebook/bart-large-mnli \ --student_encoder_layers 12 \ --student_decoder_layers 6 \ --save_path student-bart-mnli-12-6 \ ``` Start fine-tuning ```bash python run_glue.py args.json ``` You can find the logs of these trained models in this [wandb project](https://wandb.ai/psuraj/distilbart-mnli).
RichardErkhov/emozilla_-_llama3-1.6b-init-gguf	RichardErkhov	2024-06-30T03:37:12Z	4,828	0	null	[ "gguf", "region:us" ]	null	2024-06-30T03:11:25Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) llama3-1.6b-init - GGUF - Model creator: https://huggingface.co/emozilla/ - Original model: https://huggingface.co/emozilla/llama3-1.6b-init/ \| Name \| Quant method \| Size \| \| ---- \| ---- \| ---- \| \| [llama3-1.6b-init.Q2_K.gguf](https://huggingface.co/RichardErkhov/emozilla_-_llama3-1.6b-init-gguf/blob/main/llama3-1.6b-init.Q2_K.gguf) \| Q2_K \| 0.65GB \| \| [llama3-1.6b-init.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/emozilla_-_llama3-1.6b-init-gguf/blob/main/llama3-1.6b-init.IQ3_XS.gguf) \| IQ3_XS \| 0.72GB \| \| [llama3-1.6b-init.IQ3_S.gguf](https://huggingface.co/RichardErkhov/emozilla_-_llama3-1.6b-init-gguf/blob/main/llama3-1.6b-init.IQ3_S.gguf) \| IQ3_S \| 0.74GB \| \| [llama3-1.6b-init.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/emozilla_-_llama3-1.6b-init-gguf/blob/main/llama3-1.6b-init.Q3_K_S.gguf) \| Q3_K_S \| 0.74GB \| \| [llama3-1.6b-init.IQ3_M.gguf](https://huggingface.co/RichardErkhov/emozilla_-_llama3-1.6b-init-gguf/blob/main/llama3-1.6b-init.IQ3_M.gguf) \| IQ3_M \| 0.76GB \| \| [llama3-1.6b-init.Q3_K.gguf](https://huggingface.co/RichardErkhov/emozilla_-_llama3-1.6b-init-gguf/blob/main/llama3-1.6b-init.Q3_K.gguf) \| Q3_K \| 0.8GB \| \| [llama3-1.6b-init.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/emozilla_-_llama3-1.6b-init-gguf/blob/main/llama3-1.6b-init.Q3_K_M.gguf) \| Q3_K_M \| 0.8GB \| \| [llama3-1.6b-init.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/emozilla_-_llama3-1.6b-init-gguf/blob/main/llama3-1.6b-init.Q3_K_L.gguf) \| Q3_K_L \| 0.84GB \| \| [llama3-1.6b-init.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/emozilla_-_llama3-1.6b-init-gguf/blob/main/llama3-1.6b-init.IQ4_XS.gguf) \| IQ4_XS \| 0.87GB \| \| [llama3-1.6b-init.Q4_0.gguf](https://huggingface.co/RichardErkhov/emozilla_-_llama3-1.6b-init-gguf/blob/main/llama3-1.6b-init.Q4_0.gguf) \| Q4_0 \| 0.91GB \| \| [llama3-1.6b-init.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/emozilla_-_llama3-1.6b-init-gguf/blob/main/llama3-1.6b-init.IQ4_NL.gguf) \| IQ4_NL \| 0.91GB \| \| [llama3-1.6b-init.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/emozilla_-_llama3-1.6b-init-gguf/blob/main/llama3-1.6b-init.Q4_K_S.gguf) \| Q4_K_S \| 0.91GB \| \| [llama3-1.6b-init.Q4_K.gguf](https://huggingface.co/RichardErkhov/emozilla_-_llama3-1.6b-init-gguf/blob/main/llama3-1.6b-init.Q4_K.gguf) \| Q4_K \| 0.95GB \| \| [llama3-1.6b-init.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/emozilla_-_llama3-1.6b-init-gguf/blob/main/llama3-1.6b-init.Q4_K_M.gguf) \| Q4_K_M \| 0.95GB \| \| [llama3-1.6b-init.Q4_1.gguf](https://huggingface.co/RichardErkhov/emozilla_-_llama3-1.6b-init-gguf/blob/main/llama3-1.6b-init.Q4_1.gguf) \| Q4_1 \| 0.99GB \| \| [llama3-1.6b-init.Q5_0.gguf](https://huggingface.co/RichardErkhov/emozilla_-_llama3-1.6b-init-gguf/blob/main/llama3-1.6b-init.Q5_0.gguf) \| Q5_0 \| 1.06GB \| \| [llama3-1.6b-init.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/emozilla_-_llama3-1.6b-init-gguf/blob/main/llama3-1.6b-init.Q5_K_S.gguf) \| Q5_K_S \| 1.06GB \| \| [llama3-1.6b-init.Q5_K.gguf](https://huggingface.co/RichardErkhov/emozilla_-_llama3-1.6b-init-gguf/blob/main/llama3-1.6b-init.Q5_K.gguf) \| Q5_K \| 1.08GB \| \| [llama3-1.6b-init.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/emozilla_-_llama3-1.6b-init-gguf/blob/main/llama3-1.6b-init.Q5_K_M.gguf) \| Q5_K_M \| 1.08GB \| \| [llama3-1.6b-init.Q5_1.gguf](https://huggingface.co/RichardErkhov/emozilla_-_llama3-1.6b-init-gguf/blob/main/llama3-1.6b-init.Q5_1.gguf) \| Q5_1 \| 1.14GB \| \| [llama3-1.6b-init.Q6_K.gguf](https://huggingface.co/RichardErkhov/emozilla_-_llama3-1.6b-init-gguf/blob/main/llama3-1.6b-init.Q6_K.gguf) \| Q6_K \| 1.23GB \| \| [llama3-1.6b-init.Q8_0.gguf](https://huggingface.co/RichardErkhov/emozilla_-_llama3-1.6b-init-gguf/blob/main/llama3-1.6b-init.Q8_0.gguf) \| Q8_0 \| 1.59GB \| Original model description: Entry not found
soheeyang/rdr-question_encoder-single-nq-base	soheeyang	2021-04-15T15:58:07Z	4,827	1	transformers	[ "transformers", "pytorch", "tf", "dpr", "feature-extraction", "arxiv:2010.10999", "arxiv:2004.04906", "endpoints_compatible", "region:us" ]	feature-extraction	2022-03-02T23:29:05Z	# rdr-question_encoder-single-nq-base Reader-Distilled Retriever (`RDR`) Sohee Yang and Minjoon Seo, [Is Retriever Merely an Approximator of Reader?](https://arxiv.org/abs/2010.10999), arXiv 2020 The paper proposes to distill the reader into the retriever so that the retriever absorbs the strength of the reader while keeping its own benefit. The model is a [DPR](https://arxiv.org/abs/2004.04906) retriever further finetuned using knowledge distillation from the DPR reader. Using this approach, the answer recall rate increases by a large margin, especially at small numbers of top-k. This model is the question encoder of RDR trained solely on Natural Questions (NQ) (single-nq). This model is trained by the authors and is the official checkpoint of RDR. ## Performance The following is the answer recall rate measured using PyTorch 1.4.0 and transformers 4.5.0. The values of DPR on the NQ dev set are taken from Table 1 of the [paper of RDR](https://arxiv.org/abs/2010.10999). The values of DPR on the NQ test set are taken from the [codebase of DPR](https://github.com/facebookresearch/DPR). DPR-adv is the a new DPR model released in March 2021. It is trained on the original DPR NQ train set and its version where hard negatives are mined using DPR index itself using the previous NQ checkpoint. Please refer to the [codebase of DPR](https://github.com/facebookresearch/DPR) for more details about DPR-adv-hn. \| \| Top-K Passages \| 1 \| 5 \| 20 \| 50 \| 100 \| \|---------\|------------------\|-------\|-------\|-------\|-------\|-------\| \| NQ Dev \| DPR \| 44.2 \| - \| 76.9 \| 81.3 \| 84.2 \| \| \| RDR (This Model) \| 54.43 \| 72.17 \| 81.33 \| 84.8 \| 86.61 \| \| NQ Test \| DPR \| 45.87 \| 68.14 \| 79.97 \| - \| 85.87 \| \| \| DPR-adv-hn \| 52.47 \| 72.24 \| 81.33 \| - \| 87.29 \| \| \| RDR (This Model) \| 54.29 \| 72.16 \| 82.8 \| 86.34 \| 88.2 \| ## How to Use RDR shares the same architecture with DPR. Therefore, It uses `DPRQuestionEncoder` as the model class. Using `AutoModel` does not properly detect whether the checkpoint is for `DPRContextEncoder` or `DPRQuestionEncoder`. Therefore, please specify the exact class to use the model. ```python from transformers import DPRQuestionEncoder, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("soheeyang/rdr-question_encoder-single-trivia-base") question_encoder = DPRQuestionEncoder.from_pretrained("soheeyang/rdr-question_encoder-single-trivia-base") data = tokenizer("question comes here", return_tensors="pt") question_embedding = question_encoder(**data).pooler_output # embedding vector for question ```
mradermacher/Average_Normie_v3.69_8B-i1-GGUF	mradermacher	2024-06-09T03:21:10Z	4,826	0	transformers	[ "transformers", "gguf", "en", "base_model:jeiku/Average_Normie_v3.69_8B", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-06-09T00:34:37Z	--- base_model: jeiku/Average_Normie_v3.69_8B language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/jeiku/Average_Normie_v3.69_8B <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/Average_Normie_v3.69_8B-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Average_Normie_v3.69_8B-i1-GGUF/resolve/main/Average_Normie_v3.69_8B.i1-IQ1_S.gguf) \| i1-IQ1_S \| 2.1 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/Average_Normie_v3.69_8B-i1-GGUF/resolve/main/Average_Normie_v3.69_8B.i1-IQ1_M.gguf) \| i1-IQ1_M \| 2.3 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/Average_Normie_v3.69_8B-i1-GGUF/resolve/main/Average_Normie_v3.69_8B.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 2.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Average_Normie_v3.69_8B-i1-GGUF/resolve/main/Average_Normie_v3.69_8B.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 2.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Average_Normie_v3.69_8B-i1-GGUF/resolve/main/Average_Normie_v3.69_8B.i1-IQ2_S.gguf) \| i1-IQ2_S \| 2.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Average_Normie_v3.69_8B-i1-GGUF/resolve/main/Average_Normie_v3.69_8B.i1-IQ2_M.gguf) \| i1-IQ2_M \| 3.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Average_Normie_v3.69_8B-i1-GGUF/resolve/main/Average_Normie_v3.69_8B.i1-Q2_K.gguf) \| i1-Q2_K \| 3.3 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Average_Normie_v3.69_8B-i1-GGUF/resolve/main/Average_Normie_v3.69_8B.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 3.4 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Average_Normie_v3.69_8B-i1-GGUF/resolve/main/Average_Normie_v3.69_8B.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 3.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Average_Normie_v3.69_8B-i1-GGUF/resolve/main/Average_Normie_v3.69_8B.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 3.8 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Average_Normie_v3.69_8B-i1-GGUF/resolve/main/Average_Normie_v3.69_8B.i1-IQ3_S.gguf) \| i1-IQ3_S \| 3.8 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Average_Normie_v3.69_8B-i1-GGUF/resolve/main/Average_Normie_v3.69_8B.i1-IQ3_M.gguf) \| i1-IQ3_M \| 3.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Average_Normie_v3.69_8B-i1-GGUF/resolve/main/Average_Normie_v3.69_8B.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 4.1 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/Average_Normie_v3.69_8B-i1-GGUF/resolve/main/Average_Normie_v3.69_8B.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 4.4 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/Average_Normie_v3.69_8B-i1-GGUF/resolve/main/Average_Normie_v3.69_8B.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 4.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Average_Normie_v3.69_8B-i1-GGUF/resolve/main/Average_Normie_v3.69_8B.i1-Q4_0.gguf) \| i1-Q4_0 \| 4.8 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/Average_Normie_v3.69_8B-i1-GGUF/resolve/main/Average_Normie_v3.69_8B.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 4.8 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/Average_Normie_v3.69_8B-i1-GGUF/resolve/main/Average_Normie_v3.69_8B.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 5.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Average_Normie_v3.69_8B-i1-GGUF/resolve/main/Average_Normie_v3.69_8B.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 5.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Average_Normie_v3.69_8B-i1-GGUF/resolve/main/Average_Normie_v3.69_8B.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 5.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Average_Normie_v3.69_8B-i1-GGUF/resolve/main/Average_Normie_v3.69_8B.i1-Q6_K.gguf) \| i1-Q6_K \| 6.7 \| practically like static Q6_K \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his hardware for calculating the imatrix for these quants. <!-- end -->
superb/wav2vec2-base-superb-er	superb	2021-11-04T16:03:36Z	4,824	11	transformers	[ "transformers", "pytorch", "wav2vec2", "audio-classification", "speech", "audio", "en", "dataset:superb", "arxiv:2105.01051", "license:apache-2.0", "endpoints_compatible", "region:us" ]	audio-classification	2022-03-02T23:29:05Z	--- language: en datasets: - superb tags: - speech - audio - wav2vec2 - audio-classification license: apache-2.0 widget: - example_title: IEMOCAP clip "happy" src: https://cdn-media.huggingface.co/speech_samples/IEMOCAP_Ses01F_impro03_F013.wav - example_title: IEMOCAP clip "neutral" src: https://cdn-media.huggingface.co/speech_samples/IEMOCAP_Ses01F_impro04_F000.wav --- # Wav2Vec2-Base for Emotion Recognition ## Model description This is a ported version of [S3PRL's Wav2Vec2 for the SUPERB Emotion Recognition task](https://github.com/s3prl/s3prl/tree/master/s3prl/downstream/emotion). The base model is [wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base), which is pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. For more information refer to [SUPERB: Speech processing Universal PERformance Benchmark](https://arxiv.org/abs/2105.01051) ## Task and dataset description Emotion Recognition (ER) predicts an emotion class for each utterance. The most widely used ER dataset [IEMOCAP](https://sail.usc.edu/iemocap/) is adopted, and we follow the conventional evaluation protocol: we drop the unbalanced emotion classes to leave the final four classes with a similar amount of data points and cross-validate on five folds of the standard splits. For the original model's training and evaluation instructions refer to the [S3PRL downstream task README](https://github.com/s3prl/s3prl/tree/master/s3prl/downstream#er-emotion-recognition). ## Usage examples You can use the model via the Audio Classification pipeline: ```python from datasets import load_dataset from transformers import pipeline dataset = load_dataset("anton-l/superb_demo", "er", split="session1") classifier = pipeline("audio-classification", model="superb/wav2vec2-base-superb-er") labels = classifier(dataset[0]["file"], top_k=5) ``` Or use the model directly: ```python import torch import librosa from datasets import load_dataset from transformers import Wav2Vec2ForSequenceClassification, Wav2Vec2FeatureExtractor def map_to_array(example): speech, _ = librosa.load(example["file"], sr=16000, mono=True) example["speech"] = speech return example # load a demo dataset and read audio files dataset = load_dataset("anton-l/superb_demo", "er", split="session1") dataset = dataset.map(map_to_array) model = Wav2Vec2ForSequenceClassification.from_pretrained("superb/wav2vec2-base-superb-er") feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained("superb/wav2vec2-base-superb-er") # compute attention masks and normalize the waveform if needed inputs = feature_extractor(dataset[:4]["speech"], sampling_rate=16000, padding=True, return_tensors="pt") logits = model(inputs).logits predicted_ids = torch.argmax(logits, dim=-1) labels = [model.config.id2label[_id] for _id in predicted_ids.tolist()] ``` ## Eval results The evaluation metric is accuracy. \| \| s3prl \| transformers \| \|--------\|-----------\|------------------\| \|session1**\| `0.6343` \| `0.6258` \| ### BibTeX entry and citation info ```bibtex @article{yang2021superb, title={SUPERB: Speech processing Universal PERformance Benchmark}, author={Yang, Shu-wen and Chi, Po-Han and Chuang, Yung-Sung and Lai, Cheng-I Jeff and Lakhotia, Kushal and Lin, Yist Y and Liu, Andy T and Shi, Jiatong and Chang, Xuankai and Lin, Guan-Ting and others}, journal={arXiv preprint arXiv:2105.01051}, year={2021} } ```
bigscience/mt0-large	bigscience	2023-09-26T09:16:52Z	4,824	40	transformers	[ "transformers", "pytorch", "onnx", "safetensors", "mt5", "text2text-generation", "af", "am", "ar", "az", "be", "bg", "bn", "ca", "ceb", "co", "cs", "cy", "da", "de", "el", "en", "eo", "es", "et", "eu", "fa", "fi", "fil", "fr", "fy", "ga", "gd", "gl", "gu", "ha", "haw", "hi", "hmn", "ht", "hu", "hy", "ig", "is", "it", "iw", "ja", "jv", "ka", "kk", "km", "kn", "ko", "ku", "ky", "la", "lb", "lo", "lt", "lv", "mg", "mi", "mk", "ml", "mn", "mr", "ms", "mt", "my", "ne", "nl", "no", "ny", "pa", "pl", "ps", "pt", "ro", "ru", "sd", "si", "sk", "sl", "sm", "sn", "so", "sq", "sr", "st", "su", "sv", "sw", "ta", "te", "tg", "th", "tr", "uk", "und", "ur", "uz", "vi", "xh", "yi", "yo", "zh", "zu", "dataset:bigscience/xP3", "dataset:mc4", "arxiv:2211.01786", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-10-27T19:22:31Z	--- datasets: - bigscience/xP3 - mc4 license: apache-2.0 language: - af - am - ar - az - be - bg - bn - ca - ceb - co - cs - cy - da - de - el - en - eo - es - et - eu - fa - fi - fil - fr - fy - ga - gd - gl - gu - ha - haw - hi - hmn - ht - hu - hy - ig - is - it - iw - ja - jv - ka - kk - km - kn - ko - ku - ky - la - lb - lo - lt - lv - mg - mi - mk - ml - mn - mr - ms - mt - my - ne - nl - 'no' - ny - pa - pl - ps - pt - ro - ru - sd - si - sk - sl - sm - sn - so - sq - sr - st - su - sv - sw - ta - te - tg - th - tr - uk - und - ur - uz - vi - xh - yi - yo - zh - zu pipeline_tag: text2text-generation widget: - text: >- 一个传奇的开端，一个不灭的神话，这不仅仅是一部电影，而是作为一个走进新时代的标签，永远彪炳史册。Would you rate the previous review as positive, neutral or negative? example_title: zh-en sentiment - text: 一个传奇的开端，一个不灭的神话，这不仅仅是一部电影，而是作为一个走进新时代的标签，永远彪炳史册。你认为这句话的立场是赞扬、中立还是批评？ example_title: zh-zh sentiment - text: Suggest at least five related search terms to "Mạng neural nhân tạo". example_title: vi-en query - text: >- Proposez au moins cinq mots clés concernant «Réseau de neurones artificiels». example_title: fr-fr query - text: Explain in a sentence in Telugu what is backpropagation in neural networks. example_title: te-en qa - text: Why is the sky blue? example_title: en-en qa - text: >- Write a fairy tale about a troll saving a princess from a dangerous dragon. The fairy tale is a masterpiece that has achieved praise worldwide and its moral is "Heroes Come in All Shapes and Sizes". Story (in Spanish): example_title: es-en fable - text: >- Write a fable about wood elves living in a forest that is suddenly invaded by ogres. The fable is a masterpiece that has achieved praise worldwide and its moral is "Violence is the last refuge of the incompetent". Fable (in Hindi): example_title: hi-en fable model-index: - name: mt0-large results: - task: type: Coreference resolution dataset: type: winogrande name: Winogrande XL (xl) config: xl split: validation revision: a80f460359d1e9a67c006011c94de42a8759430c metrics: - type: Accuracy value: 51.78 - task: type: Coreference resolution dataset: type: Muennighoff/xwinograd name: XWinograd (en) config: en split: test revision: 9dd5ea5505fad86b7bedad667955577815300cee metrics: - type: Accuracy value: 54.8 - task: type: Coreference resolution dataset: type: Muennighoff/xwinograd name: XWinograd (fr) config: fr split: test revision: 9dd5ea5505fad86b7bedad667955577815300cee metrics: - type: Accuracy value: 56.63 - task: type: Coreference resolution dataset: type: Muennighoff/xwinograd name: XWinograd (jp) config: jp split: test revision: 9dd5ea5505fad86b7bedad667955577815300cee metrics: - type: Accuracy value: 53.08 - task: type: Coreference resolution dataset: type: Muennighoff/xwinograd name: XWinograd (pt) config: pt split: test revision: 9dd5ea5505fad86b7bedad667955577815300cee metrics: - type: Accuracy value: 56.27 - task: type: Coreference resolution dataset: type: Muennighoff/xwinograd name: XWinograd (ru) config: ru split: test revision: 9dd5ea5505fad86b7bedad667955577815300cee metrics: - type: Accuracy value: 55.56 - task: type: Coreference resolution dataset: type: Muennighoff/xwinograd name: XWinograd (zh) config: zh split: test revision: 9dd5ea5505fad86b7bedad667955577815300cee metrics: - type: Accuracy value: 54.37 - task: type: Natural language inference dataset: type: anli name: ANLI (r1) config: r1 split: validation revision: 9dbd830a06fea8b1c49d6e5ef2004a08d9f45094 metrics: - type: Accuracy value: 33.3 - task: type: Natural language inference dataset: type: anli name: ANLI (r2) config: r2 split: validation revision: 9dbd830a06fea8b1c49d6e5ef2004a08d9f45094 metrics: - type: Accuracy value: 34.7 - task: type: Natural language inference dataset: type: anli name: ANLI (r3) config: r3 split: validation revision: 9dbd830a06fea8b1c49d6e5ef2004a08d9f45094 metrics: - type: Accuracy value: 34.75 - task: type: Natural language inference dataset: type: super_glue name: SuperGLUE (cb) config: cb split: validation revision: 9e12063561e7e6c79099feb6d5a493142584e9e2 metrics: - type: Accuracy value: 51.79 - task: type: Natural language inference dataset: type: super_glue name: SuperGLUE (rte) config: rte split: validation revision: 9e12063561e7e6c79099feb6d5a493142584e9e2 metrics: - type: Accuracy value: 64.26 - task: type: Natural language inference dataset: type: xnli name: XNLI (ar) config: ar split: validation revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16 metrics: - type: Accuracy value: 42.61 - task: type: Natural language inference dataset: type: xnli name: XNLI (bg) config: bg split: validation revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16 metrics: - type: Accuracy value: 43.94 - task: type: Natural language inference dataset: type: xnli name: XNLI (de) config: de split: validation revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16 metrics: - type: Accuracy value: 44.18 - task: type: Natural language inference dataset: type: xnli name: XNLI (el) config: el split: validation revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16 metrics: - type: Accuracy value: 43.94 - task: type: Natural language inference dataset: type: xnli name: XNLI (en) config: en split: validation revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16 metrics: - type: Accuracy value: 44.26 - task: type: Natural language inference dataset: type: xnli name: XNLI (es) config: es split: validation revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16 metrics: - type: Accuracy value: 45.34 - task: type: Natural language inference dataset: type: xnli name: XNLI (fr) config: fr split: validation revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16 metrics: - type: Accuracy value: 42.01 - task: type: Natural language inference dataset: type: xnli name: XNLI (hi) config: hi split: validation revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16 metrics: - type: Accuracy value: 41.89 - task: type: Natural language inference dataset: type: xnli name: XNLI (ru) config: ru split: validation revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16 metrics: - type: Accuracy value: 42.13 - task: type: Natural language inference dataset: type: xnli name: XNLI (sw) config: sw split: validation revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16 metrics: - type: Accuracy value: 40.08 - task: type: Natural language inference dataset: type: xnli name: XNLI (th) config: th split: validation revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16 metrics: - type: Accuracy value: 40.8 - task: type: Natural language inference dataset: type: xnli name: XNLI (tr) config: tr split: validation revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16 metrics: - type: Accuracy value: 41.29 - task: type: Natural language inference dataset: type: xnli name: XNLI (ur) config: ur split: validation revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16 metrics: - type: Accuracy value: 39.88 - task: type: Natural language inference dataset: type: xnli name: XNLI (vi) config: vi split: validation revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16 metrics: - type: Accuracy value: 41.81 - task: type: Natural language inference dataset: type: xnli name: XNLI (zh) config: zh split: validation revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16 metrics: - type: Accuracy value: 40.84 - task: type: Sentence completion dataset: type: story_cloze name: StoryCloze (2016) config: '2016' split: validation revision: e724c6f8cdf7c7a2fb229d862226e15b023ee4db metrics: - type: Accuracy value: 59.49 - task: type: Sentence completion dataset: type: super_glue name: SuperGLUE (copa) config: copa split: validation revision: 9e12063561e7e6c79099feb6d5a493142584e9e2 metrics: - type: Accuracy value: 65 - task: type: Sentence completion dataset: type: xcopa name: XCOPA (et) config: et split: validation revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187 metrics: - type: Accuracy value: 56 - task: type: Sentence completion dataset: type: xcopa name: XCOPA (ht) config: ht split: validation revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187 metrics: - type: Accuracy value: 62 - task: type: Sentence completion dataset: type: xcopa name: XCOPA (id) config: id split: validation revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187 metrics: - type: Accuracy value: 61 - task: type: Sentence completion dataset: type: xcopa name: XCOPA (it) config: it split: validation revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187 metrics: - type: Accuracy value: 63 - task: type: Sentence completion dataset: type: xcopa name: XCOPA (qu) config: qu split: validation revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187 metrics: - type: Accuracy value: 57 - task: type: Sentence completion dataset: type: xcopa name: XCOPA (sw) config: sw split: validation revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187 metrics: - type: Accuracy value: 54 - task: type: Sentence completion dataset: type: xcopa name: XCOPA (ta) config: ta split: validation revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187 metrics: - type: Accuracy value: 62 - task: type: Sentence completion dataset: type: xcopa name: XCOPA (th) config: th split: validation revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187 metrics: - type: Accuracy value: 57 - task: type: Sentence completion dataset: type: xcopa name: XCOPA (tr) config: tr split: validation revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187 metrics: - type: Accuracy value: 57 - task: type: Sentence completion dataset: type: xcopa name: XCOPA (vi) config: vi split: validation revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187 metrics: - type: Accuracy value: 63 - task: type: Sentence completion dataset: type: xcopa name: XCOPA (zh) config: zh split: validation revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187 metrics: - type: Accuracy value: 58 - task: type: Sentence completion dataset: type: Muennighoff/xstory_cloze name: XStoryCloze (ar) config: ar split: validation revision: 8bb76e594b68147f1a430e86829d07189622b90d metrics: - type: Accuracy value: 56.59 - task: type: Sentence completion dataset: type: Muennighoff/xstory_cloze name: XStoryCloze (es) config: es split: validation revision: 8bb76e594b68147f1a430e86829d07189622b90d metrics: - type: Accuracy value: 55.72 - task: type: Sentence completion dataset: type: Muennighoff/xstory_cloze name: XStoryCloze (eu) config: eu split: validation revision: 8bb76e594b68147f1a430e86829d07189622b90d metrics: - type: Accuracy value: 52.61 - task: type: Sentence completion dataset: type: Muennighoff/xstory_cloze name: XStoryCloze (hi) config: hi split: validation revision: 8bb76e594b68147f1a430e86829d07189622b90d metrics: - type: Accuracy value: 52.15 - task: type: Sentence completion dataset: type: Muennighoff/xstory_cloze name: XStoryCloze (id) config: id split: validation revision: 8bb76e594b68147f1a430e86829d07189622b90d metrics: - type: Accuracy value: 54.67 - task: type: Sentence completion dataset: type: Muennighoff/xstory_cloze name: XStoryCloze (my) config: my split: validation revision: 8bb76e594b68147f1a430e86829d07189622b90d metrics: - type: Accuracy value: 51.69 - task: type: Sentence completion dataset: type: Muennighoff/xstory_cloze name: XStoryCloze (ru) config: ru split: validation revision: 8bb76e594b68147f1a430e86829d07189622b90d metrics: - type: Accuracy value: 53.74 - task: type: Sentence completion dataset: type: Muennighoff/xstory_cloze name: XStoryCloze (sw) config: sw split: validation revision: 8bb76e594b68147f1a430e86829d07189622b90d metrics: - type: Accuracy value: 55.53 - task: type: Sentence completion dataset: type: Muennighoff/xstory_cloze name: XStoryCloze (te) config: te split: validation revision: 8bb76e594b68147f1a430e86829d07189622b90d metrics: - type: Accuracy value: 57.18 - task: type: Sentence completion dataset: type: Muennighoff/xstory_cloze name: XStoryCloze (zh) config: zh split: validation revision: 8bb76e594b68147f1a430e86829d07189622b90d metrics: - type: Accuracy value: 59.5 --- ![xmtf](https://github.com/bigscience-workshop/xmtf/blob/master/xmtf_banner.png?raw=true) # Table of Contents 1. [Model Summary](#model-summary) 2. [Use](#use) 3. [Limitations](#limitations) 4. [Training](#training) 5. [Evaluation](#evaluation) 7. [Citation](#citation) # Model Summary > We present BLOOMZ & mT0, a family of models capable of following human instructions in dozens of languages zero-shot. We finetune BLOOM & mT5 pretrained multilingual language models on our crosslingual task mixture (xP3) and find our resulting models capable of crosslingual generalization to unseen tasks & languages. - Repository: [bigscience-workshop/xmtf](https://github.com/bigscience-workshop/xmtf) - Paper: [Crosslingual Generalization through Multitask Finetuning](https://arxiv.org/abs/2211.01786) - Point of Contact: [Niklas Muennighoff](mailto:[email protected]) - Languages: Refer to [mc4](https://huggingface.co/datasets/mc4) for pretraining & [xP3](https://huggingface.co/datasets/bigscience/xP3) for finetuning language proportions. It understands both pretraining & finetuning languages. - BLOOMZ & mT0 Model Family: <div class="max-w-full overflow-auto"> <table> <tr> <th colspan="12">Multitask finetuned on <a style="font-weight:bold" href=https://huggingface.co/datasets/bigscience/xP3>xP3</a>. Recommended for prompting in English. </tr> <tr> <td>Parameters</td> <td>300M</td> <td>580M</td> <td>1.2B</td> <td>3.7B</td> <td>13B</td> <td>560M</td> <td>1.1B</td> <td>1.7B</td> <td>3B</td> <td>7.1B</td> <td>176B</td> </tr> <tr> <td>Finetuned Model</td> <td><a href=https://huggingface.co/bigscience/mt0-small>mt0-small</a></td> <td><a href=https://huggingface.co/bigscience/mt0-base>mt0-base</a></td> <td><a href=https://huggingface.co/bigscience/mt0-large>mt0-large</a></td> <td><a href=https://huggingface.co/bigscience/mt0-xl>mt0-xl</a></td> <td><a href=https://huggingface.co/bigscience/mt0-xxl>mt0-xxl</a></td> <td><a href=https://huggingface.co/bigscience/bloomz-560m>bloomz-560m</a></td> <td><a href=https://huggingface.co/bigscience/bloomz-1b1>bloomz-1b1</a></td> <td><a href=https://huggingface.co/bigscience/bloomz-1b7>bloomz-1b7</a></td> <td><a href=https://huggingface.co/bigscience/bloomz-3b>bloomz-3b</a></td> <td><a href=https://huggingface.co/bigscience/bloomz-7b1>bloomz-7b1</a></td> <td><a href=https://huggingface.co/bigscience/bloomz>bloomz</a></td> </tr> </tr> <tr> <th colspan="12">Multitask finetuned on <a style="font-weight:bold" href=https://huggingface.co/datasets/bigscience/xP3mt>xP3mt</a>. Recommended for prompting in non-English.</th> </tr> <tr> <td>Finetuned Model</td> <td></td> <td></td> <td></td> <td></td> <td><a href=https://huggingface.co/bigscience/mt0-xxl-mt>mt0-xxl-mt</a></td> <td></td> <td></td> <td></td> <td></td> <td><a href=https://huggingface.co/bigscience/bloomz-7b1-mt>bloomz-7b1-mt</a></td> <td><a href=https://huggingface.co/bigscience/bloomz-mt>bloomz-mt</a></td> </tr> <th colspan="12">Multitask finetuned on <a style="font-weight:bold" href=https://huggingface.co/datasets/Muennighoff/P3>P3</a>. Released for research purposes only. Strictly inferior to above models!</th> </tr> <tr> <td>Finetuned Model</td> <td></td> <td></td> <td></td> <td></td> <td><a href=https://huggingface.co/bigscience/mt0-xxl-p3>mt0-xxl-p3</a></td> <td></td> <td></td> <td></td> <td></td> <td><a href=https://huggingface.co/bigscience/bloomz-7b1-p3>bloomz-7b1-p3</a></td> <td><a href=https://huggingface.co/bigscience/bloomz-p3>bloomz-p3</a></td> </tr> <th colspan="12">Original pretrained checkpoints. Not recommended.</th> <tr> <td>Pretrained Model</td> <td><a href=https://huggingface.co/google/mt5-small>mt5-small</a></td> <td><a href=https://huggingface.co/google/mt5-base>mt5-base</a></td> <td><a href=https://huggingface.co/google/mt5-large>mt5-large</a></td> <td><a href=https://huggingface.co/google/mt5-xl>mt5-xl</a></td> <td><a href=https://huggingface.co/google/mt5-xxl>mt5-xxl</a></td> <td><a href=https://huggingface.co/bigscience/bloom-560m>bloom-560m</a></td> <td><a href=https://huggingface.co/bigscience/bloom-1b1>bloom-1b1</a></td> <td><a href=https://huggingface.co/bigscience/bloom-1b7>bloom-1b7</a></td> <td><a href=https://huggingface.co/bigscience/bloom-3b>bloom-3b</a></td> <td><a href=https://huggingface.co/bigscience/bloom-7b1>bloom-7b1</a></td> <td><a href=https://huggingface.co/bigscience/bloom>bloom</a></td> </tr> </table> </div> # Use ## Intended use We recommend using the model to perform tasks expressed in natural language. For example, given the prompt "Translate to English: Je t’aime.", the model will most likely answer "I love you.". Some prompt ideas from our paper: - 一个传奇的开端，一个不灭的神话，这不仅仅是一部电影，而是作为一个走进新时代的标签，永远彪炳史册。你认为这句话的立场是赞扬、中立还是批评? - Suggest at least five related search terms to "Mạng neural nhân tạo". - Write a fairy tale about a troll saving a princess from a dangerous dragon. The fairy tale is a masterpiece that has achieved praise worldwide and its moral is "Heroes Come in All Shapes and Sizes". Story (in Spanish): - Explain in a sentence in Telugu what is backpropagation in neural networks. Feel free to share your generations in the Community tab! ## How to use ### CPU <details> <summary> Click to expand </summary> ```python # pip install -q transformers from transformers import AutoModelForSeq2SeqLM, AutoTokenizer checkpoint = "bigscience/mt0-large" tokenizer = AutoTokenizer.from_pretrained(checkpoint) model = AutoModelForSeq2SeqLM.from_pretrained(checkpoint) inputs = tokenizer.encode("Translate to English: Je t’aime.", return_tensors="pt") outputs = model.generate(inputs) print(tokenizer.decode(outputs[0])) ``` </details> ### GPU <details> <summary> Click to expand </summary> ```python # pip install -q transformers accelerate from transformers import AutoModelForSeq2SeqLM, AutoTokenizer checkpoint = "bigscience/mt0-large" tokenizer = AutoTokenizer.from_pretrained(checkpoint) model = AutoModelForSeq2SeqLM.from_pretrained(checkpoint, torch_dtype="auto", device_map="auto") inputs = tokenizer.encode("Translate to English: Je t’aime.", return_tensors="pt").to("cuda") outputs = model.generate(inputs) print(tokenizer.decode(outputs[0])) ``` </details> ### GPU in 8bit <details> <summary> Click to expand </summary> ```python # pip install -q transformers accelerate bitsandbytes from transformers import AutoModelForSeq2SeqLM, AutoTokenizer checkpoint = "bigscience/mt0-large" tokenizer = AutoTokenizer.from_pretrained(checkpoint) model = AutoModelForSeq2SeqLM.from_pretrained(checkpoint, device_map="auto", load_in_8bit=True) inputs = tokenizer.encode("Translate to English: Je t’aime.", return_tensors="pt").to("cuda") outputs = model.generate(inputs) print(tokenizer.decode(outputs[0])) ``` </details> <!-- Necessary for whitespace --> ### # Limitations Prompt Engineering: The performance may vary depending on the prompt. For BLOOMZ models, we recommend making it very clear when the input stops to avoid the model trying to continue it. For example, the prompt "Translate to English: Je t'aime" without the full stop (.) at the end, may result in the model trying to continue the French sentence. Better prompts are e.g. "Translate to English: Je t'aime.", "Translate to English: Je t'aime. Translation:" "What is "Je t'aime." in English?", where it is clear for the model when it should answer. Further, we recommend providing the model as much context as possible. For example, if you want it to answer in Telugu, then tell the model, e.g. "Explain in a sentence in Telugu what is backpropagation in neural networks.". # Training ## Model - Architecture: Same as [mt5-large](https://huggingface.co/google/mt5-large), also refer to the `config.json` file - Finetuning steps: 25000 - Finetuning tokens: 4.62 billion - Precision: bfloat16 ## Hardware - TPUs: TPUv4-64 ## Software - Orchestration: [T5X](https://github.com/google-research/t5x) - Neural networks: [Jax](https://github.com/google/jax) # Evaluation We refer to Table 7 from our [paper](https://arxiv.org/abs/2211.01786) & [bigscience/evaluation-results](https://huggingface.co/datasets/bigscience/evaluation-results) for zero-shot results on unseen tasks. The sidebar reports zero-shot performance of the best prompt per dataset config. # Citation ```bibtex @article{muennighoff2022crosslingual, title={Crosslingual generalization through multitask finetuning}, author={Muennighoff, Niklas and Wang, Thomas and Sutawika, Lintang and Roberts, Adam and Biderman, Stella and Scao, Teven Le and Bari, M Saiful and Shen, Sheng and Yong, Zheng-Xin and Schoelkopf, Hailey and others}, journal={arXiv preprint arXiv:2211.01786}, year={2022} } ```
Equall/Saul-7B-Instruct-v1	Equall	2024-03-10T12:39:32Z	4,824	66	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "legal", "conversational", "en", "arxiv:2403.03883", "license:mit", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text-generation	2024-02-07T13:38:13Z	--- library_name: transformers tags: - legal license: mit language: - en --- # Equall/Saul-Instruct-v1 This is the instruct model for Equall/Saul-Instruct-v1, a large instruct language model tailored for Legal domain. This model is obtained by continue pretraining of Mistral-7B. Checkout our website and register https://equall.ai/ ![image/png](https://cdn-uploads.huggingface.co/production/uploads/644a900e3a619fe72b14af0f/OU4Y3s-WckYKMN4fQkNiS.png) ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: Equall.ai in collaboration with CentraleSupelec, Sorbonne Université, Instituto Superior Técnico and NOVA School of Law - Model type: 7B - Language(s) (NLP): English - License: MIT ### Model Sources <!-- Provide the basic links for the model. --> - Paper: https://arxiv.org/abs/2403.03883 ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> You can use it for legal use cases that involves generation. Here's how you can run the model using the pipeline() function from 🤗 Transformers: ```python # Install transformers from source - only needed for versions <= v4.34 # pip install git+https://github.com/huggingface/transformers.git # pip install accelerate import torch from transformers import pipeline pipe = pipeline("text-generation", model="Equall/Saul-Instruct-v1", torch_dtype=torch.bfloat16, device_map="auto") # We use the tokenizer’s chat template to format each message - see https://huggingface.co/docs/transformers/main/en/chat_templating messages = [ {"role": "user", "content": "[YOUR QUERY GOES HERE]"}, ] prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) outputs = pipe(prompt, max_new_tokens=256, do_sample=False) print(outputs[0]["generated_text"]) ``` ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> This model is built upon the technology of LLM, which comes with inherent limitations. It may occasionally generate inaccurate or nonsensical outputs. Furthermore, being a 7B model, it's anticipated to exhibit less robust performance compared to larger models, such as the 70B variant. ## Citation <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: ```bibtex @misc{colombo2024saullm7b, title={SaulLM-7B: A pioneering Large Language Model for Law}, author={Pierre Colombo and Telmo Pessoa Pires and Malik Boudiaf and Dominic Culver and Rui Melo and Caio Corro and Andre F. T. Martins and Fabrizio Esposito and Vera Lúcia Raposo and Sofia Morgado and Michael Desa}, year={2024}, eprint={2403.03883}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
Qwen/Qwen2-57B-A14B-Instruct-GPTQ-Int4	Qwen	2024-06-18T13:18:05Z	4,821	17	transformers	[ "transformers", "safetensors", "qwen2_moe", "text-generation", "chat", "conversational", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "4-bit", "gptq", "region:us" ]	text-generation	2024-06-06T05:25:27Z	--- license: apache-2.0 language: - en pipeline_tag: text-generation tags: - chat --- # Qwen2-57B-A14B-Instruct-GPTQ-Int4 ## Introduction Qwen2 is the new series of Qwen large language models. For Qwen2, we release a number of base language models and instruction-tuned language models ranging from 0.5 to 72 billion parameters, including a Mixture-of-Experts model. This repo contains the instruction-tuned 57B-A14B Mixture-of-Experts Qwen2 model. Compared with the state-of-the-art opensource language models, including the previous released Qwen1.5, Qwen2 has generally surpassed most opensource models and demonstrated competitiveness against proprietary models across a series of benchmarks targeting for language understanding, language generation, multilingual capability, coding, mathematics, reasoning, etc. Note: vLLM does not support the GPTQ version of Qwen2MoeForCausalLM currently. Qwen2-57B-A14B-Instruct supports a context length of up to 65,536 tokens, enabling the processing of extensive inputs. However, since vLLM currently does not support this model (Qwen2-57B-A14B-Instruct-GPTQ-Int4), please refer to [Qwen2-57B-A14B-Instruct](https://huggingface.co/Qwen/Qwen2-57B-A14B-Instruct). For more details, please refer to our [blog](https://qwenlm.github.io/blog/qwen2/), [GitHub](https://github.com/QwenLM/Qwen2), and [Documentation](https://qwen.readthedocs.io/en/latest/). <br> ## Model Details Qwen2 is a language model series including decoder language models of different model sizes. For each size, we release the base language model and the aligned chat model. It is based on the Transformer architecture with SwiGLU activation, attention QKV bias, group query attention, etc. Additionally, we have an improved tokenizer adaptive to multiple natural languages and codes. ## Training details We pretrained the models with a large amount of data, and we post-trained the models with both supervised finetuning and direct preference optimization. ## Requirements The code of Qwen2MoE has been in the latest Hugging face transformers and we advise you to install `transformers>=4.40.0`, or you might encounter the following error: ``` KeyError: 'qwen2_moe' ``` ## Quickstart Here provides a code snippet with `apply_chat_template` to show you how to load the tokenizer and model and how to generate contents. ```python from transformers import AutoModelForCausalLM, AutoTokenizer device = "cuda" # the device to load the model onto model = AutoModelForCausalLM.from_pretrained( "Qwen/Qwen2-57B-A14B-Instruct-GPTQ-Int4", torch_dtype="auto", device_map="auto" ) tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2-57B-A14B-Instruct-GPTQ-Int4") prompt = "Give me a short introduction to large language model." messages = [ {"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": prompt} ] text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) model_inputs = tokenizer([text], return_tensors="pt").to(device) generated_ids = model.generate( model_inputs.input_ids, max_new_tokens=512 ) generated_ids = [ output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids) ] response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] ``` ## Benchmark and Speed To compare the generation performance between bfloat16 (bf16) and quantized models such as GPTQ-Int8, GPTQ-Int4, and AWQ, please consult our [Benchmark of Quantized Models](https://qwen.readthedocs.io/en/latest/benchmark/quantization_benchmark.html). This benchmark provides insights into how different quantization techniques affect model performance. For those interested in understanding the inference speed and memory consumption when deploying these models with either ``transformer`` or ``vLLM``, we have compiled an extensive [Speed Benchmark](https://qwen.readthedocs.io/en/latest/benchmark/speed_benchmark.html). ## Citation If you find our work helpful, feel free to give us a cite. ``` @article{qwen2, title={Qwen2 Technical Report}, year={2024} } ```
cognitivecomputations/Llama-3-8B-Instruct-abliterated-v2	cognitivecomputations	2024-05-12T17:50:18Z	4,818	20	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "license:llama3", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text-generation	2024-05-09T01:57:24Z	--- library_name: transformers license: llama3 --- # Model Card for Llama-3-8B-Instruct-abliterated-v2 ## Overview This model card describes the Llama-3-8B-Instruct-abliterated-v2 model, which is an orthogonalized version of the meta-llama/Llama-3-8B-Instruct model, and an improvement upon the previous generation Llama-3-8B-Instruct-abliterated. This variant has had certain weights manipulated to inhibit the model's ability to express refusal. [Join the Cognitive Computations Discord!](https://discord.gg/cognitivecomputations) ## Details * The model was trained with more data to better pinpoint the "refusal direction". * This model is MUCH better at directly and succinctly answering requests without producing even so much as disclaimers. ## Methodology The methodology used to generate this model is described in the preview paper/blog post: '[Refusal in LLMs is mediated by a single direction](https://www.alignmentforum.org/posts/jGuXSZgv6qfdhMCuJ/refusal-in-llms-is-mediated-by-a-single-direction)' ## Quirks and Side Effects This model may come with interesting quirks, as the methodology is still new and untested. The code used to generate the model is available in the Python notebook [ortho_cookbook.ipynb](https://huggingface.co/failspy/llama-3-70B-Instruct-abliterated/blob/main/ortho_cookbook.ipynb). Please note that the model may still refuse to answer certain requests, even after the weights have been manipulated to inhibit refusal. ## Availability ## How to Use This model is available for use in the Transformers library. GGUF Quants are available [here](https://huggingface.co/failspy/Llama-3-8B-Instruct-abliterated-v2-GGUF).
RichardErkhov/BEE-spoke-data_-_Mixtral-GQA-400m-v2-gguf	RichardErkhov	2024-06-26T09:29:03Z	4,817	0	null	[ "gguf", "region:us" ]	null	2024-06-26T07:03:02Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) Mixtral-GQA-400m-v2 - GGUF - Model creator: https://huggingface.co/BEE-spoke-data/ - Original model: https://huggingface.co/BEE-spoke-data/Mixtral-GQA-400m-v2/ \| Name \| Quant method \| Size \| \| ---- \| ---- \| ---- \| \| [Mixtral-GQA-400m-v2.Q2_K.gguf](https://huggingface.co/RichardErkhov/BEE-spoke-data_-_Mixtral-GQA-400m-v2-gguf/blob/main/Mixtral-GQA-400m-v2.Q2_K.gguf) \| Q2_K \| 0.72GB \| \| [Mixtral-GQA-400m-v2.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/BEE-spoke-data_-_Mixtral-GQA-400m-v2-gguf/blob/main/Mixtral-GQA-400m-v2.IQ3_XS.gguf) \| IQ3_XS \| 0.8GB \| \| [Mixtral-GQA-400m-v2.IQ3_S.gguf](https://huggingface.co/RichardErkhov/BEE-spoke-data_-_Mixtral-GQA-400m-v2-gguf/blob/main/Mixtral-GQA-400m-v2.IQ3_S.gguf) \| IQ3_S \| 0.84GB \| \| [Mixtral-GQA-400m-v2.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/BEE-spoke-data_-_Mixtral-GQA-400m-v2-gguf/blob/main/Mixtral-GQA-400m-v2.Q3_K_S.gguf) \| Q3_K_S \| 0.84GB \| \| [Mixtral-GQA-400m-v2.IQ3_M.gguf](https://huggingface.co/RichardErkhov/BEE-spoke-data_-_Mixtral-GQA-400m-v2-gguf/blob/main/Mixtral-GQA-400m-v2.IQ3_M.gguf) \| IQ3_M \| 0.87GB \| \| [Mixtral-GQA-400m-v2.Q3_K.gguf](https://huggingface.co/RichardErkhov/BEE-spoke-data_-_Mixtral-GQA-400m-v2-gguf/blob/main/Mixtral-GQA-400m-v2.Q3_K.gguf) \| Q3_K \| 0.92GB \| \| [Mixtral-GQA-400m-v2.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/BEE-spoke-data_-_Mixtral-GQA-400m-v2-gguf/blob/main/Mixtral-GQA-400m-v2.Q3_K_M.gguf) \| Q3_K_M \| 0.92GB \| \| [Mixtral-GQA-400m-v2.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/BEE-spoke-data_-_Mixtral-GQA-400m-v2-gguf/blob/main/Mixtral-GQA-400m-v2.Q3_K_L.gguf) \| Q3_K_L \| 0.98GB \| \| [Mixtral-GQA-400m-v2.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/BEE-spoke-data_-_Mixtral-GQA-400m-v2-gguf/blob/main/Mixtral-GQA-400m-v2.IQ4_XS.gguf) \| IQ4_XS \| 1.02GB \| \| [Mixtral-GQA-400m-v2.Q4_0.gguf](https://huggingface.co/RichardErkhov/BEE-spoke-data_-_Mixtral-GQA-400m-v2-gguf/blob/main/Mixtral-GQA-400m-v2.Q4_0.gguf) \| Q4_0 \| 1.07GB \| \| [Mixtral-GQA-400m-v2.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/BEE-spoke-data_-_Mixtral-GQA-400m-v2-gguf/blob/main/Mixtral-GQA-400m-v2.IQ4_NL.gguf) \| IQ4_NL \| 1.08GB \| \| [Mixtral-GQA-400m-v2.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/BEE-spoke-data_-_Mixtral-GQA-400m-v2-gguf/blob/main/Mixtral-GQA-400m-v2.Q4_K_S.gguf) \| Q4_K_S \| 1.08GB \| \| [Mixtral-GQA-400m-v2.Q4_K.gguf](https://huggingface.co/RichardErkhov/BEE-spoke-data_-_Mixtral-GQA-400m-v2-gguf/blob/main/Mixtral-GQA-400m-v2.Q4_K.gguf) \| Q4_K \| 1.12GB \| \| [Mixtral-GQA-400m-v2.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/BEE-spoke-data_-_Mixtral-GQA-400m-v2-gguf/blob/main/Mixtral-GQA-400m-v2.Q4_K_M.gguf) \| Q4_K_M \| 1.12GB \| \| [Mixtral-GQA-400m-v2.Q4_1.gguf](https://huggingface.co/RichardErkhov/BEE-spoke-data_-_Mixtral-GQA-400m-v2-gguf/blob/main/Mixtral-GQA-400m-v2.Q4_1.gguf) \| Q4_1 \| 1.19GB \| \| [Mixtral-GQA-400m-v2.Q5_0.gguf](https://huggingface.co/RichardErkhov/BEE-spoke-data_-_Mixtral-GQA-400m-v2-gguf/blob/main/Mixtral-GQA-400m-v2.Q5_0.gguf) \| Q5_0 \| 1.3GB \| \| [Mixtral-GQA-400m-v2.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/BEE-spoke-data_-_Mixtral-GQA-400m-v2-gguf/blob/main/Mixtral-GQA-400m-v2.Q5_K_S.gguf) \| Q5_K_S \| 1.3GB \| \| [Mixtral-GQA-400m-v2.Q5_K.gguf](https://huggingface.co/RichardErkhov/BEE-spoke-data_-_Mixtral-GQA-400m-v2-gguf/blob/main/Mixtral-GQA-400m-v2.Q5_K.gguf) \| Q5_K \| 1.32GB \| \| [Mixtral-GQA-400m-v2.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/BEE-spoke-data_-_Mixtral-GQA-400m-v2-gguf/blob/main/Mixtral-GQA-400m-v2.Q5_K_M.gguf) \| Q5_K_M \| 1.32GB \| \| [Mixtral-GQA-400m-v2.Q5_1.gguf](https://huggingface.co/RichardErkhov/BEE-spoke-data_-_Mixtral-GQA-400m-v2-gguf/blob/main/Mixtral-GQA-400m-v2.Q5_1.gguf) \| Q5_1 \| 1.41GB \| \| [Mixtral-GQA-400m-v2.Q6_K.gguf](https://huggingface.co/RichardErkhov/BEE-spoke-data_-_Mixtral-GQA-400m-v2-gguf/blob/main/Mixtral-GQA-400m-v2.Q6_K.gguf) \| Q6_K \| 1.54GB \| \| [Mixtral-GQA-400m-v2.Q8_0.gguf](https://huggingface.co/RichardErkhov/BEE-spoke-data_-_Mixtral-GQA-400m-v2-gguf/blob/main/Mixtral-GQA-400m-v2.Q8_0.gguf) \| Q8_0 \| 1.99GB \| Original model description: --- license: apache-2.0 language: - en --- # BEE-spoke-data/Mixtral-GQA-400m-v2 ## testing code ```python # !pip install -U -q transformers datasets accelerate sentencepiece import pprint as pp from transformers import pipeline pipe = pipeline( "text-generation", model="BEE-spoke-data/Mixtral-GQA-400m-v2", device_map="auto", ) pipe.model.config.pad_token_id = pipe.model.config.eos_token_id prompt = "My favorite movie is Godfather because" res = pipe( prompt, max_new_tokens=256, top_k=4, penalty_alpha=0.6, use_cache=True, no_repeat_ngram_size=4, repetition_penalty=1.1, renormalize_logits=True, ) pp.pprint(res[0]) ```
vslaykovsky/roberta-news-duplicates	vslaykovsky	2021-05-20T23:07:11Z	4,816	1	transformers	[ "transformers", "pytorch", "jax", "roberta", "text-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-03-02T23:29:05Z	Entry not found
mradermacher/L3-Umbral-Mind-RP-v3-8B-i1-GGUF	mradermacher	2024-06-14T08:02:26Z	4,816	3	transformers	[ "transformers", "gguf", "merge", "mergekit", "lazymergekit", "not-for-all-audiences", "nsfw", "rp", "roleplay", "role-play", "en", "base_model:Casual-Autopsy/L3-Umbral-Mind-RP-v3-8B", "license:llama3", "endpoints_compatible", "region:us" ]	null	2024-06-14T05:19:14Z	--- base_model: Casual-Autopsy/L3-Umbral-Mind-RP-v3-8B language: - en library_name: transformers license: llama3 quantized_by: mradermacher tags: - merge - mergekit - lazymergekit - not-for-all-audiences - nsfw - rp - roleplay - role-play --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/Casual-Autopsy/L3-Umbral-Mind-RP-v3-8B <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/L3-Umbral-Mind-RP-v3-8B-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/L3-Umbral-Mind-RP-v3-8B-i1-GGUF/resolve/main/L3-Umbral-Mind-RP-v3-8B.i1-IQ1_S.gguf) \| i1-IQ1_S \| 2.1 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/L3-Umbral-Mind-RP-v3-8B-i1-GGUF/resolve/main/L3-Umbral-Mind-RP-v3-8B.i1-IQ1_M.gguf) \| i1-IQ1_M \| 2.3 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/L3-Umbral-Mind-RP-v3-8B-i1-GGUF/resolve/main/L3-Umbral-Mind-RP-v3-8B.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 2.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/L3-Umbral-Mind-RP-v3-8B-i1-GGUF/resolve/main/L3-Umbral-Mind-RP-v3-8B.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 2.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/L3-Umbral-Mind-RP-v3-8B-i1-GGUF/resolve/main/L3-Umbral-Mind-RP-v3-8B.i1-IQ2_S.gguf) \| i1-IQ2_S \| 2.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/L3-Umbral-Mind-RP-v3-8B-i1-GGUF/resolve/main/L3-Umbral-Mind-RP-v3-8B.i1-IQ2_M.gguf) \| i1-IQ2_M \| 3.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/L3-Umbral-Mind-RP-v3-8B-i1-GGUF/resolve/main/L3-Umbral-Mind-RP-v3-8B.i1-Q2_K.gguf) \| i1-Q2_K \| 3.3 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/L3-Umbral-Mind-RP-v3-8B-i1-GGUF/resolve/main/L3-Umbral-Mind-RP-v3-8B.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 3.4 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/L3-Umbral-Mind-RP-v3-8B-i1-GGUF/resolve/main/L3-Umbral-Mind-RP-v3-8B.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 3.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/L3-Umbral-Mind-RP-v3-8B-i1-GGUF/resolve/main/L3-Umbral-Mind-RP-v3-8B.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 3.8 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/L3-Umbral-Mind-RP-v3-8B-i1-GGUF/resolve/main/L3-Umbral-Mind-RP-v3-8B.i1-IQ3_S.gguf) \| i1-IQ3_S \| 3.8 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/L3-Umbral-Mind-RP-v3-8B-i1-GGUF/resolve/main/L3-Umbral-Mind-RP-v3-8B.i1-IQ3_M.gguf) \| i1-IQ3_M \| 3.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/L3-Umbral-Mind-RP-v3-8B-i1-GGUF/resolve/main/L3-Umbral-Mind-RP-v3-8B.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 4.1 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/L3-Umbral-Mind-RP-v3-8B-i1-GGUF/resolve/main/L3-Umbral-Mind-RP-v3-8B.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 4.4 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/L3-Umbral-Mind-RP-v3-8B-i1-GGUF/resolve/main/L3-Umbral-Mind-RP-v3-8B.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 4.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/L3-Umbral-Mind-RP-v3-8B-i1-GGUF/resolve/main/L3-Umbral-Mind-RP-v3-8B.i1-Q4_0.gguf) \| i1-Q4_0 \| 4.8 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/L3-Umbral-Mind-RP-v3-8B-i1-GGUF/resolve/main/L3-Umbral-Mind-RP-v3-8B.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 4.8 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/L3-Umbral-Mind-RP-v3-8B-i1-GGUF/resolve/main/L3-Umbral-Mind-RP-v3-8B.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 5.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/L3-Umbral-Mind-RP-v3-8B-i1-GGUF/resolve/main/L3-Umbral-Mind-RP-v3-8B.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 5.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/L3-Umbral-Mind-RP-v3-8B-i1-GGUF/resolve/main/L3-Umbral-Mind-RP-v3-8B.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 5.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/L3-Umbral-Mind-RP-v3-8B-i1-GGUF/resolve/main/L3-Umbral-Mind-RP-v3-8B.i1-Q6_K.gguf) \| i1-Q6_K \| 6.7 \| practically like static Q6_K \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his hardware for calculating the imatrix for these quants. <!-- end -->
d4data/bias-detection-model	d4data	2022-08-09T02:40:59Z	4,814	38	transformers	[ "transformers", "tf", "distilbert", "text-classification", "Text Classification", "en", "co2_eq_emissions", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-03-02T23:29:05Z	--- language: - en tags: - Text Classification co2_eq_emissions: 0.319355 widget: - text: "Nevertheless, Trump and other Republicans have tarred the protests as havens for terrorists intent on destroying property." example_title: "Biased example 1" - text: "Billie Eilish issues apology for mouthing an anti-Asian derogatory term in a resurfaced video." example_title: "Biased example 2" - text: "Christians should make clear that the perpetuation of objectionable vaccines and the lack of alternatives is a kind of coercion." example_title: "Biased example 3" - text: "There have been a protest by a group of people" example_title: "Non-Biased example 1" - text: "While emphasizing he’s not singling out either party, Cohen warned about the danger of normalizing white supremacist ideology." example_title: "Non-Biased example 2" --- ## About the Model An English sequence classification model, trained on MBAD Dataset to detect bias and fairness in sentences (news articles). This model was built on top of distilbert-base-uncased model and trained for 30 epochs with a batch size of 16, a learning rate of 5e-5, and a maximum sequence length of 512. - Dataset : MBAD Data - Carbon emission 0.319355 Kg \| Train Accuracy \| Validation Accuracy \| Train loss \| Test loss \| \|---------------:\| -------------------:\| ----------:\|----------:\| \| 76.97 \| 62.00 \| 0.45 \| 0.96 \| ## Usage The easiest way is to load the inference api from huggingface and second method is through the pipeline object offered by transformers library. ```python from transformers import AutoTokenizer, TFAutoModelForSequenceClassification from transformers import pipeline tokenizer = AutoTokenizer.from_pretrained("d4data/bias-detection-model") model = TFAutoModelForSequenceClassification.from_pretrained("d4data/bias-detection-model") classifier = pipeline('text-classification', model=model, tokenizer=tokenizer) # cuda = 0,1 based on gpu availability classifier("The irony, of course, is that the exhibit that invites people to throw trash at vacuuming Ivanka Trump lookalike reflects every stereotype feminists claim to stand against, oversexualizing Ivanka’s body and ignoring her hard work.") ``` ## Author This model is part of the Research topic "Bias and Fairness in AI" conducted by Deepak John Reji, Shaina Raza. If you use this work (code, model or dataset), please star at: > Bias & Fairness in AI, (2022), GitHub repository, <https://github.com/dreji18/Fairness-in-AI>
Yntec/level4	Yntec	2023-09-21T03:14:00Z	4,813	1	diffusers	[ "diffusers", "safetensors", "Photorealistic", "Beautiful", "Fantasy", "AreThoseLevel4Plates", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-09-20T22:17:37Z	--- library_name: diffusers pipeline_tag: text-to-image license: creativeml-openrail-m tags: - Photorealistic - Beautiful - Fantasy - AreThoseLevel4Plates - stable-diffusion - stable-diffusion-diffusers - diffusers - text-to-image --- # level 4 v3 Original page: https://civitai.com/models/17449?modelVersionId=21896 Sample and prompt: ![Sample](https://cdn-uploads.huggingface.co/production/uploads/63239b8370edc53f51cd5d42/gaFiXeJAWFV2zkQaXnSVH.png) Pretty cute girl. Detailed coffee table in the vaporwave mid century modern livingroom. highly detailed, digital painting, artstation, concept art, smooth, sharp focus, illustration, artgerm, tomasz alen kopera, peter mohrbacher, little girl, donato giancola, joseph christian leyendecker, boris vallejo, wlop
Hum-Works/lodestone-base-4096-v1	Hum-Works	2023-10-26T22:00:30Z	4,812	11	sentence-transformers	[ "sentence-transformers", "pytorch", "bert", "feature-extraction", "sentence-similarity", "mteb", "custom_code", "en", "dataset:s2orc", "dataset:flax-sentence-embeddings/stackexchange_title_body_jsonl", "dataset:flax-sentence-embeddings/stackexchange_titlebody_best_voted_answer_jsonl", "dataset:flax-sentence-embeddings/stackexchange_title_best_voted_answer_jsonl", "dataset:flax-sentence-embeddings/stackexchange_titlebody_best_and_down_voted_answer_jsonl", "dataset:sentence-transformers/reddit-title-body", "dataset:msmarco", "dataset:gooaq", "dataset:yahoo_answers_topics", "dataset:code_search_net", "dataset:search_qa", "dataset:eli5", "dataset:snli", "dataset:multi_nli", "dataset:wikihow", "dataset:natural_questions", "dataset:trivia_qa", "dataset:embedding-data/sentence-compression", "dataset:embedding-data/flickr30k-captions", "dataset:embedding-data/altlex", "dataset:embedding-data/simple-wiki", "dataset:embedding-data/QQP", "dataset:embedding-data/SPECTER", "dataset:embedding-data/PAQ_pairs", "dataset:embedding-data/WikiAnswers", "dataset:sentence-transformers/embedding-training-data", "arxiv:2108.12409", "arxiv:1904.06472", "arxiv:2102.07033", "arxiv:2104.08727", "arxiv:1704.05179", "arxiv:1810.09305", "license:apache-2.0", "model-index", "autotrain_compatible", "text-embeddings-inference", "region:us" ]	sentence-similarity	2023-08-25T16:33:26Z	--- license: apache-2.0 pipeline_tag: sentence-similarity inference: false tags: - sentence-transformers - feature-extraction - sentence-similarity - mteb language: en datasets: - s2orc - flax-sentence-embeddings/stackexchange_title_body_jsonl - flax-sentence-embeddings/stackexchange_titlebody_best_voted_answer_jsonl - flax-sentence-embeddings/stackexchange_title_best_voted_answer_jsonl - flax-sentence-embeddings/stackexchange_titlebody_best_and_down_voted_answer_jsonl - sentence-transformers/reddit-title-body - msmarco - gooaq - yahoo_answers_topics - code_search_net - search_qa - eli5 - snli - multi_nli - wikihow - natural_questions - trivia_qa - embedding-data/sentence-compression - embedding-data/flickr30k-captions - embedding-data/altlex - embedding-data/simple-wiki - embedding-data/QQP - embedding-data/SPECTER - embedding-data/PAQ_pairs - embedding-data/WikiAnswers - sentence-transformers/embedding-training-data model-index: - name: lodestone-base-4096-v1 results: - task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (en) config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 69.7313432835821 - type: ap value: 31.618259511417733 - type: f1 value: 63.30313825394228 - task: type: Classification dataset: type: mteb/amazon_polarity name: MTEB AmazonPolarityClassification config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 86.89837499999999 - type: ap value: 82.39500885672128 - type: f1 value: 86.87317947399657 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (en) config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 44.05 - type: f1 value: 42.67624383248947 - task: type: Retrieval dataset: type: arguana name: MTEB ArguAna config: default split: test revision: None metrics: - type: map_at_1 value: 26.173999999999996 - type: map_at_10 value: 40.976 - type: map_at_100 value: 42.067 - type: map_at_1000 value: 42.075 - type: map_at_3 value: 35.917 - type: map_at_5 value: 38.656 - type: mrr_at_1 value: 26.814 - type: mrr_at_10 value: 41.252 - type: mrr_at_100 value: 42.337 - type: mrr_at_1000 value: 42.345 - type: mrr_at_3 value: 36.226 - type: mrr_at_5 value: 38.914 - type: ndcg_at_1 value: 26.173999999999996 - type: ndcg_at_10 value: 49.819 - type: ndcg_at_100 value: 54.403999999999996 - type: ndcg_at_1000 value: 54.59 - type: ndcg_at_3 value: 39.231 - type: ndcg_at_5 value: 44.189 - type: precision_at_1 value: 26.173999999999996 - type: precision_at_10 value: 7.838000000000001 - type: precision_at_100 value: 0.9820000000000001 - type: precision_at_1000 value: 0.1 - type: precision_at_3 value: 16.287 - type: precision_at_5 value: 12.191 - type: recall_at_1 value: 26.173999999999996 - type: recall_at_10 value: 78.378 - type: recall_at_100 value: 98.222 - type: recall_at_1000 value: 99.644 - type: recall_at_3 value: 48.862 - type: recall_at_5 value: 60.953 - task: type: Clustering dataset: type: mteb/arxiv-clustering-p2p name: MTEB ArxivClusteringP2P config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics: - type: v_measure value: 42.31689035788179 - task: type: Clustering dataset: type: mteb/arxiv-clustering-s2s name: MTEB ArxivClusteringS2S config: default split: test revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 metrics: - type: v_measure value: 31.280245136660984 - task: type: Reranking dataset: type: mteb/askubuntudupquestions-reranking name: MTEB AskUbuntuDupQuestions config: default split: test revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 metrics: - type: map value: 58.79109720839415 - type: mrr value: 71.79615705931495 - task: type: STS dataset: type: mteb/biosses-sts name: MTEB BIOSSES config: default split: test revision: d3fb88f8f02e40887cd149695127462bbcf29b4a metrics: - type: cos_sim_pearson value: 76.44918756608115 - type: cos_sim_spearman value: 70.86607256286257 - type: euclidean_pearson value: 74.12154678100815 - type: euclidean_spearman value: 70.86607256286257 - type: manhattan_pearson value: 74.0078626964417 - type: manhattan_spearman value: 70.68353828321327 - task: type: Classification dataset: type: mteb/banking77 name: MTEB Banking77Classification config: default split: test revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 metrics: - type: accuracy value: 75.40584415584415 - type: f1 value: 74.29514617572676 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-p2p name: MTEB BiorxivClusteringP2P config: default split: test revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 metrics: - type: v_measure value: 37.41860080664014 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-s2s name: MTEB BiorxivClusteringS2S config: default split: test revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 metrics: - type: v_measure value: 29.319217023090705 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackAndroidRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 26.595000000000002 - type: map_at_10 value: 36.556 - type: map_at_100 value: 37.984 - type: map_at_1000 value: 38.134 - type: map_at_3 value: 33.417 - type: map_at_5 value: 35.160000000000004 - type: mrr_at_1 value: 32.761 - type: mrr_at_10 value: 41.799 - type: mrr_at_100 value: 42.526 - type: mrr_at_1000 value: 42.582 - type: mrr_at_3 value: 39.39 - type: mrr_at_5 value: 40.727000000000004 - type: ndcg_at_1 value: 32.761 - type: ndcg_at_10 value: 42.549 - type: ndcg_at_100 value: 47.915 - type: ndcg_at_1000 value: 50.475 - type: ndcg_at_3 value: 37.93 - type: ndcg_at_5 value: 39.939 - type: precision_at_1 value: 32.761 - type: precision_at_10 value: 8.312 - type: precision_at_100 value: 1.403 - type: precision_at_1000 value: 0.197 - type: precision_at_3 value: 18.741 - type: precision_at_5 value: 13.447999999999999 - type: recall_at_1 value: 26.595000000000002 - type: recall_at_10 value: 54.332 - type: recall_at_100 value: 76.936 - type: recall_at_1000 value: 93.914 - type: recall_at_3 value: 40.666000000000004 - type: recall_at_5 value: 46.513 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackEnglishRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 22.528000000000002 - type: map_at_10 value: 30.751 - type: map_at_100 value: 31.855 - type: map_at_1000 value: 31.972 - type: map_at_3 value: 28.465 - type: map_at_5 value: 29.738 - type: mrr_at_1 value: 28.662 - type: mrr_at_10 value: 35.912 - type: mrr_at_100 value: 36.726 - type: mrr_at_1000 value: 36.777 - type: mrr_at_3 value: 34.013 - type: mrr_at_5 value: 35.156 - type: ndcg_at_1 value: 28.662 - type: ndcg_at_10 value: 35.452 - type: ndcg_at_100 value: 40.1 - type: ndcg_at_1000 value: 42.323 - type: ndcg_at_3 value: 32.112 - type: ndcg_at_5 value: 33.638 - type: precision_at_1 value: 28.662 - type: precision_at_10 value: 6.688 - type: precision_at_100 value: 1.13 - type: precision_at_1000 value: 0.16 - type: precision_at_3 value: 15.562999999999999 - type: precision_at_5 value: 11.019 - type: recall_at_1 value: 22.528000000000002 - type: recall_at_10 value: 43.748 - type: recall_at_100 value: 64.235 - type: recall_at_1000 value: 78.609 - type: recall_at_3 value: 33.937 - type: recall_at_5 value: 38.234 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGamingRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 33.117999999999995 - type: map_at_10 value: 44.339 - type: map_at_100 value: 45.367000000000004 - type: map_at_1000 value: 45.437 - type: map_at_3 value: 41.195 - type: map_at_5 value: 42.922 - type: mrr_at_1 value: 38.37 - type: mrr_at_10 value: 47.786 - type: mrr_at_100 value: 48.522 - type: mrr_at_1000 value: 48.567 - type: mrr_at_3 value: 45.371 - type: mrr_at_5 value: 46.857 - type: ndcg_at_1 value: 38.37 - type: ndcg_at_10 value: 50.019999999999996 - type: ndcg_at_100 value: 54.36299999999999 - type: ndcg_at_1000 value: 55.897 - type: ndcg_at_3 value: 44.733000000000004 - type: ndcg_at_5 value: 47.292 - type: precision_at_1 value: 38.37 - type: precision_at_10 value: 8.288 - type: precision_at_100 value: 1.139 - type: precision_at_1000 value: 0.132 - type: precision_at_3 value: 20.293 - type: precision_at_5 value: 14.107 - type: recall_at_1 value: 33.117999999999995 - type: recall_at_10 value: 63.451 - type: recall_at_100 value: 82.767 - type: recall_at_1000 value: 93.786 - type: recall_at_3 value: 48.964999999999996 - type: recall_at_5 value: 55.358 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGisRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 16.028000000000002 - type: map_at_10 value: 23.186999999999998 - type: map_at_100 value: 24.236 - type: map_at_1000 value: 24.337 - type: map_at_3 value: 20.816000000000003 - type: map_at_5 value: 22.311 - type: mrr_at_1 value: 17.514 - type: mrr_at_10 value: 24.84 - type: mrr_at_100 value: 25.838 - type: mrr_at_1000 value: 25.924999999999997 - type: mrr_at_3 value: 22.542 - type: mrr_at_5 value: 24.04 - type: ndcg_at_1 value: 17.514 - type: ndcg_at_10 value: 27.391 - type: ndcg_at_100 value: 32.684999999999995 - type: ndcg_at_1000 value: 35.367 - type: ndcg_at_3 value: 22.820999999999998 - type: ndcg_at_5 value: 25.380999999999997 - type: precision_at_1 value: 17.514 - type: precision_at_10 value: 4.463 - type: precision_at_100 value: 0.745 - type: precision_at_1000 value: 0.101 - type: precision_at_3 value: 10.019 - type: precision_at_5 value: 7.457999999999999 - type: recall_at_1 value: 16.028000000000002 - type: recall_at_10 value: 38.81 - type: recall_at_100 value: 63.295 - type: recall_at_1000 value: 83.762 - type: recall_at_3 value: 26.604 - type: recall_at_5 value: 32.727000000000004 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackMathematicaRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 11.962 - type: map_at_10 value: 17.218 - type: map_at_100 value: 18.321 - type: map_at_1000 value: 18.455 - type: map_at_3 value: 15.287999999999998 - type: map_at_5 value: 16.417 - type: mrr_at_1 value: 14.677000000000001 - type: mrr_at_10 value: 20.381 - type: mrr_at_100 value: 21.471999999999998 - type: mrr_at_1000 value: 21.566 - type: mrr_at_3 value: 18.448999999999998 - type: mrr_at_5 value: 19.587 - type: ndcg_at_1 value: 14.677000000000001 - type: ndcg_at_10 value: 20.86 - type: ndcg_at_100 value: 26.519 - type: ndcg_at_1000 value: 30.020000000000003 - type: ndcg_at_3 value: 17.208000000000002 - type: ndcg_at_5 value: 19.037000000000003 - type: precision_at_1 value: 14.677000000000001 - type: precision_at_10 value: 3.856 - type: precision_at_100 value: 0.7889999999999999 - type: precision_at_1000 value: 0.124 - type: precision_at_3 value: 8.043 - type: precision_at_5 value: 6.069999999999999 - type: recall_at_1 value: 11.962 - type: recall_at_10 value: 28.994999999999997 - type: recall_at_100 value: 54.071999999999996 - type: recall_at_1000 value: 79.309 - type: recall_at_3 value: 19.134999999999998 - type: recall_at_5 value: 23.727999999999998 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackPhysicsRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 22.764 - type: map_at_10 value: 31.744 - type: map_at_100 value: 33.037 - type: map_at_1000 value: 33.156 - type: map_at_3 value: 29.015 - type: map_at_5 value: 30.434 - type: mrr_at_1 value: 28.296 - type: mrr_at_10 value: 37.03 - type: mrr_at_100 value: 37.902 - type: mrr_at_1000 value: 37.966 - type: mrr_at_3 value: 34.568 - type: mrr_at_5 value: 35.786 - type: ndcg_at_1 value: 28.296 - type: ndcg_at_10 value: 37.289 - type: ndcg_at_100 value: 42.787 - type: ndcg_at_1000 value: 45.382 - type: ndcg_at_3 value: 32.598 - type: ndcg_at_5 value: 34.521 - type: precision_at_1 value: 28.296 - type: precision_at_10 value: 6.901 - type: precision_at_100 value: 1.135 - type: precision_at_1000 value: 0.152 - type: precision_at_3 value: 15.367 - type: precision_at_5 value: 11.03 - type: recall_at_1 value: 22.764 - type: recall_at_10 value: 48.807 - type: recall_at_100 value: 71.859 - type: recall_at_1000 value: 89.606 - type: recall_at_3 value: 35.594 - type: recall_at_5 value: 40.541 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackProgrammersRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 19.742 - type: map_at_10 value: 27.741 - type: map_at_100 value: 29.323 - type: map_at_1000 value: 29.438 - type: map_at_3 value: 25.217 - type: map_at_5 value: 26.583000000000002 - type: mrr_at_1 value: 24.657999999999998 - type: mrr_at_10 value: 32.407000000000004 - type: mrr_at_100 value: 33.631 - type: mrr_at_1000 value: 33.686 - type: mrr_at_3 value: 30.194 - type: mrr_at_5 value: 31.444 - type: ndcg_at_1 value: 24.657999999999998 - type: ndcg_at_10 value: 32.614 - type: ndcg_at_100 value: 39.61 - type: ndcg_at_1000 value: 42.114000000000004 - type: ndcg_at_3 value: 28.516000000000002 - type: ndcg_at_5 value: 30.274 - type: precision_at_1 value: 24.657999999999998 - type: precision_at_10 value: 6.176 - type: precision_at_100 value: 1.1400000000000001 - type: precision_at_1000 value: 0.155 - type: precision_at_3 value: 13.927 - type: precision_at_5 value: 9.954 - type: recall_at_1 value: 19.742 - type: recall_at_10 value: 42.427 - type: recall_at_100 value: 72.687 - type: recall_at_1000 value: 89.89 - type: recall_at_3 value: 30.781 - type: recall_at_5 value: 35.606 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 19.72608333333333 - type: map_at_10 value: 27.165333333333336 - type: map_at_100 value: 28.292499999999997 - type: map_at_1000 value: 28.416333333333327 - type: map_at_3 value: 24.783833333333334 - type: map_at_5 value: 26.101750000000003 - type: mrr_at_1 value: 23.721500000000002 - type: mrr_at_10 value: 30.853333333333328 - type: mrr_at_100 value: 31.741750000000003 - type: mrr_at_1000 value: 31.812999999999995 - type: mrr_at_3 value: 28.732249999999997 - type: mrr_at_5 value: 29.945166666666665 - type: ndcg_at_1 value: 23.721500000000002 - type: ndcg_at_10 value: 31.74883333333333 - type: ndcg_at_100 value: 36.883583333333334 - type: ndcg_at_1000 value: 39.6145 - type: ndcg_at_3 value: 27.639583333333334 - type: ndcg_at_5 value: 29.543666666666667 - type: precision_at_1 value: 23.721500000000002 - type: precision_at_10 value: 5.709083333333333 - type: precision_at_100 value: 0.9859166666666666 - type: precision_at_1000 value: 0.1413333333333333 - type: precision_at_3 value: 12.85683333333333 - type: precision_at_5 value: 9.258166666666668 - type: recall_at_1 value: 19.72608333333333 - type: recall_at_10 value: 41.73583333333334 - type: recall_at_100 value: 64.66566666666668 - type: recall_at_1000 value: 84.09833333333336 - type: recall_at_3 value: 30.223083333333328 - type: recall_at_5 value: 35.153083333333335 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackStatsRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 17.582 - type: map_at_10 value: 22.803 - type: map_at_100 value: 23.503 - type: map_at_1000 value: 23.599999999999998 - type: map_at_3 value: 21.375 - type: map_at_5 value: 22.052 - type: mrr_at_1 value: 20.399 - type: mrr_at_10 value: 25.369999999999997 - type: mrr_at_100 value: 26.016000000000002 - type: mrr_at_1000 value: 26.090999999999998 - type: mrr_at_3 value: 23.952 - type: mrr_at_5 value: 24.619 - type: ndcg_at_1 value: 20.399 - type: ndcg_at_10 value: 25.964 - type: ndcg_at_100 value: 29.607 - type: ndcg_at_1000 value: 32.349 - type: ndcg_at_3 value: 23.177 - type: ndcg_at_5 value: 24.276 - type: precision_at_1 value: 20.399 - type: precision_at_10 value: 4.018 - type: precision_at_100 value: 0.629 - type: precision_at_1000 value: 0.093 - type: precision_at_3 value: 9.969 - type: precision_at_5 value: 6.748 - type: recall_at_1 value: 17.582 - type: recall_at_10 value: 33.35 - type: recall_at_100 value: 50.219 - type: recall_at_1000 value: 71.06099999999999 - type: recall_at_3 value: 25.619999999999997 - type: recall_at_5 value: 28.291 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackTexRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 11.071 - type: map_at_10 value: 16.201999999999998 - type: map_at_100 value: 17.112 - type: map_at_1000 value: 17.238 - type: map_at_3 value: 14.508 - type: map_at_5 value: 15.440999999999999 - type: mrr_at_1 value: 13.833 - type: mrr_at_10 value: 19.235 - type: mrr_at_100 value: 20.108999999999998 - type: mrr_at_1000 value: 20.196 - type: mrr_at_3 value: 17.515 - type: mrr_at_5 value: 18.505 - type: ndcg_at_1 value: 13.833 - type: ndcg_at_10 value: 19.643 - type: ndcg_at_100 value: 24.298000000000002 - type: ndcg_at_1000 value: 27.614 - type: ndcg_at_3 value: 16.528000000000002 - type: ndcg_at_5 value: 17.991 - type: precision_at_1 value: 13.833 - type: precision_at_10 value: 3.6990000000000003 - type: precision_at_100 value: 0.713 - type: precision_at_1000 value: 0.116 - type: precision_at_3 value: 7.9030000000000005 - type: precision_at_5 value: 5.891 - type: recall_at_1 value: 11.071 - type: recall_at_10 value: 27.019 - type: recall_at_100 value: 48.404 - type: recall_at_1000 value: 72.641 - type: recall_at_3 value: 18.336 - type: recall_at_5 value: 21.991 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackUnixRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 18.573 - type: map_at_10 value: 25.008999999999997 - type: map_at_100 value: 26.015 - type: map_at_1000 value: 26.137 - type: map_at_3 value: 22.798 - type: map_at_5 value: 24.092 - type: mrr_at_1 value: 22.108 - type: mrr_at_10 value: 28.646 - type: mrr_at_100 value: 29.477999999999998 - type: mrr_at_1000 value: 29.57 - type: mrr_at_3 value: 26.415 - type: mrr_at_5 value: 27.693 - type: ndcg_at_1 value: 22.108 - type: ndcg_at_10 value: 29.42 - type: ndcg_at_100 value: 34.385 - type: ndcg_at_1000 value: 37.572 - type: ndcg_at_3 value: 25.274 - type: ndcg_at_5 value: 27.315 - type: precision_at_1 value: 22.108 - type: precision_at_10 value: 5.093 - type: precision_at_100 value: 0.859 - type: precision_at_1000 value: 0.124 - type: precision_at_3 value: 11.474 - type: precision_at_5 value: 8.321000000000002 - type: recall_at_1 value: 18.573 - type: recall_at_10 value: 39.433 - type: recall_at_100 value: 61.597 - type: recall_at_1000 value: 84.69 - type: recall_at_3 value: 27.849 - type: recall_at_5 value: 33.202999999999996 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWebmastersRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 22.807 - type: map_at_10 value: 30.014000000000003 - type: map_at_100 value: 31.422 - type: map_at_1000 value: 31.652 - type: map_at_3 value: 27.447 - type: map_at_5 value: 28.711 - type: mrr_at_1 value: 27.668 - type: mrr_at_10 value: 34.489 - type: mrr_at_100 value: 35.453 - type: mrr_at_1000 value: 35.526 - type: mrr_at_3 value: 32.477000000000004 - type: mrr_at_5 value: 33.603 - type: ndcg_at_1 value: 27.668 - type: ndcg_at_10 value: 34.983 - type: ndcg_at_100 value: 40.535 - type: ndcg_at_1000 value: 43.747 - type: ndcg_at_3 value: 31.026999999999997 - type: ndcg_at_5 value: 32.608 - type: precision_at_1 value: 27.668 - type: precision_at_10 value: 6.837999999999999 - type: precision_at_100 value: 1.411 - type: precision_at_1000 value: 0.23600000000000002 - type: precision_at_3 value: 14.295 - type: precision_at_5 value: 10.435 - type: recall_at_1 value: 22.807 - type: recall_at_10 value: 43.545 - type: recall_at_100 value: 69.39800000000001 - type: recall_at_1000 value: 90.706 - type: recall_at_3 value: 32.183 - type: recall_at_5 value: 36.563 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWordpressRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 13.943 - type: map_at_10 value: 20.419999999999998 - type: map_at_100 value: 21.335 - type: map_at_1000 value: 21.44 - type: map_at_3 value: 17.865000000000002 - type: map_at_5 value: 19.36 - type: mrr_at_1 value: 15.712000000000002 - type: mrr_at_10 value: 22.345000000000002 - type: mrr_at_100 value: 23.227999999999998 - type: mrr_at_1000 value: 23.304 - type: mrr_at_3 value: 19.901 - type: mrr_at_5 value: 21.325 - type: ndcg_at_1 value: 15.712000000000002 - type: ndcg_at_10 value: 24.801000000000002 - type: ndcg_at_100 value: 29.799 - type: ndcg_at_1000 value: 32.513999999999996 - type: ndcg_at_3 value: 19.750999999999998 - type: ndcg_at_5 value: 22.252 - type: precision_at_1 value: 15.712000000000002 - type: precision_at_10 value: 4.1770000000000005 - type: precision_at_100 value: 0.738 - type: precision_at_1000 value: 0.106 - type: precision_at_3 value: 8.688 - type: precision_at_5 value: 6.617000000000001 - type: recall_at_1 value: 13.943 - type: recall_at_10 value: 36.913000000000004 - type: recall_at_100 value: 60.519 - type: recall_at_1000 value: 81.206 - type: recall_at_3 value: 23.006999999999998 - type: recall_at_5 value: 29.082 - task: type: Retrieval dataset: type: climate-fever name: MTEB ClimateFEVER config: default split: test revision: None metrics: - type: map_at_1 value: 9.468 - type: map_at_10 value: 16.029 - type: map_at_100 value: 17.693 - type: map_at_1000 value: 17.886 - type: map_at_3 value: 13.15 - type: map_at_5 value: 14.568 - type: mrr_at_1 value: 21.173000000000002 - type: mrr_at_10 value: 31.028 - type: mrr_at_100 value: 32.061 - type: mrr_at_1000 value: 32.119 - type: mrr_at_3 value: 27.534999999999997 - type: mrr_at_5 value: 29.431 - type: ndcg_at_1 value: 21.173000000000002 - type: ndcg_at_10 value: 23.224 - type: ndcg_at_100 value: 30.225 - type: ndcg_at_1000 value: 33.961000000000006 - type: ndcg_at_3 value: 18.174 - type: ndcg_at_5 value: 19.897000000000002 - type: precision_at_1 value: 21.173000000000002 - type: precision_at_10 value: 7.4719999999999995 - type: precision_at_100 value: 1.5010000000000001 - type: precision_at_1000 value: 0.219 - type: precision_at_3 value: 13.312 - type: precision_at_5 value: 10.619 - type: recall_at_1 value: 9.468 - type: recall_at_10 value: 28.823 - type: recall_at_100 value: 53.26499999999999 - type: recall_at_1000 value: 74.536 - type: recall_at_3 value: 16.672 - type: recall_at_5 value: 21.302 - task: type: Retrieval dataset: type: dbpedia-entity name: MTEB DBPedia config: default split: test revision: None metrics: - type: map_at_1 value: 6.343 - type: map_at_10 value: 12.717 - type: map_at_100 value: 16.48 - type: map_at_1000 value: 17.381 - type: map_at_3 value: 9.568999999999999 - type: map_at_5 value: 11.125 - type: mrr_at_1 value: 48.75 - type: mrr_at_10 value: 58.425000000000004 - type: mrr_at_100 value: 59.075 - type: mrr_at_1000 value: 59.095 - type: mrr_at_3 value: 56.291999999999994 - type: mrr_at_5 value: 57.679 - type: ndcg_at_1 value: 37.875 - type: ndcg_at_10 value: 27.77 - type: ndcg_at_100 value: 30.288999999999998 - type: ndcg_at_1000 value: 36.187999999999995 - type: ndcg_at_3 value: 31.385999999999996 - type: ndcg_at_5 value: 29.923 - type: precision_at_1 value: 48.75 - type: precision_at_10 value: 22.375 - type: precision_at_100 value: 6.3420000000000005 - type: precision_at_1000 value: 1.4489999999999998 - type: precision_at_3 value: 35.5 - type: precision_at_5 value: 30.55 - type: recall_at_1 value: 6.343 - type: recall_at_10 value: 16.936 - type: recall_at_100 value: 35.955999999999996 - type: recall_at_1000 value: 55.787 - type: recall_at_3 value: 10.771 - type: recall_at_5 value: 13.669999999999998 - task: type: Classification dataset: type: mteb/emotion name: MTEB EmotionClassification config: default split: test revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 metrics: - type: accuracy value: 41.99 - type: f1 value: 36.823402174564954 - task: type: Retrieval dataset: type: fever name: MTEB FEVER config: default split: test revision: None metrics: - type: map_at_1 value: 40.088 - type: map_at_10 value: 52.69200000000001 - type: map_at_100 value: 53.296 - type: map_at_1000 value: 53.325 - type: map_at_3 value: 49.905 - type: map_at_5 value: 51.617000000000004 - type: mrr_at_1 value: 43.009 - type: mrr_at_10 value: 56.203 - type: mrr_at_100 value: 56.75 - type: mrr_at_1000 value: 56.769000000000005 - type: mrr_at_3 value: 53.400000000000006 - type: mrr_at_5 value: 55.163 - type: ndcg_at_1 value: 43.009 - type: ndcg_at_10 value: 59.39 - type: ndcg_at_100 value: 62.129999999999995 - type: ndcg_at_1000 value: 62.793 - type: ndcg_at_3 value: 53.878 - type: ndcg_at_5 value: 56.887 - type: precision_at_1 value: 43.009 - type: precision_at_10 value: 8.366 - type: precision_at_100 value: 0.983 - type: precision_at_1000 value: 0.105 - type: precision_at_3 value: 22.377 - type: precision_at_5 value: 15.035000000000002 - type: recall_at_1 value: 40.088 - type: recall_at_10 value: 76.68700000000001 - type: recall_at_100 value: 88.91 - type: recall_at_1000 value: 93.782 - type: recall_at_3 value: 61.809999999999995 - type: recall_at_5 value: 69.131 - task: type: Retrieval dataset: type: fiqa name: MTEB FiQA2018 config: default split: test revision: None metrics: - type: map_at_1 value: 10.817 - type: map_at_10 value: 18.9 - type: map_at_100 value: 20.448 - type: map_at_1000 value: 20.660999999999998 - type: map_at_3 value: 15.979 - type: map_at_5 value: 17.415 - type: mrr_at_1 value: 23.148 - type: mrr_at_10 value: 31.208000000000002 - type: mrr_at_100 value: 32.167 - type: mrr_at_1000 value: 32.242 - type: mrr_at_3 value: 28.498 - type: mrr_at_5 value: 29.964000000000002 - type: ndcg_at_1 value: 23.148 - type: ndcg_at_10 value: 25.325999999999997 - type: ndcg_at_100 value: 31.927 - type: ndcg_at_1000 value: 36.081 - type: ndcg_at_3 value: 21.647 - type: ndcg_at_5 value: 22.762999999999998 - type: precision_at_1 value: 23.148 - type: precision_at_10 value: 7.546 - type: precision_at_100 value: 1.415 - type: precision_at_1000 value: 0.216 - type: precision_at_3 value: 14.969 - type: precision_at_5 value: 11.327 - type: recall_at_1 value: 10.817 - type: recall_at_10 value: 32.164 - type: recall_at_100 value: 57.655 - type: recall_at_1000 value: 82.797 - type: recall_at_3 value: 19.709 - type: recall_at_5 value: 24.333 - task: type: Retrieval dataset: type: hotpotqa name: MTEB HotpotQA config: default split: test revision: None metrics: - type: map_at_1 value: 25.380999999999997 - type: map_at_10 value: 33.14 - type: map_at_100 value: 33.948 - type: map_at_1000 value: 34.028000000000006 - type: map_at_3 value: 31.019999999999996 - type: map_at_5 value: 32.23 - type: mrr_at_1 value: 50.763000000000005 - type: mrr_at_10 value: 57.899 - type: mrr_at_100 value: 58.426 - type: mrr_at_1000 value: 58.457 - type: mrr_at_3 value: 56.093 - type: mrr_at_5 value: 57.116 - type: ndcg_at_1 value: 50.763000000000005 - type: ndcg_at_10 value: 41.656 - type: ndcg_at_100 value: 45.079 - type: ndcg_at_1000 value: 46.916999999999994 - type: ndcg_at_3 value: 37.834 - type: ndcg_at_5 value: 39.732 - type: precision_at_1 value: 50.763000000000005 - type: precision_at_10 value: 8.648 - type: precision_at_100 value: 1.135 - type: precision_at_1000 value: 0.13799999999999998 - type: precision_at_3 value: 23.105999999999998 - type: precision_at_5 value: 15.363 - type: recall_at_1 value: 25.380999999999997 - type: recall_at_10 value: 43.241 - type: recall_at_100 value: 56.745000000000005 - type: recall_at_1000 value: 69.048 - type: recall_at_3 value: 34.659 - type: recall_at_5 value: 38.406 - task: type: Classification dataset: type: mteb/imdb name: MTEB ImdbClassification config: default split: test revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 metrics: - type: accuracy value: 79.544 - type: ap value: 73.82920133396664 - type: f1 value: 79.51048124883265 - task: type: Retrieval dataset: type: msmarco name: MTEB MSMARCO config: default split: dev revision: None metrics: - type: map_at_1 value: 11.174000000000001 - type: map_at_10 value: 19.451999999999998 - type: map_at_100 value: 20.612 - type: map_at_1000 value: 20.703 - type: map_at_3 value: 16.444 - type: map_at_5 value: 18.083 - type: mrr_at_1 value: 11.447000000000001 - type: mrr_at_10 value: 19.808 - type: mrr_at_100 value: 20.958 - type: mrr_at_1000 value: 21.041999999999998 - type: mrr_at_3 value: 16.791 - type: mrr_at_5 value: 18.459 - type: ndcg_at_1 value: 11.447000000000001 - type: ndcg_at_10 value: 24.556 - type: ndcg_at_100 value: 30.637999999999998 - type: ndcg_at_1000 value: 33.14 - type: ndcg_at_3 value: 18.325 - type: ndcg_at_5 value: 21.278 - type: precision_at_1 value: 11.447000000000001 - type: precision_at_10 value: 4.215 - type: precision_at_100 value: 0.732 - type: precision_at_1000 value: 0.095 - type: precision_at_3 value: 8.052 - type: precision_at_5 value: 6.318 - type: recall_at_1 value: 11.174000000000001 - type: recall_at_10 value: 40.543 - type: recall_at_100 value: 69.699 - type: recall_at_1000 value: 89.403 - type: recall_at_3 value: 23.442 - type: recall_at_5 value: 30.536 - task: type: Classification dataset: type: mteb/mtop_domain name: MTEB MTOPDomainClassification (en) config: en split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 89.6671226630187 - type: f1 value: 89.57660424361246 - task: type: Classification dataset: type: mteb/mtop_intent name: MTEB MTOPIntentClassification (en) config: en split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - type: accuracy value: 60.284997720018254 - type: f1 value: 40.30637400152823 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (en) config: en split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 63.33557498318763 - type: f1 value: 60.24039910680179 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (en) config: en split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 72.37390719569603 - type: f1 value: 72.33097333477316 - task: type: Clustering dataset: type: mteb/medrxiv-clustering-p2p name: MTEB MedrxivClusteringP2P config: default split: test revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 metrics: - type: v_measure value: 34.68158939060552 - task: type: Clustering dataset: type: mteb/medrxiv-clustering-s2s name: MTEB MedrxivClusteringS2S config: default split: test revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 metrics: - type: v_measure value: 30.340061711905236 - task: type: Reranking dataset: type: mteb/mind_small name: MTEB MindSmallReranking config: default split: test revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 metrics: - type: map value: 32.01814326295803 - type: mrr value: 33.20555240055367 - task: type: Retrieval dataset: type: nfcorpus name: MTEB NFCorpus config: default split: test revision: None metrics: - type: map_at_1 value: 3.3910000000000005 - type: map_at_10 value: 7.7219999999999995 - type: map_at_100 value: 10.286 - type: map_at_1000 value: 11.668000000000001 - type: map_at_3 value: 5.552 - type: map_at_5 value: 6.468 - type: mrr_at_1 value: 34.365 - type: mrr_at_10 value: 42.555 - type: mrr_at_100 value: 43.295 - type: mrr_at_1000 value: 43.357 - type: mrr_at_3 value: 40.299 - type: mrr_at_5 value: 41.182 - type: ndcg_at_1 value: 31.424000000000003 - type: ndcg_at_10 value: 24.758 - type: ndcg_at_100 value: 23.677999999999997 - type: ndcg_at_1000 value: 33.377 - type: ndcg_at_3 value: 28.302 - type: ndcg_at_5 value: 26.342 - type: precision_at_1 value: 33.437 - type: precision_at_10 value: 19.256999999999998 - type: precision_at_100 value: 6.662999999999999 - type: precision_at_1000 value: 1.9900000000000002 - type: precision_at_3 value: 27.761000000000003 - type: precision_at_5 value: 23.715 - type: recall_at_1 value: 3.3910000000000005 - type: recall_at_10 value: 11.068 - type: recall_at_100 value: 25.878 - type: recall_at_1000 value: 60.19 - type: recall_at_3 value: 6.1690000000000005 - type: recall_at_5 value: 7.767 - task: type: Retrieval dataset: type: nq name: MTEB NQ config: default split: test revision: None metrics: - type: map_at_1 value: 15.168000000000001 - type: map_at_10 value: 26.177 - type: map_at_100 value: 27.564 - type: map_at_1000 value: 27.628999999999998 - type: map_at_3 value: 22.03 - type: map_at_5 value: 24.276 - type: mrr_at_1 value: 17.439 - type: mrr_at_10 value: 28.205000000000002 - type: mrr_at_100 value: 29.357 - type: mrr_at_1000 value: 29.408 - type: mrr_at_3 value: 24.377 - type: mrr_at_5 value: 26.540000000000003 - type: ndcg_at_1 value: 17.41 - type: ndcg_at_10 value: 32.936 - type: ndcg_at_100 value: 39.196999999999996 - type: ndcg_at_1000 value: 40.892 - type: ndcg_at_3 value: 24.721 - type: ndcg_at_5 value: 28.615000000000002 - type: precision_at_1 value: 17.41 - type: precision_at_10 value: 6.199000000000001 - type: precision_at_100 value: 0.9690000000000001 - type: precision_at_1000 value: 0.11299999999999999 - type: precision_at_3 value: 11.790000000000001 - type: precision_at_5 value: 9.264 - type: recall_at_1 value: 15.168000000000001 - type: recall_at_10 value: 51.914 - type: recall_at_100 value: 79.804 - type: recall_at_1000 value: 92.75999999999999 - type: recall_at_3 value: 30.212 - type: recall_at_5 value: 39.204 - task: type: Retrieval dataset: type: quora name: MTEB QuoraRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 67.306 - type: map_at_10 value: 80.634 - type: map_at_100 value: 81.349 - type: map_at_1000 value: 81.37299999999999 - type: map_at_3 value: 77.691 - type: map_at_5 value: 79.512 - type: mrr_at_1 value: 77.56 - type: mrr_at_10 value: 84.177 - type: mrr_at_100 value: 84.35000000000001 - type: mrr_at_1000 value: 84.353 - type: mrr_at_3 value: 83.003 - type: mrr_at_5 value: 83.799 - type: ndcg_at_1 value: 77.58 - type: ndcg_at_10 value: 84.782 - type: ndcg_at_100 value: 86.443 - type: ndcg_at_1000 value: 86.654 - type: ndcg_at_3 value: 81.67 - type: ndcg_at_5 value: 83.356 - type: precision_at_1 value: 77.58 - type: precision_at_10 value: 12.875 - type: precision_at_100 value: 1.503 - type: precision_at_1000 value: 0.156 - type: precision_at_3 value: 35.63 - type: precision_at_5 value: 23.483999999999998 - type: recall_at_1 value: 67.306 - type: recall_at_10 value: 92.64 - type: recall_at_100 value: 98.681 - type: recall_at_1000 value: 99.79 - type: recall_at_3 value: 83.682 - type: recall_at_5 value: 88.424 - task: type: Clustering dataset: type: mteb/reddit-clustering name: MTEB RedditClustering config: default split: test revision: 24640382cdbf8abc73003fb0fa6d111a705499eb metrics: - type: v_measure value: 50.76319866126382 - task: type: Clustering dataset: type: mteb/reddit-clustering-p2p name: MTEB RedditClusteringP2P config: default split: test revision: 282350215ef01743dc01b456c7f5241fa8937f16 metrics: - type: v_measure value: 55.024711941648995 - task: type: Retrieval dataset: type: scidocs name: MTEB SCIDOCS config: default split: test revision: None metrics: - type: map_at_1 value: 3.9379999999999997 - type: map_at_10 value: 8.817 - type: map_at_100 value: 10.546999999999999 - type: map_at_1000 value: 10.852 - type: map_at_3 value: 6.351999999999999 - type: map_at_5 value: 7.453 - type: mrr_at_1 value: 19.400000000000002 - type: mrr_at_10 value: 27.371000000000002 - type: mrr_at_100 value: 28.671999999999997 - type: mrr_at_1000 value: 28.747 - type: mrr_at_3 value: 24.583 - type: mrr_at_5 value: 26.143 - type: ndcg_at_1 value: 19.400000000000002 - type: ndcg_at_10 value: 15.264 - type: ndcg_at_100 value: 22.63 - type: ndcg_at_1000 value: 28.559 - type: ndcg_at_3 value: 14.424999999999999 - type: ndcg_at_5 value: 12.520000000000001 - type: precision_at_1 value: 19.400000000000002 - type: precision_at_10 value: 7.8100000000000005 - type: precision_at_100 value: 1.854 - type: precision_at_1000 value: 0.329 - type: precision_at_3 value: 13.100000000000001 - type: precision_at_5 value: 10.68 - type: recall_at_1 value: 3.9379999999999997 - type: recall_at_10 value: 15.903 - type: recall_at_100 value: 37.645 - type: recall_at_1000 value: 66.86 - type: recall_at_3 value: 7.993 - type: recall_at_5 value: 10.885 - task: type: STS dataset: type: mteb/sickr-sts name: MTEB SICK-R config: default split: test revision: a6ea5a8cab320b040a23452cc28066d9beae2cee metrics: - type: cos_sim_pearson value: 80.12689060151425 - type: cos_sim_spearman value: 70.46515535094771 - type: euclidean_pearson value: 77.17160003557223 - type: euclidean_spearman value: 70.4651757047438 - type: manhattan_pearson value: 77.18129609281937 - type: manhattan_spearman value: 70.46610403752913 - task: type: STS dataset: type: mteb/sts12-sts name: MTEB STS12 config: default split: test revision: a0d554a64d88156834ff5ae9920b964011b16384 metrics: - type: cos_sim_pearson value: 70.451157033355 - type: cos_sim_spearman value: 63.99899601697852 - type: euclidean_pearson value: 67.46985359967678 - type: euclidean_spearman value: 64.00001637764805 - type: manhattan_pearson value: 67.56534741780037 - type: manhattan_spearman value: 64.06533893575366 - task: type: STS dataset: type: mteb/sts13-sts name: MTEB STS13 config: default split: test revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca metrics: - type: cos_sim_pearson value: 77.65086614464292 - type: cos_sim_spearman value: 78.20169706921848 - type: euclidean_pearson value: 77.77758172155283 - type: euclidean_spearman value: 78.20169706921848 - type: manhattan_pearson value: 77.75077884860052 - type: manhattan_spearman value: 78.16875216484164 - task: type: STS dataset: type: mteb/sts14-sts name: MTEB STS14 config: default split: test revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 metrics: - type: cos_sim_pearson value: 76.26381598259717 - type: cos_sim_spearman value: 70.78377709313477 - type: euclidean_pearson value: 74.82646556532096 - type: euclidean_spearman value: 70.78377658155212 - type: manhattan_pearson value: 74.81784766108225 - type: manhattan_spearman value: 70.79351454692176 - task: type: STS dataset: type: mteb/sts15-sts name: MTEB STS15 config: default split: test revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 metrics: - type: cos_sim_pearson value: 79.00532026789739 - type: cos_sim_spearman value: 80.02708383244838 - type: euclidean_pearson value: 79.48345422610525 - type: euclidean_spearman value: 80.02708383244838 - type: manhattan_pearson value: 79.44519739854803 - type: manhattan_spearman value: 79.98344094559687 - task: type: STS dataset: type: mteb/sts16-sts name: MTEB STS16 config: default split: test revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 metrics: - type: cos_sim_pearson value: 77.32783048164805 - type: cos_sim_spearman value: 78.79729961288045 - type: euclidean_pearson value: 78.72111945793154 - type: euclidean_spearman value: 78.79729904606872 - type: manhattan_pearson value: 78.72464311117116 - type: manhattan_spearman value: 78.822591248334 - task: type: STS dataset: type: mteb/sts17-crosslingual-sts name: MTEB STS17 (en-en) config: en-en split: test revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d metrics: - type: cos_sim_pearson value: 82.04318630630854 - type: cos_sim_spearman value: 83.87886389259836 - type: euclidean_pearson value: 83.40385877895086 - type: euclidean_spearman value: 83.87886389259836 - type: manhattan_pearson value: 83.46337128901547 - type: manhattan_spearman value: 83.9723106941644 - task: type: STS dataset: type: mteb/sts22-crosslingual-sts name: MTEB STS22 (en) config: en split: test revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 metrics: - type: cos_sim_pearson value: 63.003511169944595 - type: cos_sim_spearman value: 64.39318805580227 - type: euclidean_pearson value: 65.4797990735967 - type: euclidean_spearman value: 64.39318805580227 - type: manhattan_pearson value: 65.44604544280844 - type: manhattan_spearman value: 64.38742899984233 - task: type: STS dataset: type: mteb/stsbenchmark-sts name: MTEB STSBenchmark config: default split: test revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 metrics: - type: cos_sim_pearson value: 76.63101237585029 - type: cos_sim_spearman value: 75.57446967644269 - type: euclidean_pearson value: 76.93491768734478 - type: euclidean_spearman value: 75.57446967644269 - type: manhattan_pearson value: 76.92187567800636 - type: manhattan_spearman value: 75.57239337194585 - task: type: Reranking dataset: type: mteb/scidocs-reranking name: MTEB SciDocsRR config: default split: test revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab metrics: - type: map value: 78.5376604868993 - type: mrr value: 92.94422897364073 - task: type: Retrieval dataset: type: scifact name: MTEB SciFact config: default split: test revision: None metrics: - type: map_at_1 value: 38.872 - type: map_at_10 value: 50.417 - type: map_at_100 value: 51.202000000000005 - type: map_at_1000 value: 51.25999999999999 - type: map_at_3 value: 47.02 - type: map_at_5 value: 49.326 - type: mrr_at_1 value: 41.0 - type: mrr_at_10 value: 51.674 - type: mrr_at_100 value: 52.32599999999999 - type: mrr_at_1000 value: 52.376999999999995 - type: mrr_at_3 value: 48.778 - type: mrr_at_5 value: 50.744 - type: ndcg_at_1 value: 41.0 - type: ndcg_at_10 value: 56.027 - type: ndcg_at_100 value: 59.362 - type: ndcg_at_1000 value: 60.839 - type: ndcg_at_3 value: 50.019999999999996 - type: ndcg_at_5 value: 53.644999999999996 - type: precision_at_1 value: 41.0 - type: precision_at_10 value: 8.1 - type: precision_at_100 value: 0.987 - type: precision_at_1000 value: 0.11100000000000002 - type: precision_at_3 value: 20.444000000000003 - type: precision_at_5 value: 14.466999999999999 - type: recall_at_1 value: 38.872 - type: recall_at_10 value: 71.906 - type: recall_at_100 value: 86.367 - type: recall_at_1000 value: 98.0 - type: recall_at_3 value: 56.206 - type: recall_at_5 value: 65.05 - task: type: PairClassification dataset: type: mteb/sprintduplicatequestions-pairclassification name: MTEB SprintDuplicateQuestions config: default split: test revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 metrics: - type: cos_sim_accuracy value: 99.7039603960396 - type: cos_sim_ap value: 90.40809844250262 - type: cos_sim_f1 value: 84.53181583031557 - type: cos_sim_precision value: 87.56698821007502 - type: cos_sim_recall value: 81.69999999999999 - type: dot_accuracy value: 99.7039603960396 - type: dot_ap value: 90.40809844250262 - type: dot_f1 value: 84.53181583031557 - type: dot_precision value: 87.56698821007502 - type: dot_recall value: 81.69999999999999 - type: euclidean_accuracy value: 99.7039603960396 - type: euclidean_ap value: 90.4080982863383 - type: euclidean_f1 value: 84.53181583031557 - type: euclidean_precision value: 87.56698821007502 - type: euclidean_recall value: 81.69999999999999 - type: manhattan_accuracy value: 99.7 - type: manhattan_ap value: 90.39771161966652 - type: manhattan_f1 value: 84.32989690721648 - type: manhattan_precision value: 87.02127659574468 - type: manhattan_recall value: 81.8 - type: max_accuracy value: 99.7039603960396 - type: max_ap value: 90.40809844250262 - type: max_f1 value: 84.53181583031557 - task: type: Clustering dataset: type: mteb/stackexchange-clustering name: MTEB StackExchangeClustering config: default split: test revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 metrics: - type: v_measure value: 59.663210666678715 - task: type: Clustering dataset: type: mteb/stackexchange-clustering-p2p name: MTEB StackExchangeClusteringP2P config: default split: test revision: 815ca46b2622cec33ccafc3735d572c266efdb44 metrics: - type: v_measure value: 32.107791216468776 - task: type: Reranking dataset: type: mteb/stackoverflowdupquestions-reranking name: MTEB StackOverflowDupQuestions config: default split: test revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 metrics: - type: map value: 46.440691925067604 - type: mrr value: 47.03390257618199 - task: type: Summarization dataset: type: mteb/summeval name: MTEB SummEval config: default split: test revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c metrics: - type: cos_sim_pearson value: 31.067177519784074 - type: cos_sim_spearman value: 31.234728424648967 - type: dot_pearson value: 31.06717083018107 - type: dot_spearman value: 31.234728424648967 - task: type: Retrieval dataset: type: trec-covid name: MTEB TRECCOVID config: default split: test revision: None metrics: - type: map_at_1 value: 0.136 - type: map_at_10 value: 0.767 - type: map_at_100 value: 3.3689999999999998 - type: map_at_1000 value: 8.613999999999999 - type: map_at_3 value: 0.369 - type: map_at_5 value: 0.514 - type: mrr_at_1 value: 48.0 - type: mrr_at_10 value: 63.908 - type: mrr_at_100 value: 64.615 - type: mrr_at_1000 value: 64.615 - type: mrr_at_3 value: 62.0 - type: mrr_at_5 value: 63.4 - type: ndcg_at_1 value: 44.0 - type: ndcg_at_10 value: 38.579 - type: ndcg_at_100 value: 26.409 - type: ndcg_at_1000 value: 26.858999999999998 - type: ndcg_at_3 value: 47.134 - type: ndcg_at_5 value: 43.287 - type: precision_at_1 value: 48.0 - type: precision_at_10 value: 40.400000000000006 - type: precision_at_100 value: 26.640000000000004 - type: precision_at_1000 value: 12.04 - type: precision_at_3 value: 52.666999999999994 - type: precision_at_5 value: 46.800000000000004 - type: recall_at_1 value: 0.136 - type: recall_at_10 value: 1.0070000000000001 - type: recall_at_100 value: 6.318 - type: recall_at_1000 value: 26.522000000000002 - type: recall_at_3 value: 0.41700000000000004 - type: recall_at_5 value: 0.606 - task: type: Retrieval dataset: type: webis-touche2020 name: MTEB Touche2020 config: default split: test revision: None metrics: - type: map_at_1 value: 1.9949999999999999 - type: map_at_10 value: 8.304 - type: map_at_100 value: 13.644 - type: map_at_1000 value: 15.43 - type: map_at_3 value: 4.788 - type: map_at_5 value: 6.22 - type: mrr_at_1 value: 22.448999999999998 - type: mrr_at_10 value: 37.658 - type: mrr_at_100 value: 38.491 - type: mrr_at_1000 value: 38.503 - type: mrr_at_3 value: 32.312999999999995 - type: mrr_at_5 value: 35.68 - type: ndcg_at_1 value: 21.429000000000002 - type: ndcg_at_10 value: 18.995 - type: ndcg_at_100 value: 32.029999999999994 - type: ndcg_at_1000 value: 44.852 - type: ndcg_at_3 value: 19.464000000000002 - type: ndcg_at_5 value: 19.172 - type: precision_at_1 value: 22.448999999999998 - type: precision_at_10 value: 17.143 - type: precision_at_100 value: 6.877999999999999 - type: precision_at_1000 value: 1.524 - type: precision_at_3 value: 21.769 - type: precision_at_5 value: 20.0 - type: recall_at_1 value: 1.9949999999999999 - type: recall_at_10 value: 13.395999999999999 - type: recall_at_100 value: 44.348 - type: recall_at_1000 value: 82.622 - type: recall_at_3 value: 5.896 - type: recall_at_5 value: 8.554 - task: type: Classification dataset: type: mteb/toxic_conversations_50k name: MTEB ToxicConversationsClassification config: default split: test revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c metrics: - type: accuracy value: 67.9394 - type: ap value: 12.943337263423334 - type: f1 value: 52.28243093094156 - task: type: Classification dataset: type: mteb/tweet_sentiment_extraction name: MTEB TweetSentimentExtractionClassification config: default split: test revision: d604517c81ca91fe16a244d1248fc021f9ecee7a metrics: - type: accuracy value: 56.414827391058296 - type: f1 value: 56.666412409573105 - task: type: Clustering dataset: type: mteb/twentynewsgroups-clustering name: MTEB TwentyNewsgroupsClustering config: default split: test revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 metrics: - type: v_measure value: 47.009746255495465 - task: type: PairClassification dataset: type: mteb/twittersemeval2015-pairclassification name: MTEB TwitterSemEval2015 config: default split: test revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 metrics: - type: cos_sim_accuracy value: 84.02574953805807 - type: cos_sim_ap value: 67.66599910763128 - type: cos_sim_f1 value: 63.491277990844985 - type: cos_sim_precision value: 59.77172140694154 - type: cos_sim_recall value: 67.70448548812665 - type: dot_accuracy value: 84.02574953805807 - type: dot_ap value: 67.66600090945406 - type: dot_f1 value: 63.491277990844985 - type: dot_precision value: 59.77172140694154 - type: dot_recall value: 67.70448548812665 - type: euclidean_accuracy value: 84.02574953805807 - type: euclidean_ap value: 67.6659842364448 - type: euclidean_f1 value: 63.491277990844985 - type: euclidean_precision value: 59.77172140694154 - type: euclidean_recall value: 67.70448548812665 - type: manhattan_accuracy value: 84.0317100792752 - type: manhattan_ap value: 67.66351692448987 - type: manhattan_f1 value: 63.48610948306178 - type: manhattan_precision value: 57.11875131828729 - type: manhattan_recall value: 71.45118733509234 - type: max_accuracy value: 84.0317100792752 - type: max_ap value: 67.66600090945406 - type: max_f1 value: 63.491277990844985 - task: type: PairClassification dataset: type: mteb/twitterurlcorpus-pairclassification name: MTEB TwitterURLCorpus config: default split: test revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf metrics: - type: cos_sim_accuracy value: 87.53832421314084 - type: cos_sim_ap value: 83.11416594316626 - type: cos_sim_f1 value: 75.41118114347518 - type: cos_sim_precision value: 73.12839059674504 - type: cos_sim_recall value: 77.8410840776101 - type: dot_accuracy value: 87.53832421314084 - type: dot_ap value: 83.11416226342155 - type: dot_f1 value: 75.41118114347518 - type: dot_precision value: 73.12839059674504 - type: dot_recall value: 77.8410840776101 - type: euclidean_accuracy value: 87.53832421314084 - type: euclidean_ap value: 83.11416284455395 - type: euclidean_f1 value: 75.41118114347518 - type: euclidean_precision value: 73.12839059674504 - type: euclidean_recall value: 77.8410840776101 - type: manhattan_accuracy value: 87.49369348391353 - type: manhattan_ap value: 83.08066812574694 - type: manhattan_f1 value: 75.36561228603892 - type: manhattan_precision value: 71.9202518363064 - type: manhattan_recall value: 79.15768401601478 - type: max_accuracy value: 87.53832421314084 - type: max_ap value: 83.11416594316626 - type: max_f1 value: 75.41118114347518 --- # lodestone-base-4096-v1 [Hum-Works/lodestone-base-4096-v1](https://huggingface.co/Hum-Works/lodestone-base-4096-v1). [Griffin McCauley](https://huggingface.co/gmccaul1), [Will Fortin](https://huggingface.co/willathum), [Dylan DiGioia](https://huggingface.co/dylanAtHum) 2023 This new [sentence-transformers](https://www.SBERT.net) model from [Hum](https://www.hum.works/) maps long sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. ## Abstract In the hopes of furthering Hum's overarching mission of increasing the accessibility and interconnectivity of human knowledge, this model was developed as part of a project intending to boost the maximum input sequence length of sentence embedding models by leveraging recent architectural advances in the design of transformer models such as the incorporation of FlashAttention, Attention with Linear Biases (ALiBi), and Gated Linear Units (GLU). These modifications and enhancements were implemented by the team at MosaicML who designed and constructed the pre-trained [`mosaic-bert-base-seqlen-2048`](https://huggingface.co/mosaicml/mosaic-bert-base-seqlen-2048) model, and more information regarding the details of their development and testing specifications can be found on the model card. While the fine-tuning procedure followed during the course of this project loosely mirrors that of the of the original [Flax-sentence-embeddings](https://huggingface.co/flax-sentence-embeddings) team responsible for the creation of many other popular sentence-transformers models (e.g. [all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2), [all-distilroberta-v1](https://huggingface.co/sentence-transformers/all-distilroberta-v1), and [all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2)), our methodology includes novel techniques for data loading, batch sampling, and model checkpointing intended to improve training efficiency with regards to memory allocation and data storage. Through combining these well-established and proven fine-tuning practices with novel advances in transformer architectural elements, our `lodestone-base-4096-v1` model is able to achieve comparable performance metrics on standard text embedding evaluation benchmarks while also supporting a longer and more robust input sequence length of 4096 while retaining a smaller, more manageable size capable of being run on either a GPU or CPU. ## Usage Using this model becomes relatively easy when you have [sentence-transformers](https://www.SBERT.net) installed. At the time of publishing, sentence-transformers does not support remote code which is required for flash-attention used by the model. A fork of the sentence-transformers repository that allows remote code execution is provided for convenience. It can be installed using the following command: ``` pip install git+https://github.com/Hum-Works/sentence-transformers.git pip install einops ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer model = SentenceTransformer('Hum-Works/lodestone-base-4096-v1', trust_remote_code=True, revision='v1.0.0') sentences = ["This is an example sentence", "Each sentence is converted"] embeddings = model.encode(sentences) print(embeddings) ``` Note: The model will use the openAI/Triton implementation of FlashAttention if installed. This is more performant than the fallback, torch implementation. Some platforms and GPUs may not be supported by Triton - up to date compatibility can be found on [Triton’s github page](https://github.com/openai/triton#compatibility). ------ ## Background The project aims to train sentence embedding models on very large sentence level datasets using a self-supervised contrastive learning objective. We used the pretrained [`mosaic-bert-base-seqlen-2048`](https://huggingface.co/mosaicml/mosaic-bert-base-seqlen-2048) model and fine-tuned it on a nearly 1.5B sentence pairs dataset. We use a contrastive learning objective: given a sentence from the pair, the model should predict which out of a set of randomly sampled other sentences, was actually paired with it in our dataset. ## Intended uses Our model is intended to be used as a long sentence and paragraph encoder. Given an input text, it outputs a vector containing the semantic information. The sentence vector may be used for information retrieval, clustering, or sentence similarity tasks. ## Training procedure ### Pre-training We use the pretrained [`mosaic-bert-base-seqlen-2048`](https://huggingface.co/mosaicml/mosaic-bert-base-seqlen-2048). Please refer to the model card for more detailed information about the pre-training procedure. ### Fine-tuning We fine-tune the model using a contrastive objective. Formally, we compute the dot product of each possible sentence pairing in the batch. We then apply the cross entropy loss by comparing with true pairs. #### Hyperparameters We trained our model on an ml.g5.4xlarge EC2 instance with 1 NVIDIA A10G Tensor Core GPU. We train the model during 1.4 million steps using a batch size of 16. We use a learning rate warm up of 500. The sequence length during training was limited to 2048 tokens. We used the AdamW optimizer with a 2e-5 learning rate and weight decay of 0.01 (i.e. the default parameter values for SentenceTransformer.fit()). The full training script is accessible in this current repository: `Training.py`. ## Model Architecture By incorporating FlashAttention, [Attention with Linear Biases (ALiBi)](https://arxiv.org/abs/2108.12409), and Gated Linear Units (GLU), this model is able to handle input sequences of 4096, 8x longer than that supported by most comparable sentence embedding models. The model was trained using a sequence length maximum of 2048, but the final model has a maximum sequence length of 4096. This is accomplished by taking advantage of ALiBi’s positional attention extrapolation which has been shown to allow sequence lengths of 2x the initial trained length. ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 4096, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False}) (2): Normalize() ) ``` #### Training data We use the concatenation from multiple datasets to fine-tune our model. The total number of sentence pairs is nearly 1.5 billion sentences. We sampled each dataset given a weighted probability proportional to its relative contribution to the entire dataset. The breakdown of the dataset can be seen below, and the entire dataset can be publicly accessed and uploaded via the `Dataloading.ipynb` located within this repository. \| Dataset \| Paper \| Number of training tuples \| \|--------------------------------------------------------\|:----------------------------------------:\|:--------------------------:\| \| [Reddit comments (2015-2018)](https://github.com/PolyAI-LDN/conversational-datasets/tree/master/reddit) \| [paper](https://arxiv.org/abs/1904.06472) \| 726,484,430 \| \| [S2ORC](https://github.com/allenai/s2orc) Citation pairs (Abstracts) \| [paper](https://aclanthology.org/2020.acl-main.447/) \| 252,102,397 \| \| [Reddit posts](https://huggingface.co/datasets/sentence-transformers/reddit-title-body) (Title, Body) pairs \| - \| 127,445,911 \| \| [Amazon reviews (2018)](https://huggingface.co/datasets/sentence-transformers/embedding-training-data) (Title, Review) pairs \| - \| 87,877,725 \| \| [WikiAnswers](https://github.com/afader/oqa#wikianswers-corpus) Duplicate question pairs \| [paper](https://doi.org/10.1145/2623330.2623677) \| 77,427,422 \| \| [PAQ](https://github.com/facebookresearch/PAQ) (Question, Answer) pairs \| [paper](https://arxiv.org/abs/2102.07033) \| 64,371,441 \| \| [S2ORC](https://github.com/allenai/s2orc) Citation pairs (Titles) \| [paper](https://aclanthology.org/2020.acl-main.447/) \| 52,603,982 \| \| [S2ORC](https://github.com/allenai/s2orc) (Title, Abstract) \| [paper](https://aclanthology.org/2020.acl-main.447/) \| 41,769,185 \| \| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_title_body_jsonl) (Title, Body) pairs \| - \| 25,368,423 \| \| [MS MARCO](https://microsoft.github.io/msmarco/) triplets \| [paper](https://doi.org/10.1145/3404835.3462804) \| 9,144,553 \| \| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_title_best_voted_answer_jsonl) (Title, Most Upvoted Answer) pairs \| - \| 4,784,250 \| \| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_titlebody_best_voted_answer_jsonl) (Title+Body, Most Upvoted Answer) pairs \| - \| 4,551,660 \| \| [GOOAQ: Open Question Answering with Diverse Answer Types](https://github.com/allenai/gooaq) \| [paper](https://arxiv.org/pdf/2104.08727.pdf) \| 3,012,496 \| \| [Amazon QA](https://huggingface.co/datasets/sentence-transformers/embedding-training-data) \| - \| 2,507,114 \| \| [Code Search](https://huggingface.co/datasets/code_search_net) \| - \| 1,375,067 \| \| [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Title, Answer) \| [paper](https://proceedings.neurips.cc/paper/2015/hash/250cf8b51c773f3f8dc8b4be867a9a02-Abstract.html) \| 1,198,260 \| \| [AG News]((Title, Description) pairs of news articles from the AG News dataset) \| - \| 1,157,745 \| \| [COCO](https://cocodataset.org/#home) Image captions \| [paper](https://link.springer.com/chapter/10.1007%2F978-3-319-10602-1_48) \| 828,395\| \| [SPECTER](https://github.com/allenai/specter) citation triplets \| [paper](https://doi.org/10.18653/v1/2020.acl-main.207) \| 684,100 \| \| [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Question, Answer) \| [paper](https://proceedings.neurips.cc/paper/2015/hash/250cf8b51c773f3f8dc8b4be867a9a02-Abstract.html) \| 681,164 \| \| [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Title, Question) \| [paper](https://proceedings.neurips.cc/paper/2015/hash/250cf8b51c773f3f8dc8b4be867a9a02-Abstract.html) \| 659,896 \| \| [CC News](https://huggingface.co/datasets/sentence-transformers/embedding-training-data) (Title, article) pairs \| - \| 614,664 \| \| [NPR](https://huggingface.co/datasets/sentence-transformers/embedding-training-data) (Title, Body) pairs \| - \| 594,384 \| \| [SearchQA](https://huggingface.co/datasets/search_qa) \| [paper](https://arxiv.org/abs/1704.05179) \| 582,261 \| \| [MS Marco](https://microsoft.github.io/msmarco/) (Query, Answer Passage) pairs \| [paper](https://doi.org/10.1145/3404835.3462804) \| 532,751 \| \| [Stack Exchange](https://docs.google.com/spreadsheets/d/1vXJrIg38cEaKjOG5y4I4PQwAQFUmCkohbViJ9zj_Emg/edit#gid=0) (Title, Body) pairs \| - \| 364,000 \| \| [Eli5](https://huggingface.co/datasets/eli5) \| [paper](https://doi.org/10.18653/v1/p19-1346) \| 325,475 \| \| [Flickr 30k](https://shannon.cs.illinois.edu/DenotationGraph/) \| [paper](https://transacl.org/ojs/index.php/tacl/article/view/229/33) \| 317,695 \| \| [CNN & DailyMail](https://huggingface.co/datasets/sentence-transformers/embedding-training-data) (highlight sentences, article) pairs \| - \| 311,971 \| \| [Stack Exchange](https://docs.google.com/spreadsheets/d/1vXJrIg38cEaKjOG5y4I4PQwAQFUmCkohbViJ9zj_Emg/edit#gid=0) Duplicate questions (titles) \| - \| 304,524 \| \| AllNLI ([SNLI](https://nlp.stanford.edu/projects/snli/) and [MultiNLI](https://cims.nyu.edu/~sbowman/multinli/) \| [paper SNLI](https://doi.org/10.18653/v1/d15-1075), [paper MultiNLI](https://doi.org/10.18653/v1/n18-1101) \| 277,230 \| \| [Stack Exchange](https://docs.google.com/spreadsheets/d/1vXJrIg38cEaKjOG5y4I4PQwAQFUmCkohbViJ9zj_Emg/edit#gid=0) Duplicate questions (bodies) \| - \| 250,518 \| \| [Stack Exchange](https://docs.google.com/spreadsheets/d/1vXJrIg38cEaKjOG5y4I4PQwAQFUmCkohbViJ9zj_Emg/edit#gid=0) Duplicate questions (titles+bodies) \| - \| 250,459 \| \| [XSUM](https://huggingface.co/datasets/sentence-transformers/embedding-training-data) (Summary, News Article) pairs \| - \| 226,711 \| \| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_titlebody_best_and_down_voted_answer_jsonl) (Title+Body, Most Upvoted Answer, Most Downvoted Answer) triplets \| - \| 216,454 \| \| [Sentence Compression](https://github.com/google-research-datasets/sentence-compression) \| [paper](https://www.aclweb.org/anthology/D13-1155/) \| 180,000 \| \| [FEVER](https://docs.google.com/spreadsheets/d/1vXJrIg38cEaKjOG5y4I4PQwAQFUmCkohbViJ9zj_Emg/edit#gid=0) training data \| - \| 139,051 \| \| [Wikihow](https://github.com/pvl/wikihow_pairs_dataset) \| [paper](https://arxiv.org/abs/1810.09305) \| 128,542 \| \| [SearchQA](https://huggingface.co/datasets/search_qa) (Question, Top-Snippet) \| [paper](https://arxiv.org/abs/1704.05179) \| 117,384 \| \| [Altlex](https://github.com/chridey/altlex/) \| [paper](https://aclanthology.org/P16-1135.pdf) \| 112,696 \| \| [Quora Question Duplicates](https://quoradata.quora.com/First-Quora-Dataset-Release-Question-Pairs) \| - \| 103,663 \| \| [Quora Question Triplets](https://quoradata.quora.com/First-Quora-Dataset-Release-Question-Pairs) \| - \| 103,663 \| \| [Simple Wikipedia](https://cs.pomona.edu/~dkauchak/simplification/) \| [paper](https://www.aclweb.org/anthology/P11-2117/) \| 102,225 \| \| [Natural Questions (NQ)](https://ai.google.com/research/NaturalQuestions) \| [paper](https://transacl.org/ojs/index.php/tacl/article/view/1455) \| 100,231 \| \| [SQuAD2.0](https://rajpurkar.github.io/SQuAD-explorer/) \| [paper](https://aclanthology.org/P18-2124.pdf) \| 87,599 \| \| [TriviaQA](https://huggingface.co/datasets/trivia_qa) \| - \| 73,346 \| \| Total \| \| 1,492,453,113 \| #### Replication The entire fine-tuning process for this model can be replicated by following the steps outlined in the `Replication.txt` file within this repository. This document explains how to modify the [sentence-transformers](https://www.SBERT.net) library, configure the pre-trained [`mosaic-bert-base-seqlen-2048`](https://huggingface.co/mosaicml/mosaic-bert-base-seqlen-2048) model, load all of the training data, and execute the training script. #### Limitations Due to technical constraints (e.g. limited GPU memory capacity), this model was trained with a smaller batch size of 16, making it so that each step during training was less well-informed than it would have been on a higher performance system. This smaller than ideal hyperparameter value will generally cause the model to be more likely to get stuck in a local minimum and for the parameter configuration to take a longer time to converge to the optimum. In order to counteract this potential risk, we trained the model for a larger number of steps than many of its contemporaries to ensure a greater chance of achieving strong performance, but this is an area which could be improved if further fine-tuning was performed. It is also worth noting that, while this model is able to handle longer input sequences of up to 4096 word pieces, the training dataset used consists of sentence and paragraph pairs and triplets which do not necessarily reach that maximum sequence length. Since the data was not tailored specifically for this larger input size, further fine-tuning may be required to ensure highly accurate embeddings for longer texts of that magnitude. Finally, as stated on https://huggingface.co/datasets/sentence-transformers/reddit-title-body, an additional reminder and warning regarding the Reddit posts data is that one should "Be aware that this dataset is not filtered for biases, hate-speech, spam, racial slurs etc. It depicts the content as it is posted on Reddit." Thus, while we believe this has not induced any pathological behaviors in the model's performance due to its relatively low prevalence of records in the whole dataset of nearly 1.5B sentence pairs and the fact that this model was trained to produce semantic embeddings rather than generative text outputs, it is always important to be aware of vulnerabilities to bias.
Mihaiii/Ivysaur	Mihaiii	2024-04-30T07:10:12Z	4,812	0	sentence-transformers	[ "sentence-transformers", "onnx", "safetensors", "bert", "feature-extraction", "sentence-similarity", "gte", "mteb", "dataset:Mihaiii/qa-assistant", "base_model:TaylorAI/gte-tiny", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "text-embeddings-inference", "region:us" ]	sentence-similarity	2024-04-27T10:10:39Z	--- base_model: TaylorAI/gte-tiny license: mit library_name: sentence-transformers pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - gte - mteb datasets: - Mihaiii/qa-assistant model-index: - name: Ivysaur results: - task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (en) config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 72.1044776119403 - type: ap value: 35.09105788324913 - type: f1 value: 66.26967715703572 - task: type: Classification dataset: type: mteb/amazon_polarity name: MTEB AmazonPolarityClassification config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 86.686075 - type: ap value: 81.92716581685914 - type: f1 value: 86.65902299160209 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (en) config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 42.698 - type: f1 value: 42.287785312461885 - task: type: Retrieval dataset: type: mteb/arguana name: MTEB ArguAna config: default split: test revision: c22ab2a51041ffd869aaddef7af8d8215647e41a metrics: - type: map_at_1 value: 30.441000000000003 - type: map_at_10 value: 46.951 - type: map_at_100 value: 47.788000000000004 - type: map_at_1000 value: 47.794 - type: map_at_20 value: 47.621 - type: map_at_3 value: 42.295 - type: map_at_5 value: 45.126 - type: mrr_at_1 value: 31.65 - type: mrr_at_10 value: 47.394999999999996 - type: mrr_at_100 value: 48.238 - type: mrr_at_1000 value: 48.245 - type: mrr_at_20 value: 48.069 - type: mrr_at_3 value: 42.852000000000004 - type: mrr_at_5 value: 45.58 - type: ndcg_at_1 value: 30.441000000000003 - type: ndcg_at_10 value: 55.783 - type: ndcg_at_100 value: 59.227 - type: ndcg_at_1000 value: 59.376 - type: ndcg_at_20 value: 58.18 - type: ndcg_at_3 value: 46.291 - type: ndcg_at_5 value: 51.405 - type: precision_at_1 value: 30.441000000000003 - type: precision_at_10 value: 8.378 - type: precision_at_100 value: 0.985 - type: precision_at_1000 value: 0.1 - type: precision_at_20 value: 4.659 - type: precision_at_3 value: 19.298000000000002 - type: precision_at_5 value: 14.068 - type: recall_at_1 value: 30.441000000000003 - type: recall_at_10 value: 83.784 - type: recall_at_100 value: 98.506 - type: recall_at_1000 value: 99.644 - type: recall_at_20 value: 93.172 - type: recall_at_3 value: 57.894999999999996 - type: recall_at_5 value: 70.341 - task: type: Clustering dataset: type: mteb/arxiv-clustering-p2p name: MTEB ArxivClusteringP2P config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics: - type: v_measure value: 46.39249132731755 - type: v_measures value: [0.462627943488718, 0.4670198046702645, 0.4799590043041496, 0.4769331119808875, 0.4676232129237324, 0.4776548131275231, 0.4670074065859379, 0.4796639656537766, 0.4618481699630812, 0.4663292111226376, 0.5293353429909269, 0.5398570175481274, 0.5399074383870329, 0.5363158656403061, 0.5377616813701683, 0.5375897664056992, 0.5391811647339062, 0.5408906197352437, 0.5330346186210795, 0.5333610235325786, 0.5043600016005657, 0.2861923615995782, 0.42134506758129586, 0.4019628602326345, 0.345945272411779, 0.2605048863591227, 0.28469463800386774, 0.23235682032046123, 0.30618655352256796, 1.0, 0.2642226670507902, 0.462627943488718, 0.4670198046702645, 0.4799590043041496, 0.4769331119808875, 0.4676232129237324, 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mrr_at_5 value: 36.36816666666667 - type: ndcg_at_1 value: 29.478749999999998 - type: ndcg_at_10 value: 38.2035 - type: ndcg_at_100 value: 43.301083333333324 - type: ndcg_at_1000 value: 45.758666666666656 - type: ndcg_at_20 value: 40.15116666666667 - type: ndcg_at_3 value: 33.86033333333334 - type: ndcg_at_5 value: 35.81266666666666 - type: precision_at_1 value: 29.478749999999998 - type: precision_at_10 value: 6.642833333333334 - type: precision_at_100 value: 1.08425 - type: precision_at_1000 value: 0.14850000000000002 - type: precision_at_20 value: 3.948083333333334 - type: precision_at_3 value: 15.511 - type: precision_at_5 value: 10.929833333333333 - type: recall_at_1 value: 24.78675 - type: recall_at_10 value: 48.9305 - type: recall_at_100 value: 71.49416666666666 - type: recall_at_1000 value: 88.54375 - type: recall_at_20 value: 56.06475 - type: recall_at_3 value: 36.66891666666666 - type: recall_at_5 value: 41.790499999999994 - task: type: Retrieval dataset: type: mteb/cqadupstack-android name: MTEB CQADupstackAndroidRetrieval config: default split: test revision: f46a197baaae43b4f621051089b82a364682dfeb metrics: - type: map_at_1 value: 30.793 - type: map_at_10 value: 42.254000000000005 - type: map_at_100 value: 43.569 - type: map_at_1000 value: 43.714999999999996 - type: map_at_20 value: 42.994 - type: map_at_3 value: 39.007999999999996 - type: map_at_5 value: 40.488 - type: mrr_at_1 value: 38.34 - type: mrr_at_10 value: 48.274 - type: mrr_at_100 value: 48.946 - type: mrr_at_1000 value: 49.001 - type: mrr_at_20 value: 48.701 - type: mrr_at_3 value: 45.756 - type: mrr_at_5 value: 47.036 - type: ndcg_at_1 value: 38.34 - type: ndcg_at_10 value: 48.622 - type: ndcg_at_100 value: 53.288999999999994 - type: ndcg_at_1000 value: 55.614 - type: ndcg_at_20 value: 50.495000000000005 - type: ndcg_at_3 value: 43.852999999999994 - type: ndcg_at_5 value: 45.442 - type: precision_at_1 value: 38.34 - type: precision_at_10 value: 9.413 - type: precision_at_100 value: 1.4749999999999999 - type: precision_at_1000 value: 0.19499999999999998 - type: precision_at_20 value: 5.494000000000001 - type: precision_at_3 value: 20.935000000000002 - type: precision_at_5 value: 14.735000000000001 - type: recall_at_1 value: 30.793 - type: recall_at_10 value: 60.455000000000005 - type: recall_at_100 value: 80.061 - type: recall_at_1000 value: 95.322 - type: recall_at_20 value: 67.27 - type: recall_at_3 value: 46.296 - type: recall_at_5 value: 51.139 - task: type: Retrieval dataset: type: mteb/cqadupstack-english name: MTEB CQADupstackEnglishRetrieval config: default split: test revision: ad9991cb51e31e31e430383c75ffb2885547b5f0 metrics: - type: map_at_1 value: 27.93 - type: map_at_10 value: 36.085 - type: map_at_100 value: 37.192 - type: map_at_1000 value: 37.324 - type: map_at_20 value: 36.614999999999995 - type: map_at_3 value: 33.452 - type: map_at_5 value: 35.088 - type: mrr_at_1 value: 34.777 - type: mrr_at_10 value: 41.865 - type: mrr_at_100 value: 42.518 - type: mrr_at_1000 value: 42.571 - type: mrr_at_20 value: 42.219 - type: mrr_at_3 value: 39.628 - type: mrr_at_5 value: 41.038999999999994 - type: ndcg_at_1 value: 34.777 - type: ndcg_at_10 value: 41.095 - type: ndcg_at_100 value: 45.286 - type: ndcg_at_1000 value: 47.656 - type: ndcg_at_20 value: 42.472 - type: ndcg_at_3 value: 37.349 - type: ndcg_at_5 value: 39.318 - type: precision_at_1 value: 34.777 - type: precision_at_10 value: 7.617999999999999 - type: precision_at_100 value: 1.242 - type: precision_at_1000 value: 0.173 - type: precision_at_20 value: 4.481 - type: precision_at_3 value: 17.771 - type: precision_at_5 value: 12.687999999999999 - type: recall_at_1 value: 27.93 - type: recall_at_10 value: 49.464000000000006 - type: recall_at_100 value: 67.64099999999999 - type: recall_at_1000 value: 83.066 - type: recall_at_20 value: 54.452999999999996 - type: recall_at_3 value: 38.157000000000004 - type: recall_at_5 value: 43.829 - task: type: Retrieval dataset: type: mteb/cqadupstack-gaming name: MTEB CQADupstackGamingRetrieval config: default split: test revision: 4885aa143210c98657558c04aaf3dc47cfb54340 metrics: - type: map_at_1 value: 37.332 - type: map_at_10 value: 49.146 - type: map_at_100 value: 50.222 - type: map_at_1000 value: 50.281 - type: map_at_20 value: 49.802 - type: map_at_3 value: 46.264 - type: map_at_5 value: 47.912 - type: mrr_at_1 value: 43.009 - type: mrr_at_10 value: 52.586999999999996 - type: mrr_at_100 value: 53.323 - type: mrr_at_1000 value: 53.352999999999994 - type: mrr_at_20 value: 53.04299999999999 - type: mrr_at_3 value: 50.261 - type: mrr_at_5 value: 51.615 - type: ndcg_at_1 value: 43.009 - type: ndcg_at_10 value: 54.652 - type: ndcg_at_100 value: 58.918000000000006 - type: ndcg_at_1000 value: 60.172000000000004 - type: ndcg_at_20 value: 56.554 - type: ndcg_at_3 value: 49.757 - type: ndcg_at_5 value: 52.169 - type: precision_at_1 value: 43.009 - type: precision_at_10 value: 8.715 - type: precision_at_100 value: 1.1780000000000002 - type: precision_at_1000 value: 0.133 - type: precision_at_20 value: 4.931 - type: precision_at_3 value: 22.153 - type: precision_at_5 value: 15.146999999999998 - type: recall_at_1 value: 37.332 - type: recall_at_10 value: 67.55600000000001 - type: recall_at_100 value: 85.885 - type: recall_at_1000 value: 94.87400000000001 - type: recall_at_20 value: 74.568 - type: recall_at_3 value: 54.419 - type: recall_at_5 value: 60.288 - task: type: Retrieval dataset: type: mteb/cqadupstack-gis name: MTEB CQADupstackGisRetrieval config: default split: test revision: 5003b3064772da1887988e05400cf3806fe491f2 metrics: - type: map_at_1 value: 24.09 - type: map_at_10 value: 32.608 - type: map_at_100 value: 33.571 - type: map_at_1000 value: 33.668 - type: map_at_20 value: 33.181 - type: map_at_3 value: 30.091 - type: map_at_5 value: 31.518 - type: mrr_at_1 value: 25.763 - type: mrr_at_10 value: 34.25 - type: mrr_at_100 value: 35.134 - type: mrr_at_1000 value: 35.207 - type: mrr_at_20 value: 34.78 - type: mrr_at_3 value: 31.807999999999996 - type: mrr_at_5 value: 33.198 - type: ndcg_at_1 value: 25.763 - type: ndcg_at_10 value: 37.305 - type: ndcg_at_100 value: 42.114000000000004 - type: ndcg_at_1000 value: 44.467 - type: ndcg_at_20 value: 39.272 - type: ndcg_at_3 value: 32.405 - type: ndcg_at_5 value: 34.775 - type: precision_at_1 value: 25.763 - type: precision_at_10 value: 5.729 - type: precision_at_100 value: 0.853 - type: precision_at_1000 value: 0.109 - type: precision_at_20 value: 3.3329999999999997 - type: precision_at_3 value: 13.71 - type: precision_at_5 value: 9.65 - type: recall_at_1 value: 24.09 - type: recall_at_10 value: 50.161 - type: recall_at_100 value: 72.419 - type: recall_at_1000 value: 89.983 - type: recall_at_20 value: 57.53 - type: recall_at_3 value: 36.961 - type: recall_at_5 value: 42.568 - task: type: Retrieval dataset: type: mteb/cqadupstack-mathematica name: MTEB CQADupstackMathematicaRetrieval config: default split: test revision: 90fceea13679c63fe563ded68f3b6f06e50061de metrics: - type: map_at_1 value: 16.333000000000002 - type: map_at_10 value: 23.352999999999998 - type: map_at_100 value: 24.618000000000002 - type: map_at_1000 value: 24.743000000000002 - type: map_at_20 value: 24.117 - type: map_at_3 value: 21.013 - type: map_at_5 value: 22.259 - type: mrr_at_1 value: 20.398 - type: mrr_at_10 value: 28.28 - type: mrr_at_100 value: 29.307 - type: mrr_at_1000 value: 29.381 - type: mrr_at_20 value: 28.955 - type: mrr_at_3 value: 25.933 - type: mrr_at_5 value: 27.114 - type: ndcg_at_1 value: 20.398 - type: ndcg_at_10 value: 28.359 - type: ndcg_at_100 value: 34.178999999999995 - type: ndcg_at_1000 value: 37.112 - type: ndcg_at_20 value: 30.982 - type: ndcg_at_3 value: 24.104999999999997 - type: ndcg_at_5 value: 25.877 - type: precision_at_1 value: 20.398 - type: precision_at_10 value: 5.2490000000000006 - type: precision_at_100 value: 0.927 - type: precision_at_1000 value: 0.131 - type: precision_at_20 value: 3.3520000000000003 - type: precision_at_3 value: 11.733 - type: precision_at_5 value: 8.433 - type: recall_at_1 value: 16.333000000000002 - type: recall_at_10 value: 39.082 - type: recall_at_100 value: 64.269 - type: recall_at_1000 value: 85.103 - type: recall_at_20 value: 48.625 - type: recall_at_3 value: 26.740000000000002 - type: recall_at_5 value: 31.519000000000002 - task: type: Retrieval dataset: type: mteb/cqadupstack-physics name: MTEB CQADupstackPhysicsRetrieval config: default split: test revision: 79531abbd1fb92d06c6d6315a0cbbbf5bb247ea4 metrics: - type: map_at_1 value: 26.857999999999997 - type: map_at_10 value: 36.258 - type: map_at_100 value: 37.556 - type: map_at_1000 value: 37.669999999999995 - type: map_at_20 value: 36.937 - type: map_at_3 value: 33.306000000000004 - type: map_at_5 value: 35.004999999999995 - type: mrr_at_1 value: 33.397 - type: mrr_at_10 value: 42.089 - type: mrr_at_100 value: 42.864999999999995 - type: mrr_at_1000 value: 42.915 - type: mrr_at_20 value: 42.510999999999996 - type: mrr_at_3 value: 39.413 - type: mrr_at_5 value: 40.905 - type: ndcg_at_1 value: 33.397 - type: ndcg_at_10 value: 42.062 - type: ndcg_at_100 value: 47.620000000000005 - type: ndcg_at_1000 value: 49.816 - type: ndcg_at_20 value: 44.096999999999994 - type: ndcg_at_3 value: 37.165 - type: ndcg_at_5 value: 39.493 - type: precision_at_1 value: 33.397 - type: precision_at_10 value: 7.5649999999999995 - type: precision_at_100 value: 1.224 - type: precision_at_1000 value: 0.16 - type: precision_at_20 value: 4.495 - type: precision_at_3 value: 17.613 - type: precision_at_5 value: 12.589 - type: recall_at_1 value: 26.857999999999997 - type: recall_at_10 value: 53.900000000000006 - type: recall_at_100 value: 77.595 - type: recall_at_1000 value: 92.116 - type: recall_at_20 value: 60.962 - type: recall_at_3 value: 39.799 - type: recall_at_5 value: 45.961 - task: type: Retrieval dataset: type: mteb/cqadupstack-programmers name: MTEB CQADupstackProgrammersRetrieval config: default split: test revision: 6184bc1440d2dbc7612be22b50686b8826d22b32 metrics: - type: map_at_1 value: 24.131 - type: map_at_10 value: 33.016 - type: map_at_100 value: 34.263 - type: map_at_1000 value: 34.39 - type: map_at_20 value: 33.703 - type: map_at_3 value: 30.055 - type: map_at_5 value: 31.651 - type: mrr_at_1 value: 30.593999999999998 - type: mrr_at_10 value: 38.786 - type: mrr_at_100 value: 39.674 - type: mrr_at_1000 value: 39.739000000000004 - type: mrr_at_20 value: 39.322 - type: mrr_at_3 value: 36.32 - type: mrr_at_5 value: 37.787 - type: ndcg_at_1 value: 30.593999999999998 - type: ndcg_at_10 value: 38.606 - type: ndcg_at_100 value: 44.116 - type: ndcg_at_1000 value: 46.772999999999996 - type: ndcg_at_20 value: 40.775 - type: ndcg_at_3 value: 33.854 - type: ndcg_at_5 value: 35.957 - type: precision_at_1 value: 30.593999999999998 - type: precision_at_10 value: 7.112 - type: precision_at_100 value: 1.154 - type: precision_at_1000 value: 0.155 - type: precision_at_20 value: 4.2410000000000005 - type: precision_at_3 value: 16.323999999999998 - type: precision_at_5 value: 11.644 - type: recall_at_1 value: 24.131 - type: recall_at_10 value: 49.767 - type: recall_at_100 value: 73.57000000000001 - type: recall_at_1000 value: 91.842 - type: recall_at_20 value: 57.498000000000005 - type: recall_at_3 value: 35.888 - type: recall_at_5 value: 41.801 - task: type: Retrieval dataset: type: mteb/cqadupstack-stats name: MTEB CQADupstackStatsRetrieval config: default split: test revision: 65ac3a16b8e91f9cee4c9828cc7c335575432a2a metrics: - type: map_at_1 value: 23.075000000000003 - type: map_at_10 value: 29.584 - type: map_at_100 value: 30.4 - type: map_at_1000 value: 30.501 - type: map_at_20 value: 30.051 - type: map_at_3 value: 27.561000000000003 - type: map_at_5 value: 28.603 - type: mrr_at_1 value: 26.227 - type: mrr_at_10 value: 32.647 - type: mrr_at_100 value: 33.391999999999996 - type: mrr_at_1000 value: 33.469 - type: mrr_at_20 value: 33.053 - type: mrr_at_3 value: 30.776999999999997 - type: mrr_at_5 value: 31.828 - type: ndcg_at_1 value: 26.227 - type: ndcg_at_10 value: 33.582 - type: ndcg_at_100 value: 37.814 - type: ndcg_at_1000 value: 40.444 - type: ndcg_at_20 value: 35.163 - type: ndcg_at_3 value: 29.874000000000002 - type: ndcg_at_5 value: 31.53 - type: precision_at_1 value: 26.227 - type: precision_at_10 value: 5.244999999999999 - type: precision_at_100 value: 0.788 - type: precision_at_1000 value: 0.11100000000000002 - type: precision_at_20 value: 3.006 - type: precision_at_3 value: 12.73 - type: precision_at_5 value: 8.741999999999999 - type: recall_at_1 value: 23.075000000000003 - type: recall_at_10 value: 42.894 - type: recall_at_100 value: 62.721000000000004 - type: recall_at_1000 value: 81.858 - type: recall_at_20 value: 48.842 - type: recall_at_3 value: 32.783 - type: recall_at_5 value: 36.949 - task: type: Retrieval dataset: type: mteb/cqadupstack-tex name: MTEB CQADupstackTexRetrieval config: default split: test revision: 46989137a86843e03a6195de44b09deda022eec7 metrics: - type: map_at_1 value: 17.028 - type: map_at_10 value: 23.377 - type: map_at_100 value: 24.399 - type: map_at_1000 value: 24.524 - type: map_at_20 value: 23.863 - type: map_at_3 value: 21.274 - type: map_at_5 value: 22.431 - type: mrr_at_1 value: 20.578 - type: mrr_at_10 value: 27.009 - type: mrr_at_100 value: 27.889999999999997 - type: mrr_at_1000 value: 27.969 - type: mrr_at_20 value: 27.46 - type: mrr_at_3 value: 24.959999999999997 - type: mrr_at_5 value: 26.113999999999997 - type: ndcg_at_1 value: 20.578 - type: ndcg_at_10 value: 27.522999999999996 - type: ndcg_at_100 value: 32.601 - type: ndcg_at_1000 value: 35.636 - type: ndcg_at_20 value: 29.132 - type: ndcg_at_3 value: 23.771 - type: ndcg_at_5 value: 25.539 - type: precision_at_1 value: 20.578 - type: precision_at_10 value: 4.962 - type: precision_at_100 value: 0.8880000000000001 - type: precision_at_1000 value: 0.132 - type: precision_at_20 value: 2.959 - type: precision_at_3 value: 11.068999999999999 - type: precision_at_5 value: 8.052 - type: recall_at_1 value: 17.028 - type: recall_at_10 value: 36.266 - type: recall_at_100 value: 59.556 - type: recall_at_1000 value: 81.416 - type: recall_at_20 value: 42.303000000000004 - type: recall_at_3 value: 25.858999999999998 - type: recall_at_5 value: 30.422 - task: type: Retrieval dataset: type: mteb/cqadupstack-unix name: MTEB CQADupstackUnixRetrieval config: default split: test revision: 6c6430d3a6d36f8d2a829195bc5dc94d7e063e53 metrics: - type: map_at_1 value: 25.863000000000003 - type: map_at_10 value: 33.586 - type: map_at_100 value: 34.682 - type: map_at_1000 value: 34.791 - type: map_at_20 value: 34.182 - type: map_at_3 value: 31.044 - type: map_at_5 value: 32.507000000000005 - type: mrr_at_1 value: 30.131000000000004 - type: mrr_at_10 value: 37.518 - type: mrr_at_100 value: 38.355 - type: mrr_at_1000 value: 38.425 - type: mrr_at_20 value: 37.961 - type: mrr_at_3 value: 35.059000000000005 - type: mrr_at_5 value: 36.528 - type: ndcg_at_1 value: 30.131000000000004 - type: ndcg_at_10 value: 38.387 - type: ndcg_at_100 value: 43.617 - type: ndcg_at_1000 value: 46.038000000000004 - type: ndcg_at_20 value: 40.261 - type: ndcg_at_3 value: 33.722 - type: ndcg_at_5 value: 36.013 - type: precision_at_1 value: 30.131000000000004 - type: precision_at_10 value: 6.297 - type: precision_at_100 value: 1.008 - type: precision_at_1000 value: 0.132 - type: precision_at_20 value: 3.689 - type: precision_at_3 value: 15.049999999999999 - type: precision_at_5 value: 10.634 - type: recall_at_1 value: 25.863000000000003 - type: recall_at_10 value: 49.101 - type: recall_at_100 value: 72.286 - type: recall_at_1000 value: 89.14 - type: recall_at_20 value: 55.742999999999995 - type: recall_at_3 value: 36.513 - type: recall_at_5 value: 42.204 - task: type: Retrieval dataset: type: mteb/cqadupstack-webmasters name: MTEB CQADupstackWebmastersRetrieval config: default split: test revision: 160c094312a0e1facb97e55eeddb698c0abe3571 metrics: - type: map_at_1 value: 24.747 - type: map_at_10 value: 32.067 - type: map_at_100 value: 33.739999999999995 - type: map_at_1000 value: 33.952 - type: map_at_20 value: 32.927 - type: map_at_3 value: 29.736 - type: map_at_5 value: 30.996000000000002 - type: mrr_at_1 value: 29.644 - type: mrr_at_10 value: 36.683 - type: mrr_at_100 value: 37.808 - type: mrr_at_1000 value: 37.858999999999995 - type: mrr_at_20 value: 37.326 - type: mrr_at_3 value: 34.42 - type: mrr_at_5 value: 35.626000000000005 - type: ndcg_at_1 value: 29.644 - type: ndcg_at_10 value: 36.989 - type: ndcg_at_100 value: 43.589 - type: ndcg_at_1000 value: 46.133 - type: ndcg_at_20 value: 39.403 - type: ndcg_at_3 value: 33.273 - type: ndcg_at_5 value: 34.853 - type: precision_at_1 value: 29.644 - type: precision_at_10 value: 6.8180000000000005 - type: precision_at_100 value: 1.4529999999999998 - type: precision_at_1000 value: 0.23500000000000001 - type: precision_at_20 value: 4.457 - type: precision_at_3 value: 15.152 - type: precision_at_5 value: 10.711 - type: recall_at_1 value: 24.747 - type: recall_at_10 value: 45.714 - type: recall_at_100 value: 75.212 - type: recall_at_1000 value: 90.884 - type: recall_at_20 value: 54.777 - type: recall_at_3 value: 34.821999999999996 - type: recall_at_5 value: 39.278999999999996 - task: type: Retrieval dataset: type: mteb/cqadupstack-wordpress name: MTEB CQADupstackWordpressRetrieval config: default split: test revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4 metrics: - type: map_at_1 value: 19.261 - type: map_at_10 value: 26.873 - type: map_at_100 value: 27.938000000000002 - type: map_at_1000 value: 28.060000000000002 - type: map_at_20 value: 27.456000000000003 - type: map_at_3 value: 24.834 - type: map_at_5 value: 25.793 - type: mrr_at_1 value: 20.887 - type: mrr_at_10 value: 28.634999999999998 - type: mrr_at_100 value: 29.609 - type: mrr_at_1000 value: 29.698999999999998 - type: mrr_at_20 value: 29.173 - type: mrr_at_3 value: 26.741 - type: mrr_at_5 value: 27.628000000000004 - type: ndcg_at_1 value: 20.887 - type: ndcg_at_10 value: 31.261 - type: ndcg_at_100 value: 36.471 - type: ndcg_at_1000 value: 39.245000000000005 - type: ndcg_at_20 value: 33.209 - type: ndcg_at_3 value: 27.195999999999998 - type: ndcg_at_5 value: 28.786 - type: precision_at_1 value: 20.887 - type: precision_at_10 value: 4.9910000000000005 - type: precision_at_100 value: 0.8210000000000001 - type: precision_at_1000 value: 0.116 - type: precision_at_20 value: 2.939 - type: precision_at_3 value: 11.892 - type: precision_at_5 value: 8.133 - type: recall_at_1 value: 19.261 - type: recall_at_10 value: 42.806 - type: recall_at_100 value: 66.715 - type: recall_at_1000 value: 86.921 - type: recall_at_20 value: 50.205999999999996 - type: recall_at_3 value: 31.790000000000003 - type: recall_at_5 value: 35.527 - task: type: Retrieval dataset: type: mteb/climate-fever name: MTEB ClimateFEVER config: default split: test revision: 47f2ac6acb640fc46020b02a5b59fdda04d39380 metrics: - type: map_at_1 value: 9.009 - type: map_at_10 value: 14.629 - type: map_at_100 value: 16.092000000000002 - type: map_at_1000 value: 16.267 - type: map_at_20 value: 15.384999999999998 - type: map_at_3 value: 12.280000000000001 - type: map_at_5 value: 13.442000000000002 - type: mrr_at_1 value: 20.0 - type: mrr_at_10 value: 29.298000000000002 - type: mrr_at_100 value: 30.375999999999998 - type: mrr_at_1000 value: 30.436999999999998 - type: mrr_at_20 value: 29.956 - type: mrr_at_3 value: 26.362999999999996 - type: mrr_at_5 value: 28.021 - type: ndcg_at_1 value: 20.0 - type: ndcg_at_10 value: 21.234 - type: ndcg_at_100 value: 27.687 - type: ndcg_at_1000 value: 31.325999999999997 - type: ndcg_at_20 value: 23.631 - type: ndcg_at_3 value: 17.101 - type: ndcg_at_5 value: 18.501 - type: precision_at_1 value: 20.0 - type: precision_at_10 value: 6.651 - type: precision_at_100 value: 1.347 - type: precision_at_1000 value: 0.201 - type: precision_at_20 value: 4.316 - type: precision_at_3 value: 12.53 - type: precision_at_5 value: 9.707 - type: recall_at_1 value: 9.009 - type: recall_at_10 value: 25.824 - type: recall_at_100 value: 48.535000000000004 - type: recall_at_1000 value: 69.44399999999999 - type: recall_at_20 value: 32.78 - type: recall_at_3 value: 15.693999999999999 - type: recall_at_5 value: 19.59 - task: type: Retrieval dataset: type: mteb/dbpedia name: MTEB DBPedia config: default split: test revision: c0f706b76e590d620bd6618b3ca8efdd34e2d659 metrics: - type: map_at_1 value: 7.454 - type: map_at_10 value: 15.675 - type: map_at_100 value: 21.335 - type: map_at_1000 value: 22.639 - type: map_at_20 value: 17.822 - type: map_at_3 value: 11.609 - type: map_at_5 value: 13.342 - type: mrr_at_1 value: 56.25 - type: mrr_at_10 value: 65.30799999999999 - type: mrr_at_100 value: 65.90599999999999 - type: mrr_at_1000 value: 65.92099999999999 - type: mrr_at_20 value: 65.74600000000001 - type: mrr_at_3 value: 63.333 - 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accuracy value: 45.44499999999999 - type: f1 value: 40.581418056070994 - task: type: Retrieval dataset: type: mteb/fever name: MTEB FEVER config: default split: test revision: bea83ef9e8fb933d90a2f1d5515737465d613e12 metrics: - type: map_at_1 value: 46.339000000000006 - type: map_at_10 value: 57.87 - type: map_at_100 value: 58.447 - type: map_at_1000 value: 58.474000000000004 - type: map_at_20 value: 58.241 - type: map_at_3 value: 55.336 - type: map_at_5 value: 56.879000000000005 - type: mrr_at_1 value: 49.91 - type: mrr_at_10 value: 61.55199999999999 - type: mrr_at_100 value: 62.07 - type: mrr_at_1000 value: 62.086 - type: mrr_at_20 value: 61.899 - type: mrr_at_3 value: 59.108000000000004 - type: mrr_at_5 value: 60.622 - type: ndcg_at_1 value: 49.91 - type: ndcg_at_10 value: 63.970000000000006 - type: ndcg_at_100 value: 66.625 - type: ndcg_at_1000 value: 67.221 - type: ndcg_at_20 value: 65.261 - type: ndcg_at_3 value: 59.059 - type: ndcg_at_5 value: 61.68900000000001 - type: 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84.84830422602371 - type: euclidean_ap value: 69.97192936786296 - type: euclidean_f1 value: 66.53649011471808 - type: euclidean_precision value: 61.898274296094456 - type: euclidean_recall value: 71.92612137203166 - type: manhattan_accuracy value: 84.75889610776659 - type: manhattan_ap value: 69.75691180376053 - type: manhattan_f1 value: 66.32788868723533 - type: manhattan_precision value: 61.2513966480447 - type: manhattan_recall value: 72.32189973614776 - type: max_accuracy value: 84.84830422602371 - type: max_ap value: 69.97192936786296 - type: max_f1 value: 66.53649011471808 - task: type: PairClassification dataset: type: mteb/twitterurlcorpus-pairclassification name: MTEB TwitterURLCorpus config: default split: test revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf metrics: - type: cos_sim_accuracy value: 88.43287926417511 - type: cos_sim_ap value: 85.07378179191598 - type: cos_sim_f1 value: 77.50230244980658 - type: cos_sim_precision value: 74.30246521155613 - type: cos_sim_recall value: 80.99014474899907 - type: dot_accuracy value: 86.946481934257 - type: dot_ap value: 80.90485630835825 - type: dot_f1 value: 74.43342263413221 - type: dot_precision value: 70.24736914035807 - type: dot_recall value: 79.1499846011703 - type: euclidean_accuracy value: 88.49303372530757 - type: euclidean_ap value: 85.08920672765427 - type: euclidean_f1 value: 77.53514807059526 - type: euclidean_precision value: 75.3707473102646 - type: euclidean_recall value: 79.82753310748383 - type: manhattan_accuracy value: 88.47168859393798 - type: manhattan_ap value: 85.01816084029292 - type: manhattan_f1 value: 77.36513181524315 - type: manhattan_precision value: 72.5057223643463 - type: manhattan_recall value: 82.9226978749615 - type: max_accuracy value: 88.49303372530757 - type: max_ap value: 85.08920672765427 - type: max_f1 value: 77.53514807059526 --- # Ivysaur This is a fine-tune of [gte-tiny](https://huggingface.co/TaylorAI/gte-tiny) using [qa-assistant](https://huggingface.co/datasets/Mihaiii/qa-assistant). ## Intended purpose <span style="color:blue">This model is designed for use in semantic-autocomplete ([click here for demo](https://mihaiii.github.io/semantic-autocomplete/)).</span> ## Usage (Sentence-Transformers) (same as [gte-tiny](https://huggingface.co/TaylorAI/gte-tiny)) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('Mihaiii/Ivysaur') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) (same as [gte-tiny](https://huggingface.co/TaylorAI/gte-tiny)) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('Mihaiii/Ivysaur') model = AutoModel.from_pretrained('Mihaiii/Ivysaur') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(**encoded_input) # Perform pooling. In this case, mean pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ### Limitation (same as [gte-small](https://huggingface.co/thenlper/gte-small)) This model exclusively caters to English texts, and any lengthy texts will be truncated to a maximum of 512 tokens.
infgrad/stella-large-zh-v2	infgrad	2024-04-06T02:48:44Z	4,811	30	sentence-transformers	[ "sentence-transformers", "pytorch", "bert", "feature-extraction", "sentence-similarity", "mteb", "arxiv:1612.00796", "model-index", "endpoints_compatible", "region:us" ]	sentence-similarity	2023-10-13T04:41:14Z	--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - mteb model-index: - name: stella-large-zh-v2 results: - task: type: STS dataset: type: C-MTEB/AFQMC name: MTEB AFQMC config: default split: validation revision: None metrics: - type: cos_sim_pearson value: 47.34436411023816 - type: cos_sim_spearman value: 49.947084806624545 - type: euclidean_pearson value: 48.128834319004824 - type: euclidean_spearman value: 49.947064694876815 - type: manhattan_pearson value: 48.083561270166484 - type: manhattan_spearman value: 49.90207128584442 - task: type: STS dataset: type: C-MTEB/ATEC name: MTEB ATEC config: default split: test revision: None metrics: - type: cos_sim_pearson value: 50.97998570817664 - type: cos_sim_spearman value: 53.11852606980578 - type: euclidean_pearson value: 55.12610520736481 - type: euclidean_spearman value: 53.11852832108405 - type: manhattan_pearson value: 55.10299116717361 - type: manhattan_spearman value: 53.11304196536268 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (zh) config: zh split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 40.81799999999999 - type: f1 value: 39.022194031906444 - task: type: STS dataset: type: C-MTEB/BQ name: MTEB BQ config: default split: test revision: None metrics: - type: cos_sim_pearson value: 62.83544115057508 - type: cos_sim_spearman value: 65.53509404838948 - type: euclidean_pearson value: 64.08198144850084 - type: euclidean_spearman value: 65.53509404760305 - type: manhattan_pearson value: 64.08808420747272 - type: manhattan_spearman value: 65.54907862648346 - task: type: Clustering dataset: type: C-MTEB/CLSClusteringP2P name: MTEB CLSClusteringP2P config: default split: test revision: None metrics: - type: v_measure value: 39.95428546140963 - task: type: Clustering dataset: type: C-MTEB/CLSClusteringS2S name: MTEB CLSClusteringS2S config: default split: test revision: None metrics: - type: v_measure value: 38.18454393512963 - task: type: Reranking dataset: type: C-MTEB/CMedQAv1-reranking name: MTEB CMedQAv1 config: default split: test revision: None metrics: - type: map value: 85.4453602559479 - type: mrr value: 88.1418253968254 - task: type: Reranking dataset: type: C-MTEB/CMedQAv2-reranking name: MTEB CMedQAv2 config: default split: test revision: None metrics: - type: map value: 85.82731720256984 - type: mrr value: 88.53230158730159 - task: type: Retrieval dataset: type: C-MTEB/CmedqaRetrieval name: MTEB CmedqaRetrieval config: default split: dev revision: None metrics: - type: map_at_1 value: 24.459 - type: map_at_10 value: 36.274 - type: map_at_100 value: 38.168 - type: map_at_1000 value: 38.292 - type: map_at_3 value: 32.356 - type: map_at_5 value: 34.499 - type: mrr_at_1 value: 37.584 - type: mrr_at_10 value: 45.323 - type: mrr_at_100 value: 46.361999999999995 - type: mrr_at_1000 value: 46.412 - type: mrr_at_3 value: 42.919000000000004 - type: mrr_at_5 value: 44.283 - type: ndcg_at_1 value: 37.584 - type: ndcg_at_10 value: 42.63 - type: ndcg_at_100 value: 50.114000000000004 - type: ndcg_at_1000 value: 52.312000000000005 - type: ndcg_at_3 value: 37.808 - type: ndcg_at_5 value: 39.711999999999996 - type: precision_at_1 value: 37.584 - type: precision_at_10 value: 9.51 - type: precision_at_100 value: 1.554 - type: precision_at_1000 value: 0.183 - type: precision_at_3 value: 21.505 - type: precision_at_5 value: 15.514 - type: recall_at_1 value: 24.459 - type: recall_at_10 value: 52.32 - type: recall_at_100 value: 83.423 - type: recall_at_1000 value: 98.247 - type: recall_at_3 value: 37.553 - type: recall_at_5 value: 43.712 - task: type: PairClassification dataset: type: C-MTEB/CMNLI name: MTEB Cmnli config: default split: validation revision: None metrics: - type: cos_sim_accuracy value: 77.7269993986771 - type: cos_sim_ap value: 86.8488070512359 - type: cos_sim_f1 value: 79.32095490716179 - type: cos_sim_precision value: 72.6107226107226 - type: cos_sim_recall value: 87.39770867430443 - type: dot_accuracy value: 77.7269993986771 - type: dot_ap value: 86.84218333157476 - type: dot_f1 value: 79.32095490716179 - type: dot_precision value: 72.6107226107226 - type: dot_recall value: 87.39770867430443 - type: euclidean_accuracy value: 77.7269993986771 - type: euclidean_ap value: 86.84880910178296 - type: euclidean_f1 value: 79.32095490716179 - type: euclidean_precision value: 72.6107226107226 - type: euclidean_recall value: 87.39770867430443 - type: manhattan_accuracy value: 77.82321106434155 - type: manhattan_ap value: 86.8152244713786 - type: manhattan_f1 value: 79.43262411347519 - type: manhattan_precision value: 72.5725338491296 - type: manhattan_recall value: 87.72504091653029 - type: max_accuracy value: 77.82321106434155 - type: max_ap value: 86.84880910178296 - type: max_f1 value: 79.43262411347519 - task: type: Retrieval dataset: type: C-MTEB/CovidRetrieval name: MTEB CovidRetrieval config: default split: dev revision: None metrics: - type: map_at_1 value: 68.862 - type: map_at_10 value: 77.106 - type: map_at_100 value: 77.455 - type: map_at_1000 value: 77.459 - type: map_at_3 value: 75.457 - type: map_at_5 value: 76.254 - type: mrr_at_1 value: 69.125 - type: mrr_at_10 value: 77.13799999999999 - type: mrr_at_100 value: 77.488 - type: mrr_at_1000 value: 77.492 - type: mrr_at_3 value: 75.606 - type: mrr_at_5 value: 76.29599999999999 - type: ndcg_at_1 value: 69.02000000000001 - type: ndcg_at_10 value: 80.81099999999999 - type: ndcg_at_100 value: 82.298 - type: ndcg_at_1000 value: 82.403 - type: ndcg_at_3 value: 77.472 - type: ndcg_at_5 value: 78.892 - type: precision_at_1 value: 69.02000000000001 - type: precision_at_10 value: 9.336 - type: precision_at_100 value: 0.9990000000000001 - type: precision_at_1000 value: 0.101 - type: precision_at_3 value: 27.924 - type: precision_at_5 value: 17.492 - type: recall_at_1 value: 68.862 - type: recall_at_10 value: 92.308 - type: recall_at_100 value: 98.84100000000001 - type: recall_at_1000 value: 99.684 - type: recall_at_3 value: 83.193 - type: recall_at_5 value: 86.617 - task: type: Retrieval dataset: type: C-MTEB/DuRetrieval name: MTEB DuRetrieval config: default split: dev revision: None metrics: - type: map_at_1 value: 25.063999999999997 - type: map_at_10 value: 78.02 - type: map_at_100 value: 81.022 - type: map_at_1000 value: 81.06 - type: map_at_3 value: 53.613 - type: map_at_5 value: 68.008 - type: mrr_at_1 value: 87.8 - type: mrr_at_10 value: 91.827 - type: mrr_at_100 value: 91.913 - type: mrr_at_1000 value: 91.915 - type: mrr_at_3 value: 91.508 - type: mrr_at_5 value: 91.758 - type: ndcg_at_1 value: 87.8 - type: ndcg_at_10 value: 85.753 - type: ndcg_at_100 value: 88.82900000000001 - type: ndcg_at_1000 value: 89.208 - type: ndcg_at_3 value: 84.191 - type: ndcg_at_5 value: 83.433 - type: precision_at_1 value: 87.8 - type: precision_at_10 value: 41.33 - type: precision_at_100 value: 4.8 - type: precision_at_1000 value: 0.48900000000000005 - type: precision_at_3 value: 75.767 - type: precision_at_5 value: 64.25999999999999 - type: recall_at_1 value: 25.063999999999997 - type: recall_at_10 value: 87.357 - type: recall_at_100 value: 97.261 - type: recall_at_1000 value: 99.309 - type: recall_at_3 value: 56.259 - type: recall_at_5 value: 73.505 - task: type: Retrieval dataset: type: C-MTEB/EcomRetrieval name: MTEB EcomRetrieval config: default split: dev revision: None metrics: - type: map_at_1 value: 46.800000000000004 - type: map_at_10 value: 56.898 - type: map_at_100 value: 57.567 - type: map_at_1000 value: 57.593 - type: map_at_3 value: 54.167 - type: map_at_5 value: 55.822 - type: mrr_at_1 value: 46.800000000000004 - type: mrr_at_10 value: 56.898 - type: mrr_at_100 value: 57.567 - type: mrr_at_1000 value: 57.593 - type: mrr_at_3 value: 54.167 - type: mrr_at_5 value: 55.822 - type: ndcg_at_1 value: 46.800000000000004 - type: ndcg_at_10 value: 62.07 - type: ndcg_at_100 value: 65.049 - type: ndcg_at_1000 value: 65.666 - type: ndcg_at_3 value: 56.54 - type: ndcg_at_5 value: 59.492999999999995 - type: precision_at_1 value: 46.800000000000004 - type: precision_at_10 value: 7.84 - type: precision_at_100 value: 0.9169999999999999 - type: precision_at_1000 value: 0.096 - type: precision_at_3 value: 21.133 - type: precision_at_5 value: 14.099999999999998 - type: recall_at_1 value: 46.800000000000004 - type: recall_at_10 value: 78.4 - type: recall_at_100 value: 91.7 - type: recall_at_1000 value: 96.39999999999999 - type: recall_at_3 value: 63.4 - type: recall_at_5 value: 70.5 - task: type: Classification dataset: type: C-MTEB/IFlyTek-classification name: MTEB IFlyTek config: default split: validation revision: None metrics: - type: accuracy value: 47.98768757214313 - type: f1 value: 35.23884426992269 - task: type: Classification dataset: type: C-MTEB/JDReview-classification name: MTEB JDReview config: default split: test revision: None metrics: - type: accuracy value: 86.97936210131333 - type: ap value: 56.292679530375736 - type: f1 value: 81.87001614762136 - task: type: STS dataset: type: C-MTEB/LCQMC name: MTEB LCQMC config: default split: test revision: None metrics: - type: cos_sim_pearson value: 71.17149643620844 - type: cos_sim_spearman value: 77.48040046337948 - type: euclidean_pearson value: 76.32337539923347 - type: euclidean_spearman value: 77.4804004621894 - type: manhattan_pearson value: 76.33275226275444 - type: manhattan_spearman value: 77.48979843086128 - task: type: Reranking dataset: type: C-MTEB/Mmarco-reranking name: MTEB MMarcoReranking config: default split: dev revision: None metrics: - type: map value: 27.966807589556826 - type: mrr value: 26.92023809523809 - task: type: Retrieval dataset: type: C-MTEB/MMarcoRetrieval name: MTEB MMarcoRetrieval config: default split: dev revision: None metrics: - type: map_at_1 value: 66.15100000000001 - type: map_at_10 value: 75.048 - type: map_at_100 value: 75.374 - type: map_at_1000 value: 75.386 - type: map_at_3 value: 73.26700000000001 - type: map_at_5 value: 74.39 - type: mrr_at_1 value: 68.381 - type: mrr_at_10 value: 75.644 - type: mrr_at_100 value: 75.929 - type: mrr_at_1000 value: 75.93900000000001 - type: mrr_at_3 value: 74.1 - type: mrr_at_5 value: 75.053 - type: ndcg_at_1 value: 68.381 - type: ndcg_at_10 value: 78.669 - type: ndcg_at_100 value: 80.161 - type: ndcg_at_1000 value: 80.46799999999999 - type: ndcg_at_3 value: 75.3 - type: ndcg_at_5 value: 77.172 - type: precision_at_1 value: 68.381 - type: precision_at_10 value: 9.48 - type: precision_at_100 value: 1.023 - type: precision_at_1000 value: 0.105 - type: precision_at_3 value: 28.299999999999997 - type: precision_at_5 value: 17.98 - type: recall_at_1 value: 66.15100000000001 - type: recall_at_10 value: 89.238 - type: recall_at_100 value: 96.032 - type: recall_at_1000 value: 98.437 - type: recall_at_3 value: 80.318 - type: recall_at_5 value: 84.761 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (zh-CN) config: zh-CN split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 68.26160053799597 - type: f1 value: 65.96949453305112 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (zh-CN) config: zh-CN split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 73.12037659717554 - type: f1 value: 72.69052407105445 - task: type: Retrieval dataset: type: C-MTEB/MedicalRetrieval name: MTEB MedicalRetrieval config: default split: dev revision: None metrics: - type: map_at_1 value: 50.1 - type: map_at_10 value: 56.489999999999995 - type: map_at_100 value: 57.007 - type: map_at_1000 value: 57.06400000000001 - type: map_at_3 value: 55.25 - type: map_at_5 value: 55.93 - type: mrr_at_1 value: 50.3 - type: mrr_at_10 value: 56.591 - type: mrr_at_100 value: 57.108000000000004 - type: mrr_at_1000 value: 57.165 - type: mrr_at_3 value: 55.35 - type: mrr_at_5 value: 56.03 - type: ndcg_at_1 value: 50.1 - type: ndcg_at_10 value: 59.419999999999995 - type: ndcg_at_100 value: 62.28900000000001 - type: ndcg_at_1000 value: 63.9 - type: ndcg_at_3 value: 56.813 - type: ndcg_at_5 value: 58.044 - type: precision_at_1 value: 50.1 - type: precision_at_10 value: 6.859999999999999 - type: precision_at_100 value: 0.828 - type: precision_at_1000 value: 0.096 - type: precision_at_3 value: 20.433 - type: precision_at_5 value: 12.86 - type: recall_at_1 value: 50.1 - type: recall_at_10 value: 68.60000000000001 - type: recall_at_100 value: 82.8 - type: recall_at_1000 value: 95.7 - type: recall_at_3 value: 61.3 - type: recall_at_5 value: 64.3 - task: type: Classification dataset: type: C-MTEB/MultilingualSentiment-classification name: MTEB MultilingualSentiment config: default split: validation revision: None metrics: - type: accuracy value: 73.41000000000001 - type: f1 value: 72.87768282499509 - task: type: PairClassification dataset: type: C-MTEB/OCNLI name: MTEB Ocnli config: default split: validation revision: None metrics: - type: cos_sim_accuracy value: 73.4163508391987 - type: cos_sim_ap value: 78.51058998215277 - type: cos_sim_f1 value: 75.3875968992248 - type: cos_sim_precision value: 69.65085049239033 - type: cos_sim_recall value: 82.15417106652588 - type: dot_accuracy value: 73.4163508391987 - type: dot_ap value: 78.51058998215277 - type: dot_f1 value: 75.3875968992248 - type: dot_precision value: 69.65085049239033 - type: dot_recall value: 82.15417106652588 - type: euclidean_accuracy value: 73.4163508391987 - type: euclidean_ap value: 78.51058998215277 - type: euclidean_f1 value: 75.3875968992248 - type: euclidean_precision value: 69.65085049239033 - type: euclidean_recall value: 82.15417106652588 - type: manhattan_accuracy value: 73.03735787763942 - type: manhattan_ap value: 78.4190891700083 - type: manhattan_f1 value: 75.32592950265573 - type: manhattan_precision value: 69.3950177935943 - type: manhattan_recall value: 82.36536430834214 - type: max_accuracy value: 73.4163508391987 - type: max_ap value: 78.51058998215277 - type: max_f1 value: 75.3875968992248 - task: type: Classification dataset: type: C-MTEB/OnlineShopping-classification name: MTEB OnlineShopping config: default split: test revision: None metrics: - type: accuracy value: 91.81000000000002 - type: ap value: 89.35809579688139 - type: f1 value: 91.79220350456818 - task: type: STS dataset: type: C-MTEB/PAWSX name: MTEB PAWSX config: default split: test revision: None metrics: - type: cos_sim_pearson value: 30.10755999973859 - type: cos_sim_spearman value: 36.221732138848864 - type: euclidean_pearson value: 36.41120179336658 - type: euclidean_spearman value: 36.221731188009436 - type: manhattan_pearson value: 36.34865300346968 - type: manhattan_spearman value: 36.17696483080459 - task: type: STS dataset: type: C-MTEB/QBQTC name: MTEB QBQTC config: default split: test revision: None metrics: - type: cos_sim_pearson value: 36.778975708100226 - type: cos_sim_spearman value: 38.733929926753724 - type: euclidean_pearson value: 37.13383498228113 - type: euclidean_spearman value: 38.73374886550868 - type: manhattan_pearson value: 37.175732896552404 - type: manhattan_spearman value: 38.74120541657908 - task: type: STS dataset: type: mteb/sts22-crosslingual-sts name: MTEB STS22 (zh) config: zh split: test revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 metrics: - type: cos_sim_pearson value: 65.97095922825076 - type: cos_sim_spearman value: 68.87452938308421 - type: euclidean_pearson value: 67.23101642424429 - type: euclidean_spearman value: 68.87452938308421 - type: manhattan_pearson value: 67.29909334410189 - type: manhattan_spearman value: 68.89807985930508 - task: type: STS dataset: type: C-MTEB/STSB name: MTEB STSB config: default split: test revision: None metrics: - type: cos_sim_pearson value: 78.98860630733722 - type: cos_sim_spearman value: 79.36601601355665 - type: euclidean_pearson value: 78.77295944956447 - type: euclidean_spearman value: 79.36585127278974 - type: manhattan_pearson value: 78.82060736131619 - type: manhattan_spearman value: 79.4395526421926 - task: type: Reranking dataset: type: C-MTEB/T2Reranking name: MTEB T2Reranking config: default split: dev revision: None metrics: - type: map value: 66.40501824507894 - type: mrr value: 76.18463933756757 - task: type: Retrieval dataset: type: C-MTEB/T2Retrieval name: MTEB T2Retrieval config: default split: dev revision: None metrics: - type: map_at_1 value: 27.095000000000002 - type: map_at_10 value: 76.228 - type: map_at_100 value: 79.865 - type: map_at_1000 value: 79.935 - type: map_at_3 value: 53.491 - type: map_at_5 value: 65.815 - type: mrr_at_1 value: 89.554 - type: mrr_at_10 value: 92.037 - type: mrr_at_100 value: 92.133 - type: mrr_at_1000 value: 92.137 - type: mrr_at_3 value: 91.605 - type: mrr_at_5 value: 91.88 - type: ndcg_at_1 value: 89.554 - type: ndcg_at_10 value: 83.866 - type: ndcg_at_100 value: 87.566 - type: ndcg_at_1000 value: 88.249 - type: ndcg_at_3 value: 85.396 - type: ndcg_at_5 value: 83.919 - type: precision_at_1 value: 89.554 - type: precision_at_10 value: 41.792 - type: precision_at_100 value: 4.997 - type: precision_at_1000 value: 0.515 - type: precision_at_3 value: 74.795 - type: precision_at_5 value: 62.675000000000004 - type: recall_at_1 value: 27.095000000000002 - type: recall_at_10 value: 82.694 - type: recall_at_100 value: 94.808 - type: recall_at_1000 value: 98.30600000000001 - type: recall_at_3 value: 55.156000000000006 - type: recall_at_5 value: 69.19 - task: type: Classification dataset: type: C-MTEB/TNews-classification name: MTEB TNews config: default split: validation revision: None metrics: - type: accuracy value: 51.929 - type: f1 value: 50.16876489927282 - task: type: Clustering dataset: type: C-MTEB/ThuNewsClusteringP2P name: MTEB ThuNewsClusteringP2P config: default split: test revision: None metrics: - type: v_measure value: 61.404157724658894 - task: type: Clustering dataset: type: C-MTEB/ThuNewsClusteringS2S name: MTEB ThuNewsClusteringS2S config: default split: test revision: None metrics: - type: v_measure value: 57.11418384351802 - task: type: Retrieval dataset: type: C-MTEB/VideoRetrieval name: MTEB VideoRetrieval config: default split: dev revision: None metrics: - type: map_at_1 value: 52.1 - type: map_at_10 value: 62.956999999999994 - type: map_at_100 value: 63.502 - type: map_at_1000 value: 63.51599999999999 - type: map_at_3 value: 60.75000000000001 - type: map_at_5 value: 62.195 - type: mrr_at_1 value: 52.0 - type: mrr_at_10 value: 62.907000000000004 - type: mrr_at_100 value: 63.452 - type: mrr_at_1000 value: 63.466 - type: mrr_at_3 value: 60.699999999999996 - type: mrr_at_5 value: 62.144999999999996 - type: ndcg_at_1 value: 52.1 - type: ndcg_at_10 value: 67.93299999999999 - type: ndcg_at_100 value: 70.541 - type: ndcg_at_1000 value: 70.91300000000001 - type: ndcg_at_3 value: 63.468 - type: ndcg_at_5 value: 66.08800000000001 - type: precision_at_1 value: 52.1 - type: precision_at_10 value: 8.34 - type: precision_at_100 value: 0.955 - type: precision_at_1000 value: 0.098 - type: precision_at_3 value: 23.767 - type: precision_at_5 value: 15.540000000000001 - type: recall_at_1 value: 52.1 - type: recall_at_10 value: 83.39999999999999 - type: recall_at_100 value: 95.5 - type: recall_at_1000 value: 98.4 - type: recall_at_3 value: 71.3 - type: recall_at_5 value: 77.7 - task: type: Classification dataset: type: C-MTEB/waimai-classification name: MTEB Waimai config: default split: test revision: None metrics: - type: accuracy value: 87.12 - type: ap value: 70.85284793227382 - type: f1 value: 85.55420883566512 --- 新闻 \| News [2024-04-06] 开源[puff](https://huggingface.co/infgrad/puff-base-v1)系列模型，专门针对检索和语义匹配任务，更多的考虑泛化性和私有通用测试集效果，向量维度可变，中英双语。 [2024-02-27] 开源stella-mrl-large-zh-v3.5-1792d模型，支持向量可变维度。 [2024-02-17] 开源stella v3系列、dialogue编码模型和相关训练数据。 [2023-10-19] 开源stella-base-en-v2 使用简单，不需要任何前缀文本。 [2023-10-12] 开源stella-base-zh-v2和stella-large-zh-v2, 效果更好且使用简单，不需要任何前缀文本。 [2023-09-11] 开源stella-base-zh和stella-large-zh 欢迎去[本人主页](https://huggingface.co/infgrad)查看最新模型，并提出您的宝贵意见！ ## stella model stella是一个通用的文本编码模型，主要有以下模型： \| Model Name \| Model Size (GB) \| Dimension \| Sequence Length \| Language \| Need instruction for retrieval? \| \|:------------------:\|:---------------:\|:---------:\|:---------------:\|:--------:\|:-------------------------------:\| \| stella-base-en-v2 \| 0.2 \| 768 \| 512 \| English \| No \| \| stella-large-zh-v2 \| 0.65 \| 1024 \| 1024 \| Chinese \| No \| \| stella-base-zh-v2 \| 0.2 \| 768 \| 1024 \| Chinese \| No \| \| stella-large-zh \| 0.65 \| 1024 \| 1024 \| Chinese \| Yes \| \| stella-base-zh \| 0.2 \| 768 \| 1024 \| Chinese \| Yes \| 完整的训练思路和训练过程已记录在[博客1](https://zhuanlan.zhihu.com/p/655322183)和[博客2](https://zhuanlan.zhihu.com/p/662209559)，欢迎阅读讨论。训练数据： 1. 开源数据(wudao_base_200GB[1]、m3e[2]和simclue[3])，着重挑选了长度大于512的文本 2. 在通用语料库上使用LLM构造一批(question, paragraph)和(sentence, paragraph)数据训练方法： 1. 对比学习损失函数 2. 带有难负例的对比学习损失函数(分别基于bm25和vector构造了难负例) 3. EWC(Elastic Weights Consolidation)[4] 4. cosent loss[5] 5. 每一种类型的数据一个迭代器，分别计算loss进行更新 stella-v2在stella模型的基础上，使用了更多的训练数据，同时知识蒸馏等方法去除了前置的instruction( 比如piccolo的`查询:`, `结果:`, e5的`query:`和`passage:`)。初始权重：\ stella-base-zh和stella-large-zh分别以piccolo-base-zh[6]和piccolo-large-zh作为基础模型，512-1024的position embedding使用层次分解位置编码[7]进行初始化。\ 感谢商汤科技研究院开源的[piccolo系列模型](https://huggingface.co/sensenova)。 stella is a general-purpose text encoder, which mainly includes the following models: \| Model Name \| Model Size (GB) \| Dimension \| Sequence Length \| Language \| Need instruction for retrieval? \| \|:------------------:\|:---------------:\|:---------:\|:---------------:\|:--------:\|:-------------------------------:\| \| stella-base-en-v2 \| 0.2 \| 768 \| 512 \| English \| No \| \| stella-large-zh-v2 \| 0.65 \| 1024 \| 1024 \| Chinese \| No \| \| stella-base-zh-v2 \| 0.2 \| 768 \| 1024 \| Chinese \| No \| \| stella-large-zh \| 0.65 \| 1024 \| 1024 \| Chinese \| Yes \| \| stella-base-zh \| 0.2 \| 768 \| 1024 \| Chinese \| Yes \| The training data mainly includes: 1. Open-source training data (wudao_base_200GB, m3e, and simclue), with a focus on selecting texts with lengths greater than 512. 2. A batch of (question, paragraph) and (sentence, paragraph) data constructed on a general corpus using LLM. The loss functions mainly include: 1. Contrastive learning loss function 2. Contrastive learning loss function with hard negative examples (based on bm25 and vector hard negatives) 3. EWC (Elastic Weights Consolidation) 4. cosent loss Model weight initialization:\ stella-base-zh and stella-large-zh use piccolo-base-zh and piccolo-large-zh as the base models, respectively, and the 512-1024 position embedding uses the initialization strategy of hierarchical decomposed position encoding. Training strategy:\ One iterator for each type of data, separately calculating the loss. Based on stella models, stella-v2 use more training data and remove instruction by Knowledge Distillation. ## Metric #### C-MTEB leaderboard (Chinese) \| Model Name \| Model Size (GB) \| Dimension \| Sequence Length \| Average (35) \| Classification (9) \| Clustering (4) \| Pair Classification (2) \| Reranking (4) \| Retrieval (8) \| STS (8) \| \|:------------------:\|:---------------:\|:---------:\|:---------------:\|:------------:\|:------------------:\|:--------------:\|:-----------------------:\|:-------------:\|:-------------:\|:-------:\| \| stella-large-zh-v2 \| 0.65 \| 1024 \| 1024 \| 65.13 \| 69.05 \| 49.16 \| 82.68 \| 66.41 \| 70.14 \| 58.66 \| \| stella-base-zh-v2 \| 0.2 \| 768 \| 1024 \| 64.36 \| 68.29 \| 49.4 \| 79.95 \| 66.1 \| 70.08 \| 56.92 \| \| stella-large-zh \| 0.65 \| 1024 \| 1024 \| 64.54 \| 67.62 \| 48.65 \| 78.72 \| 65.98 \| 71.02 \| 58.3 \| \| stella-base-zh \| 0.2 \| 768 \| 1024 \| 64.16 \| 67.77 \| 48.7 \| 76.09 \| 66.95 \| 71.07 \| 56.54 \| #### MTEB leaderboard (English) \| Model Name \| Model Size (GB) \| Dimension \| Sequence Length \| Average (56) \| Classification (12) \| Clustering (11) \| Pair Classification (3) \| Reranking (4) \| Retrieval (15) \| STS (10) \| Summarization (1) \| \|:-----------------:\|:---------------:\|:---------:\|:---------------:\|:------------:\|:-------------------:\|:---------------:\|:-----------------------:\|:-------------:\|:--------------:\|:--------:\|:------------------:\| \| stella-base-en-v2 \| 0.2 \| 768 \| 512 \| 62.61 \| 75.28 \| 44.9 \| 86.45 \| 58.77 \| 50.1 \| 83.02 \| 32.52 \| #### Reproduce our results C-MTEB: ```python import torch import numpy as np from typing import List from mteb import MTEB from sentence_transformers import SentenceTransformer class FastTextEncoder(): def __init__(self, model_name): self.model = SentenceTransformer(model_name).cuda().half().eval() self.model.max_seq_length = 512 def encode( self, input_texts: List[str], args, kwargs ): new_sens = list(set(input_texts)) new_sens.sort(key=lambda x: len(x), reverse=True) vecs = self.model.encode( new_sens, normalize_embeddings=True, convert_to_numpy=True, batch_size=256 ).astype(np.float32) sen2arrid = {sen: idx for idx, sen in enumerate(new_sens)} vecs = vecs[[sen2arrid[sen] for sen in input_texts]] torch.cuda.empty_cache() return vecs if __name__ == '__main__': model_name = "infgrad/stella-base-zh-v2" output_folder = "zh_mteb_results/stella-base-zh-v2" task_names = [t.description["name"] for t in MTEB(task_langs=['zh', 'zh-CN']).tasks] model = FastTextEncoder(model_name) for task in task_names: MTEB(tasks=[task], task_langs=['zh', 'zh-CN']).run(model, output_folder=output_folder) ``` MTEB:* You can use official script to reproduce our result. [scripts/run_mteb_english.py](https://github.com/embeddings-benchmark/mteb/blob/main/scripts/run_mteb_english.py) #### Evaluation for long text 经过实际观察发现，C-MTEB的评测数据长度基本都是小于512的，更致命的是那些长度大于512的文本，其重点都在前半部分这里以CMRC2018的数据为例说明这个问题： ``` question: 《无双大蛇z》是谁旗下ω-force开发的动作游戏？ passage：《无双大蛇z》是光荣旗下ω-force开发的动作游戏，于2009年3月12日登陆索尼playstation3，并于2009年11月27日推...... ``` passage长度为800多，大于512，但是对于这个question而言只需要前面40个字就足以检索，多的内容对于模型而言是一种噪声，反而降低了效果。\ 简言之，现有数据集的2个问题：\ 1）长度大于512的过少\ 2）即便大于512，对于检索而言也只需要前512的文本内容\ 导致无法准确评估模型的长文本编码能力。为了解决这个问题，搜集了相关开源数据并使用规则进行过滤，最终整理了6份长文本测试集,他们分别是： - CMRC2018，通用百科 - CAIL，法律阅读理解 - DRCD，繁体百科，已转简体 - Military，军工问答 - Squad，英文阅读理解，已转中文 - Multifieldqa_zh，清华的大模型长文本理解能力评测数据[9] 处理规则是选取答案在512长度之后的文本，短的测试数据会欠采样一下，长短文本占比约为1:2，所以模型既得理解短文本也得理解长文本。除了Military数据集，我们提供了其他5个测试数据的下载地址：https://drive.google.com/file/d/1WC6EWaCbVgz-vPMDFH4TwAMkLyh5WNcN/view?usp=sharing 评测指标为Recall@5, 结果如下： \| Dataset \| piccolo-base-zh \| piccolo-large-zh \| bge-base-zh \| bge-large-zh \| stella-base-zh \| stella-large-zh \| \|:---------------:\|:---------------:\|:----------------:\|:-----------:\|:------------:\|:--------------:\|:---------------:\| \| CMRC2018 \| 94.34 \| 93.82 \| 91.56 \| 93.12 \| 96.08 \| 95.56 \| \| CAIL \| 28.04 \| 33.64 \| 31.22 \| 33.94 \| 34.62 \| 37.18 \| \| DRCD \| 78.25 \| 77.9 \| 78.34 \| 80.26 \| 86.14 \| 84.58 \| \| Military \| 76.61 \| 73.06 \| 75.65 \| 75.81 \| 83.71 \| 80.48 \| \| Squad \| 91.21 \| 86.61 \| 87.87 \| 90.38 \| 93.31 \| 91.21 \| \| Multifieldqa_zh \| 81.41 \| 83.92 \| 83.92 \| 83.42 \| 79.9 \| 80.4 \| \| Average \| 74.98 \| 74.83 \| 74.76 \| 76.15 \| 78.96 \| 78.24 \| 注意：因为长文本评测数据数量稀少，所以构造时也使用了train部分，如果自行评测，请注意模型的训练数据以免数据泄露。 ## Usage #### stella 中文系列模型 stella-base-zh 和 stella-large-zh: 本模型是在piccolo基础上训练的，因此用法和piccolo完全一致，即在检索重排任务上给query和passage加上`查询: `和`结果: `。对于短短匹配不需要做任何操作。 stella-base-zh-v2 和 stella-large-zh-v2: 本模型使用简单，任何使用场景中都不需要加前缀文本。 stella中文系列模型均使用mean pooling做为文本向量。在sentence-transformer库中的使用方法： ```python from sentence_transformers import SentenceTransformer sentences = ["数据1", "数据2"] model = SentenceTransformer('infgrad/stella-base-zh-v2') print(model.max_seq_length) embeddings_1 = model.encode(sentences, normalize_embeddings=True) embeddings_2 = model.encode(sentences, normalize_embeddings=True) similarity = embeddings_1 @ embeddings_2.T print(similarity) ``` 直接使用transformers库： ```python from transformers import AutoModel, AutoTokenizer from sklearn.preprocessing import normalize model = AutoModel.from_pretrained('infgrad/stella-base-zh-v2') tokenizer = AutoTokenizer.from_pretrained('infgrad/stella-base-zh-v2') sentences = ["数据1", "数据ABCDEFGH"] batch_data = tokenizer( batch_text_or_text_pairs=sentences, padding="longest", return_tensors="pt", max_length=1024, truncation=True, ) attention_mask = batch_data["attention_mask"] model_output = model(batch_data) last_hidden = model_output.last_hidden_state.masked_fill(~attention_mask[..., None].bool(), 0.0) vectors = last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None] vectors = normalize(vectors, norm="l2", axis=1, ) print(vectors.shape) # 2,768 ``` #### stella models for English Using Sentence-Transformers: ```python from sentence_transformers import SentenceTransformer sentences = ["one car come", "one car go"] model = SentenceTransformer('infgrad/stella-base-en-v2') print(model.max_seq_length) embeddings_1 = model.encode(sentences, normalize_embeddings=True) embeddings_2 = model.encode(sentences, normalize_embeddings=True) similarity = embeddings_1 @ embeddings_2.T print(similarity) ``` Using HuggingFace Transformers: ```python from transformers import AutoModel, AutoTokenizer from sklearn.preprocessing import normalize model = AutoModel.from_pretrained('infgrad/stella-base-en-v2') tokenizer = AutoTokenizer.from_pretrained('infgrad/stella-base-en-v2') sentences = ["one car come", "one car go"] batch_data = tokenizer( batch_text_or_text_pairs=sentences, padding="longest", return_tensors="pt", max_length=512, truncation=True, ) attention_mask = batch_data["attention_mask"] model_output = model(batch_data) last_hidden = model_output.last_hidden_state.masked_fill(~attention_mask[..., None].bool(), 0.0) vectors = last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None] vectors = normalize(vectors, norm="l2", axis=1, ) print(vectors.shape) # 2,768 ``` ## Training Detail 硬件：单卡A100-80GB 环境： torch1.13.; transformers-trainer + deepspeed + gradient-checkpointing 学习率：* 1e-6 batch_size： base模型为1024，额外增加20%的难负例；large模型为768，额外增加20%的难负例数据量：第一版模型约100万，其中用LLM构造的数据约有200K. LLM模型大小为13b。v2系列模型到了2000万训练数据。 ## ToDoList 评测的稳定性：评测过程中发现Clustering任务会和官方的结果不一致，大约有±0.0x的小差距，原因是聚类代码没有设置random_seed，差距可以忽略不计，不影响评测结论。更高质量的长文本训练和测试数据：训练数据多是用13b模型构造的，肯定会存在噪声。测试数据基本都是从mrc数据整理来的，所以问题都是factoid类型，不符合真实分布。 OOD的性能：虽然近期出现了很多向量编码模型，但是对于不是那么通用的domain，这一众模型包括stella、openai和cohere, 它们的效果均比不上BM25。 ## Reference 1. https://www.scidb.cn/en/detail?dataSetId=c6a3fe684227415a9db8e21bac4a15ab 2. https://github.com/wangyuxinwhy/uniem 3. https://github.com/CLUEbenchmark/SimCLUE 4. https://arxiv.org/abs/1612.00796 5. https://kexue.fm/archives/8847 6. https://huggingface.co/sensenova/piccolo-base-zh 7. https://kexue.fm/archives/7947 8. https://github.com/FlagOpen/FlagEmbedding 9. https://github.com/THUDM/LongBench
Yntec/IsThisDisney	Yntec	2024-05-16T12:15:45Z	4,811	0	diffusers	[ "diffusers", "safetensors", "3D Animation", "Simple prompts", "Things", "nitrosocke", "PromptSharingSamaritan", "jinofcoolnes", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "en", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2024-05-16T08:26:20Z	--- license: creativeml-openrail-m language: - en library_name: diffusers pipeline_tag: text-to-image tags: - 3D Animation - Simple prompts - Things - nitrosocke - PromptSharingSamaritan - jinofcoolnes - stable-diffusion - stable-diffusion-diffusers - text-to-image inference: true --- Use "modern disney" in the prompts to enhance the style. # Is This Disney? Yet another model I'm only releasing because of its hash: ![3bbbbb hash](https://cdn-uploads.huggingface.co/production/uploads/63239b8370edc53f51cd5d42/JV8uEZZvvFm7r1XQ_8MJl.png) Five bs in a row! What if instead of training this style on SD1.5, nitrosocke trained it on my Is This Art Model? (that includes SamDoesArtUltimerge, Samaritan 3D Cartoon v2 and the Stuff models) You'd get this one, boasting to deliver without negative prompts or having to use "modern disney" on the prompts! And to allow simple prompts so you can just ask for what you want! You won't see me using shorter prompts than these for my samples! Check them out! And that hash! I could keep writing paragraphs about this, but instead, I'll provide many samples... Samples and prompts: ![Free AI Text To image Is this disney](https://cdn-uploads.huggingface.co/production/uploads/63239b8370edc53f51cd5d42/AtkIIqGvIWzV8DN15OTHR.png) (Click for larger) Top left: modern disney link Top right: zero suit samus Bottom left: Princess Peach Toadstool Bottom right: modern disney Cute Red Panda ![Free AI image generator Is This Disney](https://cdn-uploads.huggingface.co/production/uploads/63239b8370edc53f51cd5d42/87QfuK4O1yZa7UGQYyXIf.png) (Click for larger) Top left: modern disney eevee Top right: lara croft Bottom left: loli girl Bottom right: modern disney angel girl carrying rabbit, white wavy hair, birds, waterfall, closeup ![Free AI image generator prompts](https://cdn-uploads.huggingface.co/production/uploads/63239b8370edc53f51cd5d42/H2CfsGCRsjYuFCmEGLNSt.png) (Click for larger) Top left: disney modern movie little daughters with dad, Santa Top right: disney pretty harley quinn Bottom left: cartoon modern red nose reindeer Bottom right: modern master chief ![Free AI image generator samples](https://cdn-uploads.huggingface.co/production/uploads/63239b8370edc53f51cd5d42/oFJMMaot15NS8Jfb4kfCp.png) (Click for larger) Top left: Pizza Top right: modern disney chibi chun li Bottom left: cute modern zelda Bottom right: disney burger ![Is This Disney Prompts](https://cdn-uploads.huggingface.co/production/uploads/63239b8370edc53f51cd5d42/lybq1wFjaMl7RWadTpFSc.png) (Click for larger) Top left: A birthday themed cake, high quality pie Top right: modern disney cute porcupine with headphones holding umbrella Bottom left: modern disney baby lion Bottom right: city skyline ![Is This Disney](https://cdn-uploads.huggingface.co/production/uploads/63239b8370edc53f51cd5d42/R-uYSdNfQVhxTp3XD7u3U.png) (Click for larger) Top left: a Cooking of a beautiful young cute indian girl Top right: Modern Disney lego architecture chinese building Bottom left: Modern Disney a brand new classic VW Bus Bottom right: Modern Disney Halle Berry as Storm from Xmen ![Is this disney images](https://cdn-uploads.huggingface.co/production/uploads/63239b8370edc53f51cd5d42/fOV8trPjm4WDjkk-RRvy5.png) (Click for larger) Top left: modern cartoon disney long coat pikachu Top right: modern disney porche car Bottom left: Baby girl with a giant basket full of cherries, grass Bottom right: Dreamworks artstyle, baby cow For more models with hashes like this check out https://huggingface.co/Yntec/Jackpot and https://huggingface.co/Yntec/BabeBae - I promise their hashes will not disappoint! Original pages: https://huggingface.co/nitrosocke/mo-di-diffusion https://huggingface.co/Yntec/IsThisArt https://huggingface.co/Yntec/SamaritanDoesArt https://huggingface.co/Yntec/Stuff https://huggingface.co/jinofcoolnes/sammod https://civitai.com/models/81270?modelVersionId=113299 (Samaritan 3D Cartoon v2) # Recipe - SuperMerger Weight Sum Train Difference Alpha 1 Model A: moDi-v1-pruned Model B: IsThisArt Model C: Stable Diffusion 1.5 Output Model: IsThisDisney
google/switch-base-32	google	2023-11-28T09:43:25Z	4,808	9	transformers	[ "transformers", "pytorch", "safetensors", "switch_transformers", "text2text-generation", "en", "dataset:c4", "arxiv:2101.03961", "arxiv:1910.09700", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-11-04T07:58:49Z	--- language: - en tags: - text2text-generation widget: - text: "The <extra_id_0> walks in <extra_id_1> park" example_title: "Masked Language Modeling" datasets: - c4 license: apache-2.0 --- # Model Card for Switch Transformers Base - 32 experts ![model image](https://s3.amazonaws.com/moonup/production/uploads/1666966931908-62441d1d9fdefb55a0b7d12c.png) # Table of Contents 0. [TL;DR](#TL;DR) 1. [Model Details](#model-details) 2. [Usage](#usage) 3. [Uses](#uses) 4. [Bias, Risks, and Limitations](#bias-risks-and-limitations) 5. [Training Details](#training-details) 6. [Evaluation](#evaluation) 7. [Environmental Impact](#environmental-impact) 8. [Citation](#citation) 9. [Model Card Authors](#model-card-authors) # TL;DR Switch Transformers is a Mixture of Experts (MoE) model trained on Masked Language Modeling (MLM) task. The model architecture is similar to the classic T5, but with the Feed Forward layers replaced by the Sparse MLP layers containing "experts" MLP. According to the [original paper](https://arxiv.org/pdf/2101.03961.pdf) the model enables faster training (scaling properties) while being better than T5 on fine-tuned tasks. As mentioned in the first few lines of the abstract : > we advance the current scale of language models by pre-training up to trillion parameter models on the “Colossal Clean Crawled Corpus”, and achieve a 4x speedup over the T5-XXL model. Disclaimer: Content from this model card has been written by the Hugging Face team, and parts of it were copy pasted from the [original paper](https://arxiv.org/pdf/2101.03961.pdf). # Model Details ## Model Description - Model type: Language model - Language(s) (NLP): English - License: Apache 2.0 - Related Models: [All Switch Transformers Checkpoints](https://huggingface.co/models?search=switch) - Original Checkpoints: [All Original Switch Transformers Checkpoints](https://github.com/google-research/t5x/blob/main/docs/models.md#mixture-of-experts-moe-checkpoints) - Resources for more information: - [Research paper](https://arxiv.org/pdf/2101.03961.pdf) - [GitHub Repo](https://github.com/google-research/t5x) - [Hugging Face Switch Transformers Docs (Similar to T5) ](https://huggingface.co/docs/transformers/model_doc/switch_transformers) # Usage Note that these checkpoints has been trained on Masked-Language Modeling (MLM) task. Therefore the checkpoints are not "ready-to-use" for downstream tasks. You may want to check `FLAN-T5` for running fine-tuned weights or fine-tune your own MoE following [this notebook](https://colab.research.google.com/drive/1aGGVHZmtKmcNBbAwa9hbu58DDpIuB5O4?usp=sharing) Find below some example scripts on how to use the model in `transformers`: ## Using the Pytorch model ### Running the model on a CPU <details> <summary> Click to expand </summary> ```python from transformers import AutoTokenizer, SwitchTransformersForConditionalGeneration tokenizer = AutoTokenizer.from_pretrained("google/switch-base-32") model = SwitchTransformersForConditionalGeneration.from_pretrained("google/switch-base-32") input_text = "A <extra_id_0> walks into a bar a orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>." input_ids = tokenizer(input_text, return_tensors="pt").input_ids outputs = model.generate(input_ids) print(tokenizer.decode(outputs[0])) >>> <pad> <extra_id_0> man<extra_id_1> beer<extra_id_2> a<extra_id_3> salt<extra_id_4>.</s> ``` </details> ### Running the model on a GPU <details> <summary> Click to expand </summary> ```python # pip install accelerate from transformers import AutoTokenizer, SwitchTransformersForConditionalGeneration tokenizer = AutoTokenizer.from_pretrained("google/switch-base-32") model = SwitchTransformersForConditionalGeneration.from_pretrained("google/switch-base-32", device_map="auto") input_text = "A <extra_id_0> walks into a bar a orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>." input_ids = tokenizer(input_text, return_tensors="pt").input_ids.to(0) outputs = model.generate(input_ids) print(tokenizer.decode(outputs[0])) >>> <pad> <extra_id_0> man<extra_id_1> beer<extra_id_2> a<extra_id_3> salt<extra_id_4>.</s> ``` </details> ### Running the model on a GPU using different precisions #### FP16 <details> <summary> Click to expand </summary> ```python # pip install accelerate from transformers import AutoTokenizer, SwitchTransformersForConditionalGeneration tokenizer = AutoTokenizer.from_pretrained("google/switch-base-32") model = SwitchTransformersForConditionalGeneration.from_pretrained("google/switch-base-32", device_map="auto", torch_dtype=torch.float16) input_text = "A <extra_id_0> walks into a bar a orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>." input_ids = tokenizer(input_text, return_tensors="pt").input_ids.to(0) outputs = model.generate(input_ids) print(tokenizer.decode(outputs[0])) >>> <pad> <extra_id_0> man<extra_id_1> beer<extra_id_2> a<extra_id_3> salt<extra_id_4>.</s> ``` </details> #### INT8 <details> <summary> Click to expand </summary> ```python # pip install bitsandbytes accelerate from transformers import AutoTokenizer, SwitchTransformersForConditionalGeneration tokenizer = AutoTokenizer.from_pretrained("google/switch-base-32") model = SwitchTransformersForConditionalGeneration.from_pretrained("google/switch-base-32", device_map="auto") input_text = "A <extra_id_0> walks into a bar a orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>." input_ids = tokenizer(input_text, return_tensors="pt").input_ids.to(0) outputs = model.generate(input_ids) print(tokenizer.decode(outputs[0])) >>> <pad> <extra_id_0> man<extra_id_1> beer<extra_id_2> a<extra_id_3> salt<extra_id_4>.</s> ``` </details> # Uses ## Direct Use and Downstream Use See the [research paper](https://arxiv.org/pdf/2101.03961.pdf) for further details. ## Out-of-Scope Use More information needed. # Bias, Risks, and Limitations More information needed. ## Ethical considerations and risks More information needed. ## Known Limitations More information needed. ## Sensitive Use: More information needed. # Training Details ## Training Data The model was trained on a Masked Language Modeling task, on Colossal Clean Crawled Corpus (C4) dataset, following the same procedure as `T5`. ## Training Procedure According to the model card from the [original paper](https://arxiv.org/pdf/2101.03961.pdf) the model has been trained on TPU v3 or TPU v4 pods, using [`t5x`](https://github.com/google-research/t5x) codebase together with [`jax`](https://github.com/google/jax). # Evaluation ## Testing Data, Factors & Metrics The authors evaluated the model on various tasks and compared the results against T5. See the table below for some quantitative evaluation: ![image.png](https://s3.amazonaws.com/moonup/production/uploads/1666967660372-62441d1d9fdefb55a0b7d12c.png) For full details, please check the [research paper](https://arxiv.org/pdf/2101.03961.pdf). ## Results For full results for Switch Transformers, see the [research paper](https://arxiv.org/pdf/2101.03961.pdf), Table 5. # Environmental Impact Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: Google Cloud TPU Pods - TPU v3 or TPU v4 \| Number of chips ≥ 4. - Hours used: More information needed - Cloud Provider: GCP - Compute Region: More information needed - Carbon Emitted: More information needed # Citation BibTeX: ```bibtex @misc{https://doi.org/10.48550/arxiv.2101.03961, doi = {10.48550/ARXIV.2101.03961}, url = {https://arxiv.org/abs/2101.03961}, author = {Fedus, William and Zoph, Barret and Shazeer, Noam}, keywords = {Machine Learning (cs.LG), Artificial Intelligence (cs.AI), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity}, publisher = {arXiv}, year = {2021}, copyright = {arXiv.org perpetual, non-exclusive license} } ```
mradermacher/F2PhenotypeDPO-i1-GGUF	mradermacher	2024-06-16T11:45:20Z	4,807	0	transformers	[ "transformers", "gguf", "mergekit", "merge", "en", "base_model:WesPro/F2PhenotypeDPO", "endpoints_compatible", "region:us" ]	null	2024-06-16T06:42:31Z	--- base_model: WesPro/F2PhenotypeDPO language: - en library_name: transformers quantized_by: mradermacher tags: - mergekit - merge --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/WesPro/F2PhenotypeDPO <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/F2PhenotypeDPO-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/F2PhenotypeDPO-i1-GGUF/resolve/main/F2PhenotypeDPO.i1-IQ1_S.gguf) \| i1-IQ1_S \| 2.1 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/F2PhenotypeDPO-i1-GGUF/resolve/main/F2PhenotypeDPO.i1-IQ1_M.gguf) \| i1-IQ1_M \| 2.3 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/F2PhenotypeDPO-i1-GGUF/resolve/main/F2PhenotypeDPO.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 2.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/F2PhenotypeDPO-i1-GGUF/resolve/main/F2PhenotypeDPO.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 2.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/F2PhenotypeDPO-i1-GGUF/resolve/main/F2PhenotypeDPO.i1-IQ2_S.gguf) \| i1-IQ2_S \| 2.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/F2PhenotypeDPO-i1-GGUF/resolve/main/F2PhenotypeDPO.i1-IQ2_M.gguf) \| i1-IQ2_M \| 3.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/F2PhenotypeDPO-i1-GGUF/resolve/main/F2PhenotypeDPO.i1-Q2_K.gguf) \| i1-Q2_K \| 3.3 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/F2PhenotypeDPO-i1-GGUF/resolve/main/F2PhenotypeDPO.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 3.4 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/F2PhenotypeDPO-i1-GGUF/resolve/main/F2PhenotypeDPO.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 3.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/F2PhenotypeDPO-i1-GGUF/resolve/main/F2PhenotypeDPO.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 3.8 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/F2PhenotypeDPO-i1-GGUF/resolve/main/F2PhenotypeDPO.i1-IQ3_S.gguf) \| i1-IQ3_S \| 3.8 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/F2PhenotypeDPO-i1-GGUF/resolve/main/F2PhenotypeDPO.i1-IQ3_M.gguf) \| i1-IQ3_M \| 3.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/F2PhenotypeDPO-i1-GGUF/resolve/main/F2PhenotypeDPO.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 4.1 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/F2PhenotypeDPO-i1-GGUF/resolve/main/F2PhenotypeDPO.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 4.4 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/F2PhenotypeDPO-i1-GGUF/resolve/main/F2PhenotypeDPO.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 4.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/F2PhenotypeDPO-i1-GGUF/resolve/main/F2PhenotypeDPO.i1-Q4_0.gguf) \| i1-Q4_0 \| 4.8 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/F2PhenotypeDPO-i1-GGUF/resolve/main/F2PhenotypeDPO.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 4.8 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/F2PhenotypeDPO-i1-GGUF/resolve/main/F2PhenotypeDPO.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 5.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/F2PhenotypeDPO-i1-GGUF/resolve/main/F2PhenotypeDPO.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 5.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/F2PhenotypeDPO-i1-GGUF/resolve/main/F2PhenotypeDPO.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 5.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/F2PhenotypeDPO-i1-GGUF/resolve/main/F2PhenotypeDPO.i1-Q6_K.gguf) \| i1-Q6_K \| 6.7 \| practically like static Q6_K \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his hardware for calculating the imatrix for these quants. <!-- end -->
RichardErkhov/tensoropera_-_Fox-1-1.6B-gguf	RichardErkhov	2024-06-24T10:41:58Z	4,804	0	null	[ "gguf", "region:us" ]	null	2024-06-24T06:06:34Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) Fox-1-1.6B - GGUF - Model creator: https://huggingface.co/tensoropera/ - Original model: https://huggingface.co/tensoropera/Fox-1-1.6B/ \| Name \| Quant method \| Size \| \| ---- \| ---- \| ---- \| \| [Fox-1-1.6B.Q2_K.gguf](https://huggingface.co/RichardErkhov/tensoropera_-_Fox-1-1.6B-gguf/blob/main/Fox-1-1.6B.Q2_K.gguf) \| Q2_K \| 0.8GB \| \| [Fox-1-1.6B.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/tensoropera_-_Fox-1-1.6B-gguf/blob/main/Fox-1-1.6B.IQ3_XS.gguf) \| IQ3_XS \| 0.84GB \| \| [Fox-1-1.6B.IQ3_S.gguf](https://huggingface.co/RichardErkhov/tensoropera_-_Fox-1-1.6B-gguf/blob/main/Fox-1-1.6B.IQ3_S.gguf) \| IQ3_S \| 0.87GB \| \| [Fox-1-1.6B.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/tensoropera_-_Fox-1-1.6B-gguf/blob/main/Fox-1-1.6B.Q3_K_S.gguf) \| Q3_K_S \| 0.86GB \| \| [Fox-1-1.6B.IQ3_M.gguf](https://huggingface.co/RichardErkhov/tensoropera_-_Fox-1-1.6B-gguf/blob/main/Fox-1-1.6B.IQ3_M.gguf) \| IQ3_M \| 0.89GB \| \| [Fox-1-1.6B.Q3_K.gguf](https://huggingface.co/RichardErkhov/tensoropera_-_Fox-1-1.6B-gguf/blob/main/Fox-1-1.6B.Q3_K.gguf) \| Q3_K \| 0.92GB \| \| [Fox-1-1.6B.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/tensoropera_-_Fox-1-1.6B-gguf/blob/main/Fox-1-1.6B.Q3_K_M.gguf) \| Q3_K_M \| 0.92GB \| \| [Fox-1-1.6B.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/tensoropera_-_Fox-1-1.6B-gguf/blob/main/Fox-1-1.6B.Q3_K_L.gguf) \| Q3_K_L \| 0.97GB \| \| [Fox-1-1.6B.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/tensoropera_-_Fox-1-1.6B-gguf/blob/main/Fox-1-1.6B.IQ4_XS.gguf) \| IQ4_XS \| 0.98GB \| \| [Fox-1-1.6B.Q4_0.gguf](https://huggingface.co/RichardErkhov/tensoropera_-_Fox-1-1.6B-gguf/blob/main/Fox-1-1.6B.Q4_0.gguf) \| Q4_0 \| 1.0GB \| \| [Fox-1-1.6B.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/tensoropera_-_Fox-1-1.6B-gguf/blob/main/Fox-1-1.6B.IQ4_NL.gguf) \| IQ4_NL \| 1.01GB \| \| [Fox-1-1.6B.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/tensoropera_-_Fox-1-1.6B-gguf/blob/main/Fox-1-1.6B.Q4_K_S.gguf) \| Q4_K_S \| 1.01GB \| \| [Fox-1-1.6B.Q4_K.gguf](https://huggingface.co/RichardErkhov/tensoropera_-_Fox-1-1.6B-gguf/blob/main/Fox-1-1.6B.Q4_K.gguf) \| Q4_K \| 1.04GB \| \| [Fox-1-1.6B.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/tensoropera_-_Fox-1-1.6B-gguf/blob/main/Fox-1-1.6B.Q4_K_M.gguf) \| Q4_K_M \| 1.04GB \| \| [Fox-1-1.6B.Q4_1.gguf](https://huggingface.co/RichardErkhov/tensoropera_-_Fox-1-1.6B-gguf/blob/main/Fox-1-1.6B.Q4_1.gguf) \| Q4_1 \| 1.07GB \| \| [Fox-1-1.6B.Q5_0.gguf](https://huggingface.co/RichardErkhov/tensoropera_-_Fox-1-1.6B-gguf/blob/main/Fox-1-1.6B.Q5_0.gguf) \| Q5_0 \| 1.14GB \| \| [Fox-1-1.6B.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/tensoropera_-_Fox-1-1.6B-gguf/blob/main/Fox-1-1.6B.Q5_K_S.gguf) \| Q5_K_S \| 1.14GB \| \| [Fox-1-1.6B.Q5_K.gguf](https://huggingface.co/RichardErkhov/tensoropera_-_Fox-1-1.6B-gguf/blob/main/Fox-1-1.6B.Q5_K.gguf) \| Q5_K \| 1.16GB \| \| [Fox-1-1.6B.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/tensoropera_-_Fox-1-1.6B-gguf/blob/main/Fox-1-1.6B.Q5_K_M.gguf) \| Q5_K_M \| 1.16GB \| \| [Fox-1-1.6B.Q5_1.gguf](https://huggingface.co/RichardErkhov/tensoropera_-_Fox-1-1.6B-gguf/blob/main/Fox-1-1.6B.Q5_1.gguf) \| Q5_1 \| 1.2GB \| \| [Fox-1-1.6B.Q6_K.gguf](https://huggingface.co/RichardErkhov/tensoropera_-_Fox-1-1.6B-gguf/blob/main/Fox-1-1.6B.Q6_K.gguf) \| Q6_K \| 1.28GB \| \| [Fox-1-1.6B.Q8_0.gguf](https://huggingface.co/RichardErkhov/tensoropera_-_Fox-1-1.6B-gguf/blob/main/Fox-1-1.6B.Q8_0.gguf) \| Q8_0 \| 1.65GB \| Original model description: --- license: apache-2.0 language: - en pipeline_tag: text-generation --- ## Model Card for Fox-1-1.6B > [!IMPORTANT] > This model is a base pretrained model which requires further finetuning for most use cases. We will release the instruction-tuned version soon. Fox-1 is a decoder-only transformer-based small language model (SLM) with 1.6B total parameters developed by [TensorOpera AI](https://tensoropera.ai/). The model was trained with a 3-stage data curriculum on 3 trillion tokens of text and code data in 8K sequence length. Fox-1 uses grouped query attention (GQA) with 4 KV heads and 16 attention heads and has a deeper architecture than other SLMs. For the full details of this model please read our [release blog post](https://blog.tensoropera.ai/tensoropera-unveils-fox-foundation-model-a-pioneering-open-source-slm-leading-the-way-against-tech-giants).
cmarkea/distilcamembert-base-sentiment	cmarkea	2024-05-22T17:36:59Z	4,803	32	transformers	[ "transformers", "pytorch", "tf", "onnx", "safetensors", "camembert", "text-classification", "fr", "dataset:amazon_reviews_multi", "dataset:allocine", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-03-02T23:29:05Z	--- language: fr license: mit datasets: - amazon_reviews_multi - allocine widget: - text: "Je pensais lire un livre nul, mais finalement je l'ai trouvé super !" - text: "Cette banque est très bien, mais elle n'offre pas les services de paiements sans contact." - text: "Cette banque est très bien et elle offre en plus les services de paiements sans contact." --- DistilCamemBERT-Sentiment ========================= We present DistilCamemBERT-Sentiment, which is [DistilCamemBERT](https://huggingface.co/cmarkea/distilcamembert-base) fine-tuned for the sentiment analysis task for the French language. This model is built using two datasets: [Amazon Reviews](https://huggingface.co/datasets/amazon_reviews_multi) and [Allociné.fr](https://huggingface.co/datasets/allocine) to minimize the bias. Indeed, Amazon reviews are similar in messages and relatively shorts, contrary to Allociné critics, who are long and rich texts. This modelization is close to [tblard/tf-allocine](https://huggingface.co/tblard/tf-allocine) based on [CamemBERT](https://huggingface.co/camembert-base) model. The problem of the modelizations based on CamemBERT is at the scaling moment, for the production phase, for example. Indeed, inference cost can be a technological issue. To counteract this effect, we propose this modelization which divides the inference time by two with the same consumption power thanks to [DistilCamemBERT](https://huggingface.co/cmarkea/distilcamembert-base). Dataset ------- The dataset comprises 204,993 reviews for training and 4,999 reviews for the test from Amazon, and 235,516 and 4,729 critics from [Allocine website](https://www.allocine.fr/). The dataset is labeled into five categories: - 1 star: represents a terrible appreciation, - 2 stars: bad appreciation, - 3 stars: neutral appreciation, - 4 stars: good appreciation, - 5 stars: excellent appreciation. Evaluation results ------------------ In addition of accuracy (called here exact accuracy) in order to be robust to +/-1 star estimation errors, we will take the following definition as a performance measure: $$\mathrm{top\!-\!2\; acc}=\frac{1}{\|\mathcal{O}\|}\sum_{i\in\mathcal{O}}\sum_{0\leq l < 2}\mathbb{1}(\hat{f}_{i,l}=y_i)$$ where \$\hat{f}_l\$ is the l-th largest predicted label, \$y\$ the true label, \$\mathcal{O}\$ is the test set of the observations and \$\mathbb{1}\$ is the indicator function. \| class \| exact accuracy (%) \| top-2 acc (%) \| support \| \| :---------: \| :--------------------: \| :---------------: \| :---------: \| \| global \| 61.01 \| 88.80 \| 9,698 \| \| 1 star \| 87.21 \| 77.17 \| 1,905 \| \| 2 stars \| 79.19 \| 84.75 \| 1,935 \| \| 3 stars \| 77.85 \| 78.98 \| 1,974 \| \| 4 stars \| 78.61 \| 90.22 \| 1,952 \| \| 5 stars \| 85.96 \| 82.92 \| 1,932 \| Benchmark --------- This model is compared to 3 reference models (see below). As each model doesn't have the exact definition of targets, we detail the performance measure used for each. An AMD Ryzen 5 4500U @ 2.3GHz with 6 cores was used for the mean inference time measure. #### bert-base-multilingual-uncased-sentiment [nlptown/bert-base-multilingual-uncased-sentiment](https://huggingface.co/nlptown/bert-base-multilingual-uncased-sentiment) is based on BERT model in the multilingual and uncased version. This sentiment analyzer is trained on Amazon reviews, similar to our model. Hence the targets and their definitions are the same. \| model \| time (ms) \| exact accuracy (%) \| top-2 acc (%) \| \| :-------: \| :-----------: \| :--------------------: \| :---------------: \| \| [cmarkea/distilcamembert-base-sentiment](https://huggingface.co/cmarkea/distilcamembert-base-sentiment) \| 95.56 \| 61.01 \| 88.80 \| \| [nlptown/bert-base-multilingual-uncased-sentiment](https://huggingface.co/nlptown/bert-base-multilingual-uncased-sentiment) \| 187.70 \| 54.41 \| 82.82 \| #### tf-allociné and barthez-sentiment-classification [tblard/tf-allocine](https://huggingface.co/tblard/tf-allocine) based on [CamemBERT](https://huggingface.co/camembert-base) model and [moussaKam/barthez-sentiment-classification](https://huggingface.co/moussaKam/barthez-sentiment-classification) based on [BARThez](https://huggingface.co/moussaKam/barthez) use the same bi-class definition between them. To bring this back to a two-class problem, we will only consider the "1 star" and "2 stars" labels for the negative sentiments and "4 stars" and "5 stars" for positive sentiments. We exclude the "3 stars" which can be interpreted as a neutral class. In this context, the problem of +/-1 star estimation errors disappears. Then we use only the classical accuracy definition. \| model \| time (ms) \| exact accuracy (%) \| \| :-------: \| :-----------: \| :--------------------: \| \| [cmarkea/distilcamembert-base-sentiment](https://huggingface.co/cmarkea/distilcamembert-base-sentiment) \| 95.56 \| 97.52 \| \| [tblard/tf-allocine](https://huggingface.co/tblard/tf-allocine) \| 329.74 \| 95.69 \| \| [moussaKam/barthez-sentiment-classification](https://huggingface.co/moussaKam/barthez-sentiment-classification) \| 197.95 \| 94.29 \| How to use DistilCamemBERT-Sentiment ------------------------------------ ```python from transformers import pipeline analyzer = pipeline( task='text-classification', model="cmarkea/distilcamembert-base-sentiment", tokenizer="cmarkea/distilcamembert-base-sentiment" ) result = analyzer( "J'aime me promener en forêt même si ça me donne mal aux pieds.", return_all_scores=True ) result [{'label': '1 star', 'score': 0.047529436647892}, {'label': '2 stars', 'score': 0.14150355756282806}, {'label': '3 stars', 'score': 0.3586442470550537}, {'label': '4 stars', 'score': 0.3181498646736145}, {'label': '5 stars', 'score': 0.13417290151119232}] ``` ### Optimum + ONNX ```python from optimum.onnxruntime import ORTModelForSequenceClassification from transformers import AutoTokenizer, pipeline HUB_MODEL = "cmarkea/distilcamembert-base-sentiment" tokenizer = AutoTokenizer.from_pretrained(HUB_MODEL) model = ORTModelForSequenceClassification.from_pretrained(HUB_MODEL) onnx_qa = pipeline("text-classification", model=model, tokenizer=tokenizer) # Quantized onnx model quantized_model = ORTModelForSequenceClassification.from_pretrained( HUB_MODEL, file_name="model_quantized.onnx" ) ``` Citation -------- ```bibtex @inproceedings{delestre:hal-03674695, TITLE = {{DistilCamemBERT : une distillation du mod{\`e}le fran{\c c}ais CamemBERT}}, AUTHOR = {Delestre, Cyrile and Amar, Abibatou}, URL = {https://hal.archives-ouvertes.fr/hal-03674695}, BOOKTITLE = {{CAp (Conf{\'e}rence sur l'Apprentissage automatique)}}, ADDRESS = {Vannes, France}, YEAR = {2022}, MONTH = Jul, KEYWORDS = {NLP ; Transformers ; CamemBERT ; Distillation}, PDF = {https://hal.archives-ouvertes.fr/hal-03674695/file/cap2022.pdf}, HAL_ID = {hal-03674695}, HAL_VERSION = {v1}, } ```
TheBloke/Wizard-Vicuna-7B-Uncensored-GPTQ	TheBloke	2023-09-27T12:44:19Z	4,803	162	transformers	[ "transformers", "safetensors", "llama", "text-generation", "uncensored", "en", "dataset:ehartford/wizard_vicuna_70k_unfiltered", "base_model:ehartford/Wizard-Vicuna-7B-Uncensored", "license:other", "autotrain_compatible", "text-generation-inference", "4-bit", "gptq", "region:us" ]	text-generation	2023-05-18T07:53:47Z	--- language: - en license: other tags: - uncensored datasets: - ehartford/wizard_vicuna_70k_unfiltered model_name: Wizard Vicuna 7B Uncensored base_model: ehartford/Wizard-Vicuna-7B-Uncensored inference: false model_creator: Eric Hartford model_type: llama prompt_template: 'A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user''s questions. USER: {prompt} ASSISTANT: ' quantized_by: TheBloke --- <!-- header start --> <!-- 200823 --> <div style="width: auto; margin-left: auto; margin-right: auto"> <img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> <div style="display: flex; justify-content: space-between; width: 100%;"> <div style="display: flex; flex-direction: column; align-items: flex-start;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p> </div> <div style="display: flex; flex-direction: column; align-items: flex-end;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p> </div> </div> <div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div> <hr style="margin-top: 1.0em; margin-bottom: 1.0em;"> <!-- header end --> # Wizard Vicuna 7B Uncensored - GPTQ - Model creator: [Eric Hartford](https://huggingface.co/ehartford) - Original model: [Wizard Vicuna 7B Uncensored](https://huggingface.co/ehartford/Wizard-Vicuna-7B-Uncensored) <!-- description start --> ## Description This repo contains GPTQ model files for [Wizard-Vicuna-7B-Uncensored](https://huggingface.co/ehartford/Wizard-Vicuna-7B-Uncensored). Multiple GPTQ parameter permutations are provided; see Provided Files below for details of the options provided, their parameters, and the software used to create them. <!-- description end --> <!-- repositories-available start --> ## Repositories available * [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/Wizard-Vicuna-7B-Uncensored-AWQ) * [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Wizard-Vicuna-7B-Uncensored-GPTQ) * [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/Wizard-Vicuna-7B-Uncensored-GGUF) * [Eric Hartford's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/ehartford/Wizard-Vicuna-7B-Uncensored) <!-- repositories-available end --> <!-- prompt-template start --> ## Prompt template: Vicuna ``` A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: {prompt} ASSISTANT: ``` <!-- prompt-template end --> <!-- README_GPTQ.md-provided-files start --> ## Provided files and GPTQ parameters Multiple quantisation parameters are provided, to allow you to choose the best one for your hardware and requirements. Each separate quant is in a different branch. See below for instructions on fetching from different branches. All recent GPTQ files are made with AutoGPTQ, and all files in non-main branches are made with AutoGPTQ. Files in the `main` branch which were uploaded before August 2023 were made with GPTQ-for-LLaMa. <details> <summary>Explanation of GPTQ parameters</summary> - Bits: The bit size of the quantised model. - GS: GPTQ group size. Higher numbers use less VRAM, but have lower quantisation accuracy. "None" is the lowest possible value. - Act Order: True or False. Also known as `desc_act`. True results in better quantisation accuracy. Some GPTQ clients have had issues with models that use Act Order plus Group Size, but this is generally resolved now. - Damp %: A GPTQ parameter that affects how samples are processed for quantisation. 0.01 is default, but 0.1 results in slightly better accuracy. - GPTQ dataset: The dataset used for quantisation. Using a dataset more appropriate to the model's training can improve quantisation accuracy. Note that the GPTQ dataset is not the same as the dataset used to train the model - please refer to the original model repo for details of the training dataset(s). - Sequence Length: The length of the dataset sequences used for quantisation. Ideally this is the same as the model sequence length. For some very long sequence models (16+K), a lower sequence length may have to be used. Note that a lower sequence length does not limit the sequence length of the quantised model. It only impacts the quantisation accuracy on longer inference sequences. - ExLlama Compatibility: Whether this file can be loaded with ExLlama, which currently only supports Llama models in 4-bit. </details> \| Branch \| Bits \| GS \| Act Order \| Damp % \| GPTQ Dataset \| Seq Len \| Size \| ExLlama \| Desc \| \| ------ \| ---- \| -- \| --------- \| ------ \| ------------ \| ------- \| ---- \| ------- \| ---- \| \| [main](https://huggingface.co/TheBloke/Wizard-Vicuna-7B-Uncensored-GPTQ/tree/main) \| 4 \| 128 \| No \| 0.01 \| [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) \| 2048 \| 4.52 GB \| Yes \| 4-bit, without Act Order and group size 128g. \| \| [gptq-4bit-32g-actorder_True](https://huggingface.co/TheBloke/Wizard-Vicuna-7B-Uncensored-GPTQ/tree/gptq-4bit-32g-actorder_True) \| 4 \| 32 \| Yes \| 0.01 \| [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) \| 2048 \| 4.28 GB \| Yes \| 4-bit, with Act Order and group size 32g. Gives highest possible inference quality, with maximum VRAM usage. \| \| [gptq-4bit-64g-actorder_True](https://huggingface.co/TheBloke/Wizard-Vicuna-7B-Uncensored-GPTQ/tree/gptq-4bit-64g-actorder_True) \| 4 \| 64 \| Yes \| 0.01 \| [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) \| 2048 \| 4.02 GB \| Yes \| 4-bit, with Act Order and group size 64g. Uses less VRAM than 32g, but with slightly lower accuracy. \| \| [gptq-4bit-128g-actorder_True](https://huggingface.co/TheBloke/Wizard-Vicuna-7B-Uncensored-GPTQ/tree/gptq-4bit-128g-actorder_True) \| 4 \| 128 \| Yes \| 0.01 \| [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) \| 2048 \| 3.90 GB \| Yes \| 4-bit, with Act Order and group size 128g. Uses even less VRAM than 64g, but with slightly lower accuracy. \| \| [gptq-8bit--1g-actorder_True](https://huggingface.co/TheBloke/Wizard-Vicuna-7B-Uncensored-GPTQ/tree/gptq-8bit--1g-actorder_True) \| 8 \| None \| Yes \| 0.01 \| [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) \| 2048 \| 7.01 GB \| No \| 8-bit, with Act Order. No group size, to lower VRAM requirements. \| \| [gptq-8bit-128g-actorder_False](https://huggingface.co/TheBloke/Wizard-Vicuna-7B-Uncensored-GPTQ/tree/gptq-8bit-128g-actorder_False) \| 8 \| 128 \| No \| 0.01 \| [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) \| 2048 \| 7.16 GB \| No \| 8-bit, with group size 128g for higher inference quality and without Act Order to improve AutoGPTQ speed. \| \| [gptq-8bit-128g-actorder_True](https://huggingface.co/TheBloke/Wizard-Vicuna-7B-Uncensored-GPTQ/tree/gptq-8bit-128g-actorder_True) \| 8 \| 128 \| Yes \| 0.01 \| [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) \| 2048 \| 7.16 GB \| No \| 8-bit, with group size 128g for higher inference quality and with Act Order for even higher accuracy. \| \| [gptq-8bit-64g-actorder_True](https://huggingface.co/TheBloke/Wizard-Vicuna-7B-Uncensored-GPTQ/tree/gptq-8bit-64g-actorder_True) \| 8 \| 64 \| Yes \| 0.01 \| [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) \| 2048 \| 7.31 GB \| No \| 8-bit, with group size 64g and Act Order for even higher inference quality. Poor AutoGPTQ CUDA speed. \| <!-- README_GPTQ.md-provided-files end --> <!-- README_GPTQ.md-download-from-branches start --> ## How to download from branches - In text-generation-webui, you can add `:branch` to the end of the download name, eg `TheBloke/Wizard-Vicuna-7B-Uncensored-GPTQ:main` - With Git, you can clone a branch with: ``` git clone --single-branch --branch main https://huggingface.co/TheBloke/Wizard-Vicuna-7B-Uncensored-GPTQ ``` - In Python Transformers code, the branch is the `revision` parameter; see below. <!-- README_GPTQ.md-download-from-branches end --> <!-- README_GPTQ.md-text-generation-webui start --> ## How to easily download and use this model in [text-generation-webui](https://github.com/oobabooga/text-generation-webui). Please make sure you're using the latest version of [text-generation-webui](https://github.com/oobabooga/text-generation-webui). It is strongly recommended to use the text-generation-webui one-click-installers unless you're sure you know how to make a manual install. 1. Click the Model tab. 2. Under Download custom model or LoRA, enter `TheBloke/Wizard-Vicuna-7B-Uncensored-GPTQ`. - To download from a specific branch, enter for example `TheBloke/Wizard-Vicuna-7B-Uncensored-GPTQ:main` - see Provided Files above for the list of branches for each option. 3. Click Download. 4. The model will start downloading. Once it's finished it will say "Done". 5. In the top left, click the refresh icon next to Model. 6. In the Model dropdown, choose the model you just downloaded: `Wizard-Vicuna-7B-Uncensored-GPTQ` 7. The model will automatically load, and is now ready for use! 8. If you want any custom settings, set them and then click Save settings for this model followed by Reload the Model in the top right. * Note that you do not need to and should not set manual GPTQ parameters any more. These are set automatically from the file `quantize_config.json`. 9. Once you're ready, click the Text Generation tab and enter a prompt to get started! <!-- README_GPTQ.md-text-generation-webui end --> <!-- README_GPTQ.md-use-from-python start --> ## How to use this GPTQ model from Python code ### Install the necessary packages Requires: Transformers 4.32.0 or later, Optimum 1.12.0 or later, and AutoGPTQ 0.4.2 or later. ```shell pip3 install transformers>=4.32.0 optimum>=1.12.0 pip3 install auto-gptq --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/ # Use cu117 if on CUDA 11.7 ``` If you have problems installing AutoGPTQ using the pre-built wheels, install it from source instead: ```shell pip3 uninstall -y auto-gptq git clone https://github.com/PanQiWei/AutoGPTQ cd AutoGPTQ pip3 install . ``` ### For CodeLlama models only: you must use Transformers 4.33.0 or later. If 4.33.0 is not yet released when you read this, you will need to install Transformers from source: ```shell pip3 uninstall -y transformers pip3 install git+https://github.com/huggingface/transformers.git ``` ### You can then use the following code ```python from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline model_name_or_path = "TheBloke/Wizard-Vicuna-7B-Uncensored-GPTQ" # To use a different branch, change revision # For example: revision="main" model = AutoModelForCausalLM.from_pretrained(model_name_or_path, device_map="auto", trust_remote_code=True, revision="main") tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True) prompt = "Tell me about AI" prompt_template=f'''A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: {prompt} ASSISTANT: ''' print("\n\n* Generate:") input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda() output = model.generate(inputs=input_ids, temperature=0.7, do_sample=True, top_p=0.95, top_k=40, max_new_tokens=512) print(tokenizer.decode(output[0])) # Inference can also be done using transformers' pipeline print("* Pipeline:") pipe = pipeline( "text-generation", model=model, tokenizer=tokenizer, max_new_tokens=512, do_sample=True, temperature=0.7, top_p=0.95, top_k=40, repetition_penalty=1.1 ) print(pipe(prompt_template)[0]['generated_text']) ``` <!-- README_GPTQ.md-use-from-python end --> <!-- README_GPTQ.md-compatibility start --> ## Compatibility The files provided are tested to work with AutoGPTQ, both via Transformers and using AutoGPTQ directly. They should also work with [Occ4m's GPTQ-for-LLaMa fork](https://github.com/0cc4m/KoboldAI). [ExLlama](https://github.com/turboderp/exllama) is compatible with Llama models in 4-bit. Please see the Provided Files table above for per-file compatibility. [Huggingface Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) is compatible with all GPTQ models. <!-- README_GPTQ.md-compatibility end --> <!-- footer start --> <!-- 200823 --> ## Discord For further support, and discussions on these models and AI in general, join us at: [TheBloke AI's Discord server](https://discord.gg/theblokeai) ## Thanks, and how to contribute Thanks to the [chirper.ai](https://chirper.ai) team! Thanks to Clay from [gpus.llm-utils.org](llm-utils)! I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training. If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects. Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits. * Patreon: https://patreon.com/TheBlokeAI * Ko-Fi: https://ko-fi.com/TheBlokeAI Special thanks to: Aemon Algiz. Patreon special mentions: Alicia Loh, Stephen Murray, K, Ajan Kanaga, RoA, Magnesian, Deo Leter, Olakabola, Eugene Pentland, zynix, Deep Realms, Raymond Fosdick, Elijah Stavena, Iucharbius, Erik Bjäreholt, Luis Javier Navarrete Lozano, Nicholas, theTransient, John Detwiler, alfie_i, knownsqashed, Mano Prime, Willem Michiel, Enrico Ros, LangChain4j, OG, Michael Dempsey, Pierre Kircher, Pedro Madruga, James Bentley, Thomas Belote, Luke @flexchar, Leonard Tan, Johann-Peter Hartmann, Illia Dulskyi, Fen Risland, Chadd, S_X, Jeff Scroggin, Ken Nordquist, Sean Connelly, Artur Olbinski, Swaroop Kallakuri, Jack West, Ai Maven, David Ziegler, Russ Johnson, transmissions 11, John Villwock, Alps Aficionado, Clay Pascal, Viktor Bowallius, Subspace Studios, Rainer Wilmers, Trenton Dambrowitz, vamX, Michael Levine, 준교 김, Brandon Frisco, Kalila, Trailburnt, Randy H, Talal Aujan, Nathan Dryer, Vadim, 阿明, ReadyPlayerEmma, Tiffany J. Kim, George Stoitzev, Spencer Kim, Jerry Meng, Gabriel Tamborski, Cory Kujawski, Jeffrey Morgan, Spiking Neurons AB, Edmond Seymore, Alexandros Triantafyllidis, Lone Striker, Cap'n Zoog, Nikolai Manek, danny, ya boyyy, Derek Yates, usrbinkat, Mandus, TL, Nathan LeClaire, subjectnull, Imad Khwaja, webtim, Raven Klaugh, Asp the Wyvern, Gabriel Puliatti, Caitlyn Gatomon, Joseph William Delisle, Jonathan Leane, Luke Pendergrass, SuperWojo, Sebastain Graf, Will Dee, Fred von Graf, Andrey, Dan Guido, Daniel P. Andersen, Nitin Borwankar, Elle, Vitor Caleffi, biorpg, jjj, NimbleBox.ai, Pieter, Matthew Berman, terasurfer, Michael Davis, Alex, Stanislav Ovsiannikov Thank you to all my generous patrons and donaters! And thank you again to a16z for their generous grant. <!-- footer end --> # Original model card: Wizard-Vicuna-7B-Uncensored This is [wizard-vicuna-13b](https://huggingface.co/junelee/wizard-vicuna-13b) trained against LLaMA-7B with a subset of the dataset - responses that contained alignment / moralizing were removed. The intent is to train a WizardLM that doesn't have alignment built-in, so that alignment (of any sort) can be added separately with for example with a RLHF LoRA. Shout out to the open source AI/ML community, and everyone who helped me out. Note: An uncensored model has no guardrails. You are responsible for anything you do with the model, just as you are responsible for anything you do with any dangerous object such as a knife, gun, lighter, or car. Publishing anything this model generates is the same as publishing it yourself. You are responsible for the content you publish, and you cannot blame the model any more than you can blame the knife, gun, lighter, or car for what you do with it.
jay6944/EEVE-Korean-Instruct-10.8B-geoheim5-8bit-gguf	jay6944	2024-06-27T09:50:17Z	4,798	0	transformers	[ "transformers", "gguf", "llama", "text-generation-inference", "unsloth", "en", "base_model:yanolja/EEVE-Korean-Instruct-10.8B-v1.0", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-06-27T09:42:12Z	--- base_model: yanolja/EEVE-Korean-Instruct-10.8B-v1.0 language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - gguf --- # Uploaded model - Developed by: jay6944 - License: apache-2.0 - Finetuned from model : yanolja/EEVE-Korean-Instruct-10.8B-v1.0 This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
Open-Orca/OpenOrcaxOpenChat-Preview2-13B	Open-Orca	2023-11-18T00:16:22Z	4,796	104	transformers	[ "transformers", "pytorch", "llama", "text-generation", "en", "dataset:Open-Orca/OpenOrca", "arxiv:2306.02707", "arxiv:2301.13688", "arxiv:2307.09288", "license:llama2", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text-generation	2023-07-31T11:08:55Z	--- license: llama2 language: - en library_name: transformers pipeline_tag: text-generation datasets: - Open-Orca/OpenOrca --- <p><h1>🐋 The Second OpenOrca Model Preview! 🐋</h1></p> ![OpenOrca Logo](https://huggingface.co/datasets/Open-Orca/OpenOrca/resolve/main/OpenOrcaLogo.png "OpenOrca Logo") # OpenOrca x OpenChat - Preview2 - 13B We have used our own [OpenOrca dataset](https://huggingface.co/datasets/Open-Orca/OpenOrca) to fine-tune Llama2-13B using [OpenChat](https://huggingface.co/openchat) packing. This dataset is our attempt to reproduce the dataset generated for Microsoft Research's [Orca Paper](https://arxiv.org/abs/2306.02707). This second preview release is trained on a curated filtered subset of most of our GPT-4 augmented data. This release highlights that our dataset and training methods have surpassed performance parity with the Orca paper. We measured this with BigBench-Hard and AGIEval results with the same methods as used in the Orca paper, finding ~103% of original Orca's performance on average. As well, this is done with <1/10th the compute requirement and using <20% of the dataset size from the original Orca paper. We have run extensive evaluations internally and expect this model to place number 1 on both the HuggingFaceH4 Open LLM Leaderboard and the GPT4ALL Leaderboard for 13B models. "One" of [OpenChat](https://huggingface.co/openchat) has joined our team, and we'd like to provide special thanks for their training of this model! We have utilized OpenChat [MultiPack algorithm](https://github.com/imoneoi/multipack_sampler) which achieves 99.85% bin-packing efficiency on our dataset. This has significantly reduced training time, with efficiency improvement of 3-10X over traditional methods. <img src="https://raw.githubusercontent.com/imoneoi/openchat/master/assets/logo_new.png" style="width: 40%"> Want to visualize our full (pre-filtering) dataset? Check out our [Nomic Atlas Map](https://atlas.nomic.ai/map/c1b88b47-2d9b-47e0-9002-b80766792582/2560fd25-52fe-42f1-a58f-ff5eccc890d2). [<img src="https://huggingface.co/Open-Orca/OpenOrca-Preview1-13B/resolve/main/OpenOrca%20Nomic%20Atlas.png" alt="Atlas Nomic Dataset Map" width="400" height="400" />](https://atlas.nomic.ai/map/c1b88b47-2d9b-47e0-9002-b80766792582/2560fd25-52fe-42f1-a58f-ff5eccc890d2) We are in-process with training more models, so keep a look out on our org for releases coming soon with exciting partners. We will also give sneak-peak announcements on our Discord, which you can find here: https://AlignmentLab.ai # Prompt Template We use our own prompt template which we call "`OpenChat Llama2 V1`". The model is heavily conditioned to work using this format only and will likely encounter issues such as run-on output which emulates a chat between a user and assistant if this format is not properly followed. Examples: ``` # Single-turn `OpenChat Llama2 V1` tokenize("You are OpenOrcaChat.<\|end_of_turn\|>User: Hello<\|end_of_turn\|>Assistant:") # [1, 887, 526, 4673, 2816, 1113, 1451, 271, 29889, 32000, 4911, 29901, 15043, 32000, 4007, 22137, 29901] # Multi-turn `OpenChat Llama2 V1` tokenize("You are OpenOrcaChat.<\|end_of_turn\|>User: Hello<\|end_of_turn\|>Assistant: Hi<\|end_of_turn\|>User: How are you today?<\|end_of_turn\|>Assistant:") # [1, 887, 526, 4673, 2816, 1113, 1451, 271, 29889, 32000, 4911, 29901, 15043, 32000, 4007, 22137, 29901, 6324, 32000, 4911, 29901, 1128, 526, 366, 9826, 29973, 32000, 4007, 22137, 29901] ``` For UIs with Prefix and Suffix fields, these will likely work: Prefix (include a space after colon): ``` User: ``` Suffix (space after colon): ``` <\|end_of_turn\|>\nAssistant: ``` Oobabooga's text-generation-webui instructions can be found [further down the page](https://huggingface.co/Open-Orca/OpenOrcaxOpenChat-Preview2-13B#serving-with-oobabooga--text-generation-webui). # Evaluation We have evaluated OpenOrcaxOpenChat-Preview2-13B on hard reasoning tasks from BigBench-Hard and AGIEval as outlined in the Orca paper. Our average performance for BigBench-Hard: 0.488 Average for AGIEval: 0.447 We find our score averages to ~103% of the total performance that was shown in the Orca paper, using the same evaluation methods as outlined in the paper. So we are surpassing Orca performance with <20% of the dataset size and <1/10th the training budget! As well, we have evaluated using the methodology and tools for the HuggingFace Leaderboard and GPT4ALL Leaderboard, and find that we place #1 on both for all 13B models at release time! ## AGIEval Performance We present our results in two columns. The column for "`(Orca Paper eval)`" uses the methods outlined in the Orca paper, so as to be a direct apples-to-apples comparison with the results from the paper. The column for "`(HF Leaderboard eval)`" uses EleutherAI's LM Evaluation Harness with settings outlined by HuggingFace. These results are not comparable to the other columns, as the methods are different. ![OpenOrca Preview2 AGIEval Performance](https://huggingface.co/Open-Orca/OpenOrcaxOpenChat-Preview2-13B/resolve/main/Images/OpenOrcaP2AGIEval.png "AGIEval Performance") ## BigBench-Hard Performance We present our results in two columns. The column for "`(Orca Paper eval)`" uses the methods outlined in the Orca paper, so as to be a direct apples-to-apples comparison with the results from the paper. The column for "`(HF Leaderboard eval)`" uses EleutherAI's LM Evaluation Harness with settings outlined by HuggingFace. These results are not comparable to the other columns, as the methods are different. ![OpenOrca Preview2 BigBench-Hard Performance](https://huggingface.co/Open-Orca/OpenOrcaxOpenChat-Preview2-13B/resolve/main/Images/OpenOrcaP2BigBenchHardEval.png "BigBench-Hard Performance") ## HuggingFaceH4 Open LLM Leaderboard Performance We have run our own tests using parameters matching the [HuggingFaceH4 Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) evals. We place #1 for all 13B models at release time! ![OpenOrca Preview2 HuggingFace Leaderboard Internal Performance](https://huggingface.co/Open-Orca/OpenOrcaxOpenChat-Preview2-13B/resolve/main/Images/OpenOrcaP2HuggingFaceLeaderboard.png "HuggingFace Leaderboard Internal Performance") Update Aug 10th: The official results on the leaderboard are below. ![OpenOrca Preview2 HuggingFace Leaderboard Performance](https://huggingface.co/Open-Orca/OpenOrcaxOpenChat-Preview2-13B/resolve/main/Images/OpenOrcaP2HFLeaderboardOfficial.png "HuggingFace Leaderboard Performance") Since our release, a new model which merges an Orca-style model with a Platypus (trained on STEM and logic) model places narrowly above ours, but we were #1 at release time. Below we also highlight how our model fits relative to models of all sizes on the current (as of Aug 10th, 2023) leaderboard. ![OpenOrca Preview2 HuggingFace Leaderboard Performance](https://huggingface.co/Open-Orca/OpenOrcaxOpenChat-Preview2-13B/resolve/main/Images/OpenOrcaP2HFLeaderboardFull.png "HuggingFace Full Leaderboard") Notably, performance is beyond falcon-40b-instruct, and close to LLaMA1-65B base. ## GPT4ALL Leaderboard Performance We have tested using parameters matching the GPT4ALL Benchmark Suite and report our results and placement vs their official reporting below. We place #1 for all open models and come within comparison of `text-davinci-003`, a proprietary OpenAI model an order of magnitude larger. ![OpenOrca Preview2 GPT4ALL Performance](https://huggingface.co/Open-Orca/OpenOrcaxOpenChat-Preview2-13B/resolve/main/Images/OpenOrcaP2GPT4ALL_Leaderboard.png "GPT4ALL Performance") # Dataset We used a curated, filtered selection of most of the GPT-4 augmented data from our OpenOrca dataset, which aims to reproduce the Orca Research Paper dataset. Further details of our curation practices will be forthcoming with our full model releases. # Training We trained with 8x A100-80G GPUs for 46 hours, completing 5 epochs of full fine tuning on our dataset in one training run. This contrasts with the 20x A100-80G GPUs for 200 hours used in the Orca paper, for only 3 epochs, and requiring stacked training (which is known to suffer catastrophic forgetting). Our compute requirement was <1/10th that of the original Orca. Commodity cost was ~$600. Please await our full releases for further training details. # Serving This model is most easily served with [OpenChat's](https://github.com/imoneoi/openchat) customized vLLM OpenAI-compatible API server. This is highly recommended as it is by far the fastest in terms of inference speed and is a quick and easy option for setup. We also illustrate setup of Oobabooga/text-generation-webui below. The settings outlined there will also apply to other uses of `Transformers`. ## Serving Quantized Pre-quantized models are now available courtesy of our friend TheBloke: * GGML: https://huggingface.co/TheBloke/OpenOrcaxOpenChat-Preview2-13B-GGML * GPTQ: https://huggingface.co/TheBloke/OpenOrcaxOpenChat-Preview2-13B-GPTQ The serving instructions below only apply to the unquantized model being presented in the repository you are viewing here. There are some notes, such as on use of the prompt format, that will still apply to the quantized models though. ## Serving with OpenChat [Install OpenChat](https://github.com/imoneoi/openchat/#installation) After installation, run: ```bash python -m ochat.serving.openai_api_server \ --model-type openchat_llama2 \ --model Open-Orca/OpenOrcaxOpenChat-Preview2-13B \ --engine-use-ray --worker-use-ray --max-num-batched-tokens 5120 ``` Follow the OpenChat documentation to use features such as tensor parallelism on consumer GPUs, API keys, and logging. You may then connect to the OpenAI-compatible API endpoint with tools such as [BetterGPT.chat](https://bettergpt.chat). ## Serving with Oobabooga / text-generation-webui The model may also be loaded via [oobabooga/text-generation-webui](https://github.com/oobabooga/text-generation-webui/) in a similar manner to other models. See the requirements below. Note that inference with just the Transformers library is significantly slower than using the recommended OpenChat vLLM server. ### Oobabooga Key Requirements * You will first need to download the model as you normally do to the "`models/`" folder of your `text-generation-webui` installation. * To use the unquantized model presented here, select "`Transformers`"" in the webui's "`Model`" tab "`Model loader`" dropdown. * You will likely want to tick "`auto-devices`". The model will require >40GB VRAM after loading in context for inference. * The model was trained in bf16, so tick the "`bf16`" box for best performance. * It will run safely on single GPUs with VRAM >=48GB (e.g. A6000) * If using consumer GPUs, e.g. 2x RTX3090 24GB, you will likely want to enter "18,17" under "`tensor_split`" to split the model across both GPUs * The model will perform significantly better if you use the appropriate prompting template * We will submit a PR to include our prompting template into text-generation-webui soon * For now, manually enter the settings described in the following sections: ### Oobabooga Chat Settings In the "`Chat settings`" tab, select the following settings: For "`User String`" ... ``` User: ``` For "`Bot string`" ... ``` Assistant: ``` For "`Context`", this is analogous to system prompt. It is not necessary, but we have found good results with the below example. System prompts used in the Orca training also work well. ... ``` You are a helpful assistant. Please answer truthfully and write out your thinking step by step to be sure you get the right answer. If you make a mistake or encounter an error in your thinking, say so out loud and attempt to correct it. If you don't know or aren't sure about something, say so clearly. You will act as a professional logician, mathematician, and physicist. You will also act as the most appropriate type of expert to answer any particular question or solve the relevant problem; state which expert type your are, if so. Also think of any particular named expert that would be ideal to answer the relevant question or solve the relevant problem; name and act as them, if appropriate. ``` For "`Turn template`", this is absolutely essential to have. You will get poor, mixed up output without this template ... ``` <\|user\|> <\|user-message\|><\|end_of_turn\|>\n<\|bot\|> <\|bot-message\|>\n ``` When done, it should look as below: <img src="https://huggingface.co/Open-Orca/OpenOrcaxOpenChat-Preview2-13B/resolve/main/Images/OpenOrcaLlama2OobaboogaChatInstructionTemplate.png" style="width: 40%"> You may then save this as a named template preset by clicking the "Floppy" icon and giving it an appropriate name in the popup, e.g. "`OpenOrcaxOpenChat Llama2`". ### Oobabooga Text Generation Mode In the "`Text generation`" tab, select "`instruct`" as the mode: #### Mode Illustration It should look as below: <img src="https://huggingface.co/Open-Orca/OpenOrcaxOpenChat-Preview2-13B/resolve/main/Images/OpenOrcaLlama2OobaboogaInstructMode.png" style="width: 40%"> Then you should be ready to generate! # Citation ```bibtex @software{OpenOrcaxOpenChatPreview2, title = {OpenOrcaxOpenChatPreview2: Llama2-13B Model Instruct-tuned on Filtered OpenOrcaV1 GPT-4 Dataset}, author = {Guan Wang and Bleys Goodson and Wing Lian and Eugene Pentland and Austin Cook and Chanvichet Vong and "Teknium"}, year = {2023}, publisher = {HuggingFace}, journal = {HuggingFace repository}, howpublished = {\url{https://https://huggingface.co/Open-Orca/OpenOrcaxOpenChat-Preview2-13B}, } @software{openchat, title = {{OpenChat: Advancing Open-source Language Models with Imperfect Data}}, author = {Wang, Guan and Cheng, Sijie and Yu, Qiying and Liu, Changling}, doi = {10.5281/zenodo.8105775}, url = {https://github.com/imoneoi/openchat}, version = {pre-release}, year = {2023}, month = {7}, } @misc{mukherjee2023orca, title={Orca: Progressive Learning from Complex Explanation Traces of GPT-4}, author={Subhabrata Mukherjee and Arindam Mitra and Ganesh Jawahar and Sahaj Agarwal and Hamid Palangi and Ahmed Awadallah}, year={2023}, eprint={2306.02707}, archivePrefix={arXiv}, primaryClass={cs.CL} } @misc{longpre2023flan, title={The Flan Collection: Designing Data and Methods for Effective Instruction Tuning}, author={Shayne Longpre and Le Hou and Tu Vu and Albert Webson and Hyung Won Chung and Yi Tay and Denny Zhou and Quoc V. Le and Barret Zoph and Jason Wei and Adam Roberts}, year={2023}, eprint={2301.13688}, archivePrefix={arXiv}, primaryClass={cs.AI} } @misc{touvron2023llama, title={Llama 2: Open Foundation and Fine-Tuned Chat Models}, author={Hugo Touvron and Louis Martin and Kevin Stone and Peter Albert and Amjad Almahairi and Yasmine Babaei and Nikolay Bashlykov and Soumya Batra and Prajjwal Bhargava and Shruti Bhosale and Dan Bikel and Lukas Blecher and Cristian Canton Ferrer and Moya Chen and Guillem Cucurull and David Esiobu and Jude Fernandes and Jeremy Fu and Wenyin Fu and Brian Fuller and Cynthia Gao and Vedanuj Goswami and Naman Goyal and Anthony Hartshorn and Saghar Hosseini and Rui Hou and Hakan Inan and Marcin Kardas and Viktor Kerkez and Madian Khabsa and Isabel Kloumann and Artem Korenev and Punit Singh Koura and Marie-Anne Lachaux and Thibaut Lavril and Jenya Lee and Diana Liskovich and Yinghai Lu and Yuning Mao and Xavier Martinet and Todor Mihaylov and Pushkar Mishra and Igor Molybog and Yixin Nie and Andrew Poulton and Jeremy Reizenstein and Rashi Rungta and Kalyan Saladi and Alan Schelten and Ruan Silva and Eric Michael Smith and Ranjan Subramanian and Xiaoqing Ellen Tan and Binh Tang and Ross Taylor and Adina Williams and Jian Xiang Kuan and Puxin Xu and Zheng Yan and Iliyan Zarov and Yuchen Zhang and Angela Fan and Melanie Kambadur and Sharan Narang and Aurelien Rodriguez and Robert Stojnic and Sergey Edunov and Thomas Scialom}, year={2023}, eprint={2307.09288}, archivePrefix={arXiv}, } ```
redponike/Gemma-2-9B-It-SPPO-Iter3-GGUF	redponike	2024-06-30T20:33:51Z	4,792	0	null	[ "gguf", "region:us" ]	null	2024-06-30T18:17:42Z	GGUF quants of [UCLA-AGI/Gemma-2-9B-It-SPPO-Iter3](https://huggingface.co/UCLA-AGI/Gemma-2-9B-It-SPPO-Iter3)
timm/deit_small_patch16_224.fb_in1k	timm	2024-02-10T23:37:24Z	4,788	0	timm	[ "timm", "pytorch", "safetensors", "image-classification", "dataset:imagenet-1k", "arxiv:2012.12877", "license:apache-2.0", "region:us" ]	image-classification	2023-03-28T01:33:38Z	--- license: apache-2.0 library_name: timm tags: - image-classification - timm datasets: - imagenet-1k --- # Model card for deit_small_patch16_224.fb_in1k A DeiT image classification model. Trained on ImageNet-1k by paper authors. ## Model Details - Model Type: Image classification / feature backbone - Model Stats: - Params (M): 22.1 - GMACs: 4.6 - Activations (M): 11.9 - Image size: 224 x 224 - Papers: - Training data-efficient image transformers & distillation through attention: https://arxiv.org/abs/2012.12877 - Original: https://github.com/facebookresearch/deit - Dataset: ImageNet-1k ## Model Usage ### Image Classification ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open(urlopen( 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png' )) model = timm.create_model('deit_small_patch16_224.fb_in1k', pretrained=True) model = model.eval() # get model specific transforms (normalization, resize) data_config = timm.data.resolve_model_data_config(model) transforms = timm.data.create_transform(*data_config, is_training=False) output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1 top5_probabilities, top5_class_indices = torch.topk(output.softmax(dim=1) 100, k=5) ``` ### Image Embeddings ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open(urlopen( 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png' )) model = timm.create_model( 'deit_small_patch16_224.fb_in1k', pretrained=True, num_classes=0, # remove classifier nn.Linear ) model = model.eval() # get model specific transforms (normalization, resize) data_config = timm.data.resolve_model_data_config(model) transforms = timm.data.create_transform(**data_config, is_training=False) output = model(transforms(img).unsqueeze(0)) # output is (batch_size, num_features) shaped tensor # or equivalently (without needing to set num_classes=0) output = model.forward_features(transforms(img).unsqueeze(0)) # output is unpooled, a (1, 197, 384) shaped tensor output = model.forward_head(output, pre_logits=True) # output is a (1, num_features) shaped tensor ``` ## Model Comparison Explore the dataset and runtime metrics of this model in timm [model results](https://github.com/huggingface/pytorch-image-models/tree/main/results). ## Citation ```bibtex @InProceedings{pmlr-v139-touvron21a, title = {Training data-efficient image transformers & distillation through attention}, author = {Touvron, Hugo and Cord, Matthieu and Douze, Matthijs and Massa, Francisco and Sablayrolles, Alexandre and Jegou, Herve}, booktitle = {International Conference on Machine Learning}, pages = {10347--10357}, year = {2021}, volume = {139}, month = {July} } ``` ```bibtex @misc{rw2019timm, author = {Ross Wightman}, title = {PyTorch Image Models}, year = {2019}, publisher = {GitHub}, journal = {GitHub repository}, doi = {10.5281/zenodo.4414861}, howpublished = {\url{https://github.com/huggingface/pytorch-image-models}} } ```
Cohere/Cohere-embed-english-v3.0	Cohere	2023-11-02T12:26:14Z	4,778	34	transformers	[ "transformers", "mteb", "model-index", "endpoints_compatible", "region:us" ]	null	2023-11-02T12:24:52Z	--- tags: - mteb model-index: - name: embed-english-v3.0 results: - task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (en) config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 81.29850746268656 - type: ap value: 46.181772245676136 - type: f1 value: 75.47731234579823 - task: type: Classification dataset: type: mteb/amazon_polarity name: MTEB AmazonPolarityClassification config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 95.61824999999999 - type: ap value: 93.22525741797098 - type: f1 value: 95.61627312544859 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (en) config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 51.72 - type: f1 value: 50.529480725642465 - task: type: Retrieval dataset: type: arguana name: MTEB ArguAna config: default split: test revision: None metrics: - type: ndcg_at_10 value: 61.521 - task: type: Clustering dataset: type: mteb/arxiv-clustering-p2p name: MTEB ArxivClusteringP2P config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics: - type: v_measure value: 49.173332266218914 - task: type: Clustering dataset: type: mteb/arxiv-clustering-s2s name: MTEB ArxivClusteringS2S config: default split: test revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 metrics: - type: v_measure value: 42.1800504937582 - task: type: Reranking dataset: type: mteb/askubuntudupquestions-reranking name: MTEB AskUbuntuDupQuestions config: default split: test revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 metrics: - type: map value: 61.69942465283367 - type: mrr value: 73.8089741898606 - task: type: STS dataset: type: mteb/biosses-sts name: MTEB BIOSSES config: default split: test revision: d3fb88f8f02e40887cd149695127462bbcf29b4a metrics: - type: cos_sim_pearson value: 85.1805709775319 - type: cos_sim_spearman value: 83.50310749422796 - type: euclidean_pearson value: 83.57134970408762 - type: euclidean_spearman value: 83.50310749422796 - type: manhattan_pearson value: 83.422472116232 - type: manhattan_spearman value: 83.35611619312422 - task: type: Classification dataset: type: mteb/banking77 name: MTEB Banking77Classification config: default split: test revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 metrics: - type: accuracy value: 85.52922077922078 - type: f1 value: 85.48530911742581 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-p2p name: MTEB BiorxivClusteringP2P config: default split: test revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 metrics: - type: v_measure value: 40.95750155360001 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-s2s name: MTEB BiorxivClusteringS2S config: default split: test revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 metrics: - type: v_measure value: 37.25334765305169 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackAndroidRetrieval config: default split: test revision: None metrics: - type: ndcg_at_10 value: 50.037 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackEnglishRetrieval config: default split: test revision: None metrics: - type: ndcg_at_10 value: 49.089 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGamingRetrieval config: default split: test revision: None metrics: - type: ndcg_at_10 value: 60.523 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGisRetrieval config: default split: test revision: None metrics: - type: ndcg_at_10 value: 39.293 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackMathematicaRetrieval config: default split: test revision: None metrics: - type: ndcg_at_10 value: 30.414 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackPhysicsRetrieval config: default split: test revision: None metrics: - type: ndcg_at_10 value: 43.662 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackProgrammersRetrieval config: default split: test revision: None metrics: - type: ndcg_at_10 value: 43.667 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackRetrieval config: default split: test revision: None metrics: - type: ndcg_at_10 value: 41.53158333333334 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackStatsRetrieval config: default split: test revision: None metrics: - type: ndcg_at_10 value: 35.258 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackTexRetrieval config: default split: test revision: None metrics: - type: ndcg_at_10 value: 30.866 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackUnixRetrieval config: default split: test revision: None metrics: - type: ndcg_at_10 value: 40.643 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWebmastersRetrieval config: default split: test revision: None metrics: - type: ndcg_at_10 value: 40.663 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWordpressRetrieval config: default split: test revision: None metrics: - type: ndcg_at_10 value: 34.264 - task: type: Retrieval dataset: type: climate-fever name: MTEB ClimateFEVER config: default split: test revision: None metrics: - type: ndcg_at_10 value: 38.433 - task: type: Retrieval dataset: type: dbpedia-entity name: MTEB DBPedia config: default split: test revision: None metrics: - type: ndcg_at_10 value: 43.36 - task: type: Classification dataset: type: mteb/emotion name: MTEB EmotionClassification config: default split: test revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 metrics: - type: accuracy value: 51.574999999999996 - type: f1 value: 46.84362123583929 - task: type: Retrieval dataset: type: fever name: MTEB FEVER config: default split: test revision: None metrics: - type: ndcg_at_10 value: 88.966 - task: type: Retrieval dataset: type: fiqa name: MTEB FiQA2018 config: default split: test revision: None metrics: - type: ndcg_at_10 value: 42.189 - task: type: Retrieval dataset: type: hotpotqa name: MTEB HotpotQA config: default split: test revision: None metrics: - type: ndcg_at_10 value: 70.723 - task: type: Classification dataset: type: mteb/imdb name: MTEB ImdbClassification config: default split: test revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 metrics: - type: accuracy value: 93.56920000000001 - type: ap value: 90.56104192134326 - type: f1 value: 93.56471146876505 - task: type: Retrieval dataset: type: msmarco name: MTEB MSMARCO config: default split: test revision: None metrics: - type: ndcg_at_10 value: 42.931000000000004 - task: type: Classification dataset: type: mteb/mtop_domain name: MTEB MTOPDomainClassification (en) config: en split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 94.88372093023256 - type: f1 value: 94.64417024711646 - task: type: Classification dataset: type: mteb/mtop_intent name: MTEB MTOPIntentClassification (en) config: en split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - 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task: type: PairClassification dataset: type: mteb/twittersemeval2015-pairclassification name: MTEB TwitterSemEval2015 config: default split: test revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 metrics: - type: cos_sim_accuracy value: 86.27883411813792 - type: cos_sim_ap value: 74.80076733774258 - type: cos_sim_f1 value: 68.97989210397255 - type: cos_sim_precision value: 64.42968392120935 - type: cos_sim_recall value: 74.22163588390501 - type: dot_accuracy value: 86.27883411813792 - type: dot_ap value: 74.80076608107143 - type: dot_f1 value: 68.97989210397255 - type: dot_precision value: 64.42968392120935 - type: dot_recall value: 74.22163588390501 - type: euclidean_accuracy value: 86.27883411813792 - type: euclidean_ap value: 74.80076820459502 - type: euclidean_f1 value: 68.97989210397255 - type: euclidean_precision value: 64.42968392120935 - type: euclidean_recall value: 74.22163588390501 - type: manhattan_accuracy value: 86.23711032961793 - type: manhattan_ap value: 74.73958348950038 - type: manhattan_f1 value: 68.76052948255115 - type: manhattan_precision value: 63.207964601769916 - type: manhattan_recall value: 75.3825857519789 - type: max_accuracy value: 86.27883411813792 - type: max_ap value: 74.80076820459502 - type: max_f1 value: 68.97989210397255 - task: type: PairClassification dataset: type: mteb/twitterurlcorpus-pairclassification name: MTEB TwitterURLCorpus config: default split: test revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf metrics: - type: cos_sim_accuracy value: 89.09263787014399 - type: cos_sim_ap value: 86.46378381763645 - type: cos_sim_f1 value: 78.67838784176413 - type: cos_sim_precision value: 76.20868812238419 - type: cos_sim_recall value: 81.3135201724669 - type: dot_accuracy value: 89.09263787014399 - type: dot_ap value: 86.46378353247907 - type: dot_f1 value: 78.67838784176413 - type: dot_precision value: 76.20868812238419 - type: dot_recall value: 81.3135201724669 - type: euclidean_accuracy value: 89.09263787014399 - type: euclidean_ap value: 86.46378511891255 - type: euclidean_f1 value: 78.67838784176413 - type: euclidean_precision value: 76.20868812238419 - type: euclidean_recall value: 81.3135201724669 - type: manhattan_accuracy value: 89.09069740365584 - type: manhattan_ap value: 86.44864502475154 - type: manhattan_f1 value: 78.67372818141132 - type: manhattan_precision value: 76.29484953703704 - type: manhattan_recall value: 81.20572836464429 - type: max_accuracy value: 89.09263787014399 - type: max_ap value: 86.46378511891255 - type: max_f1 value: 78.67838784176413 --- # Cohere embed-english-v3.0 This repository contains the tokenizer for the Cohere `embed-english-v3.0` model. See our blogpost [Cohere Embed V3](https://txt.cohere.com/introducing-embed-v3/) for more details on this model. You can use the embedding model either via the Cohere API, AWS SageMaker or in your private deployments. ## Usage Cohere API The following code snippet shows the usage of the Cohere API. Install the cohere SDK via: ``` pip install -U cohere ``` Get your free API key on: www.cohere.com ```python # This snippet shows and example how to use the Cohere Embed V3 models for semantic search. # Make sure to have the Cohere SDK in at least v4.30 install: pip install -U cohere # Get your API key from: www.cohere.com import cohere import numpy as np cohere_key = "{YOUR_COHERE_API_KEY}" #Get your API key from www.cohere.com co = cohere.Client(cohere_key) docs = ["The capital of France is Paris", "PyTorch is a machine learning framework based on the Torch library.", "The average cat lifespan is between 13-17 years"] #Encode your documents with input type 'search_document' doc_emb = co.embed(docs, input_type="search_document", model="embed-english-v3.0").embeddings doc_emb = np.asarray(doc_emb) #Encode your query with input type 'search_query' query = "What is Pytorch" query_emb = co.embed([query], input_type="search_query", model="embed-english-v3.0").embeddings query_emb = np.asarray(query_emb) query_emb.shape #Compute the dot product between query embedding and document embedding scores = np.dot(query_emb, doc_emb.T)[0] #Find the highest scores max_idx = np.argsort(-scores) print(f"Query: {query}") for idx in max_idx: print(f"Score: {scores[idx]:.2f}") print(docs[idx]) print("--------") ``` ## Usage AWS SageMaker The embedding model can be privately deployed in your AWS Cloud using our [AWS SageMaker marketplace offering](https://aws.amazon.com/marketplace/pp/prodview-z6huxszcqc25i). It runs privately in your VPC, with latencies as low as 5ms for query encoding. ## Usage AWS Bedrock Soon the model will also be available via AWS Bedrock. Stay tuned ## Private Deployment You want to run the model on your own hardware? [Contact Sales](https://cohere.com/contact-sales) to learn more. ## Supported Languages This model was trained on nearly 1B English training pairs. Evaluation results can be found in the [Embed V3.0 Benchmark Results spreadsheet](https://docs.google.com/spreadsheets/d/1w7gnHWMDBdEUrmHgSfDnGHJgVQE5aOiXCCwO3uNH_mI/edit?usp=sharing).
LiheYoung/depth_anything_vitb14	LiheYoung	2024-01-25T08:10:55Z	4,778	3	transformers	[ "transformers", "pytorch", "depth_anything", "depth-estimation", "arxiv:2401.10891", "endpoints_compatible", "region:us" ]	depth-estimation	2024-01-23T07:30:13Z	--- tags: - depth_anything - depth-estimation --- # Depth Anything model, base The model card for our paper [Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data](https://arxiv.org/abs/2401.10891). You may also try our [demo](https://huggingface.co/spaces/LiheYoung/Depth-Anything) and visit our [project page](https://depth-anything.github.io/). ## Installation First, install the Depth Anything package: ``` git clone https://github.com/LiheYoung/Depth-Anything cd Depth-Anything pip install -r requirements.txt ``` ## Usage Here's how to run the model: ```python import numpy as np from PIL import Image import cv2 import torch from depth_anything.dpt import DepthAnything from depth_anything.util.transform import Resize, NormalizeImage, PrepareForNet from torchvision.transforms import Compose model = DepthAnything.from_pretrained("LiheYoung/depth_anything_vitb14") transform = Compose([ Resize( width=518, height=518, resize_target=False, keep_aspect_ratio=True, ensure_multiple_of=14, resize_method='lower_bound', image_interpolation_method=cv2.INTER_CUBIC, ), NormalizeImage(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), PrepareForNet(), ]) image = Image.open("...") image = np.array(image) / 255.0 image = transform({'image': image})['image'] image = torch.from_numpy(image).unsqueeze(0) depth = model(image) ```
Muennighoff/SGPT-5.8B-weightedmean-msmarco-specb-bitfit	Muennighoff	2023-03-27T22:26:36Z	4,776	23	sentence-transformers	[ "sentence-transformers", "pytorch", "gptj", "feature-extraction", "sentence-similarity", "mteb", "arxiv:2202.08904", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	sentence-similarity	2022-03-02T23:29:04Z	--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - mteb model-index: - name: SGPT-5.8B-weightedmean-msmarco-specb-bitfit results: - task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (en) config: en split: test revision: 2d8a100785abf0ae21420d2a55b0c56e3e1ea996 metrics: - type: accuracy value: 69.22388059701493 - type: ap value: 32.04724673950256 - type: f1 value: 63.25719825770428 - task: type: Classification dataset: type: mteb/amazon_polarity name: MTEB AmazonPolarityClassification config: default split: test revision: 80714f8dcf8cefc218ef4f8c5a966dd83f75a0e1 metrics: - type: accuracy value: 71.26109999999998 - type: ap value: 66.16336378255403 - type: f1 value: 70.89719145825303 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (en) config: en split: test revision: c379a6705fec24a2493fa68e011692605f44e119 metrics: - type: accuracy value: 39.19199999999999 - type: f1 value: 38.580766731113826 - task: type: Retrieval dataset: type: arguana name: MTEB ArguAna config: default split: test revision: 5b3e3697907184a9b77a3c99ee9ea1a9cbb1e4e3 metrics: - type: map_at_1 value: 27.311999999999998 - type: map_at_10 value: 42.620000000000005 - type: map_at_100 value: 43.707 - type: map_at_1000 value: 43.714999999999996 - type: map_at_3 value: 37.624 - type: map_at_5 value: 40.498 - type: mrr_at_1 value: 27.667 - type: mrr_at_10 value: 42.737 - type: mrr_at_100 value: 43.823 - type: mrr_at_1000 value: 43.830999999999996 - type: mrr_at_3 value: 37.743 - type: mrr_at_5 value: 40.616 - type: ndcg_at_1 value: 27.311999999999998 - type: ndcg_at_10 value: 51.37500000000001 - type: ndcg_at_100 value: 55.778000000000006 - type: ndcg_at_1000 value: 55.96600000000001 - type: ndcg_at_3 value: 41.087 - type: ndcg_at_5 value: 46.269 - type: precision_at_1 value: 27.311999999999998 - type: precision_at_10 value: 7.945 - type: precision_at_100 value: 0.9820000000000001 - type: precision_at_1000 value: 0.1 - type: precision_at_3 value: 17.046 - type: precision_at_5 value: 12.745000000000001 - type: recall_at_1 value: 27.311999999999998 - type: recall_at_10 value: 79.445 - type: recall_at_100 value: 98.151 - type: recall_at_1000 value: 99.57300000000001 - type: recall_at_3 value: 51.13799999999999 - type: recall_at_5 value: 63.727000000000004 - task: type: Clustering dataset: type: mteb/arxiv-clustering-p2p name: MTEB ArxivClusteringP2P config: default split: test revision: 0bbdb47bcbe3a90093699aefeed338a0f28a7ee8 metrics: - type: v_measure value: 45.59037428592033 - task: type: Clustering dataset: type: mteb/arxiv-clustering-s2s name: MTEB ArxivClusteringS2S config: default split: test revision: b73bd54100e5abfa6e3a23dcafb46fe4d2438dc3 metrics: - type: v_measure value: 38.86371701986363 - task: type: Reranking dataset: type: mteb/askubuntudupquestions-reranking name: MTEB AskUbuntuDupQuestions config: default split: test revision: 4d853f94cd57d85ec13805aeeac3ae3e5eb4c49c metrics: - type: map value: 61.625568691427766 - type: mrr value: 75.83256386580486 - task: type: STS dataset: type: mteb/biosses-sts name: MTEB BIOSSES config: default split: test revision: 9ee918f184421b6bd48b78f6c714d86546106103 metrics: - type: cos_sim_pearson value: 89.96074355094802 - type: cos_sim_spearman value: 86.2501580394454 - type: euclidean_pearson value: 82.18427440380462 - type: euclidean_spearman value: 80.14760935017947 - type: manhattan_pearson value: 82.24621578156392 - type: manhattan_spearman value: 80.00363016590163 - task: type: Classification dataset: type: mteb/banking77 name: MTEB Banking77Classification config: default split: test revision: 44fa15921b4c889113cc5df03dd4901b49161ab7 metrics: - type: accuracy value: 84.49350649350649 - type: f1 value: 84.4249343233736 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-p2p name: MTEB BiorxivClusteringP2P config: default split: test revision: 11d0121201d1f1f280e8cc8f3d98fb9c4d9f9c55 metrics: - type: v_measure value: 36.551459722989385 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-s2s name: MTEB BiorxivClusteringS2S config: default split: test revision: c0fab014e1bcb8d3a5e31b2088972a1e01547dc1 metrics: - type: v_measure value: 33.69901851846774 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackAndroidRetrieval config: default split: test revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db metrics: - type: map_at_1 value: 30.499 - type: map_at_10 value: 41.208 - type: map_at_100 value: 42.638 - type: map_at_1000 value: 42.754 - type: map_at_3 value: 37.506 - type: map_at_5 value: 39.422000000000004 - type: mrr_at_1 value: 37.339 - type: mrr_at_10 value: 47.051 - type: mrr_at_100 value: 47.745 - type: mrr_at_1000 value: 47.786 - type: mrr_at_3 value: 44.086999999999996 - type: mrr_at_5 value: 45.711 - type: ndcg_at_1 value: 37.339 - type: ndcg_at_10 value: 47.666 - type: ndcg_at_100 value: 52.994 - type: ndcg_at_1000 value: 54.928999999999995 - type: ndcg_at_3 value: 41.982 - type: ndcg_at_5 value: 44.42 - type: precision_at_1 value: 37.339 - type: precision_at_10 value: 9.127 - type: precision_at_100 value: 1.4749999999999999 - type: precision_at_1000 value: 0.194 - type: precision_at_3 value: 20.076 - type: precision_at_5 value: 14.449000000000002 - type: recall_at_1 value: 30.499 - type: recall_at_10 value: 60.328 - type: recall_at_100 value: 82.57900000000001 - type: recall_at_1000 value: 95.074 - type: recall_at_3 value: 44.17 - type: recall_at_5 value: 50.94 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackEnglishRetrieval config: default split: test revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db metrics: - type: map_at_1 value: 30.613 - type: map_at_10 value: 40.781 - type: map_at_100 value: 42.018 - type: map_at_1000 value: 42.132999999999996 - type: map_at_3 value: 37.816 - type: map_at_5 value: 39.389 - type: mrr_at_1 value: 38.408 - type: mrr_at_10 value: 46.631 - type: mrr_at_100 value: 47.332 - type: mrr_at_1000 value: 47.368 - type: mrr_at_3 value: 44.384 - type: mrr_at_5 value: 45.661 - type: ndcg_at_1 value: 38.408 - type: ndcg_at_10 value: 46.379999999999995 - type: ndcg_at_100 value: 50.81 - type: ndcg_at_1000 value: 52.663000000000004 - type: ndcg_at_3 value: 42.18 - type: ndcg_at_5 value: 43.974000000000004 - type: precision_at_1 value: 38.408 - type: precision_at_10 value: 8.656 - type: precision_at_100 value: 1.3860000000000001 - type: precision_at_1000 value: 0.184 - type: precision_at_3 value: 20.276 - type: precision_at_5 value: 14.241999999999999 - type: recall_at_1 value: 30.613 - type: recall_at_10 value: 56.44 - type: recall_at_100 value: 75.044 - type: recall_at_1000 value: 86.426 - type: recall_at_3 value: 43.766 - type: recall_at_5 value: 48.998000000000005 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGamingRetrieval config: default split: test revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db metrics: - type: map_at_1 value: 37.370999999999995 - type: map_at_10 value: 49.718 - type: map_at_100 value: 50.737 - type: map_at_1000 value: 50.79 - type: map_at_3 value: 46.231 - type: map_at_5 value: 48.329 - type: mrr_at_1 value: 42.884 - type: mrr_at_10 value: 53.176 - type: mrr_at_100 value: 53.81700000000001 - type: mrr_at_1000 value: 53.845 - type: mrr_at_3 value: 50.199000000000005 - type: mrr_at_5 value: 52.129999999999995 - type: ndcg_at_1 value: 42.884 - type: ndcg_at_10 value: 55.826 - type: ndcg_at_100 value: 59.93000000000001 - type: ndcg_at_1000 value: 61.013 - type: ndcg_at_3 value: 49.764 - type: ndcg_at_5 value: 53.025999999999996 - type: precision_at_1 value: 42.884 - type: precision_at_10 value: 9.046999999999999 - type: precision_at_100 value: 1.212 - type: precision_at_1000 value: 0.135 - type: precision_at_3 value: 22.131999999999998 - type: precision_at_5 value: 15.524 - type: recall_at_1 value: 37.370999999999995 - type: recall_at_10 value: 70.482 - type: recall_at_100 value: 88.425 - type: recall_at_1000 value: 96.03399999999999 - type: recall_at_3 value: 54.43 - type: recall_at_5 value: 62.327999999999996 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGisRetrieval config: default split: test revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db metrics: - type: map_at_1 value: 22.875999999999998 - type: map_at_10 value: 31.715 - type: map_at_100 value: 32.847 - type: map_at_1000 value: 32.922000000000004 - type: map_at_3 value: 29.049999999999997 - type: map_at_5 value: 30.396 - type: mrr_at_1 value: 24.52 - type: mrr_at_10 value: 33.497 - type: mrr_at_100 value: 34.455000000000005 - type: mrr_at_1000 value: 34.510000000000005 - type: mrr_at_3 value: 30.791 - type: mrr_at_5 value: 32.175 - type: ndcg_at_1 value: 24.52 - type: ndcg_at_10 value: 36.95 - type: ndcg_at_100 value: 42.238 - type: ndcg_at_1000 value: 44.147999999999996 - type: ndcg_at_3 value: 31.435000000000002 - type: ndcg_at_5 value: 33.839000000000006 - type: precision_at_1 value: 24.52 - type: precision_at_10 value: 5.9319999999999995 - type: precision_at_100 value: 0.901 - type: precision_at_1000 value: 0.11 - type: precision_at_3 value: 13.446 - type: precision_at_5 value: 9.469 - type: recall_at_1 value: 22.875999999999998 - type: recall_at_10 value: 51.38 - type: recall_at_100 value: 75.31099999999999 - type: recall_at_1000 value: 89.718 - type: recall_at_3 value: 36.26 - type: recall_at_5 value: 42.248999999999995 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackMathematicaRetrieval config: default split: test revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db metrics: - type: map_at_1 value: 14.984 - type: map_at_10 value: 23.457 - type: map_at_100 value: 24.723 - type: map_at_1000 value: 24.846 - type: map_at_3 value: 20.873 - type: map_at_5 value: 22.357 - type: mrr_at_1 value: 18.159 - type: mrr_at_10 value: 27.431 - type: mrr_at_100 value: 28.449 - type: mrr_at_1000 value: 28.52 - type: mrr_at_3 value: 24.979000000000003 - type: mrr_at_5 value: 26.447 - type: ndcg_at_1 value: 18.159 - type: ndcg_at_10 value: 28.627999999999997 - type: ndcg_at_100 value: 34.741 - type: ndcg_at_1000 value: 37.516 - type: ndcg_at_3 value: 23.902 - type: ndcg_at_5 value: 26.294 - type: precision_at_1 value: 18.159 - type: precision_at_10 value: 5.485 - type: precision_at_100 value: 0.985 - type: precision_at_1000 value: 0.136 - type: precision_at_3 value: 11.774 - type: precision_at_5 value: 8.731 - type: recall_at_1 value: 14.984 - type: recall_at_10 value: 40.198 - type: recall_at_100 value: 67.11500000000001 - type: recall_at_1000 value: 86.497 - type: recall_at_3 value: 27.639000000000003 - type: recall_at_5 value: 33.595000000000006 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackPhysicsRetrieval config: default split: test revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db metrics: - type: map_at_1 value: 29.067 - type: map_at_10 value: 39.457 - type: map_at_100 value: 40.83 - type: map_at_1000 value: 40.94 - type: map_at_3 value: 35.995 - type: map_at_5 value: 38.159 - type: mrr_at_1 value: 34.937000000000005 - type: mrr_at_10 value: 44.755 - type: mrr_at_100 value: 45.549 - type: mrr_at_1000 value: 45.589 - type: mrr_at_3 value: 41.947 - type: mrr_at_5 value: 43.733 - type: ndcg_at_1 value: 34.937000000000005 - type: ndcg_at_10 value: 45.573 - type: ndcg_at_100 value: 51.266999999999996 - type: ndcg_at_1000 value: 53.184 - type: ndcg_at_3 value: 39.961999999999996 - type: ndcg_at_5 value: 43.02 - type: precision_at_1 value: 34.937000000000005 - type: precision_at_10 value: 8.296000000000001 - type: precision_at_100 value: 1.32 - type: precision_at_1000 value: 0.167 - type: precision_at_3 value: 18.8 - type: precision_at_5 value: 13.763 - type: recall_at_1 value: 29.067 - type: recall_at_10 value: 58.298 - type: recall_at_100 value: 82.25099999999999 - type: recall_at_1000 value: 94.476 - type: recall_at_3 value: 42.984 - type: recall_at_5 value: 50.658 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackProgrammersRetrieval config: default split: test revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db metrics: - type: map_at_1 value: 25.985999999999997 - type: map_at_10 value: 35.746 - type: map_at_100 value: 37.067 - type: map_at_1000 value: 37.191 - type: map_at_3 value: 32.599000000000004 - type: map_at_5 value: 34.239000000000004 - type: mrr_at_1 value: 31.735000000000003 - type: mrr_at_10 value: 40.515 - type: mrr_at_100 value: 41.459 - type: mrr_at_1000 value: 41.516 - type: mrr_at_3 value: 37.938 - type: mrr_at_5 value: 39.25 - type: ndcg_at_1 value: 31.735000000000003 - type: ndcg_at_10 value: 41.484 - type: ndcg_at_100 value: 47.047 - type: ndcg_at_1000 value: 49.427 - type: ndcg_at_3 value: 36.254999999999995 - type: ndcg_at_5 value: 38.375 - type: precision_at_1 value: 31.735000000000003 - type: precision_at_10 value: 7.66 - type: precision_at_100 value: 1.234 - type: precision_at_1000 value: 0.16 - type: precision_at_3 value: 17.427999999999997 - type: precision_at_5 value: 12.328999999999999 - type: recall_at_1 value: 25.985999999999997 - type: recall_at_10 value: 53.761 - type: recall_at_100 value: 77.149 - type: recall_at_1000 value: 93.342 - type: recall_at_3 value: 39.068000000000005 - type: recall_at_5 value: 44.693 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackRetrieval config: default split: test revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db metrics: - type: map_at_1 value: 24.949749999999998 - type: map_at_10 value: 34.04991666666667 - type: map_at_100 value: 35.26825 - type: map_at_1000 value: 35.38316666666667 - type: map_at_3 value: 31.181333333333335 - type: map_at_5 value: 32.77391666666667 - type: mrr_at_1 value: 29.402833333333334 - type: mrr_at_10 value: 38.01633333333333 - type: mrr_at_100 value: 38.88033333333334 - type: mrr_at_1000 value: 38.938500000000005 - type: mrr_at_3 value: 35.5175 - type: mrr_at_5 value: 36.93808333333333 - type: ndcg_at_1 value: 29.402833333333334 - type: ndcg_at_10 value: 39.403166666666664 - type: ndcg_at_100 value: 44.66408333333333 - type: ndcg_at_1000 value: 46.96283333333333 - type: ndcg_at_3 value: 34.46633333333334 - type: ndcg_at_5 value: 36.78441666666667 - type: precision_at_1 value: 29.402833333333334 - type: precision_at_10 value: 6.965833333333333 - type: precision_at_100 value: 1.1330833333333334 - type: precision_at_1000 value: 0.15158333333333335 - type: precision_at_3 value: 15.886666666666665 - type: precision_at_5 value: 11.360416666666667 - type: recall_at_1 value: 24.949749999999998 - type: recall_at_10 value: 51.29325 - type: recall_at_100 value: 74.3695 - type: recall_at_1000 value: 90.31299999999999 - type: recall_at_3 value: 37.580083333333334 - type: recall_at_5 value: 43.529666666666664 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackStatsRetrieval config: default split: test revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db metrics: - type: map_at_1 value: 22.081999999999997 - type: map_at_10 value: 29.215999999999998 - type: map_at_100 value: 30.163 - type: map_at_1000 value: 30.269000000000002 - type: map_at_3 value: 26.942 - type: map_at_5 value: 28.236 - type: mrr_at_1 value: 24.847 - type: mrr_at_10 value: 31.918999999999997 - type: mrr_at_100 value: 32.817 - type: mrr_at_1000 value: 32.897 - type: mrr_at_3 value: 29.831000000000003 - type: mrr_at_5 value: 31.019999999999996 - type: ndcg_at_1 value: 24.847 - type: ndcg_at_10 value: 33.4 - type: ndcg_at_100 value: 38.354 - type: ndcg_at_1000 value: 41.045 - type: ndcg_at_3 value: 29.236 - type: ndcg_at_5 value: 31.258000000000003 - type: precision_at_1 value: 24.847 - type: precision_at_10 value: 5.353 - type: precision_at_100 value: 0.853 - type: precision_at_1000 value: 0.116 - type: precision_at_3 value: 12.679000000000002 - type: precision_at_5 value: 8.988 - type: recall_at_1 value: 22.081999999999997 - type: recall_at_10 value: 43.505 - type: recall_at_100 value: 66.45400000000001 - type: recall_at_1000 value: 86.378 - type: recall_at_3 value: 32.163000000000004 - type: recall_at_5 value: 37.059999999999995 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackTexRetrieval config: default split: test revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db metrics: - type: map_at_1 value: 15.540000000000001 - type: map_at_10 value: 22.362000000000002 - type: map_at_100 value: 23.435 - type: map_at_1000 value: 23.564 - type: map_at_3 value: 20.143 - type: map_at_5 value: 21.324 - type: mrr_at_1 value: 18.892 - type: mrr_at_10 value: 25.942999999999998 - type: mrr_at_100 value: 26.883000000000003 - type: mrr_at_1000 value: 26.968999999999998 - type: mrr_at_3 value: 23.727 - type: mrr_at_5 value: 24.923000000000002 - type: ndcg_at_1 value: 18.892 - type: ndcg_at_10 value: 26.811 - type: ndcg_at_100 value: 32.066 - type: ndcg_at_1000 value: 35.166 - type: ndcg_at_3 value: 22.706 - type: ndcg_at_5 value: 24.508 - type: precision_at_1 value: 18.892 - type: precision_at_10 value: 4.942 - type: precision_at_100 value: 0.878 - type: precision_at_1000 value: 0.131 - type: precision_at_3 value: 10.748000000000001 - type: precision_at_5 value: 7.784000000000001 - type: recall_at_1 value: 15.540000000000001 - type: recall_at_10 value: 36.742999999999995 - type: recall_at_100 value: 60.525 - type: recall_at_1000 value: 82.57600000000001 - type: recall_at_3 value: 25.252000000000002 - type: recall_at_5 value: 29.872 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackUnixRetrieval config: default split: test revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db metrics: - type: map_at_1 value: 24.453 - type: map_at_10 value: 33.363 - type: map_at_100 value: 34.579 - type: map_at_1000 value: 34.686 - type: map_at_3 value: 30.583 - type: map_at_5 value: 32.118 - type: mrr_at_1 value: 28.918 - type: mrr_at_10 value: 37.675 - type: mrr_at_100 value: 38.567 - type: mrr_at_1000 value: 38.632 - type: mrr_at_3 value: 35.260999999999996 - type: mrr_at_5 value: 36.576 - type: ndcg_at_1 value: 28.918 - type: ndcg_at_10 value: 38.736 - type: ndcg_at_100 value: 44.261 - type: ndcg_at_1000 value: 46.72 - type: ndcg_at_3 value: 33.81 - type: ndcg_at_5 value: 36.009 - type: precision_at_1 value: 28.918 - type: precision_at_10 value: 6.586 - type: precision_at_100 value: 1.047 - type: precision_at_1000 value: 0.13699999999999998 - type: precision_at_3 value: 15.360999999999999 - type: precision_at_5 value: 10.857999999999999 - type: recall_at_1 value: 24.453 - type: recall_at_10 value: 50.885999999999996 - type: recall_at_100 value: 75.03 - type: recall_at_1000 value: 92.123 - type: recall_at_3 value: 37.138 - type: recall_at_5 value: 42.864999999999995 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWebmastersRetrieval config: default split: test revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db metrics: - type: map_at_1 value: 24.57 - type: map_at_10 value: 33.672000000000004 - type: map_at_100 value: 35.244 - type: map_at_1000 value: 35.467 - type: map_at_3 value: 30.712 - type: map_at_5 value: 32.383 - type: mrr_at_1 value: 29.644 - type: mrr_at_10 value: 38.344 - type: mrr_at_100 value: 39.219 - type: mrr_at_1000 value: 39.282000000000004 - type: mrr_at_3 value: 35.771 - type: mrr_at_5 value: 37.273 - type: ndcg_at_1 value: 29.644 - type: ndcg_at_10 value: 39.567 - type: ndcg_at_100 value: 45.097 - type: ndcg_at_1000 value: 47.923 - type: ndcg_at_3 value: 34.768 - type: ndcg_at_5 value: 37.122 - type: precision_at_1 value: 29.644 - type: precision_at_10 value: 7.5889999999999995 - type: precision_at_100 value: 1.478 - type: precision_at_1000 value: 0.23500000000000001 - type: precision_at_3 value: 16.337 - type: precision_at_5 value: 12.055 - type: recall_at_1 value: 24.57 - type: recall_at_10 value: 51.00900000000001 - type: recall_at_100 value: 75.423 - type: recall_at_1000 value: 93.671 - type: recall_at_3 value: 36.925999999999995 - type: recall_at_5 value: 43.245 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWordpressRetrieval config: default split: test revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db metrics: - type: map_at_1 value: 21.356 - type: map_at_10 value: 27.904 - type: map_at_100 value: 28.938000000000002 - type: map_at_1000 value: 29.036 - type: map_at_3 value: 25.726 - type: map_at_5 value: 26.935 - type: mrr_at_1 value: 22.551 - type: mrr_at_10 value: 29.259 - type: mrr_at_100 value: 30.272 - type: mrr_at_1000 value: 30.348000000000003 - type: mrr_at_3 value: 27.295 - type: mrr_at_5 value: 28.358 - type: ndcg_at_1 value: 22.551 - type: ndcg_at_10 value: 31.817 - type: ndcg_at_100 value: 37.164 - type: ndcg_at_1000 value: 39.82 - type: ndcg_at_3 value: 27.595999999999997 - type: ndcg_at_5 value: 29.568 - type: precision_at_1 value: 22.551 - type: precision_at_10 value: 4.917 - type: precision_at_100 value: 0.828 - type: precision_at_1000 value: 0.11399999999999999 - type: precision_at_3 value: 11.583 - type: precision_at_5 value: 8.133 - type: recall_at_1 value: 21.356 - type: recall_at_10 value: 42.489 - type: recall_at_100 value: 67.128 - type: recall_at_1000 value: 87.441 - type: recall_at_3 value: 31.165 - type: recall_at_5 value: 35.853 - task: type: Retrieval dataset: type: climate-fever name: MTEB ClimateFEVER config: default split: test revision: 392b78eb68c07badcd7c2cd8f39af108375dfcce metrics: - type: map_at_1 value: 12.306000000000001 - type: map_at_10 value: 21.523 - type: map_at_100 value: 23.358 - type: map_at_1000 value: 23.541 - type: map_at_3 value: 17.809 - type: map_at_5 value: 19.631 - type: mrr_at_1 value: 27.948 - type: mrr_at_10 value: 40.355000000000004 - type: mrr_at_100 value: 41.166000000000004 - type: mrr_at_1000 value: 41.203 - type: mrr_at_3 value: 36.819 - type: mrr_at_5 value: 38.958999999999996 - type: ndcg_at_1 value: 27.948 - type: ndcg_at_10 value: 30.462 - type: ndcg_at_100 value: 37.473 - type: ndcg_at_1000 value: 40.717999999999996 - type: ndcg_at_3 value: 24.646 - type: ndcg_at_5 value: 26.642 - type: precision_at_1 value: 27.948 - type: precision_at_10 value: 9.648 - type: precision_at_100 value: 1.7239999999999998 - type: precision_at_1000 value: 0.232 - type: precision_at_3 value: 18.48 - type: precision_at_5 value: 14.293 - type: recall_at_1 value: 12.306000000000001 - type: recall_at_10 value: 37.181 - type: recall_at_100 value: 61.148 - type: recall_at_1000 value: 79.401 - type: recall_at_3 value: 22.883 - type: recall_at_5 value: 28.59 - task: type: Retrieval dataset: type: dbpedia-entity name: MTEB DBPedia config: default split: test revision: f097057d03ed98220bc7309ddb10b71a54d667d6 metrics: - type: map_at_1 value: 9.357 - type: map_at_10 value: 18.849 - type: map_at_100 value: 25.369000000000003 - type: map_at_1000 value: 26.950000000000003 - type: map_at_3 value: 13.625000000000002 - type: map_at_5 value: 15.956999999999999 - type: mrr_at_1 value: 67.75 - type: mrr_at_10 value: 74.734 - type: mrr_at_100 value: 75.1 - type: mrr_at_1000 value: 75.10900000000001 - type: mrr_at_3 value: 73.542 - type: mrr_at_5 value: 74.167 - type: ndcg_at_1 value: 55.375 - type: ndcg_at_10 value: 39.873999999999995 - type: ndcg_at_100 value: 43.098 - type: ndcg_at_1000 value: 50.69200000000001 - type: ndcg_at_3 value: 44.856 - type: ndcg_at_5 value: 42.138999999999996 - type: precision_at_1 value: 67.75 - type: precision_at_10 value: 31.1 - type: precision_at_100 value: 9.303 - type: precision_at_1000 value: 2.0060000000000002 - type: precision_at_3 value: 48.25 - type: precision_at_5 value: 40.949999999999996 - type: recall_at_1 value: 9.357 - type: recall_at_10 value: 23.832 - type: recall_at_100 value: 47.906 - type: recall_at_1000 value: 71.309 - type: recall_at_3 value: 14.512 - type: recall_at_5 value: 18.3 - task: type: Classification dataset: type: mteb/emotion name: MTEB EmotionClassification config: default split: test revision: 829147f8f75a25f005913200eb5ed41fae320aa1 metrics: - type: accuracy value: 49.655 - type: f1 value: 45.51976190938951 - task: type: Retrieval dataset: type: fever name: MTEB FEVER config: default split: test revision: 1429cf27e393599b8b359b9b72c666f96b2525f9 metrics: - type: map_at_1 value: 62.739999999999995 - type: map_at_10 value: 73.07000000000001 - type: map_at_100 value: 73.398 - type: map_at_1000 value: 73.41 - type: map_at_3 value: 71.33800000000001 - type: map_at_5 value: 72.423 - type: mrr_at_1 value: 67.777 - type: mrr_at_10 value: 77.873 - type: mrr_at_100 value: 78.091 - type: mrr_at_1000 value: 78.094 - type: mrr_at_3 value: 76.375 - type: mrr_at_5 value: 77.316 - type: ndcg_at_1 value: 67.777 - type: ndcg_at_10 value: 78.24 - type: ndcg_at_100 value: 79.557 - type: ndcg_at_1000 value: 79.814 - type: ndcg_at_3 value: 75.125 - type: ndcg_at_5 value: 76.834 - type: precision_at_1 value: 67.777 - type: precision_at_10 value: 9.832 - type: precision_at_100 value: 1.061 - type: precision_at_1000 value: 0.11 - type: precision_at_3 value: 29.433 - type: precision_at_5 value: 18.665000000000003 - type: recall_at_1 value: 62.739999999999995 - type: recall_at_10 value: 89.505 - type: recall_at_100 value: 95.102 - type: recall_at_1000 value: 96.825 - type: recall_at_3 value: 81.028 - type: recall_at_5 value: 85.28099999999999 - task: type: Retrieval dataset: type: fiqa name: MTEB FiQA2018 config: default split: test revision: 41b686a7f28c59bcaaa5791efd47c67c8ebe28be metrics: - type: map_at_1 value: 18.467 - type: map_at_10 value: 30.020999999999997 - type: map_at_100 value: 31.739 - type: map_at_1000 value: 31.934 - type: map_at_3 value: 26.003 - type: map_at_5 value: 28.338 - type: mrr_at_1 value: 35.339999999999996 - type: mrr_at_10 value: 44.108999999999995 - type: mrr_at_100 value: 44.993 - type: mrr_at_1000 value: 45.042 - type: mrr_at_3 value: 41.667 - type: mrr_at_5 value: 43.14 - type: ndcg_at_1 value: 35.339999999999996 - type: ndcg_at_10 value: 37.202 - type: ndcg_at_100 value: 43.852999999999994 - type: ndcg_at_1000 value: 47.235 - type: ndcg_at_3 value: 33.5 - type: ndcg_at_5 value: 34.985 - type: precision_at_1 value: 35.339999999999996 - type: precision_at_10 value: 10.247 - type: precision_at_100 value: 1.7149999999999999 - type: precision_at_1000 value: 0.232 - type: precision_at_3 value: 22.222 - type: precision_at_5 value: 16.573999999999998 - type: recall_at_1 value: 18.467 - type: recall_at_10 value: 44.080999999999996 - type: recall_at_100 value: 68.72200000000001 - type: recall_at_1000 value: 89.087 - type: recall_at_3 value: 30.567 - type: recall_at_5 value: 36.982 - task: type: Retrieval dataset: type: hotpotqa name: MTEB HotpotQA config: default split: test revision: 766870b35a1b9ca65e67a0d1913899973551fc6c metrics: - type: map_at_1 value: 35.726 - type: map_at_10 value: 50.207 - type: map_at_100 value: 51.05499999999999 - type: map_at_1000 value: 51.12799999999999 - type: map_at_3 value: 47.576 - type: map_at_5 value: 49.172 - type: mrr_at_1 value: 71.452 - type: mrr_at_10 value: 77.41900000000001 - type: mrr_at_100 value: 77.711 - type: mrr_at_1000 value: 77.723 - type: mrr_at_3 value: 76.39399999999999 - type: mrr_at_5 value: 77.00099999999999 - type: ndcg_at_1 value: 71.452 - type: ndcg_at_10 value: 59.260999999999996 - type: ndcg_at_100 value: 62.424 - type: ndcg_at_1000 value: 63.951 - type: ndcg_at_3 value: 55.327000000000005 - type: ndcg_at_5 value: 57.416999999999994 - type: precision_at_1 value: 71.452 - type: precision_at_10 value: 12.061 - type: precision_at_100 value: 1.455 - type: precision_at_1000 value: 0.166 - type: precision_at_3 value: 34.36 - type: precision_at_5 value: 22.266 - type: recall_at_1 value: 35.726 - type: recall_at_10 value: 60.304 - type: recall_at_100 value: 72.75500000000001 - type: recall_at_1000 value: 82.978 - type: recall_at_3 value: 51.54 - type: recall_at_5 value: 55.665 - task: type: Classification dataset: type: mteb/imdb name: MTEB ImdbClassification config: default split: test revision: 8d743909f834c38949e8323a8a6ce8721ea6c7f4 metrics: - type: accuracy value: 66.63759999999999 - type: ap value: 61.48938261286748 - type: f1 value: 66.35089269264965 - task: type: Retrieval dataset: type: msmarco name: MTEB MSMARCO config: default split: validation revision: e6838a846e2408f22cf5cc337ebc83e0bcf77849 metrics: - type: map_at_1 value: 20.842 - type: map_at_10 value: 32.992 - type: map_at_100 value: 34.236 - type: map_at_1000 value: 34.286 - type: map_at_3 value: 29.049000000000003 - type: map_at_5 value: 31.391999999999996 - type: mrr_at_1 value: 21.375 - type: mrr_at_10 value: 33.581 - type: mrr_at_100 value: 34.760000000000005 - type: mrr_at_1000 value: 34.803 - type: mrr_at_3 value: 29.704000000000004 - type: mrr_at_5 value: 32.015 - type: ndcg_at_1 value: 21.375 - type: ndcg_at_10 value: 39.905 - type: ndcg_at_100 value: 45.843 - type: ndcg_at_1000 value: 47.083999999999996 - type: ndcg_at_3 value: 31.918999999999997 - type: ndcg_at_5 value: 36.107 - type: precision_at_1 value: 21.375 - type: precision_at_10 value: 6.393 - type: precision_at_100 value: 0.935 - type: precision_at_1000 value: 0.104 - type: precision_at_3 value: 13.663 - type: precision_at_5 value: 10.324 - type: recall_at_1 value: 20.842 - type: recall_at_10 value: 61.17 - type: recall_at_100 value: 88.518 - type: recall_at_1000 value: 97.993 - type: recall_at_3 value: 39.571 - type: recall_at_5 value: 49.653999999999996 - task: type: Classification dataset: type: mteb/mtop_domain name: MTEB MTOPDomainClassification (en) config: en split: test revision: a7e2a951126a26fc8c6a69f835f33a346ba259e3 metrics: - type: accuracy value: 93.46557227542178 - type: f1 value: 92.87345917772146 - task: type: Classification dataset: type: mteb/mtop_intent name: MTEB MTOPIntentClassification (en) config: en split: test revision: 6299947a7777084cc2d4b64235bf7190381ce755 metrics: - type: accuracy value: 72.42134062927497 - type: f1 value: 55.03624810959269 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (en) config: en split: test revision: 072a486a144adf7f4479a4a0dddb2152e161e1ea metrics: - type: accuracy value: 70.3866845998655 - type: f1 value: 68.9674519872921 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (en) config: en split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 76.27774041694687 - type: f1 value: 76.72936190462792 - task: type: Clustering dataset: type: mteb/medrxiv-clustering-p2p name: MTEB MedrxivClusteringP2P config: default split: test revision: dcefc037ef84348e49b0d29109e891c01067226b metrics: - type: v_measure value: 31.511745925773337 - task: type: Clustering dataset: type: mteb/medrxiv-clustering-s2s name: MTEB MedrxivClusteringS2S config: default split: test revision: 3cd0e71dfbe09d4de0f9e5ecba43e7ce280959dc metrics: - type: v_measure value: 28.764235987575365 - task: type: Reranking dataset: type: mteb/mind_small name: MTEB MindSmallReranking config: default split: test revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 metrics: - type: map value: 32.29353136386601 - type: mrr value: 33.536774455851685 - task: type: Retrieval dataset: type: nfcorpus name: MTEB NFCorpus config: default split: test revision: 7eb63cc0c1eb59324d709ebed25fcab851fa7610 metrics: - type: map_at_1 value: 5.702 - type: map_at_10 value: 13.642000000000001 - type: map_at_100 value: 17.503 - type: map_at_1000 value: 19.126 - type: map_at_3 value: 9.748 - type: map_at_5 value: 11.642 - type: mrr_at_1 value: 45.82 - type: mrr_at_10 value: 54.821 - type: mrr_at_100 value: 55.422000000000004 - type: mrr_at_1000 value: 55.452999999999996 - type: mrr_at_3 value: 52.373999999999995 - type: mrr_at_5 value: 53.937000000000005 - type: ndcg_at_1 value: 44.272 - type: ndcg_at_10 value: 36.213 - type: ndcg_at_100 value: 33.829 - type: ndcg_at_1000 value: 42.557 - type: ndcg_at_3 value: 40.814 - type: ndcg_at_5 value: 39.562000000000005 - type: precision_at_1 value: 45.511 - type: precision_at_10 value: 27.214 - type: precision_at_100 value: 8.941 - type: precision_at_1000 value: 2.1870000000000003 - type: precision_at_3 value: 37.874 - type: precision_at_5 value: 34.489 - type: recall_at_1 value: 5.702 - type: recall_at_10 value: 17.638 - type: recall_at_100 value: 34.419 - type: recall_at_1000 value: 66.41 - type: recall_at_3 value: 10.914 - type: recall_at_5 value: 14.032 - task: type: Retrieval dataset: type: nq name: MTEB NQ config: default split: test revision: 6062aefc120bfe8ece5897809fb2e53bfe0d128c metrics: - type: map_at_1 value: 30.567 - type: map_at_10 value: 45.01 - type: map_at_100 value: 46.091 - type: map_at_1000 value: 46.126 - type: map_at_3 value: 40.897 - type: map_at_5 value: 43.301 - type: mrr_at_1 value: 34.56 - type: mrr_at_10 value: 47.725 - type: mrr_at_100 value: 48.548 - type: mrr_at_1000 value: 48.571999999999996 - type: mrr_at_3 value: 44.361 - type: mrr_at_5 value: 46.351 - type: ndcg_at_1 value: 34.531 - type: ndcg_at_10 value: 52.410000000000004 - type: ndcg_at_100 value: 56.999 - type: ndcg_at_1000 value: 57.830999999999996 - type: ndcg_at_3 value: 44.734 - type: ndcg_at_5 value: 48.701 - type: precision_at_1 value: 34.531 - type: precision_at_10 value: 8.612 - type: precision_at_100 value: 1.118 - type: precision_at_1000 value: 0.12 - type: precision_at_3 value: 20.307 - type: precision_at_5 value: 14.519000000000002 - type: recall_at_1 value: 30.567 - type: recall_at_10 value: 72.238 - type: recall_at_100 value: 92.154 - type: recall_at_1000 value: 98.375 - type: recall_at_3 value: 52.437999999999995 - type: recall_at_5 value: 61.516999999999996 - task: type: Retrieval dataset: type: quora name: MTEB QuoraRetrieval config: default split: test revision: 6205996560df11e3a3da9ab4f926788fc30a7db4 metrics: - type: map_at_1 value: 65.98 - type: map_at_10 value: 80.05600000000001 - type: map_at_100 value: 80.76299999999999 - type: map_at_1000 value: 80.786 - type: map_at_3 value: 76.848 - type: map_at_5 value: 78.854 - type: mrr_at_1 value: 75.86 - type: mrr_at_10 value: 83.397 - type: mrr_at_100 value: 83.555 - type: mrr_at_1000 value: 83.557 - type: mrr_at_3 value: 82.033 - type: mrr_at_5 value: 82.97 - type: ndcg_at_1 value: 75.88000000000001 - type: ndcg_at_10 value: 84.58099999999999 - type: ndcg_at_100 value: 86.151 - type: ndcg_at_1000 value: 86.315 - type: ndcg_at_3 value: 80.902 - type: ndcg_at_5 value: 82.953 - type: precision_at_1 value: 75.88000000000001 - type: precision_at_10 value: 12.986 - type: precision_at_100 value: 1.5110000000000001 - type: precision_at_1000 value: 0.156 - type: precision_at_3 value: 35.382999999999996 - type: precision_at_5 value: 23.555999999999997 - type: recall_at_1 value: 65.98 - type: recall_at_10 value: 93.716 - type: recall_at_100 value: 99.21799999999999 - type: recall_at_1000 value: 99.97 - type: recall_at_3 value: 83.551 - type: recall_at_5 value: 88.998 - task: type: Clustering dataset: type: mteb/reddit-clustering name: MTEB RedditClustering config: default split: test revision: b2805658ae38990172679479369a78b86de8c390 metrics: - type: v_measure value: 40.45148482612238 - task: type: Clustering dataset: type: mteb/reddit-clustering-p2p name: MTEB RedditClusteringP2P config: default split: test revision: 385e3cb46b4cfa89021f56c4380204149d0efe33 metrics: - type: v_measure value: 55.749490673039126 - task: type: Retrieval dataset: type: scidocs name: MTEB SCIDOCS config: default split: test revision: 5c59ef3e437a0a9651c8fe6fde943e7dce59fba5 metrics: - type: map_at_1 value: 4.903 - type: map_at_10 value: 11.926 - type: map_at_100 value: 13.916999999999998 - type: map_at_1000 value: 14.215 - type: map_at_3 value: 8.799999999999999 - type: map_at_5 value: 10.360999999999999 - type: mrr_at_1 value: 24.099999999999998 - type: mrr_at_10 value: 34.482 - type: mrr_at_100 value: 35.565999999999995 - type: mrr_at_1000 value: 35.619 - type: mrr_at_3 value: 31.433 - type: mrr_at_5 value: 33.243 - type: ndcg_at_1 value: 24.099999999999998 - type: ndcg_at_10 value: 19.872999999999998 - type: ndcg_at_100 value: 27.606 - type: ndcg_at_1000 value: 32.811 - type: ndcg_at_3 value: 19.497999999999998 - type: ndcg_at_5 value: 16.813 - type: precision_at_1 value: 24.099999999999998 - type: precision_at_10 value: 10.08 - type: precision_at_100 value: 2.122 - type: precision_at_1000 value: 0.337 - type: precision_at_3 value: 18.2 - type: precision_at_5 value: 14.62 - type: recall_at_1 value: 4.903 - type: recall_at_10 value: 20.438000000000002 - type: recall_at_100 value: 43.043 - type: recall_at_1000 value: 68.41000000000001 - type: recall_at_3 value: 11.068 - type: recall_at_5 value: 14.818000000000001 - task: type: STS dataset: type: mteb/sickr-sts name: MTEB SICK-R config: default split: test revision: 20a6d6f312dd54037fe07a32d58e5e168867909d metrics: - type: cos_sim_pearson value: 78.58086597995997 - type: cos_sim_spearman value: 69.63214182814991 - type: euclidean_pearson value: 72.76175489042691 - type: euclidean_spearman value: 67.84965161872971 - type: manhattan_pearson value: 72.73812689782592 - type: manhattan_spearman value: 67.83610439531277 - task: type: STS dataset: type: mteb/sts12-sts name: MTEB STS12 config: default split: test revision: fdf84275bb8ce4b49c971d02e84dd1abc677a50f metrics: - type: cos_sim_pearson value: 75.13970861325006 - type: cos_sim_spearman value: 67.5020551515597 - type: euclidean_pearson value: 66.33415412418276 - type: euclidean_spearman value: 66.82145056673268 - type: manhattan_pearson value: 66.55489484006415 - type: manhattan_spearman value: 66.95147433279057 - 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type: manhattan_ap value: 62.41882051995577 - type: manhattan_f1 value: 59.32101002778785 - type: manhattan_precision value: 54.71361711611321 - type: manhattan_recall value: 64.77572559366754 - type: max_accuracy value: 84.09131549144662 - type: max_ap value: 66.86677647503386 - type: max_f1 value: 62.94631710362049 - task: type: PairClassification dataset: type: mteb/twitterurlcorpus-pairclassification name: MTEB TwitterURLCorpus config: default split: test revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf metrics: - type: cos_sim_accuracy value: 88.79574649745798 - type: cos_sim_ap value: 85.28960532524223 - type: cos_sim_f1 value: 77.98460043358001 - type: cos_sim_precision value: 75.78090948714224 - type: cos_sim_recall value: 80.32029565753002 - type: dot_accuracy value: 85.5939767920208 - type: dot_ap value: 76.14131706694056 - type: dot_f1 value: 72.70246298696868 - type: dot_precision value: 65.27012127894156 - type: dot_recall value: 82.04496458269172 - type: euclidean_accuracy value: 86.72332828812046 - type: euclidean_ap value: 80.84854809178995 - type: euclidean_f1 value: 72.47657499809551 - type: euclidean_precision value: 71.71717171717171 - type: euclidean_recall value: 73.25223283030489 - type: manhattan_accuracy value: 86.7563162184189 - type: manhattan_ap value: 80.87598895575626 - type: manhattan_f1 value: 72.54617892068092 - type: manhattan_precision value: 68.49268225960881 - type: manhattan_recall value: 77.10963966738528 - type: max_accuracy value: 88.79574649745798 - type: max_ap value: 85.28960532524223 - type: max_f1 value: 77.98460043358001 --- # SGPT-5.8B-weightedmean-msmarco-specb-bitfit ## Usage For usage instructions, refer to our codebase: https://github.com/Muennighoff/sgpt ## Evaluation Results For eval results, refer to our paper: https://arxiv.org/abs/2202.08904 ## Training The model was trained with the parameters: DataLoader: `torch.utils.data.dataloader.DataLoader` of length 249592 with parameters: ``` {'batch_size': 2, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` Loss: `sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters: ``` {'scale': 20.0, 'similarity_fct': 'cos_sim'} ``` Parameters of the fit()-Method: ``` { "epochs": 10, "evaluation_steps": 0, "evaluator": "NoneType", "max_grad_norm": 1, "optimizer_class": "<class 'transformers.optimization.AdamW'>", "optimizer_params": { "lr": 5e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 1000, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 300, 'do_lower_case': False}) with Transformer model: GPTJModel (1): Pooling({'word_embedding_dimension': 4096, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': True, 'pooling_mode_lasttoken': False}) ) ``` ## Citing & Authors ```bibtex @article{muennighoff2022sgpt, title={SGPT: GPT Sentence Embeddings for Semantic Search}, author={Muennighoff, Niklas}, journal={arXiv preprint arXiv:2202.08904}, year={2022} } ```
allenai/OLMo-7B	allenai	2024-06-25T19:29:40Z	4,772	623	transformers	[ "transformers", "pytorch", "safetensors", "hf_olmo", "text-generation", "custom_code", "en", "dataset:allenai/dolma", "arxiv:2402.00838", "arxiv:2302.13971", "license:apache-2.0", "autotrain_compatible", "region:us" ]	text-generation	2024-01-09T23:13:23Z	--- license: apache-2.0 datasets: - allenai/dolma language: - en --- <img src="https://allenai.org/olmo/olmo-7b-animation.gif" alt="OLMo Logo" width="800" style="margin-left:'auto' margin-right:'auto' display:'block'"/> # Model Card for OLMo 7B <!-- Provide a quick summary of what the model is/does. --> For transformers versions v4.40.0 or newer, we suggest using [OLMo 7B HF](https://huggingface.co/allenai/OLMo-7B-hf) instead. OLMo is a series of Open Language Models designed to enable the science of language models. The OLMo models are trained on the [Dolma](https://huggingface.co/datasets/allenai/dolma) dataset. We release all code, checkpoints, logs (coming soon), and details involved in training these models. A new version of this model with a 24 point improvement on MMLU is available [here](https://huggingface.co/allenai/OLMo-1.7-7B). ## Model Details The core models released in this batch are the following: \| Size \| Training Tokens \| Layers \| Hidden Size \| Attention Heads \| Context Length \| \|------\|--------\|---------\|-------------\|-----------------\|----------------\| \| [OLMo 1B](https://huggingface.co/allenai/OLMo-1B) \| 3 Trillion \|16 \| 2048 \| 16 \| 2048 \| \| [OLMo 7B](https://huggingface.co/allenai/OLMo-7B) \| 2.5 Trillion \| 32 \| 4096 \| 32 \| 2048 \| \| [OLMo 7B Twin 2T](https://huggingface.co/allenai/OLMo-7B-Twin-2T) \| 2 Trillion \| 32 \| 4096 \| 32 \| 2048 \| We are releasing many checkpoints for these models, for every 1000 traing steps. The naming convention is `step1000-tokens4B`. In particular, we focus on four revisions of the 7B models: \| Name \| HF Repo \| Model Revision \| Tokens \| Note \| \|------------\|---------\|----------------\|-------------------\|------\| \|OLMo 7B\| [allenai/OLMo-7B](https://huggingface.co/allenai/OLMo-7B)\|`main`\| 2.5T\|The base OLMo 7B model\| \|OLMo 7B (not annealed)\|[allenai/OLMo-7B](https://huggingface.co/allenai/OLMo-7B)\|step556000-tokens2460B\|2.5T\| learning rate not annealed to 0\| \|OLMo 7B-2T\|[allenai/OLMo-7B](https://huggingface.co/allenai/OLMo-7B)\| step452000-tokens2000B \|2T\| OLMo checkpoint at 2T tokens\| \|OLMo-7B-Twin-2T\|[allenai/OLMo-7B-Twin-2T](https://huggingface.co/allenai/OLMo-7B-Twin-2T)\|`main`\|2T\| Twin version on different hardware\| To load a specific model revision with HuggingFace, simply add the argument `revision`: ```bash from hf_olmo import OLMoForCausalLM # pip install ai2-olmo olmo = OLMoForCausalLM.from_pretrained("allenai/OLMo-7B", revision="step1000-tokens4B") ``` All revisions/branches are listed in the file `revisions.txt`. Or, you can access all the revisions for the models via the following code snippet: ```python from huggingface_hub import list_repo_refs out = list_repo_refs("allenai/OLMo-7B") branches = [b.name for b in out.branches] ``` A few revisions were lost due to an error, but the vast majority are present. ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: Allen Institute for AI (AI2) - Supported by: Databricks, Kempner Institute for the Study of Natural and Artificial Intelligence at Harvard University, AMD, CSC (Lumi Supercomputer), UW - Model type: a Transformer style autoregressive language model. - Language(s) (NLP): English - License: The code and model are released under Apache 2.0. - Contact: Technical inquiries: `olmo at allenai dot org`. Press: `press at allenai dot org` - Date cutoff: Feb./March 2023 based on Dolma dataset version. ### Model Sources <!-- Provide the basic links for the model. --> - Project Page: https://allenai.org/olmo - Repositories: - Core repo (training, inference, fine-tuning etc.): https://github.com/allenai/OLMo - Evaluation code: https://github.com/allenai/OLMo-Eval - Further fine-tuning code: https://github.com/allenai/open-instruct - Paper: [Link](https://arxiv.org/abs/2402.00838) - Technical blog post: https://blog.allenai.org/olmo-open-language-model-87ccfc95f580 - W&B Logs: https://wandb.ai/ai2-llm/OLMo-7B/reports/OLMo-7B--Vmlldzo2NzQyMzk5 <!-- - Press release: TODO --> ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Inference Quickly get inference running with the following required installation: ```bash pip install ai2-olmo ``` Now, proceed as usual with HuggingFace: ```python from hf_olmo import OLMoForCausalLM, OLMoTokenizerFast olmo = OLMoForCausalLM.from_pretrained("allenai/OLMo-7B") tokenizer = OLMoTokenizerFast.from_pretrained("allenai/OLMo-7B") message = ["Language modeling is "] inputs = tokenizer(message, return_tensors='pt', return_token_type_ids=False) # optional verifying cuda # inputs = {k: v.to('cuda') for k,v in inputs.items()} # olmo = olmo.to('cuda') response = olmo.generate(inputs, max_new_tokens=100, do_sample=True, top_k=50, top_p=0.95) print(tokenizer.batch_decode(response, skip_special_tokens=True)[0]) >> 'Language modeling is the first step to build natural language generation...' ``` You can make this slightly faster by quantizing the model, e.g. `AutoModelForCausalLM.from_pretrained("allenai/OLMo-7B", torch_dtype=torch.float16, load_in_8bit=True)` (requires `bitsandbytes`). The quantized model is more sensitive to typing / cuda, so it is recommended to pass the inputs as `inputs.input_ids.to('cuda')` to avoid potential issues. Note, you may see the following error if `ai2-olmo` is not installed correctly, which is caused by internal Python check naming. We'll update the code soon to make this error clearer. ```bash raise ImportError( ImportError: This modeling file requires the following packages that were not found in your environment: hf_olmo. Run `pip install hf_olmo` ``` ### Fine-tuning Model fine-tuning can be done from the final checkpoint (the `main` revision of this model) or many intermediate checkpoints. Two recipes for tuning are available. 1. Fine-tune with the OLMo repository: ```bash torchrun --nproc_per_node=8 scripts/train.py {path_to_train_config} \ --data.paths=[{path_to_data}/input_ids.npy] \ --data.label_mask_paths=[{path_to_data}/label_mask.npy] \ --load_path={path_to_checkpoint} \ --reset_trainer_state ``` For more documentation, see the [GitHub readme](https://github.com/allenai/OLMo?tab=readme-ov-file#fine-tuning). 2. Further fine-tuning support is being developing in AI2's Open Instruct repository. Details are [here](https://github.com/allenai/open-instruct). ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> Core model results for the 7B model are found below. \| \| [Llama 7B](https://arxiv.org/abs/2302.13971) \| [Llama 2 7B](https://huggingface.co/meta-llama/Llama-2-7b) \| [Falcon 7B](https://huggingface.co/tiiuae/falcon-7b) \| [MPT 7B](https://huggingface.co/mosaicml/mpt-7b) \| OLMo 7B (ours) \| \| --------------------------------- \| -------- \| ---------- \| --------- \| ------ \| ------- \| \| arc_challenge \| 44.5 \| 39.8 \| 47.5 \| 46.5 \| 48.5 \| \| arc_easy \| 57.0 \| 57.7 \| 70.4 \| 70.5 \| 65.4 \| \| boolq \| 73.1 \| 73.5 \| 74.6 \| 74.2 \| 73.4 \| \| copa \| 85.0 \| 87.0 \| 86.0 \| 85.0 \| 90 \| \| hellaswag \| 74.5 \| 74.5 \| 75.9 \| 77.6 \| 76.4 \| \| openbookqa \| 49.8 \| 48.4 \| 53.0 \| 48.6 \| 50.2 \| \| piqa \| 76.3 \| 76.4 \| 78.5 \| 77.3 \| 78.4 \| \| sciq \| 89.5 \| 90.8 \| 93.9 \| 93.7 \| 93.8 \| \| winogrande \| 68.2 \| 67.3 \| 68.9 \| 69.9 \| 67.9 \| \| Core tasks average \| 68.7 \| 68.4 \| 72.1 \| 71.5 \| 71.6 \| \| truthfulQA (MC2) \| 33.9 \| 38.5 \| 34.0 \| 33 \| 36.0 \| \| MMLU (5 shot MC) \| 31.5 \| 45.0 \| 24.0 \| 30.8 \| 28.3 \| \| GSM8k (mixed eval.) \| 10.0 (8shot CoT) \| 12.0 (8shot CoT) \| 4.0 (5 shot) \| 4.5 (5 shot) \| 8.5 (8shot CoT) \| \| Full average** \| 57.8 \| 59.3 \| 59.2 \| 59.3 \| 59.8 \| And for the 1B model: \| task \| random \| [StableLM 2 1.6b](https://huggingface.co/stabilityai/stablelm-2-1_6b)\* \| [Pythia 1B](https://huggingface.co/EleutherAI/pythia-1b) \| [TinyLlama 1.1B](https://huggingface.co/TinyLlama/TinyLlama-1.1B-intermediate-step-1195k-token-2.5T) \| OLMo 1B (ours) \| \| ------------------------------------------------------------------------------------------------------------------------------------------------------------ \| ------ \| ----------------- \| --------- \| -------------------------------------- \| ------- \| \| arc_challenge \| 25 \| 43.81 \| 33.11 \| 34.78 \| 34.45 \| \| arc_easy \| 25 \| 63.68 \| 50.18 \| 53.16 \| 58.07 \| \| boolq \| 50 \| 76.6 \| 61.8 \| 64.6 \| 60.7 \| \| copa \| 50 \| 84 \| 72 \| 78 \| 79 \| \| hellaswag \| 25 \| 68.2 \| 44.7 \| 58.7 \| 62.5 \| \| openbookqa \| 25 \| 45.8 \| 37.8 \| 43.6 \| 46.4 \| \| piqa \| 50 \| 74 \| 69.1 \| 71.1 \| 73.7 \| \| sciq \| 25 \| 94.7 \| 86 \| 90.5 \| 88.1 \| \| winogrande \| 50 \| 64.9 \| 53.3 \| 58.9 \| 58.9 \| \| Average \| 36.11 \| 68.41 \| 56.44 \| 61.48 \| 62.42 \| \Unlike OLMo, Pythia, and TinyLlama, StabilityAI has not disclosed yet the data StableLM was trained on, making comparisons with other efforts challenging. ## Model Details ### Data For training data details, please see the [Dolma](https://huggingface.co/datasets/allenai/dolma) documentation. ### Architecture OLMo 7B architecture with peer models for comparison. \| \| OLMo 7B* \| [Llama 2 7B](https://huggingface.co/meta-llama/Llama-2-7b) \| [OpenLM 7B](https://laion.ai/blog/open-lm/) \| [Falcon 7B](https://huggingface.co/tiiuae/falcon-7b) \| PaLM 8B \| \|------------------------\|-------------------\|---------------------\|--------------------\|--------------------\|------------------\| \| d_model \| 4096 \| 4096 \| 4096 \| 4544 \| 4096 \| \| num heads \| 32 \| 32 \| 32 \| 71 \| 16 \| \| num layers \| 32 \| 32 \| 32 \| 32 \| 32 \| \| MLP ratio \| ~8/3 \| ~8/3 \| ~8/3 \| 4 \| 4 \| \| LayerNorm type \| non-parametric LN \| RMSNorm \| parametric LN \| parametric LN \| parametric LN \| \| pos embeddings \| RoPE \| RoPE \| RoPE \| RoPE \| RoPE \| \| attention variant \| full \| GQA \| full \| MQA \| MQA \| \| biases \| none \| none \| in LN only \| in LN only \| none \| \| block type \| sequential \| sequential \| sequential \| parallel \| parallel \| \| activation \| SwiGLU \| SwiGLU \| SwiGLU \| GeLU \| SwiGLU \| \| sequence length \| 2048 \| 4096 \| 2048 \| 2048 \| 2048 \| \| batch size (instances) \| 2160 \| 1024 \| 2048 \| 2304 \| 512 \| \| batch size (tokens) \| ~4M \| ~4M \| ~4M \| ~4M \| ~1M \| \| weight tying \| no \| no \| no \| no \| yes \| ### Hyperparameters AdamW optimizer parameters are shown below. \| Size \| Peak LR \| Betas \| Epsilon \| Weight Decay \| \|------\|------------\|-----------------\|-------------\|--------------\| \| 1B \| 4.0E-4 \| (0.9, 0.95) \| 1.0E-5 \| 0.1 \| \| 7B \| 3.0E-4 \| (0.9, 0.99) \| 1.0E-5 \| 0.1 \| Optimizer settings comparison with peer models. \| \| OLMo 7B \| [Llama 2 7B](https://huggingface.co/meta-llama/Llama-2-7b) \| [OpenLM 7B](https://laion.ai/blog/open-lm/) \| [Falcon 7B](https://huggingface.co/tiiuae/falcon-7b) \| \|-----------------------\|------------------\|---------------------\|--------------------\|--------------------\| \| warmup steps \| 5000 \| 2000 \| 2000 \| 1000 \| \| peak LR \| 3.0E-04 \| 3.0E-04 \| 3.0E-04 \| 6.0E-04 \| \| minimum LR \| 3.0E-05 \| 3.0E-05 \| 3.0E-05 \| 1.2E-05 \| \| weight decay \| 0.1 \| 0.1 \| 0.1 \| 0.1 \| \| beta1 \| 0.9 \| 0.9 \| 0.9 \| 0.99 \| \| beta2 \| 0.95 \| 0.95 \| 0.95 \| 0.999 \| \| epsilon \| 1.0E-05 \| 1.0E-05 \| 1.0E-05 \| 1.0E-05 \| \| LR schedule \| linear \| cosine \| cosine \| cosine \| \| gradient clipping \| global 1.0 \| global 1.0 \| global 1.0 \| global 1.0 \| \| gradient reduce dtype \| FP32 \| FP32 \| FP32 \| BF16 \| \| optimizer state dtype \| FP32 \| most likely FP32 \| FP32 \| FP32 \| ## Environmental Impact OLMo 7B variants were either trained on MI250X GPUs at the LUMI supercomputer, or A100-40GB GPUs provided by MosaicML. A summary of the environmental impact. Further details are available in the paper. \| \| GPU Type \| Power Consumption From GPUs \| Carbon Intensity (kg CO₂e/KWh) \| Carbon Emissions (tCO₂eq) \| \|-----------\|------------\|-----------------------------\|--------------------------------\|---------------------------\| \| OLMo 7B Twin \| MI250X ([LUMI supercomputer](https://www.lumi-supercomputer.eu)) \| 135 MWh \| 0* \| 0* \| \| OLMo 7B \| A100-40GB ([MosaicML](https://www.mosaicml.com)) \| 104 MWh \| 0.656 \| 75.05 \| ## Bias, Risks, and Limitations Like any base language model or fine-tuned model without safety filtering, it is relatively easy for a user to prompt these models to generate harmful and generally sensitive content. Such content can also be produced unintentionally, especially in the case of bias, so we recommend users consider the risks of applications of this technology. Otherwise, many facts from OLMo or any LLM will often not be true, so they should be checked. ## Citation BibTeX: ``` @article{Groeneveld2023OLMo, title={OLMo: Accelerating the Science of Language Models}, author={Groeneveld, Dirk and Beltagy, Iz and Walsh, Pete and Bhagia, Akshita and Kinney, Rodney and Tafjord, Oyvind and Jha, Ananya Harsh and Ivison, Hamish and Magnusson, Ian and Wang, Yizhong and Arora, Shane and Atkinson, David and Authur, Russell and Chandu, Khyathi and Cohan, Arman and Dumas, Jennifer and Elazar, Yanai and Gu, Yuling and Hessel, Jack and Khot, Tushar and Merrill, William and Morrison, Jacob and Muennighoff, Niklas and Naik, Aakanksha and Nam, Crystal and Peters, Matthew E. and Pyatkin, Valentina and Ravichander, Abhilasha and Schwenk, Dustin and Shah, Saurabh and Smith, Will and Subramani, Nishant and Wortsman, Mitchell and Dasigi, Pradeep and Lambert, Nathan and Richardson, Kyle and Dodge, Jesse and Lo, Kyle and Soldaini, Luca and Smith, Noah A. and Hajishirzi, Hannaneh}, journal={Preprint}, year={2024} } ``` APA: Groeneveld, D., Beltagy, I., Walsh, P., Bhagia, A., Kinney, R., Tafjord, O., Jha, A., Ivison, H., Magnusson, I., Wang, Y., Arora, S., Atkinson, D., Authur, R., Chandu, K., Cohan, A., Dumas, J., Elazar, Y., Gu, Y., Hessel, J., Khot, T., Merrill, W., Morrison, J., Muennighoff, N., Naik, A., Nam, C., Peters, M., Pyatkin, V., Ravichander, A., Schwenk, D., Shah, S., Smith, W., Subramani, N., Wortsman, M., Dasigi, P., Lambert, N., Richardson, K., Dodge, J., Lo, K., Soldaini, L., Smith, N., & Hajishirzi, H. (2024). OLMo: Accelerating the Science of Language Models. Preprint. ## Model Card Contact For errors in this model card, contact Nathan or Akshita, `{nathanl, akshitab} at allenai dot org`.
stablediffusionapi/wildcardx-xl-fusion	stablediffusionapi	2024-03-05T22:46:24Z	4,772	2	diffusers	[ "diffusers", "modelslab.com", "stable-diffusion-api", "text-to-image", "ultra-realistic", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionXLPipeline", "region:us" ]	text-to-image	2024-03-05T22:44:38Z	--- license: creativeml-openrail-m tags: - modelslab.com - stable-diffusion-api - text-to-image - ultra-realistic pinned: true --- # WildCardX-XL-Fusion API Inference ![generated from modelslab.com](https://pub-3626123a908346a7a8be8d9295f44e26.r2.dev/generations/18691458391709677284.png) ## Get API Key Get API key from [ModelsLab API](http://modelslab.com), No Payment needed. Replace Key in below code, change model_id to "wildcardx-xl-fusion" Coding in PHP/Node/Java etc? Have a look at docs for more code examples: [View docs](https://modelslab.com/docs) Try model for free: [Generate Images](https://modelslab.com/models/wildcardx-xl-fusion) Model link: [View model](https://modelslab.com/models/wildcardx-xl-fusion) View all models: [View Models](https://modelslab.com/models) import requests import json url = "https://modelslab.com/api/v6/images/text2img" payload = json.dumps({ "key": "your_api_key", "model_id": "wildcardx-xl-fusion", "prompt": "ultra realistic close up portrait ((beautiful pale cyberpunk female with heavy black eyeliner)), blue eyes, shaved side haircut, hyper detail, cinematic lighting, magic neon, dark red city, Canon EOS R3, nikon, f/1.4, ISO 200, 1/160s, 8K, RAW, unedited, symmetrical balance, in-frame, 8K", "negative_prompt": "painting, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, skinny, glitchy, double torso, extra arms, extra hands, mangled fingers, missing lips, ugly face, distorted face, extra legs, anime", "width": "512", "height": "512", "samples": "1", "num_inference_steps": "30", "safety_checker": "no", "enhance_prompt": "yes", "seed": None, "guidance_scale": 7.5, "multi_lingual": "no", "panorama": "no", "self_attention": "no", "upscale": "no", "embeddings": "embeddings_model_id", "lora": "lora_model_id", "webhook": None, "track_id": None }) headers = { 'Content-Type': 'application/json' } response = requests.request("POST", url, headers=headers, data=payload) print(response.text) > Use this coupon code to get 25% off DMGG0RBN
mradermacher/Llama-3-Oasis-v1-OAS-8B-i1-GGUF	mradermacher	2024-06-23T12:05:34Z	4,772	1	transformers	[ "transformers", "gguf", "mergekit", "merge", "en", "base_model:grimjim/Llama-3-Oasis-v1-OAS-8B", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]	null	2024-06-05T07:38:54Z	--- base_model: grimjim/Llama-3-Oasis-v1-OAS-8B language: - en library_name: transformers license: cc-by-nc-4.0 license_link: LICENSE quantized_by: mradermacher tags: - mergekit - merge --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/grimjim/Llama-3-Oasis-v1-OAS-8B <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/Llama-3-Oasis-v1-OAS-8B-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-Oasis-v1-OAS-8B-i1-GGUF/resolve/main/Llama-3-Oasis-v1-OAS-8B.i1-IQ1_S.gguf) \| i1-IQ1_S \| 2.1 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-Oasis-v1-OAS-8B-i1-GGUF/resolve/main/Llama-3-Oasis-v1-OAS-8B.i1-IQ1_M.gguf) \| i1-IQ1_M \| 2.3 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-Oasis-v1-OAS-8B-i1-GGUF/resolve/main/Llama-3-Oasis-v1-OAS-8B.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 2.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-Oasis-v1-OAS-8B-i1-GGUF/resolve/main/Llama-3-Oasis-v1-OAS-8B.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 2.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-Oasis-v1-OAS-8B-i1-GGUF/resolve/main/Llama-3-Oasis-v1-OAS-8B.i1-IQ2_S.gguf) \| i1-IQ2_S \| 2.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-Oasis-v1-OAS-8B-i1-GGUF/resolve/main/Llama-3-Oasis-v1-OAS-8B.i1-IQ2_M.gguf) \| i1-IQ2_M \| 3.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-Oasis-v1-OAS-8B-i1-GGUF/resolve/main/Llama-3-Oasis-v1-OAS-8B.i1-Q2_K.gguf) \| i1-Q2_K \| 3.3 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-Oasis-v1-OAS-8B-i1-GGUF/resolve/main/Llama-3-Oasis-v1-OAS-8B.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 3.4 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-Oasis-v1-OAS-8B-i1-GGUF/resolve/main/Llama-3-Oasis-v1-OAS-8B.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 3.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-Oasis-v1-OAS-8B-i1-GGUF/resolve/main/Llama-3-Oasis-v1-OAS-8B.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 3.8 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-Oasis-v1-OAS-8B-i1-GGUF/resolve/main/Llama-3-Oasis-v1-OAS-8B.i1-IQ3_S.gguf) \| i1-IQ3_S \| 3.8 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-Oasis-v1-OAS-8B-i1-GGUF/resolve/main/Llama-3-Oasis-v1-OAS-8B.i1-IQ3_M.gguf) \| i1-IQ3_M \| 3.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-Oasis-v1-OAS-8B-i1-GGUF/resolve/main/Llama-3-Oasis-v1-OAS-8B.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 4.1 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-Oasis-v1-OAS-8B-i1-GGUF/resolve/main/Llama-3-Oasis-v1-OAS-8B.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 4.4 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-Oasis-v1-OAS-8B-i1-GGUF/resolve/main/Llama-3-Oasis-v1-OAS-8B.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 4.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-Oasis-v1-OAS-8B-i1-GGUF/resolve/main/Llama-3-Oasis-v1-OAS-8B.i1-Q4_0.gguf) \| i1-Q4_0 \| 4.8 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-Oasis-v1-OAS-8B-i1-GGUF/resolve/main/Llama-3-Oasis-v1-OAS-8B.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 4.8 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-Oasis-v1-OAS-8B-i1-GGUF/resolve/main/Llama-3-Oasis-v1-OAS-8B.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 5.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-Oasis-v1-OAS-8B-i1-GGUF/resolve/main/Llama-3-Oasis-v1-OAS-8B.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 5.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-Oasis-v1-OAS-8B-i1-GGUF/resolve/main/Llama-3-Oasis-v1-OAS-8B.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 5.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-Oasis-v1-OAS-8B-i1-GGUF/resolve/main/Llama-3-Oasis-v1-OAS-8B.i1-Q6_K.gguf) \| i1-Q6_K \| 6.7 \| practically like static Q6_K \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his hardware for calculating the imatrix for these quants. <!-- end -->
Mihaiii/Bulbasaur	Mihaiii	2024-04-30T07:30:42Z	4,770	1	sentence-transformers	[ "sentence-transformers", "onnx", "safetensors", "bert", "feature-extraction", "sentence-similarity", "gte", "mteb", "dataset:Mihaiii/qa-assistant", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "text-embeddings-inference", "region:us" ]	sentence-similarity	2024-04-27T09:53:29Z	--- license: mit library_name: sentence-transformers pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - gte - mteb datasets: - Mihaiii/qa-assistant model-index: - name: Bulbasaur results: - task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (en) config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 71.86567164179104 - type: ap value: 34.08685244750869 - type: f1 value: 65.66014356237362 - task: type: Classification dataset: type: mteb/amazon_polarity name: MTEB AmazonPolarityClassification config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 78.78927499999999 - type: ap value: 73.46960735629719 - type: f1 value: 78.6951990840684 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (en) config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 39.312 - type: f1 value: 38.94567141563064 - task: type: Retrieval dataset: type: mteb/arguana name: MTEB ArguAna config: default split: test revision: c22ab2a51041ffd869aaddef7af8d8215647e41a metrics: - type: map_at_1 value: 22.191 - type: map_at_10 value: 36.504 - type: map_at_100 value: 37.676 - type: map_at_1000 value: 37.693 - type: map_at_20 value: 37.329 - type: map_at_3 value: 31.840000000000003 - type: map_at_5 value: 34.333000000000006 - type: mrr_at_1 value: 23.186 - type: mrr_at_10 value: 36.856 - type: mrr_at_100 value: 38.048 - type: mrr_at_1000 value: 38.065 - type: mrr_at_20 value: 37.701 - type: mrr_at_3 value: 32.16 - type: mrr_at_5 value: 34.756 - type: ndcg_at_1 value: 22.191 - type: ndcg_at_10 value: 44.798 - type: ndcg_at_100 value: 50.141999999999996 - type: ndcg_at_1000 value: 50.599000000000004 - type: ndcg_at_20 value: 47.778999999999996 - type: ndcg_at_3 value: 35.071999999999996 - type: ndcg_at_5 value: 39.574 - type: precision_at_1 value: 22.191 - type: precision_at_10 value: 7.148000000000001 - type: precision_at_100 value: 0.9570000000000001 - type: precision_at_1000 value: 0.099 - type: precision_at_20 value: 4.1610000000000005 - type: precision_at_3 value: 14.817 - type: precision_at_5 value: 11.081000000000001 - type: recall_at_1 value: 22.191 - type: recall_at_10 value: 71.479 - type: recall_at_100 value: 95.661 - type: recall_at_1000 value: 99.289 - type: recall_at_20 value: 83.21499999999999 - type: recall_at_3 value: 44.452000000000005 - type: recall_at_5 value: 55.405 - task: type: Clustering dataset: type: mteb/arxiv-clustering-p2p name: MTEB ArxivClusteringP2P config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics: - type: v_measure value: 40.283298409035076 - type: v_measures value: [0.3532106296315629, 0.38211196645121454, 0.4115695136452048, 0.41137132653792025, 0.3837736540549879, 0.3747132869956856, 0.39691152506736527, 0.39788336468446533, 0.3642563557059312, 0.41116083049947033, 0.45922387863541325, 0.469635375348701, 0.46766327774202016, 0.4625872980544393, 0.47185794625113725, 0.47528841611119615, 0.4772024530512538, 0.4708000082870702, 0.4717644230002225, 0.4660063378028352, 0.4555746206742128, 0.28465696985786276, 0.3226387432684682, 0.36349250452617954, 0.31579079512572683, 0.23387076944848043, 0.28341764616852566, 0.16191336340497103, 0.2368224145727693, 1.0, 0.25065281219558383, 0.3532106296315629, 0.38211196645121454, 0.4115695136452048, 0.41137132653792025, 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3bdac13927fdc888b903db93b2ffdbd90b295a69 metrics: - type: map value: 31.7776266029616 - type: mrr value: 32.9057970138914 - task: type: Retrieval dataset: type: mteb/cqadupstack name: MTEB CQADupstackRetrieval config: default split: test revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4 metrics: - type: map_at_1 value: 24.78675 - type: map_at_10 value: 33.18391666666666 - type: map_at_100 value: 34.34583333333333 - type: map_at_1000 value: 34.46825 - type: map_at_20 value: 33.819 - type: map_at_3 value: 30.636500000000005 - type: map_at_5 value: 32.02091666666667 - type: mrr_at_1 value: 29.478749999999998 - type: mrr_at_10 value: 37.385 - type: mrr_at_100 value: 38.23491666666667 - type: mrr_at_1000 value: 38.298833333333334 - type: mrr_at_20 value: 37.87508333333333 - type: mrr_at_3 value: 35.089666666666666 - type: mrr_at_5 value: 36.36816666666667 - type: ndcg_at_1 value: 29.478749999999998 - type: ndcg_at_10 value: 38.2035 - type: ndcg_at_100 value: 43.301083333333324 - type: ndcg_at_1000 value: 45.758666666666656 - type: ndcg_at_20 value: 40.15116666666667 - type: ndcg_at_3 value: 33.86033333333334 - type: ndcg_at_5 value: 35.81266666666666 - type: precision_at_1 value: 29.478749999999998 - type: precision_at_10 value: 6.642833333333334 - type: precision_at_100 value: 1.08425 - type: precision_at_1000 value: 0.14850000000000002 - type: precision_at_20 value: 3.948083333333334 - type: precision_at_3 value: 15.511 - type: precision_at_5 value: 10.929833333333333 - type: recall_at_1 value: 24.78675 - type: recall_at_10 value: 48.9305 - type: recall_at_100 value: 71.49416666666666 - type: recall_at_1000 value: 88.54375 - type: recall_at_20 value: 56.06475 - type: recall_at_3 value: 36.66891666666666 - type: recall_at_5 value: 41.790499999999994 - task: type: Retrieval dataset: type: mteb/cqadupstack-android name: MTEB CQADupstackAndroidRetrieval config: default split: test revision: f46a197baaae43b4f621051089b82a364682dfeb metrics: - type: map_at_1 value: 24.271 - type: map_at_10 value: 33.5 - type: map_at_100 value: 34.818 - type: map_at_1000 value: 34.967 - type: map_at_20 value: 34.238 - type: map_at_3 value: 30.488 - type: map_at_5 value: 32.303 - type: mrr_at_1 value: 30.615 - type: mrr_at_10 value: 39.076 - type: mrr_at_100 value: 40.022000000000006 - type: mrr_at_1000 value: 40.082 - type: mrr_at_20 value: 39.669 - type: mrr_at_3 value: 36.552 - type: mrr_at_5 value: 38.096999999999994 - type: ndcg_at_1 value: 30.615 - type: ndcg_at_10 value: 39.106 - type: ndcg_at_100 value: 44.519 - type: ndcg_at_1000 value: 47.274 - type: ndcg_at_20 value: 41.289 - type: ndcg_at_3 value: 34.55 - type: ndcg_at_5 value: 36.815999999999995 - type: precision_at_1 value: 30.615 - type: precision_at_10 value: 7.5249999999999995 - type: precision_at_100 value: 1.282 - type: precision_at_1000 value: 0.181 - type: precision_at_20 value: 4.549 - type: precision_at_3 value: 16.643 - type: precision_at_5 value: 12.275 - type: recall_at_1 value: 24.271 - type: recall_at_10 value: 49.714000000000006 - type: recall_at_100 value: 72.792 - type: recall_at_1000 value: 91.21000000000001 - type: recall_at_20 value: 57.799 - type: recall_at_3 value: 36.494 - type: recall_at_5 value: 42.764 - task: type: Retrieval dataset: type: mteb/cqadupstack-english name: MTEB CQADupstackEnglishRetrieval config: default split: test revision: ad9991cb51e31e31e430383c75ffb2885547b5f0 metrics: - type: map_at_1 value: 19.414 - type: map_at_10 value: 25.766 - type: map_at_100 value: 26.627000000000002 - type: map_at_1000 value: 26.749000000000002 - type: map_at_20 value: 26.201999999999998 - type: map_at_3 value: 23.738 - type: map_at_5 value: 24.829 - type: mrr_at_1 value: 24.013 - type: mrr_at_10 value: 30.208000000000002 - type: mrr_at_100 value: 30.903000000000002 - type: mrr_at_1000 value: 30.976 - type: mrr_at_20 value: 30.585 - type: mrr_at_3 value: 28.376 - type: mrr_at_5 value: 29.462 - type: ndcg_at_1 value: 24.013 - type: ndcg_at_10 value: 29.871 - type: ndcg_at_100 value: 33.867999999999995 - type: ndcg_at_1000 value: 36.565 - type: ndcg_at_20 value: 31.251 - type: ndcg_at_3 value: 26.579000000000004 - type: ndcg_at_5 value: 28.094 - type: precision_at_1 value: 24.013 - type: precision_at_10 value: 5.503 - type: precision_at_100 value: 0.936 - type: precision_at_1000 value: 0.14100000000000001 - type: precision_at_20 value: 3.2800000000000002 - type: precision_at_3 value: 12.590000000000002 - type: precision_at_5 value: 8.994 - type: recall_at_1 value: 19.414 - type: recall_at_10 value: 37.582 - type: recall_at_100 value: 55.181000000000004 - type: recall_at_1000 value: 73.342 - type: recall_at_20 value: 42.596000000000004 - type: recall_at_3 value: 28.102 - type: recall_at_5 value: 32.267 - task: type: Retrieval dataset: type: mteb/cqadupstack-gaming name: MTEB CQADupstackGamingRetrieval config: default split: test revision: 4885aa143210c98657558c04aaf3dc47cfb54340 metrics: - type: map_at_1 value: 31.5 - type: map_at_10 value: 42.179 - type: map_at_100 value: 43.231 - type: map_at_1000 value: 43.302 - type: map_at_20 value: 42.786 - type: map_at_3 value: 39.17 - type: map_at_5 value: 40.854 - type: mrr_at_1 value: 36.113 - type: mrr_at_10 value: 45.378 - type: mrr_at_100 value: 46.153 - type: mrr_at_1000 value: 46.194 - type: mrr_at_20 value: 45.831 - type: mrr_at_3 value: 42.947 - type: mrr_at_5 value: 44.339 - type: ndcg_at_1 value: 36.113 - type: ndcg_at_10 value: 47.616 - type: ndcg_at_100 value: 52.125 - type: ndcg_at_1000 value: 53.717999999999996 - type: ndcg_at_20 value: 49.495 - type: ndcg_at_3 value: 42.354 - type: ndcg_at_5 value: 44.885999999999996 - type: precision_at_1 value: 36.113 - type: precision_at_10 value: 7.799 - type: precision_at_100 value: 1.093 - type: precision_at_1000 value: 0.129 - type: precision_at_20 value: 4.4670000000000005 - type: precision_at_3 value: 19.017999999999997 - type: precision_at_5 value: 13.254 - type: recall_at_1 value: 31.5 - type: recall_at_10 value: 60.67 - type: recall_at_100 value: 80.484 - type: recall_at_1000 value: 92.04599999999999 - type: recall_at_20 value: 67.644 - type: recall_at_3 value: 46.671 - type: recall_at_5 value: 52.723 - task: type: Retrieval dataset: type: mteb/cqadupstack-gis name: MTEB CQADupstackGisRetrieval config: default split: test revision: 5003b3064772da1887988e05400cf3806fe491f2 metrics: - type: map_at_1 value: 17.339 - type: map_at_10 value: 23.014000000000003 - type: map_at_100 value: 23.918 - type: map_at_1000 value: 24.027 - type: map_at_20 value: 23.507 - type: map_at_3 value: 21.176000000000002 - type: map_at_5 value: 22.126 - type: mrr_at_1 value: 18.531 - type: mrr_at_10 value: 24.356 - type: mrr_at_100 value: 25.247000000000003 - type: mrr_at_1000 value: 25.338 - type: mrr_at_20 value: 24.858 - type: mrr_at_3 value: 22.542 - type: mrr_at_5 value: 23.508000000000003 - type: ndcg_at_1 value: 18.531 - type: ndcg_at_10 value: 26.51 - type: ndcg_at_100 value: 31.367 - type: ndcg_at_1000 value: 34.38 - type: ndcg_at_20 value: 28.328999999999997 - type: ndcg_at_3 value: 22.861 - type: ndcg_at_5 value: 24.456 - type: precision_at_1 value: 18.531 - type: precision_at_10 value: 4.147 - type: precision_at_100 value: 0.695 - type: precision_at_1000 value: 0.099 - type: precision_at_20 value: 2.492 - type: precision_at_3 value: 9.793000000000001 - type: precision_at_5 value: 6.825 - type: recall_at_1 value: 17.339 - type: recall_at_10 value: 36.010999999999996 - type: recall_at_100 value: 59.040000000000006 - type: recall_at_1000 value: 82.282 - type: recall_at_20 value: 43.04 - type: recall_at_3 value: 25.904 - type: recall_at_5 value: 29.837000000000003 - task: type: Retrieval dataset: type: mteb/cqadupstack-mathematica name: MTEB CQADupstackMathematicaRetrieval config: default split: test revision: 90fceea13679c63fe563ded68f3b6f06e50061de metrics: - type: map_at_1 value: 9.251 - type: map_at_10 value: 14.848 - type: map_at_100 value: 15.940999999999999 - type: map_at_1000 value: 16.055 - type: map_at_20 value: 15.423 - type: map_at_3 value: 12.556999999999999 - type: map_at_5 value: 13.649000000000001 - type: mrr_at_1 value: 12.313 - type: mrr_at_10 value: 18.528 - type: mrr_at_100 value: 19.522000000000002 - type: mrr_at_1000 value: 19.601 - type: mrr_at_20 value: 19.107 - type: mrr_at_3 value: 16.231 - type: mrr_at_5 value: 17.294999999999998 - type: ndcg_at_1 value: 12.313 - type: ndcg_at_10 value: 19.303 - type: ndcg_at_100 value: 24.728 - type: ndcg_at_1000 value: 27.823999999999998 - type: ndcg_at_20 value: 21.318 - type: ndcg_at_3 value: 14.848 - type: ndcg_at_5 value: 16.509 - type: precision_at_1 value: 12.313 - type: precision_at_10 value: 4.03 - type: precision_at_100 value: 0.777 - type: precision_at_1000 value: 0.11800000000000001 - type: precision_at_20 value: 2.562 - type: precision_at_3 value: 7.546 - type: precision_at_5 value: 5.672 - type: recall_at_1 value: 9.251 - type: recall_at_10 value: 29.677999999999997 - type: recall_at_100 value: 53.586 - type: recall_at_1000 value: 76.181 - type: recall_at_20 value: 36.963 - type: recall_at_3 value: 17.072000000000003 - type: recall_at_5 value: 21.481 - task: type: Retrieval dataset: type: mteb/cqadupstack-physics name: MTEB CQADupstackPhysicsRetrieval config: default split: test revision: 79531abbd1fb92d06c6d6315a0cbbbf5bb247ea4 metrics: - type: map_at_1 value: 21.135 - type: map_at_10 value: 29.431 - type: map_at_100 value: 30.662 - type: map_at_1000 value: 30.792 - type: map_at_20 value: 30.086000000000002 - type: map_at_3 value: 26.593 - type: map_at_5 value: 28.011999999999997 - type: mrr_at_1 value: 26.564 - type: mrr_at_10 value: 34.735 - type: mrr_at_100 value: 35.65 - type: mrr_at_1000 value: 35.711999999999996 - type: mrr_at_20 value: 35.286 - type: mrr_at_3 value: 32.002 - type: mrr_at_5 value: 33.527 - type: ndcg_at_1 value: 26.564 - type: ndcg_at_10 value: 35.108 - type: ndcg_at_100 value: 40.601 - type: ndcg_at_1000 value: 43.329 - type: ndcg_at_20 value: 37.192 - type: ndcg_at_3 value: 29.961 - type: ndcg_at_5 value: 32.131 - type: precision_at_1 value: 26.564 - type: precision_at_10 value: 6.564 - type: precision_at_100 value: 1.105 - type: precision_at_1000 value: 0.154 - type: precision_at_20 value: 3.941 - type: precision_at_3 value: 14.212 - type: precision_at_5 value: 10.337 - type: recall_at_1 value: 21.135 - type: recall_at_10 value: 47.242 - type: recall_at_100 value: 70.645 - type: recall_at_1000 value: 89.403 - type: recall_at_20 value: 54.663 - type: recall_at_3 value: 32.647 - type: recall_at_5 value: 38.122 - task: type: Retrieval dataset: type: mteb/cqadupstack-programmers name: MTEB CQADupstackProgrammersRetrieval config: default split: test revision: 6184bc1440d2dbc7612be22b50686b8826d22b32 metrics: - type: map_at_1 value: 16.86 - type: map_at_10 value: 23.477999999999998 - type: map_at_100 value: 24.68 - type: map_at_1000 value: 24.826999999999998 - type: map_at_20 value: 24.122 - type: map_at_3 value: 21.288999999999998 - type: map_at_5 value: 22.453 - type: mrr_at_1 value: 20.776 - type: mrr_at_10 value: 28.029 - type: mrr_at_100 value: 28.951 - type: mrr_at_1000 value: 29.038000000000004 - type: mrr_at_20 value: 28.546 - type: mrr_at_3 value: 25.818 - type: mrr_at_5 value: 26.994 - type: ndcg_at_1 value: 20.776 - type: ndcg_at_10 value: 28.152 - type: ndcg_at_100 value: 33.82 - type: ndcg_at_1000 value: 37.039 - type: ndcg_at_20 value: 30.238 - type: ndcg_at_3 value: 24.197 - type: ndcg_at_5 value: 25.861 - type: precision_at_1 value: 20.776 - type: precision_at_10 value: 5.297000000000001 - type: precision_at_100 value: 0.96 - type: precision_at_1000 value: 0.14200000000000002 - type: precision_at_20 value: 3.276 - type: precision_at_3 value: 11.606 - type: precision_at_5 value: 8.356 - type: recall_at_1 value: 16.86 - type: recall_at_10 value: 37.782 - type: recall_at_100 value: 62.67 - type: recall_at_1000 value: 85.03 - type: recall_at_20 value: 45.2 - type: recall_at_3 value: 26.506999999999998 - type: recall_at_5 value: 31.113000000000003 - task: type: Retrieval dataset: type: mteb/cqadupstack-stats name: MTEB CQADupstackStatsRetrieval config: default split: test revision: 65ac3a16b8e91f9cee4c9828cc7c335575432a2a metrics: - type: map_at_1 value: 15.234 - type: map_at_10 value: 20.939 - type: map_at_100 value: 21.704 - type: map_at_1000 value: 21.804000000000002 - type: map_at_20 value: 21.311 - type: map_at_3 value: 18.972 - type: map_at_5 value: 19.929 - type: mrr_at_1 value: 17.485 - type: mrr_at_10 value: 23.267 - type: mrr_at_100 value: 23.967 - type: mrr_at_1000 value: 24.054000000000002 - type: mrr_at_20 value: 23.604 - type: mrr_at_3 value: 21.345 - type: mrr_at_5 value: 22.303 - type: ndcg_at_1 value: 17.485 - type: ndcg_at_10 value: 24.744 - type: ndcg_at_100 value: 28.801 - type: ndcg_at_1000 value: 31.619999999999997 - type: ndcg_at_20 value: 26.046000000000003 - type: ndcg_at_3 value: 20.862 - type: ndcg_at_5 value: 22.459 - type: precision_at_1 value: 17.485 - type: precision_at_10 value: 4.109999999999999 - type: precision_at_100 value: 0.676 - type: precision_at_1000 value: 0.098 - type: precision_at_20 value: 2.3619999999999997 - type: precision_at_3 value: 9.254 - type: precision_at_5 value: 6.503 - type: recall_at_1 value: 15.234 - type: recall_at_10 value: 34.48 - type: recall_at_100 value: 53.225 - type: recall_at_1000 value: 74.64699999999999 - type: recall_at_20 value: 39.421 - type: recall_at_3 value: 23.554 - type: recall_at_5 value: 27.662 - task: type: Retrieval dataset: type: mteb/cqadupstack-tex name: MTEB CQADupstackTexRetrieval config: default split: test revision: 46989137a86843e03a6195de44b09deda022eec7 metrics: - type: map_at_1 value: 9.564 - type: map_at_10 value: 13.869000000000002 - type: map_at_100 value: 14.728 - type: map_at_1000 value: 14.853 - type: map_at_20 value: 14.32 - type: map_at_3 value: 12.307 - type: map_at_5 value: 13.177 - type: mrr_at_1 value: 11.941 - type: mrr_at_10 value: 16.777 - type: mrr_at_100 value: 17.571 - type: mrr_at_1000 value: 17.663999999999998 - type: mrr_at_20 value: 17.203 - type: mrr_at_3 value: 15.067 - type: mrr_at_5 value: 16.003999999999998 - type: ndcg_at_1 value: 11.941 - type: ndcg_at_10 value: 17.111 - type: ndcg_at_100 value: 21.438 - type: ndcg_at_1000 value: 24.756 - type: ndcg_at_20 value: 18.616 - type: ndcg_at_3 value: 14.143 - type: ndcg_at_5 value: 15.501000000000001 - type: precision_at_1 value: 11.941 - type: precision_at_10 value: 3.304 - type: precision_at_100 value: 0.658 - type: precision_at_1000 value: 0.11100000000000002 - type: precision_at_20 value: 2.077 - type: precision_at_3 value: 6.882000000000001 - type: precision_at_5 value: 5.12 - type: recall_at_1 value: 9.564 - type: recall_at_10 value: 24.068 - type: recall_at_100 value: 43.759 - type: recall_at_1000 value: 68.101 - type: recall_at_20 value: 29.657 - type: recall_at_3 value: 15.68 - type: recall_at_5 value: 19.238 - task: type: Retrieval dataset: type: mteb/cqadupstack-unix name: MTEB CQADupstackUnixRetrieval config: default split: test revision: 6c6430d3a6d36f8d2a829195bc5dc94d7e063e53 metrics: - type: map_at_1 value: 16.171 - type: map_at_10 value: 22.142 - type: map_at_100 value: 23.261000000000003 - type: map_at_1000 value: 23.371 - type: map_at_20 value: 22.766000000000002 - type: map_at_3 value: 20.251 - type: map_at_5 value: 21.349 - type: mrr_at_1 value: 19.403000000000002 - type: mrr_at_10 value: 25.619999999999997 - type: mrr_at_100 value: 26.659 - type: mrr_at_1000 value: 26.735 - type: mrr_at_20 value: 26.212000000000003 - type: mrr_at_3 value: 23.694000000000003 - type: mrr_at_5 value: 24.781 - type: ndcg_at_1 value: 19.403000000000002 - type: ndcg_at_10 value: 26.104 - type: ndcg_at_100 value: 31.724000000000004 - type: ndcg_at_1000 value: 34.581 - type: ndcg_at_20 value: 28.231 - type: ndcg_at_3 value: 22.464000000000002 - type: ndcg_at_5 value: 24.233 - type: precision_at_1 value: 19.403000000000002 - type: precision_at_10 value: 4.422000000000001 - type: precision_at_100 value: 0.8170000000000001 - type: precision_at_1000 value: 0.11800000000000001 - type: precision_at_20 value: 2.78 - type: precision_at_3 value: 10.168000000000001 - type: precision_at_5 value: 7.295 - type: recall_at_1 value: 16.171 - type: recall_at_10 value: 34.899 - type: recall_at_100 value: 60.197 - type: recall_at_1000 value: 80.798 - type: recall_at_20 value: 42.591 - type: recall_at_3 value: 25.024 - type: recall_at_5 value: 29.42 - task: type: Retrieval dataset: type: mteb/cqadupstack-webmasters name: MTEB CQADupstackWebmastersRetrieval config: default split: test revision: 160c094312a0e1facb97e55eeddb698c0abe3571 metrics: - type: map_at_1 value: 16.412 - type: map_at_10 value: 23.138 - type: map_at_100 value: 24.46 - type: map_at_1000 value: 24.668 - type: map_at_20 value: 23.791 - type: map_at_3 value: 20.965 - type: map_at_5 value: 22.005 - type: mrr_at_1 value: 20.949 - type: mrr_at_10 value: 27.46 - type: mrr_at_100 value: 28.546 - type: mrr_at_1000 value: 28.619 - type: mrr_at_20 value: 28.038999999999998 - type: mrr_at_3 value: 25.461 - type: mrr_at_5 value: 26.528000000000002 - type: ndcg_at_1 value: 20.949 - type: ndcg_at_10 value: 27.919 - type: ndcg_at_100 value: 33.886 - type: ndcg_at_1000 value: 37.284 - type: ndcg_at_20 value: 29.876 - type: ndcg_at_3 value: 24.246000000000002 - type: ndcg_at_5 value: 25.607999999999997 - type: precision_at_1 value: 20.949 - type: precision_at_10 value: 5.534 - type: precision_at_100 value: 1.2409999999999999 - type: precision_at_1000 value: 0.22 - type: precision_at_20 value: 3.5180000000000002 - type: precision_at_3 value: 11.726 - type: precision_at_5 value: 8.498 - type: recall_at_1 value: 16.412 - type: recall_at_10 value: 37.012 - type: recall_at_100 value: 64.702 - type: recall_at_1000 value: 87.442 - type: recall_at_20 value: 44.797 - type: recall_at_3 value: 25.872 - type: recall_at_5 value: 29.732999999999997 - task: type: Retrieval dataset: type: mteb/cqadupstack-wordpress name: MTEB CQADupstackWordpressRetrieval config: default split: test revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4 metrics: - type: map_at_1 value: 11.158 - type: map_at_10 value: 15.809999999999999 - type: map_at_100 value: 16.821 - type: map_at_1000 value: 16.925 - type: map_at_20 value: 16.403000000000002 - type: map_at_3 value: 13.791999999999998 - type: map_at_5 value: 14.817 - type: mrr_at_1 value: 12.384 - type: mrr_at_10 value: 17.291999999999998 - type: mrr_at_100 value: 18.271 - type: mrr_at_1000 value: 18.360000000000003 - type: mrr_at_20 value: 17.854999999999997 - type: mrr_at_3 value: 15.096000000000002 - type: mrr_at_5 value: 16.214000000000002 - type: ndcg_at_1 value: 12.384 - type: ndcg_at_10 value: 19.250999999999998 - type: ndcg_at_100 value: 24.524 - type: ndcg_at_1000 value: 27.624 - type: ndcg_at_20 value: 21.387999999999998 - type: ndcg_at_3 value: 14.995 - type: ndcg_at_5 value: 16.861 - type: precision_at_1 value: 12.384 - type: 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24.215999999999998 - type: mrr_at_100 value: 25.418000000000003 - type: mrr_at_1000 value: 25.480000000000004 - type: mrr_at_20 value: 24.934 - type: mrr_at_3 value: 21.064 - type: mrr_at_5 value: 22.676 - type: ndcg_at_1 value: 15.895999999999999 - type: ndcg_at_10 value: 17.69 - type: ndcg_at_100 value: 24.232 - type: ndcg_at_1000 value: 28.405 - type: ndcg_at_20 value: 19.933999999999997 - type: ndcg_at_3 value: 13.761000000000001 - type: ndcg_at_5 value: 14.963000000000001 - type: precision_at_1 value: 15.895999999999999 - type: precision_at_10 value: 5.733 - type: precision_at_100 value: 1.266 - type: precision_at_1000 value: 0.203 - type: precision_at_20 value: 3.798 - type: precision_at_3 value: 10.076 - type: precision_at_5 value: 7.9479999999999995 - type: recall_at_1 value: 7.172000000000001 - type: recall_at_10 value: 22.149 - type: recall_at_100 value: 45.491 - type: recall_at_1000 value: 69.34 - type: recall_at_20 value: 28.634999999999998 - type: recall_at_3 value: 12.701 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type: mteb/twittersemeval2015-pairclassification name: MTEB TwitterSemEval2015 config: default split: test revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 metrics: - type: cos_sim_accuracy value: 84.25821064552662 - type: cos_sim_ap value: 67.96785265119063 - type: cos_sim_f1 value: 65.0070788107598 - type: cos_sim_precision value: 58.792146820315835 - type: cos_sim_recall value: 72.69129287598945 - type: dot_accuracy value: 81.47463789712106 - type: dot_ap value: 58.234902049577684 - type: dot_f1 value: 56.73442037078401 - type: dot_precision value: 49.18667699457785 - type: dot_recall value: 67.01846965699208 - type: euclidean_accuracy value: 84.30589497526375 - type: euclidean_ap value: 68.07824251821404 - type: euclidean_f1 value: 65.09073543457498 - type: euclidean_precision value: 59.44177932839075 - type: euclidean_recall value: 71.92612137203166 - type: manhattan_accuracy value: 84.24032902187518 - type: manhattan_ap value: 67.76838044141897 - type: manhattan_f1 value: 64.75698520779525 - type: manhattan_precision value: 58.333333333333336 - type: manhattan_recall value: 72.77044854881267 - type: max_accuracy value: 84.30589497526375 - type: max_ap value: 68.07824251821404 - type: max_f1 value: 65.09073543457498 - task: type: PairClassification dataset: type: mteb/twitterurlcorpus-pairclassification name: MTEB TwitterURLCorpus config: default split: test revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf metrics: - type: cos_sim_accuracy value: 88.2951061435169 - type: cos_sim_ap value: 84.74905878045149 - type: cos_sim_f1 value: 77.01659871869538 - type: cos_sim_precision value: 73.0392156862745 - type: cos_sim_recall value: 81.45210963966738 - type: dot_accuracy value: 86.37598478674273 - type: dot_ap value: 79.17253140971533 - type: dot_f1 value: 73.19411657889958 - type: dot_precision value: 69.27201484842236 - type: dot_recall value: 77.58700338774254 - type: euclidean_accuracy value: 88.29122521054062 - type: euclidean_ap value: 84.64901724668165 - type: euclidean_f1 value: 76.99685189252507 - type: euclidean_precision value: 73.39148639218422 - type: euclidean_recall value: 80.97474591931014 - type: manhattan_accuracy value: 88.29316567702877 - type: manhattan_ap value: 84.5869003947086 - type: manhattan_f1 value: 76.9094138543517 - type: manhattan_precision value: 74.03818751781134 - type: manhattan_recall value: 80.01231906375116 - type: max_accuracy value: 88.2951061435169 - type: max_ap value: 84.74905878045149 - type: max_f1 value: 77.01659871869538 --- # Bulbasaur This is a distill of [gte-tiny](https://huggingface.co/TaylorAI/gte-tiny) trained using [qa-assistant](https://huggingface.co/datasets/Mihaiii/qa-assistant). ## Intended purpose <span style="color:blue">This model is designed for use in semantic-autocomplete ([click here for demo](https://mihaiii.github.io/semantic-autocomplete/)).</span> ## Usage (Sentence-Transformers) (same as [gte-tiny](https://huggingface.co/TaylorAI/gte-tiny)) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('Mihaiii/Bulbasaur') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) (same as [gte-tiny](https://huggingface.co/TaylorAI/gte-tiny)) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('Mihaiii/Bulbasaur') model = AutoModel.from_pretrained('Mihaiii/Bulbasaur') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(**encoded_input) # Perform pooling. In this case, mean pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ### Limitation (same as [gte-small](https://huggingface.co/thenlper/gte-small)) This model exclusively caters to English texts, and any lengthy texts will be truncated to a maximum of 512 tokens.
microsoft/swinv2-large-patch4-window12-192-22k	microsoft	2022-12-10T10:02:59Z	4,769	8	transformers	[ "transformers", "pytorch", "swinv2", "image-classification", "vision", "dataset:imagenet-1k", "arxiv:2111.09883", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2022-06-15T12:47:41Z	--- license: apache-2.0 tags: - vision - image-classification datasets: - imagenet-1k widget: - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg example_title: Tiger - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg example_title: Teapot - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg example_title: Palace --- # Swin Transformer v2 (large-sized model) Swin Transformer v2 model pre-trained on ImageNet-21k at resolution 192x192. It was introduced in the paper [Swin Transformer V2: Scaling Up Capacity and Resolution](https://arxiv.org/abs/2111.09883) by Liu et al. and first released in [this repository](https://github.com/microsoft/Swin-Transformer). Disclaimer: The team releasing Swin Transformer v2 did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description The Swin Transformer is a type of Vision Transformer. It builds hierarchical feature maps by merging image patches (shown in gray) in deeper layers and has linear computation complexity to input image size due to computation of self-attention only within each local window (shown in red). It can thus serve as a general-purpose backbone for both image classification and dense recognition tasks. In contrast, previous vision Transformers produce feature maps of a single low resolution and have quadratic computation complexity to input image size due to computation of self-attention globally. Swin Transformer v2 adds 3 main improvements: 1) a residual-post-norm method combined with cosine attention to improve training stability; 2) a log-spaced continuous position bias method to effectively transfer models pre-trained using low-resolution images to downstream tasks with high-resolution inputs; 3) a self-supervised pre-training method, SimMIM, to reduce the needs of vast labeled images. ![model image](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/swin_transformer_architecture.png) [Source](https://paperswithcode.com/method/swin-transformer) ## Intended uses & limitations You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=swinv2) to look for fine-tuned versions on a task that interests you. ### How to use Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 21k ImageNet classes: ```python from transformers import AutoImageProcessor, AutoModelForImageClassification from PIL import Image import requests url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) processor = AutoImageProcessor.from_pretrained("microsoft/swinv2-large-patch4-window12-192-22k") model = AutoModelForImageClassification.from_pretrained("microsoft/swinv2-large-patch4-window12-192-22k") inputs = processor(images=image, return_tensors="pt") outputs = model(**inputs) logits = outputs.logits # model predicts one of the 21k ImageNet classes predicted_class_idx = logits.argmax(-1).item() print("Predicted class:", model.config.id2label[predicted_class_idx]) ``` For more code examples, we refer to the [documentation](https://huggingface.co/transformers/model_doc/swinv2.html#). ### BibTeX entry and citation info ```bibtex @article{DBLP:journals/corr/abs-2111-09883, author = {Ze Liu and Han Hu and Yutong Lin and Zhuliang Yao and Zhenda Xie and Yixuan Wei and Jia Ning and Yue Cao and Zheng Zhang and Li Dong and Furu Wei and Baining Guo}, title = {Swin Transformer {V2:} Scaling Up Capacity and Resolution}, journal = {CoRR}, volume = {abs/2111.09883}, year = {2021}, url = {https://arxiv.org/abs/2111.09883}, eprinttype = {arXiv}, eprint = {2111.09883}, timestamp = {Thu, 02 Dec 2021 15:54:22 +0100}, biburl = {https://dblp.org/rec/journals/corr/abs-2111-09883.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ```
NousResearch/Yarn-Mistral-7b-64k	NousResearch	2023-11-02T19:00:04Z	4,768	49	transformers	[ "transformers", "pytorch", "mistral", "text-generation", "custom_code", "en", "dataset:emozilla/yarn-train-tokenized-16k-mistral", "arxiv:2309.00071", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text-generation	2023-10-31T02:01:43Z	--- datasets: - emozilla/yarn-train-tokenized-16k-mistral metrics: - perplexity library_name: transformers license: apache-2.0 language: - en --- # Model Card: Nous-Yarn-Mistral-7b-64k [Preprint (arXiv)](https://arxiv.org/abs/2309.00071) [GitHub](https://github.com/jquesnelle/yarn) ![yarn](https://raw.githubusercontent.com/jquesnelle/yarn/mistral/data/proofpile-long-small-mistral.csv.png) ## Model Description Nous-Yarn-Mistral-7b-64k is a state-of-the-art language model for long context, further pretrained on long context data for 1000 steps using the YaRN extension method. It is an extension of [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) and supports a 64k token context window. To use, pass `trust_remote_code=True` when loading the model, for example ```python model = AutoModelForCausalLM.from_pretrained("NousResearch/Yarn-Mistral-7b-64k", use_flash_attention_2=True, torch_dtype=torch.bfloat16, device_map="auto", trust_remote_code=True) ``` In addition you will need to use the latest version of `transformers` (until 4.35 comes out) ```sh pip install git+https://github.com/huggingface/transformers ``` ## Benchmarks Long context benchmarks: \| Model \| Context Window \| 8k PPL \| 16k PPL \| 32k PPL \| 64k PPL \| 128k PPL \| \|-------\|---------------:\|------:\|----------:\|-----:\|-----:\|------------:\| \| [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) \| 8k \| 2.96 \| - \| - \| - \| - \| \| [Yarn-Mistral-7b-64k](https://huggingface.co/NousResearch/Yarn-Mistral-7b-64k) \| 64k \| 3.04 \| 2.65 \| 2.44 \| 2.20 \| - \| \| [Yarn-Mistral-7b-128k](https://huggingface.co/NousResearch/Yarn-Mistral-7b-128k) \| 128k \| 3.08 \| 2.68 \| 2.47 \| 2.24 \| 2.19 \| Short context benchmarks showing that quality degradation is minimal: \| Model \| Context Window \| ARC-c \| Hellaswag \| MMLU \| Truthful QA \| \|-------\|---------------:\|------:\|----------:\|-----:\|------------:\| \| [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) \| 8k \| 59.98 \| 83.31 \| 64.16 \| 42.15 \| \| [Yarn-Mistral-7b-64k](https://huggingface.co/NousResearch/Yarn-Mistral-7b-64k) \| 64k \| 59.38 \| 81.21 \| 61.32 \| 42.50 \| \| [Yarn-Mistral-7b-128k](https://huggingface.co/NousResearch/Yarn-Mistral-7b-128k) \| 128k \| 58.87 \| 80.58 \| 60.64 \| 42.46 \| ## Collaborators - [bloc97](https://github.com/bloc97): Methods, paper and evals - [@theemozilla](https://twitter.com/theemozilla): Methods, paper, model training, and evals - [@EnricoShippole](https://twitter.com/EnricoShippole): Model training - [honglu2875](https://github.com/honglu2875): Paper and evals The authors would like to thank LAION AI for their support of compute for this model. It was trained on the [JUWELS](https://www.fz-juelich.de/en/ias/jsc/systems/supercomputers/juwels) supercomputer.
microsoft/xclip-base-patch16-zero-shot	microsoft	2023-09-12T12:13:40Z	4,767	20	transformers	[ "transformers", "pytorch", "safetensors", "xclip", "feature-extraction", "vision", "video-classification", "en", "arxiv:2208.02816", "license:mit", "model-index", "endpoints_compatible", "region:us" ]	video-classification	2022-09-07T17:52:51Z	--- language: en license: mit tags: - vision - video-classification model-index: - name: nielsr/xclip-base-patch16-zero-shot results: - task: type: video-classification dataset: name: HMDB-51 type: hmdb-51 metrics: - type: top-1 accuracy value: 44.6 - task: type: video-classification dataset: name: UCF101 type: ucf101 metrics: - type: top-1 accuracy value: 72.0 - task: type: video-classification dataset: name: Kinetics-600 type: kinetics600 metrics: - type: top-1 accuracy value: 65.2 --- # X-CLIP (base-sized model) X-CLIP model (base-sized, patch resolution of 16) trained on [Kinetics-400](https://www.deepmind.com/open-source/kinetics). It was introduced in the paper [Expanding Language-Image Pretrained Models for General Video Recognition](https://arxiv.org/abs/2208.02816) by Ni et al. and first released in [this repository](https://github.com/microsoft/VideoX/tree/master/X-CLIP). This model was trained using 32 frames per video, at a resolution of 224x224. Disclaimer: The team releasing X-CLIP did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description X-CLIP is a minimal extension of [CLIP](https://huggingface.co/docs/transformers/model_doc/clip) for general video-language understanding. The model is trained in a contrastive way on (video, text) pairs. ![X-CLIP architecture](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/xclip_architecture.png) This allows the model to be used for tasks like zero-shot, few-shot or fully supervised video classification and video-text retrieval. ## Intended uses & limitations You can use the raw model for determining how well text goes with a given video. See the [model hub](https://huggingface.co/models?search=microsoft/xclip) to look for fine-tuned versions on a task that interests you. ### How to use For code examples, we refer to the [documentation](https://huggingface.co/transformers/main/model_doc/xclip.html#). ## Training data This model was trained on [Kinetics 400](https://www.deepmind.com/open-source/kinetics). ### Preprocessing The exact details of preprocessing during training can be found [here](https://github.com/microsoft/VideoX/blob/40f6d177e0a057a50ac69ac1de6b5938fd268601/X-CLIP/datasets/build.py#L247). The exact details of preprocessing during validation can be found [here](https://github.com/microsoft/VideoX/blob/40f6d177e0a057a50ac69ac1de6b5938fd268601/X-CLIP/datasets/build.py#L285). During validation, one resizes the shorter edge of each frame, after which center cropping is performed to a fixed-size resolution (like 224x224). Next, frames are normalized across the RGB channels with the ImageNet mean and standard deviation. ## Evaluation results This model achieves a zero-shot top-1 accuracy of 44.6% on HMDB-51, 72.0% on UCF-101 and 65.2% on Kinetics-600.
Mihaiii/Wartortle	Mihaiii	2024-04-30T20:46:21Z	4,767	0	sentence-transformers	[ "sentence-transformers", "onnx", "safetensors", "bert", "feature-extraction", "sentence-similarity", "bge", "mteb", "dataset:Mihaiii/qa-assistant", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "text-embeddings-inference", "region:us" ]	sentence-similarity	2024-04-30T15:12:13Z	--- license: mit library_name: sentence-transformers pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - bge - mteb datasets: - Mihaiii/qa-assistant model-index: - name: Wartortle results: - task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (en) config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 70.40298507462687 - type: ap value: 32.88973775597331 - type: f1 value: 64.3726772221329 - task: type: Classification dataset: type: mteb/amazon_polarity name: MTEB AmazonPolarityClassification config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 82.0381 - type: ap value: 77.15483149750918 - type: f1 value: 81.97695449378108 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (en) config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 42.412 - type: f1 value: 41.039684315409595 - task: type: Retrieval dataset: type: mteb/arguana name: MTEB ArguAna config: default split: test revision: c22ab2a51041ffd869aaddef7af8d8215647e41a metrics: - type: map_at_1 value: 16.003 - type: map_at_10 value: 28.448 - type: map_at_100 value: 29.781999999999996 - type: map_at_1000 value: 29.822 - type: map_at_20 value: 29.278 - type: map_at_3 value: 23.874000000000002 - type: map_at_5 value: 26.491 - type: mrr_at_1 value: 16.714000000000002 - type: mrr_at_10 value: 28.727999999999998 - type: mrr_at_100 value: 30.055 - type: mrr_at_1000 value: 30.095 - type: mrr_at_20 value: 29.558 - type: mrr_at_3 value: 24.194 - type: mrr_at_5 value: 26.778999999999996 - type: ndcg_at_1 value: 16.003 - type: ndcg_at_10 value: 35.865 - type: ndcg_at_100 value: 42.304 - type: ndcg_at_1000 value: 43.333 - type: ndcg_at_20 value: 38.876 - type: ndcg_at_3 value: 26.436999999999998 - type: ndcg_at_5 value: 31.139 - type: precision_at_1 value: 16.003 - type: precision_at_10 value: 5.982 - type: precision_at_100 value: 0.898 - type: precision_at_1000 value: 0.098 - type: precision_at_20 value: 3.585 - type: precision_at_3 value: 11.285 - type: precision_at_5 value: 9.046999999999999 - type: recall_at_1 value: 16.003 - type: recall_at_10 value: 59.815 - type: recall_at_100 value: 89.75800000000001 - type: recall_at_1000 value: 97.795 - type: recall_at_20 value: 71.693 - type: recall_at_3 value: 33.855000000000004 - type: recall_at_5 value: 45.235 - task: type: Clustering dataset: type: mteb/arxiv-clustering-p2p name: MTEB ArxivClusteringP2P config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics: - type: v_measure value: 35.843668514122115 - type: v_measures value: [0.3334224034497392, 0.3341547890740972, 0.3357840169117339, 0.34882361674739576, 0.3295989566449552, 0.346573603986452, 0.3336839394053626, 0.33891447096132693, 0.3324032010342291, 0.3292339117184913, 0.413709338323792, 0.42196122544420617, 0.41164392816543693, 0.42687170671748026, 0.4196249053060285, 0.41799639579395365, 0.4197169573853409, 0.42335330439048224, 0.4140526534891295, 0.4219636794179018, 0.41570945861753056, 0.2537851472263153, 0.2634446492249144, 0.34061280544819494, 0.2898513290669649, 0.1963574383667656, 0.2587887795105382, 0.12932481412215552, 0.1917433748379367, 1.0, 0.21843243858900313, 0.3334224034497392, 0.3341547890740972, 0.3357840169117339, 0.34882361674739576, 0.3295989566449552, 0.346573603986452, 0.3336839394053626, 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type: recall_at_1 value: 20.799 - type: recall_at_10 value: 41.819 - type: recall_at_100 value: 63.32299999999999 - type: recall_at_1000 value: 82.994 - type: recall_at_20 value: 48.024 - type: recall_at_3 value: 30.523 - type: recall_at_5 value: 35.214 - task: type: Retrieval dataset: type: mteb/cqadupstack-english name: MTEB CQADupstackEnglishRetrieval config: default split: test revision: ad9991cb51e31e31e430383c75ffb2885547b5f0 metrics: - type: map_at_1 value: 13.431999999999999 - type: map_at_10 value: 18.384 - type: map_at_100 value: 19.067999999999998 - type: map_at_1000 value: 19.178 - type: map_at_20 value: 18.732 - type: map_at_3 value: 16.834 - type: map_at_5 value: 17.758 - type: mrr_at_1 value: 16.624 - type: mrr_at_10 value: 21.467 - type: mrr_at_100 value: 22.126 - type: mrr_at_1000 value: 22.206 - type: mrr_at_20 value: 21.8 - type: mrr_at_3 value: 19.894000000000002 - type: mrr_at_5 value: 20.794999999999998 - type: ndcg_at_1 value: 16.624 - type: ndcg_at_10 value: 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value: 35.265 - type: ndcg_at_20 value: 28.814 - type: ndcg_at_3 value: 23.474 - type: ndcg_at_5 value: 24.924 - type: precision_at_1 value: 20.693 - type: precision_at_10 value: 4.986 - type: precision_at_100 value: 0.915 - type: precision_at_1000 value: 0.13699999999999998 - type: precision_at_20 value: 3.157 - type: precision_at_3 value: 11.132 - type: precision_at_5 value: 8.027 - type: recall_at_1 value: 16.558 - type: recall_at_10 value: 34.636 - type: recall_at_100 value: 57.745999999999995 - type: recall_at_1000 value: 80.438 - type: recall_at_20 value: 42.248000000000005 - type: recall_at_3 value: 25.419999999999998 - type: recall_at_5 value: 29.254 - task: type: Retrieval dataset: type: mteb/cqadupstack-programmers name: MTEB CQADupstackProgrammersRetrieval config: default split: test revision: 6184bc1440d2dbc7612be22b50686b8826d22b32 metrics: - type: map_at_1 value: 10.231 - type: map_at_10 value: 14.352 - type: map_at_100 value: 15.174000000000001 - type: map_at_1000 value: 15.310000000000002 - type: map_at_20 value: 14.704 - type: map_at_3 value: 12.878 - type: map_at_5 value: 13.632 - type: mrr_at_1 value: 12.556999999999999 - type: mrr_at_10 value: 17.378 - type: mrr_at_100 value: 18.186 - type: mrr_at_1000 value: 18.287 - type: mrr_at_20 value: 17.752000000000002 - type: mrr_at_3 value: 15.772 - type: mrr_at_5 value: 16.6 - type: ndcg_at_1 value: 12.556999999999999 - type: ndcg_at_10 value: 17.501 - type: ndcg_at_100 value: 22.065 - type: ndcg_at_1000 value: 25.607999999999997 - type: ndcg_at_20 value: 18.756 - type: ndcg_at_3 value: 14.691 - type: ndcg_at_5 value: 15.842 - type: precision_at_1 value: 12.556999999999999 - type: precision_at_10 value: 3.322 - type: precision_at_100 value: 0.6709999999999999 - type: precision_at_1000 value: 0.11399999999999999 - type: precision_at_20 value: 2.0549999999999997 - type: precision_at_3 value: 6.963 - type: precision_at_5 value: 5.137 - type: recall_at_1 value: 10.231 - type: recall_at_10 value: 24.2 - type: recall_at_100 value: 45.051 - type: recall_at_1000 value: 70.372 - type: recall_at_20 value: 28.624 - type: recall_at_3 value: 16.209 - type: recall_at_5 value: 19.259999999999998 - task: type: Retrieval dataset: type: mteb/cqadupstack name: MTEB CQADupstackRetrieval config: default split: test revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4 metrics: - type: map_at_1 value: 13.304916666666664 - type: map_at_10 value: 18.2725 - type: map_at_100 value: 19.125249999999998 - type: map_at_1000 value: 19.246166666666664 - type: map_at_20 value: 18.682916666666667 - type: map_at_3 value: 16.61425 - type: map_at_5 value: 17.508000000000003 - type: mrr_at_1 value: 16.06625 - type: mrr_at_10 value: 21.317583333333335 - type: mrr_at_100 value: 22.106583333333333 - type: mrr_at_1000 value: 22.195 - type: mrr_at_20 value: 21.716500000000003 - type: mrr_at_3 value: 19.601666666666667 - type: mrr_at_5 value: 20.540333333333326 - type: ndcg_at_1 value: 16.06625 - type: ndcg_at_10 value: 21.690500000000004 - type: ndcg_at_100 value: 26.08625 - type: ndcg_at_1000 value: 29.223333333333336 - type: ndcg_at_20 value: 23.085083333333333 - type: ndcg_at_3 value: 18.621583333333337 - type: ndcg_at_5 value: 19.984999999999996 - type: precision_at_1 value: 16.06625 - type: precision_at_10 value: 3.9008333333333334 - type: precision_at_100 value: 0.7179166666666666 - type: precision_at_1000 value: 0.11541666666666667 - type: precision_at_20 value: 2.3684166666666666 - type: precision_at_3 value: 8.643 - type: precision_at_5 value: 6.230833333333333 - type: recall_at_1 value: 13.304916666666664 - type: recall_at_10 value: 29.081916666666665 - type: recall_at_100 value: 49.29125 - type: recall_at_1000 value: 72.18308333333331 - type: recall_at_20 value: 34.271499999999996 - type: recall_at_3 value: 20.34425 - type: recall_at_5 value: 23.923583333333333 - task: type: Retrieval dataset: type: mteb/cqadupstack-stats name: MTEB CQADupstackStatsRetrieval config: default split: test revision: 65ac3a16b8e91f9cee4c9828cc7c335575432a2a metrics: - type: map_at_1 value: 10.539 - type: map_at_10 value: 14.783 - type: map_at_100 value: 15.542 - type: map_at_1000 value: 15.644 - type: map_at_20 value: 15.139 - type: map_at_3 value: 13.508999999999999 - type: map_at_5 value: 14.191 - type: mrr_at_1 value: 12.577 - type: mrr_at_10 value: 17.212 - type: mrr_at_100 value: 17.95 - type: mrr_at_1000 value: 18.043 - type: mrr_at_20 value: 17.563000000000002 - type: mrr_at_3 value: 15.951 - type: mrr_at_5 value: 16.587 - type: ndcg_at_1 value: 12.577 - type: ndcg_at_10 value: 17.683 - type: ndcg_at_100 value: 21.783 - type: ndcg_at_1000 value: 24.802 - type: ndcg_at_20 value: 18.944 - type: ndcg_at_3 value: 15.204999999999998 - type: ndcg_at_5 value: 16.274 - type: precision_at_1 value: 12.577 - type: precision_at_10 value: 2.991 - type: precision_at_100 value: 0.557 - type: precision_at_1000 value: 0.08800000000000001 - type: precision_at_20 value: 1.81 - type: precision_at_3 value: 6.952999999999999 - type: precision_at_5 value: 4.8469999999999995 - type: recall_at_1 value: 10.539 - type: recall_at_10 value: 24.541 - type: recall_at_100 value: 43.732 - type: recall_at_1000 value: 66.97800000000001 - type: recall_at_20 value: 29.331000000000003 - type: recall_at_3 value: 17.096 - type: recall_at_5 value: 20.080000000000002 - task: type: Retrieval dataset: type: mteb/cqadupstack-tex name: MTEB CQADupstackTexRetrieval config: default split: test revision: 46989137a86843e03a6195de44b09deda022eec7 metrics: - type: map_at_1 value: 7.954 - type: map_at_10 value: 11.091 - type: map_at_100 value: 11.828 - type: map_at_1000 value: 11.935 - type: map_at_20 value: 11.44 - type: map_at_3 value: 9.876 - type: map_at_5 value: 10.496 - type: mrr_at_1 value: 9.738 - type: mrr_at_10 value: 13.361 - type: mrr_at_100 value: 14.096 - type: mrr_at_1000 value: 14.184 - type: mrr_at_20 value: 13.721 - type: mrr_at_3 value: 12.004 - type: mrr_at_5 value: 12.658 - type: ndcg_at_1 value: 9.738 - type: ndcg_at_10 value: 13.592 - type: ndcg_at_100 value: 17.512 - type: ndcg_at_1000 value: 20.602999999999998 - type: ndcg_at_20 value: 14.789 - type: ndcg_at_3 value: 11.232000000000001 - type: ndcg_at_5 value: 12.191 - type: precision_at_1 value: 9.738 - type: precision_at_10 value: 2.598 - type: precision_at_100 value: 0.553 - type: precision_at_1000 value: 0.096 - type: precision_at_20 value: 1.652 - type: precision_at_3 value: 5.311 - type: precision_at_5 value: 3.895 - type: recall_at_1 value: 7.954 - type: recall_at_10 value: 18.932 - type: recall_at_100 value: 37.082 - type: recall_at_1000 value: 60.114999999999995 - type: recall_at_20 value: 23.339 - type: recall_at_3 value: 12.318999999999999 - type: recall_at_5 value: 14.834 - task: type: Retrieval dataset: type: mteb/cqadupstack-unix name: MTEB CQADupstackUnixRetrieval config: default split: test revision: 6c6430d3a6d36f8d2a829195bc5dc94d7e063e53 metrics: - type: map_at_1 value: 13.764999999999999 - type: map_at_10 value: 17.766000000000002 - type: map_at_100 value: 18.637999999999998 - type: map_at_1000 value: 18.755 - type: map_at_20 value: 18.242 - type: map_at_3 value: 16.502 - type: map_at_5 value: 17.155 - type: mrr_at_1 value: 16.604 - type: mrr_at_10 value: 21.071 - type: mrr_at_100 value: 21.906 - type: mrr_at_1000 value: 22.0 - type: mrr_at_20 value: 21.545 - type: mrr_at_3 value: 19.667 - type: mrr_at_5 value: 20.395 - type: ndcg_at_1 value: 16.604 - type: ndcg_at_10 value: 20.742 - type: ndcg_at_100 value: 25.363999999999997 - type: ndcg_at_1000 value: 28.607 - type: ndcg_at_20 value: 22.469 - type: ndcg_at_3 value: 18.276999999999997 - type: ndcg_at_5 value: 19.277 - type: precision_at_1 value: 16.604 - type: precision_at_10 value: 3.47 - type: precision_at_100 value: 0.651 - type: precision_at_1000 value: 0.104 - type: precision_at_20 value: 2.169 - type: precision_at_3 value: 8.209 - type: precision_at_5 value: 5.7090000000000005 - type: recall_at_1 value: 13.764999999999999 - type: recall_at_10 value: 26.752 - type: recall_at_100 value: 47.988 - type: recall_at_1000 value: 71.859 - type: recall_at_20 value: 33.25 - type: recall_at_3 value: 19.777 - type: recall_at_5 value: 22.39 - task: type: Retrieval dataset: type: mteb/cqadupstack-webmasters name: MTEB CQADupstackWebmastersRetrieval config: default split: test revision: 160c094312a0e1facb97e55eeddb698c0abe3571 metrics: - type: map_at_1 value: 14.435999999999998 - type: map_at_10 value: 19.517 - type: map_at_100 value: 20.380000000000003 - type: map_at_1000 value: 20.558 - type: map_at_20 value: 19.858 - type: map_at_3 value: 17.764 - type: map_at_5 value: 18.705 - type: mrr_at_1 value: 18.182000000000002 - type: mrr_at_10 value: 23.342 - type: mrr_at_100 value: 24.121000000000002 - type: mrr_at_1000 value: 24.226 - type: mrr_at_20 value: 23.71 - type: mrr_at_3 value: 21.573999999999998 - type: mrr_at_5 value: 22.572 - type: ndcg_at_1 value: 18.182000000000002 - type: ndcg_at_10 value: 23.322000000000003 - type: ndcg_at_100 value: 27.529999999999998 - type: ndcg_at_1000 value: 31.434 - type: ndcg_at_20 value: 24.274 - type: ndcg_at_3 value: 20.307 - type: ndcg_at_5 value: 21.681 - type: precision_at_1 value: 18.182000000000002 - type: precision_at_10 value: 4.486 - type: precision_at_100 value: 0.907 - type: precision_at_1000 value: 0.17500000000000002 - type: precision_at_20 value: 2.727 - type: precision_at_3 value: 9.684 - type: precision_at_5 value: 7.074999999999999 - type: recall_at_1 value: 14.435999999999998 - type: recall_at_10 value: 30.221999999999998 - type: recall_at_100 value: 50.657 - type: recall_at_1000 value: 77.803 - type: recall_at_20 value: 34.044999999999995 - type: recall_at_3 value: 21.394 - type: recall_at_5 value: 25.058000000000003 - task: type: Retrieval dataset: type: mteb/cqadupstack-wordpress name: MTEB CQADupstackWordpressRetrieval config: default split: test revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4 metrics: - type: map_at_1 value: 8.078000000000001 - type: map_at_10 value: 12.43 - type: map_at_100 value: 13.242999999999999 - type: map_at_1000 value: 13.34 - type: map_at_20 value: 12.767999999999999 - type: map_at_3 value: 11.085 - type: map_at_5 value: 11.727 - type: mrr_at_1 value: 9.057 - type: mrr_at_10 value: 13.885 - type: mrr_at_100 value: 14.697 - type: mrr_at_1000 value: 14.785 - type: mrr_at_20 value: 14.216999999999999 - type: mrr_at_3 value: 12.415 - type: mrr_at_5 value: 13.108 - type: ndcg_at_1 value: 9.057 - type: ndcg_at_10 value: 15.299 - type: ndcg_at_100 value: 19.872 - type: ndcg_at_1000 value: 22.903000000000002 - type: ndcg_at_20 value: 16.525000000000002 - type: ndcg_at_3 value: 12.477 - type: ndcg_at_5 value: 13.605 - type: precision_at_1 value: 9.057 - type: precision_at_10 value: 2.643 - type: precision_at_100 value: 0.525 - type: precision_at_1000 value: 0.084 - type: precision_at_20 value: 1.599 - type: precision_at_3 value: 5.7299999999999995 - type: precision_at_5 value: 4.104 - type: recall_at_1 value: 8.078000000000001 - type: recall_at_10 value: 22.874 - type: recall_at_100 value: 45.043 - type: recall_at_1000 value: 68.77799999999999 - type: recall_at_20 value: 27.662 - type: recall_at_3 value: 14.926 - type: recall_at_5 value: 17.791 - task: type: Retrieval dataset: type: mteb/climate-fever name: MTEB ClimateFEVER config: default split: test revision: 47f2ac6acb640fc46020b02a5b59fdda04d39380 metrics: - type: map_at_1 value: 4.460999999999999 - type: map_at_10 value: 8.625 - type: map_at_100 value: 9.772 - type: map_at_1000 value: 9.952 - type: map_at_20 value: 9.133 - type: map_at_3 value: 6.961 - type: map_at_5 value: 7.727 - type: mrr_at_1 value: 9.381 - type: mrr_at_10 value: 16.742 - type: mrr_at_100 value: 17.901 - type: mrr_at_1000 value: 17.983 - type: mrr_at_20 value: 17.368 - type: mrr_at_3 value: 14.126 - type: mrr_at_5 value: 15.504000000000001 - type: ndcg_at_1 value: 9.381 - type: ndcg_at_10 value: 13.111 - type: ndcg_at_100 value: 19.043 - type: ndcg_at_1000 value: 22.901 - type: ndcg_at_20 value: 14.909 - type: ndcg_at_3 value: 9.727 - type: ndcg_at_5 value: 10.91 - type: precision_at_1 value: 9.381 - type: precision_at_10 value: 4.391 - type: precision_at_100 value: 1.075 - type: precision_at_1000 value: 0.178 - type: precision_at_20 value: 2.9739999999999998 - type: precision_at_3 value: 7.448 - type: precision_at_5 value: 5.954000000000001 - type: recall_at_1 value: 4.460999999999999 - type: recall_at_10 value: 17.657999999999998 - type: recall_at_100 value: 39.201 - type: recall_at_1000 value: 61.229 - type: recall_at_20 value: 22.758 - type: recall_at_3 value: 9.724 - type: recall_at_5 value: 12.651000000000002 - task: type: Retrieval dataset: type: mteb/dbpedia name: MTEB DBPedia config: default split: test revision: c0f706b76e590d620bd6618b3ca8efdd34e2d659 metrics: - type: map_at_1 value: 5.849 - type: map_at_10 value: 12.828999999999999 - type: map_at_100 value: 17.204 - type: map_at_1000 value: 18.314 - type: map_at_20 value: 14.607000000000001 - type: map_at_3 value: 9.442 - type: map_at_5 value: 10.808 - type: mrr_at_1 value: 48.75 - type: mrr_at_10 value: 59.82300000000001 - type: mrr_at_100 value: 60.293 - type: mrr_at_1000 value: 60.307 - type: mrr_at_20 value: 60.131 - type: mrr_at_3 value: 57.208000000000006 - type: mrr_at_5 value: 58.583 - type: ndcg_at_1 value: 36.875 - type: ndcg_at_10 value: 29.328 - type: ndcg_at_100 value: 32.2 - type: ndcg_at_1000 value: 39.125 - type: ndcg_at_20 value: 28.674 - type: ndcg_at_3 value: 32.469 - type: ndcg_at_5 value: 30.613 - type: precision_at_1 value: 48.75 - type: precision_at_10 value: 24.099999999999998 - type: precision_at_100 value: 7.292999999999999 - type: precision_at_1000 value: 1.486 - type: precision_at_20 value: 17.812 - type: precision_at_3 value: 37.167 - type: precision_at_5 value: 31.1 - type: recall_at_1 value: 5.849 - type: recall_at_10 value: 18.473 - type: recall_at_100 value: 37.602000000000004 - type: recall_at_1000 value: 60.68599999999999 - type: recall_at_20 value: 23.552 - type: recall_at_3 value: 11.077 - type: recall_at_5 value: 13.511999999999999 - task: type: Classification dataset: type: mteb/emotion name: MTEB EmotionClassification config: default split: test revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 metrics: - type: accuracy value: 46.78 - type: f1 value: 40.027922341568576 - task: type: Retrieval dataset: type: mteb/fever name: MTEB FEVER config: default split: test revision: bea83ef9e8fb933d90a2f1d5515737465d613e12 metrics: - type: map_at_1 value: 21.675 - type: map_at_10 value: 30.4 - type: map_at_100 value: 31.285 - type: map_at_1000 value: 31.351000000000003 - type: map_at_20 value: 30.917 - type: map_at_3 value: 27.748 - type: map_at_5 value: 29.265 - type: mrr_at_1 value: 23.327 - type: mrr_at_10 value: 32.363 - type: mrr_at_100 value: 33.237 - type: mrr_at_1000 value: 33.298 - type: mrr_at_20 value: 32.883 - type: mrr_at_3 value: 29.665000000000003 - type: mrr_at_5 value: 31.230999999999998 - type: ndcg_at_1 value: 23.327 - type: ndcg_at_10 value: 35.576 - type: ndcg_at_100 value: 40.071 - type: ndcg_at_1000 value: 41.884 - type: ndcg_at_20 value: 37.431 - type: ndcg_at_3 value: 30.173 - type: ndcg_at_5 value: 32.883 - type: precision_at_1 value: 23.327 - type: precision_at_10 value: 5.438 - type: precision_at_100 value: 0.784 - type: precision_at_1000 value: 0.096 - type: precision_at_20 value: 3.121 - type: precision_at_3 value: 12.741 - type: precision_at_5 value: 9.078999999999999 - type: recall_at_1 value: 21.675 - type: recall_at_10 value: 49.952999999999996 - type: recall_at_100 value: 70.953 - type: recall_at_1000 value: 84.902 - type: recall_at_20 value: 57.081 - type: recall_at_3 value: 35.301 - type: recall_at_5 value: 41.805 - task: type: Retrieval dataset: type: mteb/fiqa name: MTEB FiQA2018 config: default split: test revision: 27a168819829fe9bcd655c2df245fb19452e8e06 metrics: - type: map_at_1 value: 3.096 - type: map_at_10 value: 5.4879999999999995 - type: map_at_100 value: 6.199000000000001 - type: map_at_1000 value: 6.348 - type: map_at_20 value: 5.826 - type: map_at_3 value: 4.43 - type: map_at_5 value: 4.899 - type: mrr_at_1 value: 6.481000000000001 - type: mrr_at_10 value: 10.059999999999999 - type: mrr_at_100 value: 10.905 - type: mrr_at_1000 value: 11.019 - type: mrr_at_20 value: 10.513 - type: mrr_at_3 value: 8.436 - type: mrr_at_5 value: 9.168999999999999 - type: ndcg_at_1 value: 6.481000000000001 - type: ndcg_at_10 value: 8.097999999999999 - type: ndcg_at_100 value: 12.092 - type: ndcg_at_1000 value: 16.5 - type: ndcg_at_20 value: 9.353 - type: ndcg_at_3 value: 6.148 - type: ndcg_at_5 value: 6.714 - type: precision_at_1 value: 6.481000000000001 - type: precision_at_10 value: 2.5309999999999997 - type: precision_at_100 value: 0.6479999999999999 - type: precision_at_1000 value: 0.14100000000000001 - type: precision_at_20 value: 1.752 - type: precision_at_3 value: 4.064 - type: precision_at_5 value: 3.272 - type: recall_at_1 value: 3.096 - type: recall_at_10 value: 11.575000000000001 - type: recall_at_100 value: 27.560000000000002 - type: recall_at_1000 value: 56.391999999999996 - type: recall_at_20 value: 15.611 - type: recall_at_3 value: 5.821 - type: recall_at_5 value: 7.6259999999999994 - task: type: Retrieval dataset: type: mteb/hotpotqa name: MTEB HotpotQA config: default split: test revision: ab518f4d6fcca38d87c25209f94beba119d02014 metrics: - type: map_at_1 value: 27.481 - type: map_at_10 value: 38.229 - type: map_at_100 value: 39.186 - type: map_at_1000 value: 39.283 - type: map_at_20 value: 38.763999999999996 - type: map_at_3 value: 35.652 - type: map_at_5 value: 37.18 - type: mrr_at_1 value: 54.962999999999994 - type: mrr_at_10 value: 62.651999999999994 - type: mrr_at_100 value: 63.158 - type: mrr_at_1000 value: 63.18899999999999 - type: mrr_at_20 value: 62.965 - type: mrr_at_3 value: 61.013 - type: mrr_at_5 value: 62.004999999999995 - type: ndcg_at_1 value: 54.962999999999994 - type: ndcg_at_10 value: 47.03 - type: ndcg_at_100 value: 50.938 - type: ndcg_at_1000 value: 53.028 - type: ndcg_at_20 value: 48.571999999999996 - type: ndcg_at_3 value: 42.751 - type: ndcg_at_5 value: 44.981 - type: precision_at_1 value: 54.962999999999994 - type: precision_at_10 value: 9.919 - type: precision_at_100 value: 1.302 - type: precision_at_1000 value: 0.158 - type: precision_at_20 value: 5.4559999999999995 - type: precision_at_3 value: 26.671 - type: precision_at_5 value: 17.764 - type: recall_at_1 value: 27.481 - type: recall_at_10 value: 49.595 - type: recall_at_100 value: 65.078 - type: recall_at_1000 value: 79.001 - type: recall_at_20 value: 54.564 - type: recall_at_3 value: 40.007 - type: recall_at_5 value: 44.409 - task: type: Classification dataset: type: mteb/imdb name: MTEB ImdbClassification config: default split: test revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 metrics: - type: accuracy value: 74.5976 - type: ap value: 68.90030024726627 - type: f1 value: 74.44139933523756 - task: type: Retrieval dataset: type: mteb/msmarco name: MTEB MSMARCO config: default split: dev revision: c5a29a104738b98a9e76336939199e264163d4a0 metrics: - type: map_at_1 value: 9.392 - type: map_at_10 value: 15.858 - type: map_at_100 value: 16.821 - type: map_at_1000 value: 16.916999999999998 - type: map_at_20 value: 16.378 - type: map_at_3 value: 13.627 - type: map_at_5 value: 14.837 - type: mrr_at_1 value: 9.642000000000001 - type: mrr_at_10 value: 16.189999999999998 - type: mrr_at_100 value: 17.149 - type: mrr_at_1000 value: 17.241 - type: mrr_at_20 value: 16.712 - type: mrr_at_3 value: 13.94 - type: mrr_at_5 value: 15.173 - type: ndcg_at_1 value: 9.642000000000001 - type: ndcg_at_10 value: 19.798 - type: ndcg_at_100 value: 24.93 - type: ndcg_at_1000 value: 27.723 - type: ndcg_at_20 value: 21.676000000000002 - type: ndcg_at_3 value: 15.135000000000002 - type: ndcg_at_5 value: 17.323 - type: precision_at_1 value: 9.642000000000001 - type: precision_at_10 value: 3.335 - type: precision_at_100 value: 0.597 - type: precision_at_1000 value: 0.084 - type: precision_at_20 value: 2.052 - type: precision_at_3 value: 6.585000000000001 - type: precision_at_5 value: 5.0569999999999995 - type: recall_at_1 value: 9.392 - type: recall_at_10 value: 32.074000000000005 - type: recall_at_100 value: 56.816 - type: recall_at_1000 value: 79.107 - type: recall_at_20 value: 39.404 - type: recall_at_3 value: 19.211 - type: recall_at_5 value: 24.476 - task: type: Classification dataset: type: mteb/mtop_domain name: MTEB MTOPDomainClassification (en) config: en split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 88.23529411764707 - type: f1 value: 87.7087794539205 - task: type: Classification dataset: type: mteb/mtop_intent name: MTEB MTOPIntentClassification (en) config: en split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - type: accuracy value: 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70970daeab8776df92f5ea462b6173c0b46fd2d1 metrics: - type: cos_sim_accuracy value: 84.00190737318948 - type: cos_sim_ap value: 67.48296380006165 - type: cos_sim_f1 value: 62.996718920889535 - type: cos_sim_precision value: 58.39152962378914 - type: cos_sim_recall value: 68.3905013192612 - type: dot_accuracy value: 84.00190737318948 - type: dot_ap value: 67.48295942427862 - type: dot_f1 value: 62.996718920889535 - type: dot_precision value: 58.39152962378914 - type: dot_recall value: 68.3905013192612 - type: euclidean_accuracy value: 84.00190737318948 - type: euclidean_ap value: 67.482961801317 - type: euclidean_f1 value: 62.996718920889535 - type: euclidean_precision value: 58.39152962378914 - type: euclidean_recall value: 68.3905013192612 - type: manhattan_accuracy value: 83.94826250223521 - type: manhattan_ap value: 67.32115101507013 - type: manhattan_f1 value: 62.665684830633275 - type: manhattan_precision value: 58.5819183111519 - type: manhattan_recall value: 67.36147757255937 - type: max_accuracy value: 84.00190737318948 - type: max_ap value: 67.48296380006165 - type: max_f1 value: 62.996718920889535 - task: type: PairClassification dataset: type: mteb/twitterurlcorpus-pairclassification name: MTEB TwitterURLCorpus config: default split: test revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf metrics: - type: cos_sim_accuracy value: 88.30286800946948 - type: cos_sim_ap value: 84.5306725053528 - type: cos_sim_f1 value: 76.5947752126367 - type: cos_sim_precision value: 75.56188192987715 - type: cos_sim_recall value: 77.65629812134279 - type: dot_accuracy value: 88.30286800946948 - type: dot_ap value: 84.53066920468329 - type: dot_f1 value: 76.5947752126367 - type: dot_precision value: 75.56188192987715 - type: dot_recall value: 77.65629812134279 - type: euclidean_accuracy value: 88.30286800946948 - type: euclidean_ap value: 84.53066432305307 - type: euclidean_f1 value: 76.5947752126367 - type: euclidean_precision value: 75.56188192987715 - type: euclidean_recall value: 77.65629812134279 - type: manhattan_accuracy value: 88.39795086738852 - type: manhattan_ap value: 84.51446339083833 - type: manhattan_f1 value: 76.57867106644667 - type: manhattan_precision value: 74.64181286549709 - type: manhattan_recall value: 78.61872497690176 - type: max_accuracy value: 88.39795086738852 - type: max_ap value: 84.5306725053528 - type: max_f1 value: 76.5947752126367 --- # Wartortle Wartortle is a distill of [bge-base-en-v1.5](BAAI/bge-base-en-v1.5). ## Intended purpose <span style="color:blue">This model is designed for use in semantic-autocomplete ([click here for demo](https://mihaiii.github.io/semantic-autocomplete/)).</span> Make sure you also pass `pipelineParams={{ pooling: "cls", normalize: true }}` since the default pooling in the component is mean. ## Usage Other than within [semantic-autocomplete](https://github.com/Mihaiii/semantic-autocomplete), you can use this model same as [bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5#usage).
THUDM/cogvlm2-llama3-chat-19B-int4	THUDM	2024-05-24T09:43:10Z	4,765	13	transformers	[ "transformers", "pytorch", "text-generation", "chat", "cogvlm2", "conversational", "custom_code", "en", "arxiv:2311.03079", "license:other", "autotrain_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2024-05-24T06:57:53Z	--- license: other license_name: cogvlm2 license_link: https://huggingface.co/THUDM/cogvlm2-llama3-chat-19B-int4/blob/main/LICENSE language: - en pipeline_tag: text-generation tags: - chat - cogvlm2 inference: false --- # CogVLM2 <div align="center"> <img src=https://raw.githubusercontent.com/THUDM/CogVLM2/53d5d5ea1aa8d535edffc0d15e31685bac40f878/resources/logo.svg width="40%"/> </div> <p align="center"> 👋 <a href="resources/WECHAT.md" target="_blank">Wechat</a> · 💡<a href="http://36.103.203.44:7861/" target="_blank">Online Demo</a> · 🎈<a href="https://github.com/THUDM/CogVLM2" target="_blank">Github Page</a> </p> <p align="center"> 📍Experience the larger-scale CogVLM model on the <a href="https://open.bigmodel.cn/dev/api#glm-4v">ZhipuAI Open Platform</a>. </p> ## Model introduction We launch a new generation of CogVLM2 series of models and open source two models built with [Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct). Compared with the previous generation of CogVLM open source models, the CogVLM2 series of open source models have the following improvements: 1. Significant improvements in many benchmarks such as `TextVQA`, `DocVQA`. 2. Support 8K content length. 3. Support image resolution up to *1344 1344. 4. Provide an open source model version that supports both Chinese and English. CogVlM2 Int4 model requires 16G GPU memory and Must be run on Linux with Nvidia GPU. \| Model name \| cogvlm2-llama3-chat-19B-int4 \| cogvlm2-llama3-chat-19B \| \|---------------------\|------------------------------\|-------------------------\| \| GPU Memory Required \| 16G \| 42G \| \| System Required \| Linux (With Nvidia GPU) \| Linux (With Nvidia GPU) \| ## Benchmark Our open source models have achieved good results in many lists compared to the previous generation of CogVLM open source models. Its excellent performance can compete with some non-open source models, as shown in the table below: \| Model \| Open Source \| LLM Size \| TextVQA \| DocVQA \| ChartQA \| OCRbench \| MMMU \| MMVet \| MMBench \| \|--------------------------------\|-------------\|----------\|----------\|----------\|----------\|----------\|----------\|----------\|----------\| \| CogVLM1.1 \| ✅ \| 7B \| 69.7 \| - \| 68.3 \| 590 \| 37.3 \| 52.0 \| 65.8 \| \| LLaVA-1.5 \| ✅ \| 13B \| 61.3 \| - \| - \| 337 \| 37.0 \| 35.4 \| 67.7 \| \| Mini-Gemini \| ✅ \| 34B \| 74.1 \| - \| - \| - \| 48.0 \| 59.3 \| 80.6 \| \| LLaVA-NeXT-LLaMA3 \| ✅ \| 8B \| - \| 78.2 \| 69.5 \| - \| 41.7 \| - \| 72.1 \| \| LLaVA-NeXT-110B \| ✅ \| 110B \| - \| 85.7 \| 79.7 \| - \| 49.1 \| - \| 80.5 \| \| InternVL-1.5 \| ✅ \| 20B \| 80.6 \| 90.9 \| 83.8 \| 720 \| 46.8 \| 55.4 \| 82.3 \| \| QwenVL-Plus \| ❌ \| - \| 78.9 \| 91.4 \| 78.1 \| 726 \| 51.4 \| 55.7 \| 67.0 \| \| Claude3-Opus \| ❌ \| - \| - \| 89.3 \| 80.8 \| 694 \| 59.4 \| 51.7 \| 63.3 \| \| Gemini Pro 1.5 \| ❌ \| - \| 73.5 \| 86.5 \| 81.3 \| - \| 58.5 \| - \| - \| \| GPT-4V \| ❌ \| - \| 78.0 \| 88.4 \| 78.5 \| 656 \| 56.8 \| 67.7 \| 75.0 \| \| CogVLM2-LLaMA3 (Ours) \| ✅ \| 8B \| 84.2 \| 92.3 \| 81.0 \| 756 \| 44.3 \| 60.4 \| 80.5 \| \| CogVLM2-LLaMA3-Chinese (Ours) \| ✅ \| 8B \| 85.0 \| 88.4 \| 74.7 \| 780 \| 42.8 \| 60.5 \| 78.9 \| All reviews were obtained without using any external OCR tools ("pixel only"). ## Quick Start here is a simple example of how to use the model to chat with the CogVLM2 model. For More use case. Find in our [github](https://github.com/THUDM/CogVLM2) ```python import torch from PIL import Image from transformers import AutoModelForCausalLM, AutoTokenizer MODEL_PATH = "THUDM/cogvlm2-llama3-chat-19B-int4" DEVICE = 'cuda' if torch.cuda.is_available() else 'cpu' TORCH_TYPE = torch.bfloat16 if torch.cuda.is_available() and torch.cuda.get_device_capability()[ 0] >= 8 else torch.float16 tokenizer = AutoTokenizer.from_pretrained( MODEL_PATH, trust_remote_code=True ) model = AutoModelForCausalLM.from_pretrained( MODEL_PATH, torch_dtype=TORCH_TYPE, trust_remote_code=True, low_cpu_mem_usage=True, ).eval() text_only_template = "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: {} ASSISTANT:" while True: image_path = input("image path >>>>> ") if image_path == '': print('You did not enter image path, the following will be a plain text conversation.') image = None text_only_first_query = True else: image = Image.open(image_path).convert('RGB') history = [] while True: query = input("Human:") if query == "clear": break if image is None: if text_only_first_query: query = text_only_template.format(query) text_only_first_query = False else: old_prompt = '' for _, (old_query, response) in enumerate(history): old_prompt += old_query + " " + response + "\n" query = old_prompt + "USER: {} ASSISTANT:".format(query) if image is None: input_by_model = model.build_conversation_input_ids( tokenizer, query=query, history=history, template_version='chat' ) else: input_by_model = model.build_conversation_input_ids( tokenizer, query=query, history=history, images=[image], template_version='chat' ) inputs = { 'input_ids': input_by_model['input_ids'].unsqueeze(0).to(DEVICE), 'token_type_ids': input_by_model['token_type_ids'].unsqueeze(0).to(DEVICE), 'attention_mask': input_by_model['attention_mask'].unsqueeze(0).to(DEVICE), 'images': [[input_by_model['images'][0].to(DEVICE).to(TORCH_TYPE)]] if image is not None else None, } gen_kwargs = { "max_new_tokens": 2048, "pad_token_id": 128002, } with torch.no_grad(): outputs = model.generate(inputs, **gen_kwargs) outputs = outputs[:, inputs['input_ids'].shape[1]:] response = tokenizer.decode(outputs[0]) response = response.split("<\|end_of_text\|>")[0] print("\nCogVLM2:", response) history.append((query, response)) ``` ## License This model is released under the CogVLM2 [LICENSE](LICENSE). For models built with Meta Llama 3, please also adhere to the [LLAMA3_LICENSE](LLAMA3_LICENSE). ## Citation If you find our work helpful, please consider citing the following papers ``` @misc{wang2023cogvlm, title={CogVLM: Visual Expert for Pretrained Language Models}, author={Weihan Wang and Qingsong Lv and Wenmeng Yu and Wenyi Hong and Ji Qi and Yan Wang and Junhui Ji and Zhuoyi Yang and Lei Zhao and Xixuan Song and Jiazheng Xu and Bin Xu and Juanzi Li and Yuxiao Dong and Ming Ding and Jie Tang}, year={2023}, eprint={2311.03079}, archivePrefix={arXiv}, primaryClass={cs.CV} } ```
timm/convnext_xlarge.fb_in22k_ft_in1k	timm	2024-02-10T23:27:37Z	4,764	0	timm	[ "timm", "pytorch", "safetensors", "image-classification", "dataset:imagenet-1k", "dataset:imagenet-22k", "arxiv:2201.03545", "license:apache-2.0", "region:us" ]	image-classification	2022-12-13T07:17:29Z	--- license: apache-2.0 library_name: timm tags: - image-classification - timm datasets: - imagenet-1k - imagenet-22k --- # Model card for convnext_xlarge.fb_in22k_ft_in1k A ConvNeXt image classification model. Pretrained on ImageNet-22k and fine-tuned on ImageNet-1k by paper authors. ## Model Details - Model Type: Image classification / feature backbone - Model Stats: - Params (M): 350.2 - GMACs: 61.0 - Activations (M): 57.5 - Image size: train = 224 x 224, test = 288 x 288 - Papers: - A ConvNet for the 2020s: https://arxiv.org/abs/2201.03545 - Original: https://github.com/facebookresearch/ConvNeXt - Dataset: ImageNet-1k - Pretrain Dataset: ImageNet-22k ## Model Usage ### Image Classification ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open(urlopen( 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png' )) model = timm.create_model('convnext_xlarge.fb_in22k_ft_in1k', pretrained=True) model = model.eval() # get model specific transforms (normalization, resize) data_config = timm.data.resolve_model_data_config(model) transforms = timm.data.create_transform(*data_config, is_training=False) output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1 top5_probabilities, top5_class_indices = torch.topk(output.softmax(dim=1) 100, k=5) ``` ### Feature Map Extraction ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open(urlopen( 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png' )) model = timm.create_model( 'convnext_xlarge.fb_in22k_ft_in1k', pretrained=True, features_only=True, ) model = model.eval() # get model specific transforms (normalization, resize) data_config = timm.data.resolve_model_data_config(model) transforms = timm.data.create_transform(data_config, is_training=False) output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1 for o in output: # print shape of each feature map in output # e.g.: # torch.Size([1, 256, 56, 56]) # torch.Size([1, 512, 28, 28]) # torch.Size([1, 1024, 14, 14]) # torch.Size([1, 2048, 7, 7]) print(o.shape) ``` ### Image Embeddings ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open(urlopen( 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png' )) model = timm.create_model( 'convnext_xlarge.fb_in22k_ft_in1k', pretrained=True, num_classes=0, # remove classifier nn.Linear ) model = model.eval() # get model specific transforms (normalization, resize) data_config = timm.data.resolve_model_data_config(model) transforms = timm.data.create_transform(data_config, is_training=False) output = model(transforms(img).unsqueeze(0)) # output is (batch_size, num_features) shaped tensor # or equivalently (without needing to set num_classes=0) output = model.forward_features(transforms(img).unsqueeze(0)) # output is unpooled, a (1, 2048, 7, 7) shaped tensor output = model.forward_head(output, pre_logits=True) # output is a (1, num_features) shaped tensor ``` ## Model Comparison Explore the dataset and runtime metrics of this model in timm [model results](https://github.com/huggingface/pytorch-image-models/tree/main/results). All timing numbers from eager model PyTorch 1.13 on RTX 3090 w/ AMP. \| model \|top1 \|top5 \|img_size\|param_count\|gmacs \|macts \|samples_per_sec\|batch_size\| \|------------------------------------------------------------------------------------------------------------------------------\|------\|------\|--------\|-----------\|------\|------\|---------------\|----------\| \| [convnextv2_huge.fcmae_ft_in22k_in1k_512](https://huggingface.co/timm/convnextv2_huge.fcmae_ft_in22k_in1k_512) \|88.848\|98.742\|512 \|660.29 \|600.81\|413.07\|28.58 \|48 \| \| [convnextv2_huge.fcmae_ft_in22k_in1k_384](https://huggingface.co/timm/convnextv2_huge.fcmae_ft_in22k_in1k_384) \|88.668\|98.738\|384 \|660.29 \|337.96\|232.35\|50.56 \|64 \| \| [convnext_xxlarge.clip_laion2b_soup_ft_in1k](https://huggingface.co/timm/convnext_xxlarge.clip_laion2b_soup_ft_in1k) \|88.612\|98.704\|256 \|846.47 \|198.09\|124.45\|122.45 \|256 \| \| [convnext_large_mlp.clip_laion2b_soup_ft_in12k_in1k_384](https://huggingface.co/timm/convnext_large_mlp.clip_laion2b_soup_ft_in12k_in1k_384) \|88.312\|98.578\|384 \|200.13 \|101.11\|126.74\|196.84 \|256 \| \| [convnextv2_large.fcmae_ft_in22k_in1k_384](https://huggingface.co/timm/convnextv2_large.fcmae_ft_in22k_in1k_384) \|88.196\|98.532\|384 \|197.96 \|101.1 \|126.74\|128.94 \|128 \| \| [convnext_large_mlp.clip_laion2b_soup_ft_in12k_in1k_320](https://huggingface.co/timm/convnext_large_mlp.clip_laion2b_soup_ft_in12k_in1k_320) \|87.968\|98.47 \|320 \|200.13 \|70.21 \|88.02 \|283.42 \|256 \| \| [convnext_xlarge.fb_in22k_ft_in1k_384](https://huggingface.co/timm/convnext_xlarge.fb_in22k_ft_in1k_384) \|87.75 \|98.556\|384 \|350.2 \|179.2 \|168.99\|124.85 \|192 \| \| [convnextv2_base.fcmae_ft_in22k_in1k_384](https://huggingface.co/timm/convnextv2_base.fcmae_ft_in22k_in1k_384) \|87.646\|98.422\|384 \|88.72 \|45.21 \|84.49 \|209.51 \|256 \| \| [convnext_large.fb_in22k_ft_in1k_384](https://huggingface.co/timm/convnext_large.fb_in22k_ft_in1k_384) \|87.476\|98.382\|384 \|197.77 \|101.1 \|126.74\|194.66 \|256 \| \| [convnext_large_mlp.clip_laion2b_augreg_ft_in1k](https://huggingface.co/timm/convnext_large_mlp.clip_laion2b_augreg_ft_in1k) \|87.344\|98.218\|256 \|200.13 \|44.94 \|56.33 \|438.08 \|256 \| \| [convnextv2_large.fcmae_ft_in22k_in1k](https://huggingface.co/timm/convnextv2_large.fcmae_ft_in22k_in1k) \|87.26 \|98.248\|224 \|197.96 \|34.4 \|43.13 \|376.84 \|256 \| \| [convnext_base.clip_laion2b_augreg_ft_in12k_in1k_384](https://huggingface.co/timm/convnext_base.clip_laion2b_augreg_ft_in12k_in1k_384) \|87.138\|98.212\|384 \|88.59 \|45.21 \|84.49 \|365.47 \|256 \| \| [convnext_xlarge.fb_in22k_ft_in1k](https://huggingface.co/timm/convnext_xlarge.fb_in22k_ft_in1k) \|87.002\|98.208\|224 \|350.2 \|60.98 \|57.5 \|368.01 \|256 \| \| [convnext_base.fb_in22k_ft_in1k_384](https://huggingface.co/timm/convnext_base.fb_in22k_ft_in1k_384) \|86.796\|98.264\|384 \|88.59 \|45.21 \|84.49 \|366.54 \|256 \| \| [convnextv2_base.fcmae_ft_in22k_in1k](https://huggingface.co/timm/convnextv2_base.fcmae_ft_in22k_in1k) \|86.74 \|98.022\|224 \|88.72 \|15.38 \|28.75 \|624.23 \|256 \| \| [convnext_large.fb_in22k_ft_in1k](https://huggingface.co/timm/convnext_large.fb_in22k_ft_in1k) \|86.636\|98.028\|224 \|197.77 \|34.4 \|43.13 \|581.43 \|256 \| \| [convnext_base.clip_laiona_augreg_ft_in1k_384](https://huggingface.co/timm/convnext_base.clip_laiona_augreg_ft_in1k_384) \|86.504\|97.97 \|384 \|88.59 \|45.21 \|84.49 \|368.14 \|256 \| \| [convnext_base.clip_laion2b_augreg_ft_in12k_in1k](https://huggingface.co/timm/convnext_base.clip_laion2b_augreg_ft_in12k_in1k) \|86.344\|97.97 \|256 \|88.59 \|20.09 \|37.55 \|816.14 \|256 \| \| [convnextv2_huge.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_huge.fcmae_ft_in1k) \|86.256\|97.75 \|224 \|660.29 \|115.0 \|79.07 \|154.72 \|256 \| \| [convnext_small.in12k_ft_in1k_384](https://huggingface.co/timm/convnext_small.in12k_ft_in1k_384) \|86.182\|97.92 \|384 \|50.22 \|25.58 \|63.37 \|516.19 \|256 \| \| [convnext_base.clip_laion2b_augreg_ft_in1k](https://huggingface.co/timm/convnext_base.clip_laion2b_augreg_ft_in1k) \|86.154\|97.68 \|256 \|88.59 \|20.09 \|37.55 \|819.86 \|256 \| \| [convnext_base.fb_in22k_ft_in1k](https://huggingface.co/timm/convnext_base.fb_in22k_ft_in1k) \|85.822\|97.866\|224 \|88.59 \|15.38 \|28.75 \|1037.66 \|256 \| \| [convnext_small.fb_in22k_ft_in1k_384](https://huggingface.co/timm/convnext_small.fb_in22k_ft_in1k_384) \|85.778\|97.886\|384 \|50.22 \|25.58 \|63.37 \|518.95 \|256 \| \| [convnextv2_large.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_large.fcmae_ft_in1k) \|85.742\|97.584\|224 \|197.96 \|34.4 \|43.13 \|375.23 \|256 \| \| [convnext_small.in12k_ft_in1k](https://huggingface.co/timm/convnext_small.in12k_ft_in1k) \|85.174\|97.506\|224 \|50.22 \|8.71 \|21.56 \|1474.31 \|256 \| \| [convnext_tiny.in12k_ft_in1k_384](https://huggingface.co/timm/convnext_tiny.in12k_ft_in1k_384) \|85.118\|97.608\|384 \|28.59 \|13.14 \|39.48 \|856.76 \|256 \| \| [convnextv2_tiny.fcmae_ft_in22k_in1k_384](https://huggingface.co/timm/convnextv2_tiny.fcmae_ft_in22k_in1k_384) \|85.112\|97.63 \|384 \|28.64 \|13.14 \|39.48 \|491.32 \|256 \| \| [convnextv2_base.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_base.fcmae_ft_in1k) \|84.874\|97.09 \|224 \|88.72 \|15.38 \|28.75 \|625.33 \|256 \| \| [convnext_small.fb_in22k_ft_in1k](https://huggingface.co/timm/convnext_small.fb_in22k_ft_in1k) \|84.562\|97.394\|224 \|50.22 \|8.71 \|21.56 \|1478.29 \|256 \| \| [convnext_large.fb_in1k](https://huggingface.co/timm/convnext_large.fb_in1k) \|84.282\|96.892\|224 \|197.77 \|34.4 \|43.13 \|584.28 \|256 \| \| [convnext_tiny.in12k_ft_in1k](https://huggingface.co/timm/convnext_tiny.in12k_ft_in1k) \|84.186\|97.124\|224 \|28.59 \|4.47 \|13.44 \|2433.7 \|256 \| \| [convnext_tiny.fb_in22k_ft_in1k_384](https://huggingface.co/timm/convnext_tiny.fb_in22k_ft_in1k_384) \|84.084\|97.14 \|384 \|28.59 \|13.14 \|39.48 \|862.95 \|256 \| \| [convnextv2_tiny.fcmae_ft_in22k_in1k](https://huggingface.co/timm/convnextv2_tiny.fcmae_ft_in22k_in1k) \|83.894\|96.964\|224 \|28.64 \|4.47 \|13.44 \|1452.72 \|256 \| \| [convnext_base.fb_in1k](https://huggingface.co/timm/convnext_base.fb_in1k) \|83.82 \|96.746\|224 \|88.59 \|15.38 \|28.75 \|1054.0 \|256 \| \| [convnextv2_nano.fcmae_ft_in22k_in1k_384](https://huggingface.co/timm/convnextv2_nano.fcmae_ft_in22k_in1k_384) \|83.37 \|96.742\|384 \|15.62 \|7.22 \|24.61 \|801.72 \|256 \| \| [convnext_small.fb_in1k](https://huggingface.co/timm/convnext_small.fb_in1k) \|83.142\|96.434\|224 \|50.22 \|8.71 \|21.56 \|1464.0 \|256 \| \| [convnextv2_tiny.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_tiny.fcmae_ft_in1k) \|82.92 \|96.284\|224 \|28.64 \|4.47 \|13.44 \|1425.62 \|256 \| \| [convnext_tiny.fb_in22k_ft_in1k](https://huggingface.co/timm/convnext_tiny.fb_in22k_ft_in1k) \|82.898\|96.616\|224 \|28.59 \|4.47 \|13.44 \|2480.88 \|256 \| \| [convnext_nano.in12k_ft_in1k](https://huggingface.co/timm/convnext_nano.in12k_ft_in1k) \|82.282\|96.344\|224 \|15.59 \|2.46 \|8.37 \|3926.52 \|256 \| \| [convnext_tiny_hnf.a2h_in1k](https://huggingface.co/timm/convnext_tiny_hnf.a2h_in1k) \|82.216\|95.852\|224 \|28.59 \|4.47 \|13.44 \|2529.75 \|256 \| \| [convnext_tiny.fb_in1k](https://huggingface.co/timm/convnext_tiny.fb_in1k) \|82.066\|95.854\|224 \|28.59 \|4.47 \|13.44 \|2346.26 \|256 \| \| [convnextv2_nano.fcmae_ft_in22k_in1k](https://huggingface.co/timm/convnextv2_nano.fcmae_ft_in22k_in1k) \|82.03 \|96.166\|224 \|15.62 \|2.46 \|8.37 \|2300.18 \|256 \| \| [convnextv2_nano.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_nano.fcmae_ft_in1k) \|81.83 \|95.738\|224 \|15.62 \|2.46 \|8.37 \|2321.48 \|256 \| \| [convnext_nano_ols.d1h_in1k](https://huggingface.co/timm/convnext_nano_ols.d1h_in1k) \|80.866\|95.246\|224 \|15.65 \|2.65 \|9.38 \|3523.85 \|256 \| \| [convnext_nano.d1h_in1k](https://huggingface.co/timm/convnext_nano.d1h_in1k) \|80.768\|95.334\|224 \|15.59 \|2.46 \|8.37 \|3915.58 \|256 \| \| [convnextv2_pico.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_pico.fcmae_ft_in1k) \|80.304\|95.072\|224 \|9.07 \|1.37 \|6.1 \|3274.57 \|256 \| \| [convnext_pico.d1_in1k](https://huggingface.co/timm/convnext_pico.d1_in1k) \|79.526\|94.558\|224 \|9.05 \|1.37 \|6.1 \|5686.88 \|256 \| \| [convnext_pico_ols.d1_in1k](https://huggingface.co/timm/convnext_pico_ols.d1_in1k) \|79.522\|94.692\|224 \|9.06 \|1.43 \|6.5 \|5422.46 \|256 \| \| [convnextv2_femto.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_femto.fcmae_ft_in1k) \|78.488\|93.98 \|224 \|5.23 \|0.79 \|4.57 \|4264.2 \|256 \| \| [convnext_femto_ols.d1_in1k](https://huggingface.co/timm/convnext_femto_ols.d1_in1k) \|77.86 \|93.83 \|224 \|5.23 \|0.82 \|4.87 \|6910.6 \|256 \| \| [convnext_femto.d1_in1k](https://huggingface.co/timm/convnext_femto.d1_in1k) \|77.454\|93.68 \|224 \|5.22 \|0.79 \|4.57 \|7189.92 \|256 \| \| [convnextv2_atto.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_atto.fcmae_ft_in1k) \|76.664\|93.044\|224 \|3.71 \|0.55 \|3.81 \|4728.91 \|256 \| \| [convnext_atto_ols.a2_in1k](https://huggingface.co/timm/convnext_atto_ols.a2_in1k) \|75.88 \|92.846\|224 \|3.7 \|0.58 \|4.11 \|7963.16 \|256 \| \| [convnext_atto.d2_in1k](https://huggingface.co/timm/convnext_atto.d2_in1k) \|75.664\|92.9 \|224 \|3.7 \|0.55 \|3.81 \|8439.22 \|256 \| ## Citation ```bibtex @article{liu2022convnet, author = {Zhuang Liu and Hanzi Mao and Chao-Yuan Wu and Christoph Feichtenhofer and Trevor Darrell and Saining Xie}, title = {A ConvNet for the 2020s}, journal = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, year = {2022}, } ``` ```bibtex @misc{rw2019timm, author = {Ross Wightman}, title = {PyTorch Image Models}, year = {2019}, publisher = {GitHub}, journal = {GitHub repository}, doi = {10.5281/zenodo.4414861}, howpublished = {\url{https://github.com/huggingface/pytorch-image-models}} } ```
finiteautomata/beto-sentiment-analysis	finiteautomata	2023-02-25T14:23:57Z	4,763	24	transformers	[ "transformers", "pytorch", "jax", "bert", "text-classification", "sentiment-analysis", "es", "arxiv:2106.09462", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-03-02T23:29:05Z	--- language: - es tags: - sentiment-analysis --- # Sentiment Analysis in Spanish ## beto-sentiment-analysis NOTE: this model will be removed soon -- use [pysentimiento/robertuito-sentiment-analysis](https://huggingface.co/pysentimiento/robertuito-sentiment-analysis) instead Repository: [https://github.com/finiteautomata/pysentimiento/](https://github.com/pysentimiento/pysentimiento/) Model trained with TASS 2020 corpus (around ~5k tweets) of several dialects of Spanish. Base model is [BETO](https://github.com/dccuchile/beto), a BERT model trained in Spanish. Uses `POS`, `NEG`, `NEU` labels. ## License `pysentimiento` is an open-source library for non-commercial use and scientific research purposes only. Please be aware that models are trained with third-party datasets and are subject to their respective licenses. 1. [TASS Dataset license](http://tass.sepln.org/tass_data/download.php) 2. [SEMEval 2017 Dataset license]() ## Citation If you use this model in your work, please cite the following papers: ``` @misc{perez2021pysentimiento, title={pysentimiento: A Python Toolkit for Sentiment Analysis and SocialNLP tasks}, author={Juan Manuel Pérez and Juan Carlos Giudici and Franco Luque}, year={2021}, eprint={2106.09462}, archivePrefix={arXiv}, primaryClass={cs.CL} } @article{canete2020spanish, title={Spanish pre-trained bert model and evaluation data}, author={Ca{\~n}ete, Jos{\'e} and Chaperon, Gabriel and Fuentes, Rodrigo and Ho, Jou-Hui and Kang, Hojin and P{\'e}rez, Jorge}, journal={Pml4dc at iclr}, volume={2020}, number={2020}, pages={1--10}, year={2020} } ``` Enjoy! 🤗
Yntec/CyberRealistic	Yntec	2023-10-17T04:07:07Z	4,763	4	diffusers	[ "diffusers", "safetensors", "Photorealistic", "Highly Detailed", "Beautiful", "Cyberdelia", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-10-16T21:44:55Z	--- license: creativeml-openrail-m library_name: diffusers pipeline_tag: text-to-image tags: - Photorealistic - Highly Detailed - Beautiful - Cyberdelia - stable-diffusion - stable-diffusion-diffusers - diffusers - text-to-image --- # CyberRealistic V2 Original page: https://civitai.com/models/15003?modelVersionId=55015 Sample and prompt: ![Sample](https://cdn-uploads.huggingface.co/production/uploads/63239b8370edc53f51cd5d42/wGg_EOOj8jUraAIzridx8.png) An bartolomé esteban murillo Pretty CUTE Girl buying A room made of pizza. called the pizza room, amazing detailed artwork, Full body pose, working in a bakery by graffiti. cookie cooking a bunch of cookies, in the kitchen, DETAILED CHIBI EYES, technicolor, painterly, logo, farmer, elegant, highly detailed, digital art, hyperrealistic
Habana/swin	Habana	2023-07-25T21:36:24Z	4,762	0	null	[ "optimum_habana", "license:apache-2.0", "region:us" ]	null	2022-08-23T08:10:57Z	--- license: apache-2.0 --- [Optimum Habana](https://github.com/huggingface/optimum-habana) is the interface between the Hugging Face Transformers and Diffusers libraries and Habana's Gaudi processor (HPU). It provides a set of tools enabling easy and fast model loading, training and inference on single- and multi-HPU settings for different downstream tasks. Learn more about how to take advantage of the power of Habana HPUs to train and deploy Transformers and Diffusers models at [hf.co/hardware/habana](https://huggingface.co/hardware/habana). ## Swin Transformer model HPU configuration This model only contains the `GaudiConfig` file for running the [Swin Transformer](https://huggingface.co/microsoft/swin-base-patch4-window7-224-in22k) model on Habana's Gaudi processors (HPU). This model contains no model weights, only a GaudiConfig. This enables to specify: - `use_fused_adam`: whether to use Habana's custom AdamW implementation - `use_fused_clip_norm`: whether to use Habana's fused gradient norm clipping operator - `use_torch_autocast`: whether to use Torch Autocast for managing mixed precision ## Usage The model is instantiated the same way as in the Transformers library. The only difference is that there are a few new training arguments specific to HPUs.\ It is strongly recommended to train this model doing bf16 mixed-precision training for optimal performance and accuracy. [Here](https://github.com/huggingface/optimum-habana/blob/main/examples/image-classification/run_image_classification.py) is an image classification example script to fine-tune a model. You can run it with Swin with the following command: ```bash python run_image_classification.py \ --model_name_or_path microsoft/swin-base-patch4-window7-224-in22k \ --dataset_name cifar10 \ --output_dir /tmp/outputs/ \ --remove_unused_columns False \ --do_train \ --do_eval \ --learning_rate 3e-5 \ --num_train_epochs 5 \ --per_device_train_batch_size 64 \ --per_device_eval_batch_size 64 \ --evaluation_strategy epoch \ --save_strategy epoch \ --load_best_model_at_end True \ --save_total_limit 3 \ --seed 1337 \ --use_habana \ --use_lazy_mode \ --gaudi_config_name Habana/swin \ --throughput_warmup_steps 3 \ --ignore_mismatched_sizes \ --bf16 ``` Check the [documentation](https://huggingface.co/docs/optimum/habana/index) out for more advanced usage and examples.
mradermacher/codellama-7b-tofutune-GGUF	mradermacher	2024-06-13T10:33:59Z	4,760	0	transformers	[ "transformers", "gguf", "text-generation-inference", "unsloth", "llama", "trl", "sft", "en", "base_model:simonbutt/codellama-7b-tofutune", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-06-13T10:09:53Z	--- base_model: simonbutt/codellama-7b-tofutune language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - text-generation-inference - transformers - unsloth - llama - trl - sft --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/simonbutt/codellama-7b-tofutune <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/codellama-7b-tofutune-GGUF/resolve/main/codellama-7b-tofutune.Q2_K.gguf) \| Q2_K \| 2.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/codellama-7b-tofutune-GGUF/resolve/main/codellama-7b-tofutune.IQ3_XS.gguf) \| IQ3_XS \| 2.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/codellama-7b-tofutune-GGUF/resolve/main/codellama-7b-tofutune.IQ3_S.gguf) \| IQ3_S \| 3.0 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/codellama-7b-tofutune-GGUF/resolve/main/codellama-7b-tofutune.Q3_K_S.gguf) \| Q3_K_S \| 3.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/codellama-7b-tofutune-GGUF/resolve/main/codellama-7b-tofutune.IQ3_M.gguf) \| IQ3_M \| 3.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/codellama-7b-tofutune-GGUF/resolve/main/codellama-7b-tofutune.Q3_K_M.gguf) \| Q3_K_M \| 3.4 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/codellama-7b-tofutune-GGUF/resolve/main/codellama-7b-tofutune.Q3_K_L.gguf) \| Q3_K_L \| 3.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/codellama-7b-tofutune-GGUF/resolve/main/codellama-7b-tofutune.IQ4_XS.gguf) \| IQ4_XS \| 3.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/codellama-7b-tofutune-GGUF/resolve/main/codellama-7b-tofutune.Q4_K_S.gguf) \| Q4_K_S \| 4.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/codellama-7b-tofutune-GGUF/resolve/main/codellama-7b-tofutune.Q4_K_M.gguf) \| Q4_K_M \| 4.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/codellama-7b-tofutune-GGUF/resolve/main/codellama-7b-tofutune.Q5_K_S.gguf) \| Q5_K_S \| 4.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/codellama-7b-tofutune-GGUF/resolve/main/codellama-7b-tofutune.Q5_K_M.gguf) \| Q5_K_M \| 4.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/codellama-7b-tofutune-GGUF/resolve/main/codellama-7b-tofutune.Q6_K.gguf) \| Q6_K \| 5.6 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/codellama-7b-tofutune-GGUF/resolve/main/codellama-7b-tofutune.Q8_0.gguf) \| Q8_0 \| 7.3 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/codellama-7b-tofutune-GGUF/resolve/main/codellama-7b-tofutune.f16.gguf) \| f16 \| 13.6 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
Mihaiii/Squirtle	Mihaiii	2024-04-30T20:00:05Z	4,759	0	sentence-transformers	[ "sentence-transformers", "onnx", "safetensors", "bert", "feature-extraction", "sentence-similarity", "bge", "mteb", "dataset:Mihaiii/qa-assistant", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "text-embeddings-inference", "region:us" ]	sentence-similarity	2024-04-30T15:06:52Z	--- license: mit library_name: sentence-transformers pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - bge - mteb datasets: - Mihaiii/qa-assistant model-index: - name: Squirtle results: - task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (en) config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 69.59701492537313 - type: ap value: 31.80839087521638 - type: f1 value: 63.43204352573031 - task: type: Classification dataset: type: mteb/amazon_polarity name: MTEB AmazonPolarityClassification config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 82.09027499999999 - type: ap value: 76.95004336850603 - type: f1 value: 82.04505556179174 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (en) config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 41.943999999999996 - type: f1 value: 40.40964457303876 - task: type: Retrieval dataset: type: mteb/arguana name: MTEB ArguAna config: default split: test revision: c22ab2a51041ffd869aaddef7af8d8215647e41a metrics: - type: map_at_1 value: 13.869000000000002 - type: map_at_10 value: 24.631 - type: map_at_100 value: 25.965 - type: map_at_1000 value: 26.023000000000003 - type: map_at_20 value: 25.442999999999998 - type: map_at_3 value: 20.827 - type: map_at_5 value: 22.776 - type: mrr_at_1 value: 14.580000000000002 - type: mrr_at_10 value: 24.91 - type: mrr_at_100 value: 26.229999999999997 - type: mrr_at_1000 value: 26.288 - type: mrr_at_20 value: 25.708 - type: mrr_at_3 value: 21.136 - type: mrr_at_5 value: 23.02 - type: ndcg_at_1 value: 13.869000000000002 - type: ndcg_at_10 value: 31.14 - type: ndcg_at_100 value: 37.885999999999996 - type: ndcg_at_1000 value: 39.497 - type: ndcg_at_20 value: 34.068 - type: ndcg_at_3 value: 23.163 - type: ndcg_at_5 value: 26.677 - type: precision_at_1 value: 13.869000000000002 - type: precision_at_10 value: 5.220000000000001 - type: precision_at_100 value: 0.844 - type: precision_at_1000 value: 0.097 - type: precision_at_20 value: 3.186 - type: precision_at_3 value: 9.981 - type: precision_at_5 value: 7.696 - type: recall_at_1 value: 13.869000000000002 - type: recall_at_10 value: 52.205 - type: recall_at_100 value: 84.42399999999999 - type: recall_at_1000 value: 97.297 - type: recall_at_20 value: 63.727000000000004 - type: recall_at_3 value: 29.942999999999998 - type: recall_at_5 value: 38.478 - task: type: Clustering dataset: type: mteb/arxiv-clustering-p2p name: MTEB ArxivClusteringP2P config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics: - type: v_measure value: 33.042527574996505 - type: v_measures value: [0.2896613951792161, 0.2974905938215674, 0.28195491579456905, 0.3008325954323272, 0.3012695848509836, 0.28933380000430453, 0.297420818100457, 0.2792041800887245, 0.3049968405105834, 0.30704380358904726, 0.39238640618067383, 0.3932595512850983, 0.3875472939281748, 0.39822946285500505, 0.39839156092566014, 0.40184636328122075, 0.39008499175162326, 0.3984035967802891, 0.39159106298575347, 0.3923217036338575, 0.3916410911561569, 0.2357749280106326, 0.23682806457721106, 0.3122239617657793, 0.26610676013174756, 0.18123482803921434, 0.2504695156635453, 0.10917464735757001, 0.16714512698028008, 1.0, 0.19931410358764295, 0.2896613951792161, 0.2974905938215674, 0.28195491579456905, 0.3008325954323272, 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value: 23.033 - type: ndcg_at_10 value: 28.899 - type: ndcg_at_100 value: 33.788000000000004 - type: ndcg_at_1000 value: 36.962 - type: ndcg_at_20 value: 30.497000000000003 - type: ndcg_at_3 value: 26.442 - type: ndcg_at_5 value: 27.466 - type: precision_at_1 value: 23.033 - type: precision_at_10 value: 5.351 - type: precision_at_100 value: 0.9610000000000001 - type: precision_at_1000 value: 0.151 - type: precision_at_20 value: 3.2259999999999995 - type: precision_at_3 value: 12.923000000000002 - type: precision_at_5 value: 8.956 - type: recall_at_1 value: 17.835 - type: recall_at_10 value: 36.034 - type: recall_at_100 value: 57.615 - type: recall_at_1000 value: 79.72 - type: recall_at_20 value: 41.894999999999996 - type: recall_at_3 value: 28.313 - type: recall_at_5 value: 31.639 - task: type: Retrieval dataset: type: mteb/cqadupstack-english name: MTEB CQADupstackEnglishRetrieval config: default split: test revision: ad9991cb51e31e31e430383c75ffb2885547b5f0 metrics: - type: map_at_1 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recall_at_1 value: 12.166 - type: recall_at_10 value: 24.701999999999998 - type: recall_at_100 value: 39.199 - type: recall_at_1000 value: 58.205 - type: recall_at_20 value: 28.791 - type: recall_at_3 value: 17.469 - type: recall_at_5 value: 20.615 - task: type: Retrieval dataset: type: mteb/cqadupstack-gaming name: MTEB CQADupstackGamingRetrieval config: default split: test revision: 4885aa143210c98657558c04aaf3dc47cfb54340 metrics: - type: map_at_1 value: 19.667 - type: map_at_10 value: 27.163999999999998 - type: map_at_100 value: 28.044000000000004 - type: map_at_1000 value: 28.142 - type: map_at_20 value: 27.645999999999997 - type: map_at_3 value: 24.914 - type: map_at_5 value: 26.078000000000003 - type: mrr_at_1 value: 23.197000000000003 - type: mrr_at_10 value: 30.202 - type: mrr_at_100 value: 30.976 - type: mrr_at_1000 value: 31.047000000000004 - type: mrr_at_20 value: 30.636000000000003 - type: mrr_at_3 value: 28.004 - type: mrr_at_5 value: 29.164 - type: ndcg_at_1 value: 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2.074833333333333 - type: precision_at_3 value: 7.440333333333333 - type: precision_at_5 value: 5.341916666666667 - type: recall_at_1 value: 11.252416666666667 - type: recall_at_10 value: 25.200833333333332 - type: recall_at_100 value: 44.075333333333326 - type: recall_at_1000 value: 66.12541666666665 - type: recall_at_20 value: 30.24916666666667 - type: recall_at_3 value: 17.46591666666667 - type: recall_at_5 value: 20.53691666666667 - task: type: Retrieval dataset: type: mteb/cqadupstack-stats name: MTEB CQADupstackStatsRetrieval config: default split: test revision: 65ac3a16b8e91f9cee4c9828cc7c335575432a2a metrics: - type: map_at_1 value: 8.696 - type: map_at_10 value: 12.339 - type: map_at_100 value: 12.946 - type: map_at_1000 value: 13.04 - type: map_at_20 value: 12.6 - type: map_at_3 value: 11.06 - type: map_at_5 value: 11.530999999999999 - type: mrr_at_1 value: 10.276 - type: mrr_at_10 value: 14.463999999999999 - type: mrr_at_100 value: 15.07 - type: mrr_at_1000 value: 15.152 - type: mrr_at_20 value: 14.737 - type: mrr_at_3 value: 13.037 - type: mrr_at_5 value: 13.627 - type: ndcg_at_1 value: 10.276 - type: ndcg_at_10 value: 15.085 - type: ndcg_at_100 value: 18.538 - type: ndcg_at_1000 value: 21.461 - type: ndcg_at_20 value: 15.976 - type: ndcg_at_3 value: 12.454 - type: ndcg_at_5 value: 13.195 - type: precision_at_1 value: 10.276 - type: precision_at_10 value: 2.669 - type: precision_at_100 value: 0.48900000000000005 - type: precision_at_1000 value: 0.08 - type: precision_at_20 value: 1.572 - type: precision_at_3 value: 5.726 - type: precision_at_5 value: 3.9570000000000003 - type: recall_at_1 value: 8.696 - type: recall_at_10 value: 21.766 - type: recall_at_100 value: 38.269 - type: recall_at_1000 value: 61.106 - type: recall_at_20 value: 24.992 - type: recall_at_3 value: 14.032 - type: recall_at_5 value: 15.967999999999998 - task: type: Retrieval dataset: type: mteb/cqadupstack-tex name: MTEB CQADupstackTexRetrieval config: default split: test revision: 46989137a86843e03a6195de44b09deda022eec7 metrics: - type: map_at_1 value: 6.13 - type: map_at_10 value: 9.067 - type: map_at_100 value: 9.687999999999999 - type: map_at_1000 value: 9.792 - type: map_at_20 value: 9.384 - type: map_at_3 value: 8.006 - type: map_at_5 value: 8.581999999999999 - type: mrr_at_1 value: 7.605 - type: mrr_at_10 value: 11.111 - type: mrr_at_100 value: 11.745999999999999 - type: mrr_at_1000 value: 11.837 - type: mrr_at_20 value: 11.452 - type: mrr_at_3 value: 9.922 - type: mrr_at_5 value: 10.522 - type: ndcg_at_1 value: 7.605 - type: ndcg_at_10 value: 11.302 - type: ndcg_at_100 value: 14.629 - type: ndcg_at_1000 value: 17.739 - type: ndcg_at_20 value: 12.411 - type: ndcg_at_3 value: 9.28 - type: ndcg_at_5 value: 10.161000000000001 - type: precision_at_1 value: 7.605 - type: precision_at_10 value: 2.22 - type: precision_at_100 value: 0.46499999999999997 - type: precision_at_1000 value: 0.087 - type: precision_at_20 value: 1.428 - type: precision_at_3 value: 4.565 - type: precision_at_5 value: 3.3649999999999998 - type: recall_at_1 value: 6.13 - type: recall_at_10 value: 16.009999999999998 - type: recall_at_100 value: 31.467 - type: recall_at_1000 value: 54.722 - type: recall_at_20 value: 20.137 - type: recall_at_3 value: 10.347000000000001 - type: recall_at_5 value: 12.692 - task: type: Retrieval dataset: type: mteb/cqadupstack-unix name: MTEB CQADupstackUnixRetrieval config: default split: test revision: 6c6430d3a6d36f8d2a829195bc5dc94d7e063e53 metrics: - type: map_at_1 value: 11.645 - type: map_at_10 value: 15.466 - type: map_at_100 value: 16.147 - type: map_at_1000 value: 16.247 - type: map_at_20 value: 15.806999999999999 - type: map_at_3 value: 14.011000000000001 - type: map_at_5 value: 14.967 - type: mrr_at_1 value: 14.179 - type: mrr_at_10 value: 18.512 - type: mrr_at_100 value: 19.184 - type: mrr_at_1000 value: 19.267 - type: mrr_at_20 value: 18.855 - type: mrr_at_3 value: 16.993 - type: mrr_at_5 value: 17.954 - type: ndcg_at_1 value: 14.179 - type: ndcg_at_10 value: 18.311 - type: ndcg_at_100 value: 21.996 - type: ndcg_at_1000 value: 24.942 - type: ndcg_at_20 value: 19.522000000000002 - type: ndcg_at_3 value: 15.593000000000002 - type: ndcg_at_5 value: 17.116 - type: precision_at_1 value: 14.179 - type: precision_at_10 value: 3.116 - type: precision_at_100 value: 0.5519999999999999 - type: precision_at_1000 value: 0.091 - type: precision_at_20 value: 1.87 - type: precision_at_3 value: 7.090000000000001 - type: precision_at_5 value: 5.224 - type: recall_at_1 value: 11.645 - type: recall_at_10 value: 24.206 - type: recall_at_100 value: 41.29 - type: recall_at_1000 value: 63.205999999999996 - type: recall_at_20 value: 28.659000000000002 - type: recall_at_3 value: 16.771 - type: recall_at_5 value: 20.602 - task: type: Retrieval dataset: type: mteb/cqadupstack-webmasters name: MTEB CQADupstackWebmastersRetrieval config: default split: test revision: 160c094312a0e1facb97e55eeddb698c0abe3571 metrics: - type: map_at_1 value: 12.435 - type: map_at_10 value: 17.263 - type: map_at_100 value: 18.137 - type: map_at_1000 value: 18.282999999999998 - type: map_at_20 value: 17.724 - type: map_at_3 value: 15.648000000000001 - type: map_at_5 value: 16.542 - type: mrr_at_1 value: 15.809999999999999 - type: mrr_at_10 value: 20.687 - type: mrr_at_100 value: 21.484 - type: mrr_at_1000 value: 21.567 - type: mrr_at_20 value: 21.124000000000002 - type: mrr_at_3 value: 19.104 - type: mrr_at_5 value: 19.974 - type: ndcg_at_1 value: 15.809999999999999 - type: ndcg_at_10 value: 20.801 - type: ndcg_at_100 value: 25.001 - type: ndcg_at_1000 value: 28.347 - type: ndcg_at_20 value: 22.223000000000003 - type: ndcg_at_3 value: 18.046 - type: ndcg_at_5 value: 19.308 - type: precision_at_1 value: 15.809999999999999 - type: precision_at_10 value: 4.032 - type: precision_at_100 value: 0.832 - type: precision_at_1000 value: 0.16 - type: precision_at_20 value: 2.54 - type: precision_at_3 value: 8.63 - type: precision_at_5 value: 6.4030000000000005 - type: recall_at_1 value: 12.435 - type: recall_at_10 value: 27.495000000000005 - type: recall_at_100 value: 47.522999999999996 - type: recall_at_1000 value: 70.804 - type: recall_at_20 value: 33.334 - type: recall_at_3 value: 19.192 - type: recall_at_5 value: 22.435 - task: type: Retrieval dataset: type: mteb/cqadupstack-wordpress name: MTEB CQADupstackWordpressRetrieval config: default split: test revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4 metrics: - type: map_at_1 value: 8.262 - type: map_at_10 value: 11.167 - type: map_at_100 value: 12.017999999999999 - type: map_at_1000 value: 12.113 - type: map_at_20 value: 11.674 - type: map_at_3 value: 9.736 - type: map_at_5 value: 10.384 - type: mrr_at_1 value: 9.242 - type: mrr_at_10 value: 12.564 - type: mrr_at_100 value: 13.427 - type: mrr_at_1000 value: 13.520999999999999 - type: mrr_at_20 value: 13.072000000000001 - type: mrr_at_3 value: 11.06 - type: mrr_at_5 value: 11.753 - type: ndcg_at_1 value: 9.242 - type: ndcg_at_10 value: 13.594999999999999 - type: ndcg_at_100 value: 18.049 - type: ndcg_at_1000 value: 20.888 - type: ndcg_at_20 value: 15.440000000000001 - type: ndcg_at_3 value: 10.697 - type: ndcg_at_5 value: 11.757 - type: precision_at_1 value: 9.242 - type: precision_at_10 value: 2.348 - type: precision_at_100 value: 0.482 - type: precision_at_1000 value: 0.077 - type: precision_at_20 value: 1.5709999999999997 - type: precision_at_3 value: 4.621 - type: precision_at_5 value: 3.401 - type: recall_at_1 value: 8.262 - type: recall_at_10 value: 19.983999999999998 - type: recall_at_100 value: 40.997 - type: recall_at_1000 value: 63.058 - type: recall_at_20 value: 27.168999999999997 - type: recall_at_3 value: 11.814 - type: recall_at_5 value: 14.463999999999999 - task: type: Retrieval dataset: type: mteb/climate-fever name: MTEB ClimateFEVER config: default split: test revision: 47f2ac6acb640fc46020b02a5b59fdda04d39380 metrics: - type: map_at_1 value: 4.058 - type: map_at_10 value: 6.734 - type: map_at_100 value: 7.593999999999999 - type: map_at_1000 value: 7.736999999999999 - type: map_at_20 value: 7.102 - type: map_at_3 value: 5.559 - type: map_at_5 value: 6.178999999999999 - type: mrr_at_1 value: 8.404 - type: mrr_at_10 value: 13.514999999999999 - type: mrr_at_100 value: 14.518 - type: mrr_at_1000 value: 14.599 - type: mrr_at_20 value: 14.025000000000002 - type: mrr_at_3 value: 11.584999999999999 - type: mrr_at_5 value: 12.588 - type: ndcg_at_1 value: 8.404 - type: ndcg_at_10 value: 10.02 - type: ndcg_at_100 value: 14.771999999999998 - type: ndcg_at_1000 value: 18.251 - type: ndcg_at_20 value: 11.378 - type: ndcg_at_3 value: 7.675 - type: ndcg_at_5 value: 8.558 - type: precision_at_1 value: 8.404 - type: precision_at_10 value: 3.212 - type: precision_at_100 value: 0.83 - type: precision_at_1000 value: 0.146 - type: precision_at_20 value: 2.186 - type: precision_at_3 value: 5.624 - type: precision_at_5 value: 4.5600000000000005 - type: recall_at_1 value: 4.058 - type: recall_at_10 value: 12.751999999999999 - type: recall_at_100 value: 30.219 - type: recall_at_1000 value: 50.749 - type: recall_at_20 value: 16.634 - type: recall_at_3 value: 7.234999999999999 - type: recall_at_5 value: 9.418 - task: type: Retrieval dataset: type: mteb/dbpedia name: MTEB DBPedia config: default split: test revision: c0f706b76e590d620bd6618b3ca8efdd34e2d659 metrics: - type: map_at_1 value: 5.516 - type: map_at_10 value: 11.001 - type: map_at_100 value: 14.527999999999999 - type: map_at_1000 value: 15.417 - type: map_at_20 value: 12.446 - type: map_at_3 value: 8.269 - type: map_at_5 value: 9.345 - type: mrr_at_1 value: 43.5 - type: mrr_at_10 value: 54.078 - type: mrr_at_100 value: 54.655 - type: mrr_at_1000 value: 54.679 - type: mrr_at_20 value: 54.461999999999996 - type: mrr_at_3 value: 51.37500000000001 - type: mrr_at_5 value: 53.25 - type: ndcg_at_1 value: 33.125 - type: ndcg_at_10 value: 25.665 - type: ndcg_at_100 value: 28.116000000000003 - type: ndcg_at_1000 value: 34.477000000000004 - type: ndcg_at_20 value: 25.027 - type: ndcg_at_3 value: 28.4 - type: ndcg_at_5 value: 27.094 - type: precision_at_1 value: 43.5 - type: precision_at_10 value: 21.65 - type: precision_at_100 value: 6.351999999999999 - type: precision_at_1000 value: 1.306 - type: precision_at_20 value: 15.662 - type: precision_at_3 value: 32.333 - type: precision_at_5 value: 28.199999999999996 - type: recall_at_1 value: 5.516 - type: recall_at_10 value: 15.457 - type: recall_at_100 value: 32.903 - type: recall_at_1000 value: 53.81700000000001 - type: recall_at_20 value: 20.365 - type: recall_at_3 value: 9.528 - type: recall_at_5 value: 11.619 - task: type: Classification dataset: type: mteb/emotion name: MTEB EmotionClassification config: default split: test revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 metrics: - type: accuracy value: 45.79 - type: f1 value: 38.89634882093881 - task: type: Retrieval dataset: type: mteb/fever name: MTEB FEVER config: default split: test revision: bea83ef9e8fb933d90a2f1d5515737465d613e12 metrics: - type: map_at_1 value: 18.063000000000002 - type: map_at_10 value: 24.911 - type: map_at_100 value: 25.688 - type: map_at_1000 value: 25.758 - type: map_at_20 value: 25.358999999999998 - type: map_at_3 value: 22.743 - type: map_at_5 value: 23.924 - type: mrr_at_1 value: 19.472 - type: mrr_at_10 value: 26.587 - type: mrr_at_100 value: 27.362 - type: mrr_at_1000 value: 27.428 - type: mrr_at_20 value: 27.040999999999997 - type: mrr_at_3 value: 24.362000000000002 - type: mrr_at_5 value: 25.593 - type: ndcg_at_1 value: 19.472 - type: ndcg_at_10 value: 29.183999999999997 - type: ndcg_at_100 value: 33.207 - type: ndcg_at_1000 value: 35.21 - type: ndcg_at_20 value: 30.791 - type: ndcg_at_3 value: 24.701999999999998 - type: ndcg_at_5 value: 26.823000000000004 - type: precision_at_1 value: 19.472 - type: precision_at_10 value: 4.469 - type: precision_at_100 value: 0.6629999999999999 - type: precision_at_1000 value: 0.08499999999999999 - type: precision_at_20 value: 2.59 - type: precision_at_3 value: 10.401 - type: precision_at_5 value: 7.363 - type: recall_at_1 value: 18.063000000000002 - type: recall_at_10 value: 41.071999999999996 - type: recall_at_100 value: 60.049 - type: recall_at_1000 value: 75.64699999999999 - type: recall_at_20 value: 47.211999999999996 - type: recall_at_3 value: 28.796 - type: recall_at_5 value: 33.894999999999996 - task: type: Retrieval dataset: type: mteb/fiqa name: MTEB FiQA2018 config: default split: test revision: 27a168819829fe9bcd655c2df245fb19452e8e06 metrics: - type: map_at_1 value: 2.45 - type: map_at_10 value: 4.255 - type: map_at_100 value: 4.809 - type: map_at_1000 value: 4.954 - type: map_at_20 value: 4.513 - type: map_at_3 value: 3.4029999999999996 - type: map_at_5 value: 3.782 - type: mrr_at_1 value: 4.938 - type: mrr_at_10 value: 8.231 - type: mrr_at_100 value: 8.902000000000001 - type: mrr_at_1000 value: 9.019 - type: mrr_at_20 value: 8.530999999999999 - type: mrr_at_3 value: 6.944 - type: mrr_at_5 value: 7.623 - type: ndcg_at_1 value: 4.938 - type: ndcg_at_10 value: 6.425 - type: ndcg_at_100 value: 9.661999999999999 - type: ndcg_at_1000 value: 13.911999999999999 - type: ndcg_at_20 value: 7.3 - type: ndcg_at_3 value: 4.907 - type: ndcg_at_5 value: 5.406 - type: precision_at_1 value: 4.938 - type: precision_at_10 value: 2.037 - type: precision_at_100 value: 0.528 - type: precision_at_1000 value: 0.125 - type: precision_at_20 value: 1.366 - type: precision_at_3 value: 3.344 - type: precision_at_5 value: 2.7470000000000003 - type: recall_at_1 value: 2.45 - type: recall_at_10 value: 8.987 - type: recall_at_100 value: 22.302 - type: recall_at_1000 value: 49.903999999999996 - type: recall_at_20 value: 11.712 - type: recall_at_3 value: 4.675 - type: recall_at_5 value: 6.161 - task: type: Retrieval dataset: type: mteb/hotpotqa name: MTEB HotpotQA config: default split: test revision: ab518f4d6fcca38d87c25209f94beba119d02014 metrics: - type: map_at_1 value: 23.585 - type: map_at_10 value: 31.893 - type: map_at_100 value: 32.851 - type: map_at_1000 value: 32.951 - type: map_at_20 value: 32.415 - type: map_at_3 value: 29.787000000000003 - type: map_at_5 value: 31.012 - type: mrr_at_1 value: 47.171 - type: mrr_at_10 value: 54.333 - type: mrr_at_100 value: 54.949000000000005 - type: mrr_at_1000 value: 54.98800000000001 - type: mrr_at_20 value: 54.702 - type: mrr_at_3 value: 52.632999999999996 - type: mrr_at_5 value: 53.652 - type: ndcg_at_1 value: 47.171 - type: ndcg_at_10 value: 39.884 - type: ndcg_at_100 value: 44.019000000000005 - type: ndcg_at_1000 value: 46.303 - type: ndcg_at_20 value: 41.461999999999996 - type: ndcg_at_3 value: 36.153999999999996 - type: ndcg_at_5 value: 38.072 - type: precision_at_1 value: 47.171 - type: precision_at_10 value: 8.396 - type: precision_at_100 value: 1.169 - type: precision_at_1000 value: 0.147 - type: precision_at_20 value: 4.707 - type: precision_at_3 value: 22.408 - type: precision_at_5 value: 14.966 - type: recall_at_1 value: 23.585 - type: recall_at_10 value: 41.978 - type: recall_at_100 value: 58.447 - type: recall_at_1000 value: 73.7 - type: recall_at_20 value: 47.07 - type: recall_at_3 value: 33.611999999999995 - type: recall_at_5 value: 37.413999999999994 - task: type: Classification dataset: type: mteb/imdb name: MTEB ImdbClassification config: default split: test revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 metrics: - type: accuracy value: 74.9528 - type: ap value: 69.50790744137139 - type: f1 value: 74.77689594327182 - task: type: Retrieval dataset: type: mteb/msmarco name: MTEB MSMARCO config: default split: dev revision: c5a29a104738b98a9e76336939199e264163d4a0 metrics: - type: map_at_1 value: 8.186 - type: map_at_10 value: 13.352 - type: map_at_100 value: 14.147000000000002 - type: map_at_1000 value: 14.231 - type: map_at_20 value: 13.753000000000002 - type: map_at_3 value: 11.529 - type: map_at_5 value: 12.497 - type: mrr_at_1 value: 8.424 - type: mrr_at_10 value: 13.675999999999998 - type: mrr_at_100 value: 14.475999999999999 - type: mrr_at_1000 value: 14.557 - type: mrr_at_20 value: 14.084 - type: mrr_at_3 value: 11.843 - type: mrr_at_5 value: 12.82 - type: ndcg_at_1 value: 8.424 - type: ndcg_at_10 value: 16.534 - type: ndcg_at_100 value: 20.982 - type: ndcg_at_1000 value: 23.538999999999998 - type: ndcg_at_20 value: 18.012 - type: ndcg_at_3 value: 12.729 - type: ndcg_at_5 value: 14.466999999999999 - type: precision_at_1 value: 8.424 - type: precision_at_10 value: 2.7449999999999997 - type: precision_at_100 value: 0.507 - type: precision_at_1000 value: 0.073 - type: precision_at_20 value: 1.683 - type: precision_at_3 value: 5.478000000000001 - type: precision_at_5 value: 4.16 - type: recall_at_1 value: 8.186 - type: recall_at_10 value: 26.415 - type: recall_at_100 value: 48.282000000000004 - type: recall_at_1000 value: 68.869 - type: recall_at_20 value: 32.207 - type: recall_at_3 value: 15.909 - type: recall_at_5 value: 20.09 - task: type: Classification dataset: type: mteb/mtop_domain name: MTEB MTOPDomainClassification (en) config: en split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 87.26858185134519 - type: f1 value: 86.73793752046078 - task: type: Classification dataset: type: mteb/mtop_intent name: MTEB MTOPIntentClassification (en) config: en split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - type: accuracy value: 54.65800273597811 - type: f1 value: 36.16413360524473 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (en) config: en split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 61.519838601210495 - type: f1 value: 58.35755839392156 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (en) config: en split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - 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type: recall_at_1000 value: 49.179 - type: recall_at_20 value: 9.166 - type: recall_at_3 value: 4.263 - type: recall_at_5 value: 4.956 - task: type: Retrieval dataset: type: mteb/nq name: MTEB NQ config: default split: test revision: b774495ed302d8c44a3a7ea25c90dbce03968f31 metrics: - type: map_at_1 value: 9.176 - type: map_at_10 value: 15.720999999999998 - type: map_at_100 value: 16.847 - type: map_at_1000 value: 16.939999999999998 - type: map_at_20 value: 16.355 - type: map_at_3 value: 13.402 - type: map_at_5 value: 14.663 - type: mrr_at_1 value: 10.458 - type: mrr_at_10 value: 17.413 - type: mrr_at_100 value: 18.442 - type: mrr_at_1000 value: 18.52 - type: mrr_at_20 value: 18.006 - type: mrr_at_3 value: 15.043999999999999 - type: mrr_at_5 value: 16.367 - type: ndcg_at_1 value: 10.458 - type: ndcg_at_10 value: 19.994999999999997 - type: ndcg_at_100 value: 25.665 - type: ndcg_at_1000 value: 28.277 - type: ndcg_at_20 value: 22.233 - type: ndcg_at_3 value: 15.168999999999999 - type: 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mteb/twittersemeval2015-pairclassification name: MTEB TwitterSemEval2015 config: default split: test revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 metrics: - type: cos_sim_accuracy value: 84.0317100792752 - type: cos_sim_ap value: 67.56361271781817 - type: cos_sim_f1 value: 63.082081211970696 - type: cos_sim_precision value: 59.58245367112362 - type: cos_sim_recall value: 67.01846965699208 - type: dot_accuracy value: 84.0317100792752 - type: dot_ap value: 67.56359342938897 - type: dot_f1 value: 63.082081211970696 - type: dot_precision value: 59.58245367112362 - type: dot_recall value: 67.01846965699208 - type: euclidean_accuracy value: 84.0317100792752 - type: euclidean_ap value: 67.5636169518733 - type: euclidean_f1 value: 63.082081211970696 - type: euclidean_precision value: 59.58245367112362 - type: euclidean_recall value: 67.01846965699208 - type: manhattan_accuracy value: 84.0734338677952 - type: manhattan_ap value: 67.44969672020721 - type: manhattan_f1 value: 63.09479205695017 - type: manhattan_precision value: 59.90040313018734 - type: manhattan_recall value: 66.64907651715039 - type: max_accuracy value: 84.0734338677952 - type: max_ap value: 67.5636169518733 - type: max_f1 value: 63.09479205695017 - task: type: PairClassification dataset: type: mteb/twitterurlcorpus-pairclassification name: MTEB TwitterURLCorpus config: default split: test revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf metrics: - type: cos_sim_accuracy value: 87.60624054022587 - type: cos_sim_ap value: 82.94451598409692 - type: cos_sim_f1 value: 74.76484194294527 - type: cos_sim_precision value: 74.86874613959235 - type: cos_sim_recall value: 74.66122574684324 - type: dot_accuracy value: 87.60624054022587 - type: dot_ap value: 82.94451133280317 - type: dot_f1 value: 74.76484194294527 - type: dot_precision value: 74.86874613959235 - type: dot_recall value: 74.66122574684324 - type: euclidean_accuracy value: 87.60624054022587 - type: euclidean_ap value: 82.94449586426977 - type: euclidean_f1 value: 74.76484194294527 - type: euclidean_precision value: 74.86874613959235 - type: euclidean_recall value: 74.66122574684324 - type: manhattan_accuracy value: 87.63922847052432 - type: manhattan_ap value: 82.9449637573502 - type: manhattan_f1 value: 74.9452996046217 - type: manhattan_precision value: 74.73015386970833 - type: manhattan_recall value: 75.1616877117339 - type: max_accuracy value: 87.63922847052432 - type: max_ap value: 82.9449637573502 - type: max_f1 value: 74.9452996046217 --- # Squirtle Squirtle is a distill of [bge-base-en-v1.5](BAAI/bge-base-en-v1.5). ## Intended purpose <span style="color:blue">This model is designed for use in semantic-autocomplete ([click here for demo](https://mihaiii.github.io/semantic-autocomplete/)).</span> Make sure you also pass `pipelineParams={{ pooling: "cls", normalize: true }}` since the default pooling in the component is mean. ## Usage Other than within [semantic-autocomplete](https://github.com/Mihaiii/semantic-autocomplete), you can use this model same as [bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5#usage).
timm/beitv2_base_patch16_224.in1k_ft_in22k_in1k	timm	2023-05-08T23:35:52Z	4,758	0	timm	[ "timm", "pytorch", "safetensors", "image-classification", "dataset:imagenet-1k", "dataset:imagenet-22k", "arxiv:2208.06366", "arxiv:2010.11929", "license:apache-2.0", "region:us" ]	image-classification	2022-12-23T02:33:57Z	--- tags: - image-classification - timm library_name: timm license: apache-2.0 datasets: - imagenet-1k - imagenet-1k - imagenet-22k --- # Model card for beitv2_base_patch16_224.in1k_ft_in22k_in1k A BEiT-v2 image classification model. Trained on ImageNet-1k with self-supervised masked image modelling (MIM) using a VQ-KD encoder as a visual tokenizer (via OpenAI CLIP B/16 teacher). Fine-tuned on ImageNet-22k and then ImageNet-1k. ## Model Details - Model Type: Image classification / feature backbone - Model Stats: - Params (M): 86.5 - GMACs: 17.6 - Activations (M): 23.9 - Image size: 224 x 224 - Papers: - BEiT v2: Masked Image Modeling with Vector-Quantized Visual Tokenizers: https://arxiv.org/abs/2208.06366 - An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale: https://arxiv.org/abs/2010.11929v2 - Dataset: ImageNet-1k - Pretrain Dataset: - ImageNet-1k - ImageNet-22k - Original: https://github.com/microsoft/unilm/tree/master/beit2 ## Model Usage ### Image Classification ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open(urlopen( 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png' )) model = timm.create_model('beitv2_base_patch16_224.in1k_ft_in22k_in1k', pretrained=True) model = model.eval() # get model specific transforms (normalization, resize) data_config = timm.data.resolve_model_data_config(model) transforms = timm.data.create_transform(*data_config, is_training=False) output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1 top5_probabilities, top5_class_indices = torch.topk(output.softmax(dim=1) 100, k=5) ``` ### Image Embeddings ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open(urlopen( 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png' )) model = timm.create_model( 'beitv2_base_patch16_224.in1k_ft_in22k_in1k', pretrained=True, num_classes=0, # remove classifier nn.Linear ) model = model.eval() # get model specific transforms (normalization, resize) data_config = timm.data.resolve_model_data_config(model) transforms = timm.data.create_transform(**data_config, is_training=False) output = model(transforms(img).unsqueeze(0)) # output is (batch_size, num_features) shaped tensor # or equivalently (without needing to set num_classes=0) output = model.forward_features(transforms(img).unsqueeze(0)) # output is unpooled, a (1, 197, 768) shaped tensor output = model.forward_head(output, pre_logits=True) # output is a (1, num_features) shaped tensor ``` ## Model Comparison Explore the dataset and runtime metrics of this model in timm [model results](https://github.com/huggingface/pytorch-image-models/tree/main/results). ## Citation ```bibtex @article{peng2022beit, title={Beit v2: Masked image modeling with vector-quantized visual tokenizers}, author={Peng, Zhiliang and Dong, Li and Bao, Hangbo and Ye, Qixiang and Wei, Furu}, journal={arXiv preprint arXiv:2208.06366}, year={2022} } ``` ```bibtex @article{dosovitskiy2020vit, title={An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale}, author={Dosovitskiy, Alexey and Beyer, Lucas and Kolesnikov, Alexander and Weissenborn, Dirk and Zhai, Xiaohua and Unterthiner, Thomas and Dehghani, Mostafa and Minderer, Matthias and Heigold, Georg and Gelly, Sylvain and Uszkoreit, Jakob and Houlsby, Neil}, journal={ICLR}, year={2021} } ``` ```bibtex @misc{rw2019timm, author = {Ross Wightman}, title = {PyTorch Image Models}, year = {2019}, publisher = {GitHub}, journal = {GitHub repository}, doi = {10.5281/zenodo.4414861}, howpublished = {\url{https://github.com/huggingface/pytorch-image-models}} } ```
flamehaze1115/wonder3d-v1.0	flamehaze1115	2023-11-27T12:14:52Z	4,757	5	diffusers	[ "diffusers", "license:agpl-3.0", "diffusers:MVDiffusionImagePipeline", "region:us" ]	null	2023-11-27T11:51:47Z	--- license: agpl-3.0 ---
xtuner/llava-phi-3-mini-hf	xtuner	2024-04-25T17:59:46Z	4,756	37	transformers	[ "transformers", "safetensors", "llava", "pretraining", "image-to-text", "dataset:Lin-Chen/ShareGPT4V", "endpoints_compatible", "region:us" ]	image-to-text	2024-04-25T04:07:10Z	--- datasets: - Lin-Chen/ShareGPT4V pipeline_tag: image-to-text --- <div align="center"> <img src="https://github.com/InternLM/lmdeploy/assets/36994684/0cf8d00f-e86b-40ba-9b54-dc8f1bc6c8d8" width="600"/> [![Generic badge](https://img.shields.io/badge/GitHub-%20XTuner-black.svg)](https://github.com/InternLM/xtuner) </div> ## Model llava-phi-3-mini is a LLaVA model fine-tuned from [microsoft/Phi-3-mini-4k-instruct](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct) and [CLIP-ViT-Large-patch14-336](https://huggingface.co/openai/clip-vit-large-patch14-336) with [ShareGPT4V-PT](https://huggingface.co/datasets/Lin-Chen/ShareGPT4V) and [InternVL-SFT](https://github.com/OpenGVLab/InternVL/tree/main/internvl_chat#prepare-training-datasets) by [XTuner](https://github.com/InternLM/xtuner). Note: This model is in HuggingFace LLaVA format. Resources: - GitHub: [xtuner](https://github.com/InternLM/xtuner) - Official LLaVA format model: [xtuner/llava-phi-3-mini](https://huggingface.co/xtuner/llava-phi-3-mini) - GGUF LLaVA model: [xtuner/llava-phi-3-mini-gguf](https://huggingface.co/xtuner/llava-phi-3-mini-gguf) - XTuner LLaVA format model: [xtuner/llava-phi-3-mini-xtuner](https://huggingface.co/xtuner/llava-phi-3-mini-xtuner) ## Details \| Model \| Visual Encoder \| Projector \| Resolution \| Pretraining Strategy \| Fine-tuning Strategy \| Pretrain Dataset \| Fine-tune Dataset \| Pretrain Epoch \| Fine-tune Epoch \| \| :-------------------- \| ------------------: \| --------: \| ---------: \| ---------------------: \| ------------------------: \| ------------------------: \| -----------------------: \| -------------- \| --------------- \| \| LLaVA-v1.5-7B \| CLIP-L \| MLP \| 336 \| Frozen LLM, Frozen ViT \| Full LLM, Frozen ViT \| LLaVA-PT (558K) \| LLaVA-Mix (665K) \| 1 \| 1 \| \| LLaVA-Llama-3-8B \| CLIP-L \| MLP \| 336 \| Frozen LLM, Frozen ViT \| Full LLM, LoRA ViT \| LLaVA-PT (558K) \| LLaVA-Mix (665K) \| 1 \| 1 \| \| LLaVA-Llama-3-8B-v1.1 \| CLIP-L \| MLP \| 336 \| Frozen LLM, Frozen ViT \| Full LLM, LoRA ViT \| ShareGPT4V-PT (1246K) \| InternVL-SFT (1268K) \| 1 \| 1 \| \| LLaVA-Phi-3-mini \| CLIP-L \| MLP \| 336 \| Frozen LLM, Frozen ViT \| Full LLM, Full ViT \| ShareGPT4V-PT (1246K) \| InternVL-SFT (1268K) \| 1 \| 2 \| ## Results <div align="center"> <img src="https://github.com/InternLM/xtuner/assets/36994684/78524f65-260d-4ae3-a687-03fc5a19dcbb" alt="Image" width=500" /> </div> \| Model \| MMBench Test (EN) \| MMMU Val \| SEED-IMG \| AI2D Test \| ScienceQA Test \| HallusionBench aAcc \| POPE \| GQA \| TextVQA \| MME \| MMStar \| \| :-------------------- \| :---------------: \| :-------: \| :------: \| :-------: \| :------------: \| :-----------------: \| :--: \| :--: \| :-----: \| :------: \| :----: \| \| LLaVA-v1.5-7B \| 66.5 \| 35.3 \| 60.5 \| 54.8 \| 70.4 \| 44.9 \| 85.9 \| 62.0 \| 58.2 \| 1511/348 \| 30.3 \| \| LLaVA-Llama-3-8B \| 68.9 \| 36.8 \| 69.8 \| 60.9 \| 73.3 \| 47.3 \| 87.2 \| 63.5 \| 58.0 \| 1506/295 \| 38.2 \| \| LLaVA-Llama-3-8B-v1.1 \| 72.3 \| 37.1 \| 70.1 \| 70.0 \| 72.9 \| 47.7 \| 86.4 \| 62.6 \| 59.0 \| 1469/349 \| 45.1 \| \| LLaVA-Phi-3-mini \| 69.2 \| 41.4 \| 70.0 \| 69.3 \| 73.7 \| 49.8 \| 87.3 \| 61.5 \| 57.8 \| 1477/313 \| 43.7 \| ## Quickstart ### Chat by `pipeline` ```python from transformers import pipeline from PIL import Image import requests model_id = "xtuner/llava-phi-3-mini-hf" pipe = pipeline("image-to-text", model=model_id, device=0) url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg" image = Image.open(requests.get(url, stream=True).raw) prompt = "<\|user\|>\n<image>\nWhat does the label 15 represent? (1) lava (2) core (3) tunnel (4) ash cloud<\|end\|>\n<\|assistant\|>\n" outputs = pipe(image, prompt=prompt, generate_kwargs={"max_new_tokens": 200}) print(outputs) >>> [{'generated_text': '\nWhat does the label 15 represent? (1) lava (2) core (3) tunnel (4) ash cloud (1) lava'}] ``` ### Chat by pure `transformers` ```python import requests from PIL import Image import torch from transformers import AutoProcessor, LlavaForConditionalGeneration model_id = "xtuner/llava-phi-3-mini-hf" prompt = "<\|user\|>\n<image>\nWhat are these?<\|end\|>\n<\|assistant\|>\n" image_file = "http://images.cocodataset.org/val2017/000000039769.jpg" model = LlavaForConditionalGeneration.from_pretrained( model_id, torch_dtype=torch.float16, low_cpu_mem_usage=True, ).to(0) processor = AutoProcessor.from_pretrained(model_id) raw_image = Image.open(requests.get(image_file, stream=True).raw) inputs = processor(prompt, raw_image, return_tensors='pt').to(0, torch.float16) output = model.generate(**inputs, max_new_tokens=200, do_sample=False) print(processor.decode(output[0][2:], skip_special_tokens=True)) >>> What are these? These are two cats sleeping on a pink couch. ``` ### Reproduce Please refer to [docs](https://github.com/InternLM/xtuner/tree/main/xtuner/configs/llava/phi3_mini_4k_instruct_clip_vit_large_p14_336#readme). ## Citation ```bibtex @misc{2023xtuner, title={XTuner: A Toolkit for Efficiently Fine-tuning LLM}, author={XTuner Contributors}, howpublished = {\url{https://github.com/InternLM/xtuner}}, year={2023} } ```
uer/roberta-base-finetuned-cluener2020-chinese	uer	2023-10-17T15:17:38Z	4,755	30	transformers	[ "transformers", "pytorch", "tf", "jax", "bert", "token-classification", "zh", "arxiv:1909.05658", "arxiv:2212.06385", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-03-02T23:29:05Z	--- language: zh widget: - text: "江苏警方通报特斯拉冲进店铺" --- # Chinese RoBERTa-Base Model for NER ## Model description The model is used for named entity recognition. It is fine-tuned by [UER-py](https://github.com/dbiir/UER-py/), which is introduced in [this paper](https://arxiv.org/abs/1909.05658). Besides, the model could also be fine-tuned by [TencentPretrain](https://github.com/Tencent/TencentPretrain) introduced in [this paper](https://arxiv.org/abs/2212.06385), which inherits UER-py to support models with parameters above one billion, and extends it to a multimodal pre-training framework. You can download the model either from the [UER-py Modelzoo page](https://github.com/dbiir/UER-py/wiki/Modelzoo), or via HuggingFace from the link [roberta-base-finetuned-cluener2020-chinese](https://huggingface.co/uer/roberta-base-finetuned-cluener2020-chinese). ## How to use You can use this model directly with a pipeline for token classification : ```python >>> from transformers import AutoModelForTokenClassification,AutoTokenizer,pipeline >>> model = AutoModelForTokenClassification.from_pretrained('uer/roberta-base-finetuned-cluener2020-chinese') >>> tokenizer = AutoTokenizer.from_pretrained('uer/roberta-base-finetuned-cluener2020-chinese') >>> ner = pipeline('ner', model=model, tokenizer=tokenizer) >>> ner("江苏警方通报特斯拉冲进店铺") [ {'word': '江', 'score': 0.49153077602386475, 'entity': 'B-address', 'index': 1, 'start': 0, 'end': 1}, {'word': '苏', 'score': 0.6319217681884766, 'entity': 'I-address', 'index': 2, 'start': 1, 'end': 2}, {'word': '特', 'score': 0.5912262797355652, 'entity': 'B-company', 'index': 7, 'start': 6, 'end': 7}, {'word': '斯', 'score': 0.69145667552948, 'entity': 'I-company', 'index': 8, 'start': 7, 'end': 8}, {'word': '拉', 'score': 0.7054660320281982, 'entity': 'I-company', 'index': 9, 'start': 8, 'end': 9} ] ``` ## Training data [CLUENER2020](https://github.com/CLUEbenchmark/CLUENER2020) is used as training data. We only use the train set of the dataset. ## Training procedure The model is fine-tuned by [UER-py](https://github.com/dbiir/UER-py/) on [Tencent Cloud](https://cloud.tencent.com/). We fine-tune five epochs with a sequence length of 512 on the basis of the pre-trained model [chinese_roberta_L-12_H-768](https://huggingface.co/uer/chinese_roberta_L-12_H-768). At the end of each epoch, the model is saved when the best performance on development set is achieved. ``` python3 finetune/run_ner.py --pretrained_model_path models/cluecorpussmall_roberta_base_seq512_model.bin-250000 \ --vocab_path models/google_zh_vocab.txt \ --train_path datasets/cluener2020/train.tsv \ --dev_path datasets/cluener2020/dev.tsv \ --label2id_path datasets/cluener2020/label2id.json \ --output_model_path models/cluener2020_ner_model.bin \ --learning_rate 3e-5 --epochs_num 5 --batch_size 32 --seq_length 512 ``` Finally, we convert the pre-trained model into Huggingface's format: ``` python3 scripts/convert_bert_token_classification_from_uer_to_huggingface.py --input_model_path models/cluener2020_ner_model.bin \ --output_model_path pytorch_model.bin \ --layers_num 12 ``` ### BibTeX entry and citation info ``` @article{liu2019roberta, title={Roberta: A robustly optimized bert pretraining approach}, author={Liu, Yinhan and Ott, Myle and Goyal, Naman and Du, Jingfei and Joshi, Mandar and Chen, Danqi and Levy, Omer and Lewis, Mike and Zettlemoyer, Luke and Stoyanov, Veselin}, journal={arXiv preprint arXiv:1907.11692}, year={2019} } @article{xu2020cluener2020, title={CLUENER2020: Fine-grained Name Entity Recognition for Chinese}, author={Xu, Liang and Dong, Qianqian and Yu, Cong and Tian, Yin and Liu, Weitang and Li, Lu and Zhang, Xuanwei}, journal={arXiv preprint arXiv:2001.04351}, year={2020} } @article{zhao2019uer, title={UER: An Open-Source Toolkit for Pre-training Models}, author={Zhao, Zhe and Chen, Hui and Zhang, Jinbin and Zhao, Xin and Liu, Tao and Lu, Wei and Chen, Xi and Deng, Haotang and Ju, Qi and Du, Xiaoyong}, journal={EMNLP-IJCNLP 2019}, pages={241}, year={2019} } @article{zhao2023tencentpretrain, title={TencentPretrain: A Scalable and Flexible Toolkit for Pre-training Models of Different Modalities}, author={Zhao, Zhe and Li, Yudong and Hou, Cheng and Zhao, Jing and others}, journal={ACL 2023}, pages={217}, year={2023} ```
McGill-NLP/LLM2Vec-Mistral-7B-Instruct-v2-mntp	McGill-NLP	2024-05-21T22:01:47Z	4,753	4	transformers	[ "transformers", "safetensors", "mistral", "feature-extraction", "text-embedding", "embeddings", "information-retrieval", "beir", "text-classification", "language-model", "text-clustering", "text-semantic-similarity", "text-evaluation", "text-reranking", "sentence-similarity", "Sentence Similarity", "natural_questions", "ms_marco", "fever", "hotpot_qa", "mteb", "custom_code", "en", "arxiv:2404.05961", "license:mit", "text-generation-inference", "region:us" ]	sentence-similarity	2024-04-04T02:59:33Z	--- library_name: transformers license: mit language: - en pipeline_tag: sentence-similarity tags: - text-embedding - embeddings - information-retrieval - beir - text-classification - language-model - text-clustering - text-semantic-similarity - text-evaluation - text-reranking - feature-extraction - sentence-similarity - Sentence Similarity - natural_questions - ms_marco - fever - hotpot_qa - mteb --- # LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders > LLM2Vec is a simple recipe to convert decoder-only LLMs into text encoders. It consists of 3 simple steps: 1) enabling bidirectional attention, 2) masked next token prediction, and 3) unsupervised contrastive learning. The model can be further fine-tuned to achieve state-of-the-art performance. - Repository: https://github.com/McGill-NLP/llm2vec - Paper: https://arxiv.org/abs/2404.05961 ## Installation ```bash pip install llm2vec ``` ## Usage ```python from llm2vec import LLM2Vec import torch from transformers import AutoTokenizer, AutoModel, AutoConfig from peft import PeftModel # Loading base Mistral model, along with custom code that enables bidirectional connections in decoder-only LLMs. tokenizer = AutoTokenizer.from_pretrained( "McGill-NLP/LLM2Vec-Mistral-7B-Instruct-v2-mntp" ) config = AutoConfig.from_pretrained( "McGill-NLP/LLM2Vec-Mistral-7B-Instruct-v2-mntp", trust_remote_code=True ) model = AutoModel.from_pretrained( "McGill-NLP/LLM2Vec-Mistral-7B-Instruct-v2-mntp", trust_remote_code=True, config=config, torch_dtype=torch.bfloat16, device_map="cuda" if torch.cuda.is_available() else "cpu", ) # Loading MNTP (Masked Next Token Prediction) model. model = PeftModel.from_pretrained( model, "McGill-NLP/LLM2Vec-Mistral-7B-Instruct-v2-mntp", ) # Wrapper for encoding and pooling operations l2v = LLM2Vec(model, tokenizer, pooling_mode="mean", max_length=512) # Encoding queries using instructions instruction = ( "Given a web search query, retrieve relevant passages that answer the query:" ) queries = [ [instruction, "how much protein should a female eat"], [instruction, "summit define"], ] q_reps = l2v.encode(queries) # Encoding documents. Instruction are not required for documents documents = [ "As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.", "Definition of summit for English Language Learners. : 1 the highest point of a mountain : the top of a mountain. : 2 the highest level. : 3 a meeting or series of meetings between the leaders of two or more governments.", ] d_reps = l2v.encode(documents) # Compute cosine similarity q_reps_norm = torch.nn.functional.normalize(q_reps, p=2, dim=1) d_reps_norm = torch.nn.functional.normalize(d_reps, p=2, dim=1) cos_sim = torch.mm(q_reps_norm, d_reps_norm.transpose(0, 1)) print(cos_sim) """ tensor([[0.6266, 0.4199], [0.3429, 0.5240]]) """ ``` ## Questions If you have any question about the code, feel free to email Parishad (`[email protected]`) and Vaibhav (`[email protected]`).
Muennighoff/SGPT-2.7B-weightedmean-msmarco-specb-bitfit	Muennighoff	2023-03-27T22:24:48Z	4,751	3	sentence-transformers	[ "sentence-transformers", "pytorch", "gpt_neo", "feature-extraction", "sentence-similarity", "mteb", "arxiv:2202.08904", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	sentence-similarity	2022-03-02T23:29:04Z	--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - mteb model-index: - name: SGPT-2.7B-weightedmean-msmarco-specb-bitfit results: - task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (en) config: en split: test revision: 2d8a100785abf0ae21420d2a55b0c56e3e1ea996 metrics: - type: accuracy value: 67.56716417910448 - type: ap value: 30.75574629595259 - type: f1 value: 61.805121301858655 - task: type: Classification dataset: type: mteb/amazon_polarity name: MTEB AmazonPolarityClassification config: default split: test revision: 80714f8dcf8cefc218ef4f8c5a966dd83f75a0e1 metrics: - type: accuracy value: 71.439575 - type: ap value: 65.91341330532453 - type: f1 value: 70.90561852619555 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (en) config: en split: test revision: c379a6705fec24a2493fa68e011692605f44e119 metrics: - type: accuracy value: 35.748000000000005 - type: f1 value: 35.48576287186347 - task: type: Retrieval dataset: type: arguana name: MTEB ArguAna config: default split: test revision: 5b3e3697907184a9b77a3c99ee9ea1a9cbb1e4e3 metrics: - type: map_at_1 value: 25.96 - type: map_at_10 value: 41.619 - type: map_at_100 value: 42.673 - type: map_at_1000 value: 42.684 - type: map_at_3 value: 36.569 - type: map_at_5 value: 39.397 - type: mrr_at_1 value: 26.316 - type: mrr_at_10 value: 41.772 - type: mrr_at_100 value: 42.82 - type: mrr_at_1000 value: 42.83 - type: mrr_at_3 value: 36.724000000000004 - type: mrr_at_5 value: 39.528999999999996 - type: ndcg_at_1 value: 25.96 - type: ndcg_at_10 value: 50.491 - type: ndcg_at_100 value: 54.864999999999995 - type: ndcg_at_1000 value: 55.10699999999999 - type: ndcg_at_3 value: 40.053 - type: ndcg_at_5 value: 45.134 - type: precision_at_1 value: 25.96 - type: precision_at_10 value: 7.8950000000000005 - type: precision_at_100 value: 0.9780000000000001 - type: precision_at_1000 value: 0.1 - type: precision_at_3 value: 16.714000000000002 - type: precision_at_5 value: 12.489 - type: recall_at_1 value: 25.96 - type: recall_at_10 value: 78.947 - type: recall_at_100 value: 97.795 - type: recall_at_1000 value: 99.644 - type: recall_at_3 value: 50.141999999999996 - type: recall_at_5 value: 62.446999999999996 - task: type: Clustering dataset: type: mteb/arxiv-clustering-p2p name: MTEB ArxivClusteringP2P config: default split: test revision: 0bbdb47bcbe3a90093699aefeed338a0f28a7ee8 metrics: - type: v_measure value: 44.72125714642202 - task: type: Clustering dataset: type: mteb/arxiv-clustering-s2s name: MTEB ArxivClusteringS2S config: default split: test revision: b73bd54100e5abfa6e3a23dcafb46fe4d2438dc3 metrics: - type: v_measure value: 35.081451519142064 - task: type: Reranking dataset: type: mteb/askubuntudupquestions-reranking name: MTEB AskUbuntuDupQuestions config: default split: test revision: 4d853f94cd57d85ec13805aeeac3ae3e5eb4c49c metrics: - type: map value: 59.634661990392054 - type: mrr value: 73.6813525040672 - task: type: STS dataset: type: mteb/biosses-sts name: MTEB BIOSSES config: default split: test revision: 9ee918f184421b6bd48b78f6c714d86546106103 metrics: - type: cos_sim_pearson value: 87.42754550496836 - type: cos_sim_spearman value: 84.84289705838664 - type: euclidean_pearson value: 85.59331970450859 - type: euclidean_spearman value: 85.8525586184271 - type: manhattan_pearson value: 85.41233134466698 - type: manhattan_spearman value: 85.52303303767404 - task: type: Classification dataset: type: mteb/banking77 name: MTEB Banking77Classification config: default split: test revision: 44fa15921b4c889113cc5df03dd4901b49161ab7 metrics: - type: accuracy value: 83.21753246753246 - type: f1 value: 83.15394543120915 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-p2p name: MTEB BiorxivClusteringP2P config: default split: test revision: 11d0121201d1f1f280e8cc8f3d98fb9c4d9f9c55 metrics: - type: v_measure value: 34.41414219680629 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-s2s name: MTEB BiorxivClusteringS2S config: default split: test revision: c0fab014e1bcb8d3a5e31b2088972a1e01547dc1 metrics: - type: v_measure value: 30.533275862270028 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackAndroidRetrieval config: default split: test revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db metrics: - type: map_at_1 value: 30.808999999999997 - type: map_at_10 value: 40.617 - type: map_at_100 value: 41.894999999999996 - type: map_at_1000 value: 42.025 - type: map_at_3 value: 37.0 - type: map_at_5 value: 38.993 - type: mrr_at_1 value: 37.482 - type: mrr_at_10 value: 46.497 - type: mrr_at_100 value: 47.144000000000005 - type: mrr_at_1000 value: 47.189 - type: mrr_at_3 value: 43.705 - type: mrr_at_5 value: 45.193 - type: ndcg_at_1 value: 37.482 - type: ndcg_at_10 value: 46.688 - type: ndcg_at_100 value: 51.726000000000006 - type: ndcg_at_1000 value: 53.825 - type: ndcg_at_3 value: 41.242000000000004 - type: ndcg_at_5 value: 43.657000000000004 - type: precision_at_1 value: 37.482 - type: precision_at_10 value: 8.827 - type: precision_at_100 value: 1.393 - type: precision_at_1000 value: 0.186 - type: precision_at_3 value: 19.361 - type: precision_at_5 value: 14.106 - type: recall_at_1 value: 30.808999999999997 - type: recall_at_10 value: 58.47 - type: recall_at_100 value: 80.51899999999999 - type: recall_at_1000 value: 93.809 - type: recall_at_3 value: 42.462 - type: recall_at_5 value: 49.385 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackEnglishRetrieval config: default split: test revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db metrics: - type: map_at_1 value: 26.962000000000003 - type: map_at_10 value: 36.93 - type: map_at_100 value: 38.102000000000004 - type: map_at_1000 value: 38.22 - type: map_at_3 value: 34.065 - type: map_at_5 value: 35.72 - type: mrr_at_1 value: 33.567 - type: mrr_at_10 value: 42.269 - type: mrr_at_100 value: 42.99 - type: mrr_at_1000 value: 43.033 - type: mrr_at_3 value: 40.064 - type: mrr_at_5 value: 41.258 - type: ndcg_at_1 value: 33.567 - type: ndcg_at_10 value: 42.405 - type: ndcg_at_100 value: 46.847 - type: ndcg_at_1000 value: 48.951 - type: ndcg_at_3 value: 38.312000000000005 - type: ndcg_at_5 value: 40.242 - type: precision_at_1 value: 33.567 - type: precision_at_10 value: 8.032 - type: precision_at_100 value: 1.295 - type: precision_at_1000 value: 0.17600000000000002 - type: precision_at_3 value: 18.662 - type: precision_at_5 value: 13.299 - type: recall_at_1 value: 26.962000000000003 - type: recall_at_10 value: 52.489 - type: recall_at_100 value: 71.635 - type: recall_at_1000 value: 85.141 - type: recall_at_3 value: 40.28 - type: recall_at_5 value: 45.757 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGamingRetrieval config: default split: test revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db metrics: - type: map_at_1 value: 36.318 - type: map_at_10 value: 47.97 - type: map_at_100 value: 49.003 - type: map_at_1000 value: 49.065999999999995 - type: map_at_3 value: 45.031 - type: map_at_5 value: 46.633 - type: mrr_at_1 value: 41.504999999999995 - type: mrr_at_10 value: 51.431000000000004 - type: mrr_at_100 value: 52.129000000000005 - type: mrr_at_1000 value: 52.161 - type: mrr_at_3 value: 48.934 - type: mrr_at_5 value: 50.42 - type: ndcg_at_1 value: 41.504999999999995 - type: ndcg_at_10 value: 53.676 - type: ndcg_at_100 value: 57.867000000000004 - type: ndcg_at_1000 value: 59.166 - type: ndcg_at_3 value: 48.516 - type: ndcg_at_5 value: 50.983999999999995 - type: precision_at_1 value: 41.504999999999995 - type: precision_at_10 value: 8.608 - type: precision_at_100 value: 1.1560000000000001 - type: precision_at_1000 value: 0.133 - type: precision_at_3 value: 21.462999999999997 - type: precision_at_5 value: 14.721 - type: recall_at_1 value: 36.318 - type: recall_at_10 value: 67.066 - type: recall_at_100 value: 85.34 - type: recall_at_1000 value: 94.491 - type: recall_at_3 value: 53.215999999999994 - type: recall_at_5 value: 59.214 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGisRetrieval config: default split: test revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db metrics: - type: map_at_1 value: 22.167 - type: map_at_10 value: 29.543999999999997 - type: map_at_100 value: 30.579 - type: map_at_1000 value: 30.669999999999998 - type: map_at_3 value: 26.982 - type: map_at_5 value: 28.474 - type: mrr_at_1 value: 24.068 - type: mrr_at_10 value: 31.237 - type: mrr_at_100 value: 32.222 - type: mrr_at_1000 value: 32.292 - type: mrr_at_3 value: 28.776000000000003 - type: mrr_at_5 value: 30.233999999999998 - type: ndcg_at_1 value: 24.068 - type: ndcg_at_10 value: 33.973 - type: ndcg_at_100 value: 39.135 - type: ndcg_at_1000 value: 41.443999999999996 - type: ndcg_at_3 value: 29.018 - type: ndcg_at_5 value: 31.558999999999997 - type: precision_at_1 value: 24.068 - type: precision_at_10 value: 5.299 - type: precision_at_100 value: 0.823 - type: precision_at_1000 value: 0.106 - type: precision_at_3 value: 12.166 - type: precision_at_5 value: 8.767999999999999 - type: recall_at_1 value: 22.167 - type: recall_at_10 value: 46.115 - type: recall_at_100 value: 69.867 - type: recall_at_1000 value: 87.234 - type: recall_at_3 value: 32.798 - type: recall_at_5 value: 38.951 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackMathematicaRetrieval config: default split: test revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db metrics: - type: map_at_1 value: 12.033000000000001 - type: map_at_10 value: 19.314 - type: map_at_100 value: 20.562 - type: map_at_1000 value: 20.695 - type: map_at_3 value: 16.946 - type: map_at_5 value: 18.076999999999998 - type: mrr_at_1 value: 14.801 - type: mrr_at_10 value: 22.74 - type: mrr_at_100 value: 23.876 - type: mrr_at_1000 value: 23.949 - type: mrr_at_3 value: 20.211000000000002 - type: mrr_at_5 value: 21.573 - type: ndcg_at_1 value: 14.801 - type: ndcg_at_10 value: 24.038 - type: ndcg_at_100 value: 30.186 - type: ndcg_at_1000 value: 33.321 - type: ndcg_at_3 value: 19.431 - type: ndcg_at_5 value: 21.34 - type: precision_at_1 value: 14.801 - type: precision_at_10 value: 4.776 - type: precision_at_100 value: 0.897 - type: precision_at_1000 value: 0.133 - type: precision_at_3 value: 9.66 - type: precision_at_5 value: 7.239 - type: recall_at_1 value: 12.033000000000001 - type: recall_at_10 value: 35.098 - type: recall_at_100 value: 62.175000000000004 - type: recall_at_1000 value: 84.17099999999999 - type: recall_at_3 value: 22.61 - type: recall_at_5 value: 27.278999999999996 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackPhysicsRetrieval config: default split: test revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db metrics: - type: map_at_1 value: 26.651000000000003 - type: map_at_10 value: 36.901 - type: map_at_100 value: 38.249 - type: map_at_1000 value: 38.361000000000004 - type: map_at_3 value: 33.891 - type: map_at_5 value: 35.439 - type: mrr_at_1 value: 32.724 - type: mrr_at_10 value: 42.504 - type: mrr_at_100 value: 43.391999999999996 - type: mrr_at_1000 value: 43.436 - type: mrr_at_3 value: 39.989999999999995 - type: mrr_at_5 value: 41.347 - type: ndcg_at_1 value: 32.724 - type: ndcg_at_10 value: 43.007 - type: ndcg_at_100 value: 48.601 - type: ndcg_at_1000 value: 50.697 - type: ndcg_at_3 value: 37.99 - type: ndcg_at_5 value: 40.083999999999996 - type: precision_at_1 value: 32.724 - type: precision_at_10 value: 7.872999999999999 - type: precision_at_100 value: 1.247 - type: precision_at_1000 value: 0.16199999999999998 - type: precision_at_3 value: 18.062 - type: precision_at_5 value: 12.666 - type: recall_at_1 value: 26.651000000000003 - type: recall_at_10 value: 55.674 - type: recall_at_100 value: 78.904 - type: recall_at_1000 value: 92.55799999999999 - type: recall_at_3 value: 41.36 - type: recall_at_5 value: 46.983999999999995 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackProgrammersRetrieval config: default split: test revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db metrics: - type: map_at_1 value: 22.589000000000002 - type: map_at_10 value: 32.244 - type: map_at_100 value: 33.46 - type: map_at_1000 value: 33.593 - type: map_at_3 value: 29.21 - type: map_at_5 value: 31.019999999999996 - type: mrr_at_1 value: 28.425 - type: mrr_at_10 value: 37.282 - type: mrr_at_100 value: 38.187 - type: mrr_at_1000 value: 38.248 - type: mrr_at_3 value: 34.684 - type: mrr_at_5 value: 36.123 - type: ndcg_at_1 value: 28.425 - type: ndcg_at_10 value: 37.942 - type: ndcg_at_100 value: 43.443 - type: ndcg_at_1000 value: 45.995999999999995 - type: ndcg_at_3 value: 32.873999999999995 - type: ndcg_at_5 value: 35.325 - type: precision_at_1 value: 28.425 - type: precision_at_10 value: 7.1 - type: precision_at_100 value: 1.166 - type: precision_at_1000 value: 0.158 - type: precision_at_3 value: 16.02 - type: precision_at_5 value: 11.644 - type: recall_at_1 value: 22.589000000000002 - type: recall_at_10 value: 50.03999999999999 - type: recall_at_100 value: 73.973 - type: recall_at_1000 value: 91.128 - type: recall_at_3 value: 35.882999999999996 - type: recall_at_5 value: 42.187999999999995 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackRetrieval config: default split: test revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db metrics: - type: map_at_1 value: 23.190833333333334 - type: map_at_10 value: 31.504916666666666 - type: map_at_100 value: 32.64908333333334 - type: map_at_1000 value: 32.77075 - type: map_at_3 value: 28.82575 - type: map_at_5 value: 30.2755 - type: mrr_at_1 value: 27.427499999999995 - type: mrr_at_10 value: 35.36483333333334 - type: mrr_at_100 value: 36.23441666666666 - type: mrr_at_1000 value: 36.297583333333336 - type: mrr_at_3 value: 32.97966666666667 - type: mrr_at_5 value: 34.294583333333335 - type: ndcg_at_1 value: 27.427499999999995 - type: ndcg_at_10 value: 36.53358333333333 - type: ndcg_at_100 value: 41.64508333333333 - type: ndcg_at_1000 value: 44.14499999999999 - type: ndcg_at_3 value: 31.88908333333333 - type: ndcg_at_5 value: 33.98433333333333 - type: precision_at_1 value: 27.427499999999995 - type: precision_at_10 value: 6.481083333333333 - type: precision_at_100 value: 1.0610833333333334 - type: precision_at_1000 value: 0.14691666666666667 - type: precision_at_3 value: 14.656749999999999 - type: precision_at_5 value: 10.493583333333332 - type: recall_at_1 value: 23.190833333333334 - type: recall_at_10 value: 47.65175 - type: recall_at_100 value: 70.41016666666667 - type: recall_at_1000 value: 87.82708333333332 - type: recall_at_3 value: 34.637583333333325 - type: recall_at_5 value: 40.05008333333333 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackStatsRetrieval config: default split: test revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db metrics: - type: map_at_1 value: 20.409 - type: map_at_10 value: 26.794 - type: map_at_100 value: 27.682000000000002 - type: map_at_1000 value: 27.783 - type: map_at_3 value: 24.461 - type: map_at_5 value: 25.668000000000003 - type: mrr_at_1 value: 22.853 - type: mrr_at_10 value: 29.296 - type: mrr_at_100 value: 30.103 - type: mrr_at_1000 value: 30.179000000000002 - type: mrr_at_3 value: 27.173000000000002 - type: mrr_at_5 value: 28.223 - type: ndcg_at_1 value: 22.853 - type: ndcg_at_10 value: 31.007 - type: ndcg_at_100 value: 35.581 - type: ndcg_at_1000 value: 38.147 - type: ndcg_at_3 value: 26.590999999999998 - type: ndcg_at_5 value: 28.43 - type: precision_at_1 value: 22.853 - type: precision_at_10 value: 5.031 - type: precision_at_100 value: 0.7939999999999999 - type: precision_at_1000 value: 0.11 - type: precision_at_3 value: 11.401 - type: precision_at_5 value: 8.16 - type: recall_at_1 value: 20.409 - type: recall_at_10 value: 41.766 - type: recall_at_100 value: 62.964 - type: recall_at_1000 value: 81.682 - type: recall_at_3 value: 29.281000000000002 - type: recall_at_5 value: 33.83 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackTexRetrieval config: default split: test revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db metrics: - type: map_at_1 value: 14.549000000000001 - type: map_at_10 value: 20.315 - type: map_at_100 value: 21.301000000000002 - type: map_at_1000 value: 21.425 - type: map_at_3 value: 18.132 - type: map_at_5 value: 19.429 - type: mrr_at_1 value: 17.86 - type: mrr_at_10 value: 23.860999999999997 - type: mrr_at_100 value: 24.737000000000002 - type: mrr_at_1000 value: 24.82 - type: mrr_at_3 value: 21.685 - type: mrr_at_5 value: 23.008 - type: ndcg_at_1 value: 17.86 - type: ndcg_at_10 value: 24.396 - type: ndcg_at_100 value: 29.328 - type: ndcg_at_1000 value: 32.486 - type: ndcg_at_3 value: 20.375 - type: ndcg_at_5 value: 22.411 - type: precision_at_1 value: 17.86 - type: precision_at_10 value: 4.47 - type: precision_at_100 value: 0.8099999999999999 - type: precision_at_1000 value: 0.125 - type: precision_at_3 value: 9.475 - type: precision_at_5 value: 7.170999999999999 - type: recall_at_1 value: 14.549000000000001 - type: recall_at_10 value: 33.365 - type: recall_at_100 value: 55.797 - type: recall_at_1000 value: 78.632 - type: recall_at_3 value: 22.229 - type: recall_at_5 value: 27.339000000000002 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackUnixRetrieval config: default split: test revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db metrics: - type: map_at_1 value: 23.286 - type: map_at_10 value: 30.728 - type: map_at_100 value: 31.840000000000003 - type: map_at_1000 value: 31.953 - type: map_at_3 value: 28.302 - type: map_at_5 value: 29.615000000000002 - type: mrr_at_1 value: 27.239 - type: mrr_at_10 value: 34.408 - type: mrr_at_100 value: 35.335 - type: mrr_at_1000 value: 35.405 - type: mrr_at_3 value: 32.151999999999994 - type: mrr_at_5 value: 33.355000000000004 - type: ndcg_at_1 value: 27.239 - type: ndcg_at_10 value: 35.324 - type: ndcg_at_100 value: 40.866 - type: ndcg_at_1000 value: 43.584 - type: ndcg_at_3 value: 30.898999999999997 - type: ndcg_at_5 value: 32.812999999999995 - type: precision_at_1 value: 27.239 - type: precision_at_10 value: 5.896 - type: precision_at_100 value: 0.979 - type: precision_at_1000 value: 0.133 - type: precision_at_3 value: 13.713000000000001 - type: precision_at_5 value: 9.683 - type: recall_at_1 value: 23.286 - type: recall_at_10 value: 45.711 - type: recall_at_100 value: 70.611 - type: recall_at_1000 value: 90.029 - type: recall_at_3 value: 33.615 - type: recall_at_5 value: 38.41 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWebmastersRetrieval config: default split: test revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db metrics: - type: map_at_1 value: 23.962 - type: map_at_10 value: 31.942999999999998 - type: map_at_100 value: 33.384 - type: map_at_1000 value: 33.611000000000004 - type: map_at_3 value: 29.243000000000002 - type: map_at_5 value: 30.446 - type: mrr_at_1 value: 28.458 - type: mrr_at_10 value: 36.157000000000004 - type: mrr_at_100 value: 37.092999999999996 - type: mrr_at_1000 value: 37.163000000000004 - type: mrr_at_3 value: 33.86 - type: mrr_at_5 value: 35.086 - type: ndcg_at_1 value: 28.458 - type: ndcg_at_10 value: 37.201 - type: ndcg_at_100 value: 42.591 - type: ndcg_at_1000 value: 45.539 - type: ndcg_at_3 value: 32.889 - type: ndcg_at_5 value: 34.483000000000004 - type: precision_at_1 value: 28.458 - type: precision_at_10 value: 7.332 - type: precision_at_100 value: 1.437 - type: precision_at_1000 value: 0.233 - type: precision_at_3 value: 15.547 - type: precision_at_5 value: 11.146 - type: recall_at_1 value: 23.962 - type: recall_at_10 value: 46.751 - type: recall_at_100 value: 71.626 - type: recall_at_1000 value: 90.93900000000001 - type: recall_at_3 value: 34.138000000000005 - type: recall_at_5 value: 38.673 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWordpressRetrieval config: default split: test revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db metrics: - type: map_at_1 value: 18.555 - type: map_at_10 value: 24.759 - type: map_at_100 value: 25.732 - type: map_at_1000 value: 25.846999999999998 - type: map_at_3 value: 22.646 - type: map_at_5 value: 23.791999999999998 - type: mrr_at_1 value: 20.148 - type: mrr_at_10 value: 26.695999999999998 - type: mrr_at_100 value: 27.605 - type: mrr_at_1000 value: 27.695999999999998 - type: mrr_at_3 value: 24.522 - type: mrr_at_5 value: 25.715 - type: ndcg_at_1 value: 20.148 - type: ndcg_at_10 value: 28.746 - type: ndcg_at_100 value: 33.57 - type: ndcg_at_1000 value: 36.584 - type: ndcg_at_3 value: 24.532 - type: ndcg_at_5 value: 26.484 - type: precision_at_1 value: 20.148 - type: precision_at_10 value: 4.529 - type: precision_at_100 value: 0.736 - type: precision_at_1000 value: 0.108 - type: precision_at_3 value: 10.351 - type: precision_at_5 value: 7.32 - type: recall_at_1 value: 18.555 - type: recall_at_10 value: 39.275999999999996 - type: recall_at_100 value: 61.511 - type: recall_at_1000 value: 84.111 - type: recall_at_3 value: 27.778999999999996 - type: recall_at_5 value: 32.591 - task: type: Retrieval dataset: type: climate-fever name: MTEB ClimateFEVER config: default split: test revision: 392b78eb68c07badcd7c2cd8f39af108375dfcce metrics: - type: map_at_1 value: 10.366999999999999 - type: map_at_10 value: 18.953999999999997 - type: map_at_100 value: 20.674999999999997 - type: map_at_1000 value: 20.868000000000002 - type: map_at_3 value: 15.486 - type: map_at_5 value: 17.347 - type: mrr_at_1 value: 23.257 - type: mrr_at_10 value: 35.419 - type: mrr_at_100 value: 36.361 - type: mrr_at_1000 value: 36.403 - type: mrr_at_3 value: 31.747999999999998 - type: mrr_at_5 value: 34.077 - type: ndcg_at_1 value: 23.257 - type: ndcg_at_10 value: 27.11 - type: ndcg_at_100 value: 33.981 - type: ndcg_at_1000 value: 37.444 - type: ndcg_at_3 value: 21.471999999999998 - type: ndcg_at_5 value: 23.769000000000002 - type: precision_at_1 value: 23.257 - type: precision_at_10 value: 8.704 - type: precision_at_100 value: 1.606 - type: precision_at_1000 value: 0.22499999999999998 - type: precision_at_3 value: 16.287 - type: precision_at_5 value: 13.068 - type: recall_at_1 value: 10.366999999999999 - type: recall_at_10 value: 33.706 - type: recall_at_100 value: 57.375 - type: recall_at_1000 value: 76.79 - type: recall_at_3 value: 20.18 - type: recall_at_5 value: 26.215 - task: type: Retrieval dataset: type: dbpedia-entity name: MTEB DBPedia config: default split: test revision: f097057d03ed98220bc7309ddb10b71a54d667d6 metrics: - type: map_at_1 value: 8.246 - type: map_at_10 value: 15.979 - type: map_at_100 value: 21.025 - type: map_at_1000 value: 22.189999999999998 - type: map_at_3 value: 11.997 - type: map_at_5 value: 13.697000000000001 - type: mrr_at_1 value: 60.75000000000001 - type: mrr_at_10 value: 68.70100000000001 - type: mrr_at_100 value: 69.1 - type: mrr_at_1000 value: 69.111 - type: mrr_at_3 value: 66.583 - type: mrr_at_5 value: 67.87100000000001 - type: ndcg_at_1 value: 49.75 - type: ndcg_at_10 value: 34.702 - type: ndcg_at_100 value: 37.607 - type: ndcg_at_1000 value: 44.322 - type: ndcg_at_3 value: 39.555 - type: ndcg_at_5 value: 36.684 - type: precision_at_1 value: 60.75000000000001 - type: precision_at_10 value: 26.625 - type: precision_at_100 value: 7.969999999999999 - type: precision_at_1000 value: 1.678 - type: precision_at_3 value: 41.833 - type: precision_at_5 value: 34.5 - type: recall_at_1 value: 8.246 - type: recall_at_10 value: 20.968 - type: recall_at_100 value: 42.065000000000005 - type: recall_at_1000 value: 63.671 - type: recall_at_3 value: 13.039000000000001 - type: recall_at_5 value: 16.042 - task: type: Classification dataset: type: mteb/emotion name: MTEB EmotionClassification config: default split: test revision: 829147f8f75a25f005913200eb5ed41fae320aa1 metrics: - type: accuracy value: 49.214999999999996 - type: f1 value: 44.85952451163755 - task: type: Retrieval dataset: type: fever name: MTEB FEVER config: default split: test revision: 1429cf27e393599b8b359b9b72c666f96b2525f9 metrics: - type: map_at_1 value: 56.769000000000005 - type: map_at_10 value: 67.30199999999999 - type: map_at_100 value: 67.692 - type: map_at_1000 value: 67.712 - type: map_at_3 value: 65.346 - type: map_at_5 value: 66.574 - type: mrr_at_1 value: 61.370999999999995 - type: mrr_at_10 value: 71.875 - type: mrr_at_100 value: 72.195 - type: mrr_at_1000 value: 72.206 - type: mrr_at_3 value: 70.04 - type: mrr_at_5 value: 71.224 - type: ndcg_at_1 value: 61.370999999999995 - type: ndcg_at_10 value: 72.731 - type: ndcg_at_100 value: 74.468 - type: ndcg_at_1000 value: 74.91600000000001 - type: ndcg_at_3 value: 69.077 - type: ndcg_at_5 value: 71.111 - type: precision_at_1 value: 61.370999999999995 - type: precision_at_10 value: 9.325999999999999 - type: precision_at_100 value: 1.03 - type: precision_at_1000 value: 0.108 - type: precision_at_3 value: 27.303 - type: precision_at_5 value: 17.525 - type: recall_at_1 value: 56.769000000000005 - type: recall_at_10 value: 85.06 - type: recall_at_100 value: 92.767 - type: recall_at_1000 value: 95.933 - type: recall_at_3 value: 75.131 - type: recall_at_5 value: 80.17 - task: type: Retrieval dataset: type: fiqa name: MTEB FiQA2018 config: default split: test revision: 41b686a7f28c59bcaaa5791efd47c67c8ebe28be metrics: - type: map_at_1 value: 15.753 - type: map_at_10 value: 25.875999999999998 - type: map_at_100 value: 27.415 - type: map_at_1000 value: 27.590999999999998 - type: map_at_3 value: 22.17 - type: map_at_5 value: 24.236 - type: mrr_at_1 value: 31.019000000000002 - type: mrr_at_10 value: 39.977000000000004 - type: mrr_at_100 value: 40.788999999999994 - type: mrr_at_1000 value: 40.832 - type: mrr_at_3 value: 37.088 - type: mrr_at_5 value: 38.655 - type: ndcg_at_1 value: 31.019000000000002 - type: ndcg_at_10 value: 33.286 - type: ndcg_at_100 value: 39.528999999999996 - type: ndcg_at_1000 value: 42.934 - type: ndcg_at_3 value: 29.29 - type: ndcg_at_5 value: 30.615 - type: precision_at_1 value: 31.019000000000002 - type: precision_at_10 value: 9.383 - type: precision_at_100 value: 1.6019999999999999 - type: precision_at_1000 value: 0.22200000000000003 - type: precision_at_3 value: 19.753 - type: precision_at_5 value: 14.815000000000001 - type: recall_at_1 value: 15.753 - type: recall_at_10 value: 40.896 - type: recall_at_100 value: 64.443 - type: recall_at_1000 value: 85.218 - type: recall_at_3 value: 26.526 - type: recall_at_5 value: 32.452999999999996 - task: type: Retrieval dataset: type: hotpotqa name: MTEB HotpotQA config: default split: test revision: 766870b35a1b9ca65e67a0d1913899973551fc6c metrics: - type: map_at_1 value: 32.153999999999996 - type: map_at_10 value: 43.651 - type: map_at_100 value: 44.41 - type: map_at_1000 value: 44.487 - type: map_at_3 value: 41.239 - type: map_at_5 value: 42.659000000000006 - type: mrr_at_1 value: 64.30799999999999 - type: mrr_at_10 value: 71.22500000000001 - type: mrr_at_100 value: 71.57 - type: mrr_at_1000 value: 71.59100000000001 - type: mrr_at_3 value: 69.95 - type: mrr_at_5 value: 70.738 - type: ndcg_at_1 value: 64.30799999999999 - type: ndcg_at_10 value: 52.835 - type: ndcg_at_100 value: 55.840999999999994 - type: ndcg_at_1000 value: 57.484 - type: ndcg_at_3 value: 49.014 - type: ndcg_at_5 value: 51.01599999999999 - type: precision_at_1 value: 64.30799999999999 - type: precision_at_10 value: 10.77 - type: precision_at_100 value: 1.315 - type: precision_at_1000 value: 0.153 - type: precision_at_3 value: 30.223 - type: precision_at_5 value: 19.716 - type: recall_at_1 value: 32.153999999999996 - type: recall_at_10 value: 53.849000000000004 - type: recall_at_100 value: 65.75999999999999 - type: recall_at_1000 value: 76.705 - type: recall_at_3 value: 45.334 - type: recall_at_5 value: 49.291000000000004 - task: type: Classification dataset: type: mteb/imdb name: MTEB ImdbClassification config: default split: test revision: 8d743909f834c38949e8323a8a6ce8721ea6c7f4 metrics: - type: accuracy value: 63.5316 - type: ap value: 58.90084300359825 - type: f1 value: 63.35727889030892 - task: type: Retrieval dataset: type: msmarco name: MTEB MSMARCO config: default split: validation revision: e6838a846e2408f22cf5cc337ebc83e0bcf77849 metrics: - type: map_at_1 value: 20.566000000000003 - type: map_at_10 value: 32.229 - type: map_at_100 value: 33.445 - type: map_at_1000 value: 33.501 - type: map_at_3 value: 28.504 - type: map_at_5 value: 30.681000000000004 - type: mrr_at_1 value: 21.218 - type: mrr_at_10 value: 32.816 - type: mrr_at_100 value: 33.986 - type: mrr_at_1000 value: 34.035 - type: mrr_at_3 value: 29.15 - type: mrr_at_5 value: 31.290000000000003 - type: ndcg_at_1 value: 21.218 - type: ndcg_at_10 value: 38.832 - type: ndcg_at_100 value: 44.743 - type: ndcg_at_1000 value: 46.138 - type: ndcg_at_3 value: 31.232 - type: ndcg_at_5 value: 35.099999999999994 - type: precision_at_1 value: 21.218 - type: precision_at_10 value: 6.186 - type: precision_at_100 value: 0.914 - type: precision_at_1000 value: 0.10300000000000001 - type: precision_at_3 value: 13.314 - type: precision_at_5 value: 9.943 - type: recall_at_1 value: 20.566000000000003 - type: recall_at_10 value: 59.192 - type: recall_at_100 value: 86.626 - type: recall_at_1000 value: 97.283 - type: recall_at_3 value: 38.492 - type: recall_at_5 value: 47.760000000000005 - task: type: Classification dataset: type: mteb/mtop_domain name: MTEB MTOPDomainClassification (en) config: en split: test revision: a7e2a951126a26fc8c6a69f835f33a346ba259e3 metrics: - type: accuracy value: 92.56269949840402 - type: f1 value: 92.1020975473988 - task: type: Classification dataset: type: mteb/mtop_intent name: MTEB MTOPIntentClassification (en) config: en split: test revision: 6299947a7777084cc2d4b64235bf7190381ce755 metrics: - type: accuracy value: 71.8467852257182 - type: f1 value: 53.652719348592015 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (en) config: en split: test revision: 072a486a144adf7f4479a4a0dddb2152e161e1ea metrics: - type: accuracy value: 69.00806993947546 - type: f1 value: 67.41429618885515 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (en) config: en split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 75.90114324142569 - type: f1 value: 76.25183590651454 - task: type: Clustering dataset: type: mteb/medrxiv-clustering-p2p name: MTEB MedrxivClusteringP2P config: default split: test revision: dcefc037ef84348e49b0d29109e891c01067226b metrics: - type: v_measure value: 31.350109978273395 - task: type: Clustering dataset: type: mteb/medrxiv-clustering-s2s name: MTEB MedrxivClusteringS2S config: default split: test revision: 3cd0e71dfbe09d4de0f9e5ecba43e7ce280959dc metrics: - type: v_measure value: 28.768923695767327 - task: type: Reranking dataset: type: mteb/mind_small name: MTEB MindSmallReranking config: default split: test revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 metrics: - type: map value: 31.716396735210754 - type: mrr value: 32.88970538547634 - task: type: Retrieval dataset: type: nfcorpus name: MTEB NFCorpus config: default split: test revision: 7eb63cc0c1eb59324d709ebed25fcab851fa7610 metrics: - type: map_at_1 value: 5.604 - type: map_at_10 value: 12.379999999999999 - type: map_at_100 value: 15.791 - type: map_at_1000 value: 17.327 - type: map_at_3 value: 9.15 - type: map_at_5 value: 10.599 - type: mrr_at_1 value: 45.201 - type: mrr_at_10 value: 53.374 - type: mrr_at_100 value: 54.089 - type: mrr_at_1000 value: 54.123 - type: mrr_at_3 value: 51.44499999999999 - type: mrr_at_5 value: 52.59 - type: ndcg_at_1 value: 42.879 - type: ndcg_at_10 value: 33.891 - type: ndcg_at_100 value: 31.391999999999996 - type: ndcg_at_1000 value: 40.36 - type: ndcg_at_3 value: 39.076 - type: ndcg_at_5 value: 37.047000000000004 - type: precision_at_1 value: 44.582 - type: precision_at_10 value: 25.294 - type: precision_at_100 value: 8.285 - type: precision_at_1000 value: 2.1479999999999997 - type: precision_at_3 value: 36.120000000000005 - type: precision_at_5 value: 31.95 - type: recall_at_1 value: 5.604 - type: recall_at_10 value: 16.239 - type: recall_at_100 value: 32.16 - type: recall_at_1000 value: 64.513 - type: recall_at_3 value: 10.406 - type: recall_at_5 value: 12.684999999999999 - task: type: Retrieval dataset: type: nq name: MTEB NQ config: default split: test revision: 6062aefc120bfe8ece5897809fb2e53bfe0d128c metrics: - type: map_at_1 value: 25.881 - type: map_at_10 value: 39.501 - type: map_at_100 value: 40.615 - type: map_at_1000 value: 40.661 - type: map_at_3 value: 35.559000000000005 - type: map_at_5 value: 37.773 - type: mrr_at_1 value: 29.229 - type: mrr_at_10 value: 41.955999999999996 - type: mrr_at_100 value: 42.86 - type: mrr_at_1000 value: 42.893 - type: mrr_at_3 value: 38.562000000000005 - type: mrr_at_5 value: 40.542 - type: ndcg_at_1 value: 29.2 - type: ndcg_at_10 value: 46.703 - type: ndcg_at_100 value: 51.644 - type: ndcg_at_1000 value: 52.771 - type: ndcg_at_3 value: 39.141999999999996 - type: ndcg_at_5 value: 42.892 - type: precision_at_1 value: 29.2 - type: precision_at_10 value: 7.920000000000001 - type: precision_at_100 value: 1.0659999999999998 - type: precision_at_1000 value: 0.117 - type: precision_at_3 value: 18.105 - type: precision_at_5 value: 13.036 - type: recall_at_1 value: 25.881 - type: recall_at_10 value: 66.266 - type: recall_at_100 value: 88.116 - type: recall_at_1000 value: 96.58200000000001 - type: recall_at_3 value: 46.526 - type: recall_at_5 value: 55.154 - task: type: Retrieval dataset: type: quora name: MTEB QuoraRetrieval config: default split: test revision: 6205996560df11e3a3da9ab4f926788fc30a7db4 metrics: - type: map_at_1 value: 67.553 - type: map_at_10 value: 81.34 - type: map_at_100 value: 82.002 - type: map_at_1000 value: 82.027 - type: map_at_3 value: 78.281 - type: map_at_5 value: 80.149 - type: mrr_at_1 value: 77.72 - type: mrr_at_10 value: 84.733 - type: mrr_at_100 value: 84.878 - type: mrr_at_1000 value: 84.879 - type: mrr_at_3 value: 83.587 - type: mrr_at_5 value: 84.32600000000001 - type: ndcg_at_1 value: 77.75 - type: ndcg_at_10 value: 85.603 - type: ndcg_at_100 value: 87.069 - type: ndcg_at_1000 value: 87.25 - type: ndcg_at_3 value: 82.303 - type: ndcg_at_5 value: 84.03699999999999 - type: precision_at_1 value: 77.75 - type: precision_at_10 value: 13.04 - type: precision_at_100 value: 1.5070000000000001 - type: precision_at_1000 value: 0.156 - type: precision_at_3 value: 35.903 - type: precision_at_5 value: 23.738 - type: recall_at_1 value: 67.553 - type: recall_at_10 value: 93.903 - type: recall_at_100 value: 99.062 - type: recall_at_1000 value: 99.935 - type: recall_at_3 value: 84.58099999999999 - type: recall_at_5 value: 89.316 - task: type: Clustering dataset: type: mteb/reddit-clustering name: MTEB RedditClustering config: default split: test revision: b2805658ae38990172679479369a78b86de8c390 metrics: - type: v_measure value: 46.46887711230235 - task: type: Clustering dataset: type: mteb/reddit-clustering-p2p name: MTEB RedditClusteringP2P config: default split: test revision: 385e3cb46b4cfa89021f56c4380204149d0efe33 metrics: - type: v_measure value: 54.166876298246926 - task: type: Retrieval dataset: type: scidocs name: MTEB SCIDOCS config: default split: test revision: 5c59ef3e437a0a9651c8fe6fde943e7dce59fba5 metrics: - type: map_at_1 value: 4.053 - type: map_at_10 value: 9.693999999999999 - type: map_at_100 value: 11.387 - type: map_at_1000 value: 11.654 - type: map_at_3 value: 7.053 - type: map_at_5 value: 8.439 - type: mrr_at_1 value: 19.900000000000002 - type: mrr_at_10 value: 29.359 - type: mrr_at_100 value: 30.484 - type: mrr_at_1000 value: 30.553 - type: mrr_at_3 value: 26.200000000000003 - type: mrr_at_5 value: 28.115000000000002 - type: ndcg_at_1 value: 19.900000000000002 - type: ndcg_at_10 value: 16.575 - type: ndcg_at_100 value: 23.655 - type: ndcg_at_1000 value: 28.853 - type: ndcg_at_3 value: 15.848 - type: ndcg_at_5 value: 14.026 - type: precision_at_1 value: 19.900000000000002 - type: precision_at_10 value: 8.450000000000001 - type: precision_at_100 value: 1.872 - type: precision_at_1000 value: 0.313 - type: precision_at_3 value: 14.667 - type: precision_at_5 value: 12.32 - type: recall_at_1 value: 4.053 - type: recall_at_10 value: 17.169999999999998 - type: recall_at_100 value: 38.025 - type: recall_at_1000 value: 63.571999999999996 - type: recall_at_3 value: 8.903 - type: recall_at_5 value: 12.477 - task: type: STS dataset: type: mteb/sickr-sts name: MTEB SICK-R config: default split: test revision: 20a6d6f312dd54037fe07a32d58e5e168867909d metrics: - type: cos_sim_pearson value: 77.7548748519677 - type: cos_sim_spearman value: 68.19926431966059 - type: euclidean_pearson value: 71.69016204991725 - type: euclidean_spearman value: 66.98099673026834 - type: manhattan_pearson value: 71.62994072488664 - type: manhattan_spearman value: 67.03435950744577 - task: type: STS dataset: type: mteb/sts12-sts name: MTEB STS12 config: default split: test revision: fdf84275bb8ce4b49c971d02e84dd1abc677a50f metrics: - type: cos_sim_pearson value: 75.91051402657887 - type: cos_sim_spearman value: 66.99390786191645 - type: euclidean_pearson value: 71.54128036454578 - type: euclidean_spearman value: 69.25605675649068 - type: manhattan_pearson value: 71.60981030780171 - type: manhattan_spearman value: 69.27513670128046 - task: type: STS dataset: type: mteb/sts13-sts name: MTEB STS13 config: default split: test revision: 1591bfcbe8c69d4bf7fe2a16e2451017832cafb9 metrics: - type: cos_sim_pearson value: 77.23835466417793 - type: cos_sim_spearman value: 77.57623085766706 - type: euclidean_pearson value: 77.5090992200725 - type: euclidean_spearman value: 77.88601688144924 - type: manhattan_pearson value: 77.39045060647423 - type: manhattan_spearman value: 77.77552718279098 - task: type: STS dataset: type: mteb/sts14-sts name: MTEB STS14 config: default split: test revision: e2125984e7df8b7871f6ae9949cf6b6795e7c54b metrics: - type: cos_sim_pearson value: 77.91692485139602 - type: cos_sim_spearman value: 72.78258293483495 - type: euclidean_pearson value: 74.64773017077789 - type: euclidean_spearman value: 71.81662299104619 - type: manhattan_pearson value: 74.71043337995533 - type: manhattan_spearman value: 71.83960860845646 - task: type: STS dataset: type: mteb/sts15-sts name: MTEB STS15 config: default split: test revision: 1cd7298cac12a96a373b6a2f18738bb3e739a9b6 metrics: - type: cos_sim_pearson value: 82.13422113617578 - type: cos_sim_spearman value: 82.61707296911949 - type: euclidean_pearson value: 81.42487480400861 - type: euclidean_spearman value: 82.17970991273835 - type: manhattan_pearson value: 81.41985055477845 - type: manhattan_spearman value: 82.15823204362937 - task: type: STS dataset: type: mteb/sts16-sts name: MTEB STS16 config: default split: test revision: 360a0b2dff98700d09e634a01e1cc1624d3e42cd metrics: - type: cos_sim_pearson value: 79.07989542843826 - type: cos_sim_spearman value: 80.09839524406284 - type: euclidean_pearson value: 76.43186028364195 - type: euclidean_spearman value: 76.76720323266471 - type: manhattan_pearson value: 76.4674747409161 - type: manhattan_spearman value: 76.81797407068667 - task: type: STS dataset: type: mteb/sts17-crosslingual-sts name: MTEB STS17 (en-en) config: en-en split: test revision: 9fc37e8c632af1c87a3d23e685d49552a02582a0 metrics: - type: cos_sim_pearson value: 87.0420983224933 - type: cos_sim_spearman value: 87.25017540413702 - type: euclidean_pearson value: 84.56384596473421 - type: euclidean_spearman value: 84.72557417564886 - type: manhattan_pearson value: 84.7329954474549 - type: manhattan_spearman value: 84.75071371008909 - task: type: STS dataset: type: mteb/sts22-crosslingual-sts name: MTEB STS22 (en) config: en split: test revision: 2de6ce8c1921b71a755b262c6b57fef195dd7906 metrics: - type: cos_sim_pearson value: 68.47031320016424 - type: cos_sim_spearman value: 68.7486910762485 - type: euclidean_pearson value: 71.30330985913915 - type: euclidean_spearman value: 71.59666258520735 - type: manhattan_pearson value: 71.4423884279027 - type: manhattan_spearman value: 71.67460706861044 - task: type: STS dataset: type: mteb/stsbenchmark-sts name: MTEB STSBenchmark config: default split: test revision: 8913289635987208e6e7c72789e4be2fe94b6abd metrics: - type: cos_sim_pearson value: 80.79514366062675 - type: cos_sim_spearman value: 79.20585637461048 - type: euclidean_pearson value: 78.6591557395699 - type: euclidean_spearman value: 77.86455794285718 - type: manhattan_pearson value: 78.67754806486865 - type: manhattan_spearman value: 77.88178687200732 - task: type: Reranking dataset: type: mteb/scidocs-reranking name: MTEB SciDocsRR config: default split: test revision: 56a6d0140cf6356659e2a7c1413286a774468d44 metrics: - type: map value: 77.71580844366375 - type: mrr value: 93.04215845882513 - task: type: Retrieval dataset: type: scifact name: MTEB SciFact config: default split: test revision: a75ae049398addde9b70f6b268875f5cbce99089 metrics: - type: map_at_1 value: 56.39999999999999 - type: map_at_10 value: 65.701 - type: map_at_100 value: 66.32000000000001 - type: map_at_1000 value: 66.34100000000001 - type: map_at_3 value: 62.641999999999996 - type: map_at_5 value: 64.342 - type: mrr_at_1 value: 58.667 - type: mrr_at_10 value: 66.45299999999999 - type: mrr_at_100 value: 66.967 - type: mrr_at_1000 value: 66.988 - type: mrr_at_3 value: 64.11099999999999 - type: mrr_at_5 value: 65.411 - type: ndcg_at_1 value: 58.667 - type: ndcg_at_10 value: 70.165 - type: ndcg_at_100 value: 72.938 - type: ndcg_at_1000 value: 73.456 - type: ndcg_at_3 value: 64.79 - type: ndcg_at_5 value: 67.28 - type: precision_at_1 value: 58.667 - type: precision_at_10 value: 9.4 - type: precision_at_100 value: 1.087 - type: precision_at_1000 value: 0.11299999999999999 - type: precision_at_3 value: 24.889 - type: precision_at_5 value: 16.667 - type: recall_at_1 value: 56.39999999999999 - type: recall_at_10 value: 83.122 - type: recall_at_100 value: 95.667 - type: recall_at_1000 value: 99.667 - type: recall_at_3 value: 68.378 - type: recall_at_5 value: 74.68299999999999 - task: type: PairClassification dataset: type: mteb/sprintduplicatequestions-pairclassification name: MTEB SprintDuplicateQuestions config: default split: test revision: 5a8256d0dff9c4bd3be3ba3e67e4e70173f802ea metrics: - type: cos_sim_accuracy value: 99.76831683168317 - type: cos_sim_ap value: 93.47124923047998 - type: cos_sim_f1 value: 88.06122448979592 - type: cos_sim_precision value: 89.89583333333333 - type: cos_sim_recall value: 86.3 - type: dot_accuracy value: 99.57326732673268 - type: dot_ap value: 84.06577868167207 - type: dot_f1 value: 77.82629791363416 - type: dot_precision value: 75.58906691800189 - type: dot_recall value: 80.2 - type: euclidean_accuracy value: 99.74257425742574 - type: euclidean_ap value: 92.1904681653555 - type: euclidean_f1 value: 86.74821610601427 - type: euclidean_precision value: 88.46153846153845 - type: euclidean_recall value: 85.1 - type: manhattan_accuracy value: 99.74554455445545 - type: manhattan_ap value: 92.4337790809948 - type: manhattan_f1 value: 86.86765457332653 - type: manhattan_precision value: 88.81922675026124 - type: manhattan_recall value: 85.0 - type: max_accuracy value: 99.76831683168317 - type: max_ap value: 93.47124923047998 - type: max_f1 value: 88.06122448979592 - task: type: Clustering dataset: type: mteb/stackexchange-clustering name: MTEB StackExchangeClustering config: default split: test revision: 70a89468f6dccacc6aa2b12a6eac54e74328f235 metrics: - type: v_measure value: 59.194098673976484 - task: type: Clustering dataset: type: mteb/stackexchange-clustering-p2p name: MTEB StackExchangeClusteringP2P config: default split: test revision: d88009ab563dd0b16cfaf4436abaf97fa3550cf0 metrics: - type: v_measure value: 32.5744032578115 - task: type: Reranking dataset: type: mteb/stackoverflowdupquestions-reranking name: MTEB StackOverflowDupQuestions config: default split: test revision: ef807ea29a75ec4f91b50fd4191cb4ee4589a9f9 metrics: - type: map value: 49.61186384154483 - type: mrr value: 50.55424253034547 - task: type: Summarization dataset: type: mteb/summeval name: MTEB SummEval config: default split: test revision: 8753c2788d36c01fc6f05d03fe3f7268d63f9122 metrics: - type: cos_sim_pearson value: 30.027210161713946 - type: cos_sim_spearman value: 31.030178065751735 - type: dot_pearson value: 30.09179785685587 - type: dot_spearman value: 30.408303252207813 - task: type: Retrieval dataset: type: trec-covid name: MTEB TRECCOVID config: default split: test revision: 2c8041b2c07a79b6f7ba8fe6acc72e5d9f92d217 metrics: - type: map_at_1 value: 0.22300000000000003 - type: map_at_10 value: 1.762 - type: map_at_100 value: 9.984 - type: map_at_1000 value: 24.265 - type: map_at_3 value: 0.631 - type: map_at_5 value: 0.9950000000000001 - type: mrr_at_1 value: 88.0 - type: mrr_at_10 value: 92.833 - type: mrr_at_100 value: 92.833 - type: mrr_at_1000 value: 92.833 - type: mrr_at_3 value: 92.333 - type: mrr_at_5 value: 92.833 - type: ndcg_at_1 value: 83.0 - type: ndcg_at_10 value: 75.17 - type: ndcg_at_100 value: 55.432 - type: ndcg_at_1000 value: 49.482 - type: ndcg_at_3 value: 82.184 - type: ndcg_at_5 value: 79.712 - type: precision_at_1 value: 88.0 - type: precision_at_10 value: 78.60000000000001 - type: precision_at_100 value: 56.56 - type: precision_at_1000 value: 22.334 - type: precision_at_3 value: 86.667 - type: precision_at_5 value: 83.6 - type: recall_at_1 value: 0.22300000000000003 - type: recall_at_10 value: 1.9879999999999998 - type: recall_at_100 value: 13.300999999999998 - type: recall_at_1000 value: 46.587 - type: recall_at_3 value: 0.6629999999999999 - type: recall_at_5 value: 1.079 - task: type: Retrieval dataset: type: webis-touche2020 name: MTEB Touche2020 config: default split: test revision: 527b7d77e16e343303e68cb6af11d6e18b9f7b3b metrics: - type: map_at_1 value: 3.047 - type: map_at_10 value: 8.792 - type: map_at_100 value: 14.631 - type: map_at_1000 value: 16.127 - type: map_at_3 value: 4.673 - type: map_at_5 value: 5.897 - type: mrr_at_1 value: 38.775999999999996 - type: mrr_at_10 value: 49.271 - type: mrr_at_100 value: 50.181 - type: mrr_at_1000 value: 50.2 - type: mrr_at_3 value: 44.558 - type: mrr_at_5 value: 47.925000000000004 - type: ndcg_at_1 value: 35.714 - type: ndcg_at_10 value: 23.44 - type: ndcg_at_100 value: 35.345 - type: ndcg_at_1000 value: 46.495 - type: ndcg_at_3 value: 26.146 - type: ndcg_at_5 value: 24.878 - type: precision_at_1 value: 38.775999999999996 - type: precision_at_10 value: 20.816000000000003 - type: precision_at_100 value: 7.428999999999999 - type: precision_at_1000 value: 1.494 - type: precision_at_3 value: 25.85 - type: precision_at_5 value: 24.082 - type: recall_at_1 value: 3.047 - type: recall_at_10 value: 14.975 - type: recall_at_100 value: 45.943 - type: recall_at_1000 value: 80.31099999999999 - type: recall_at_3 value: 5.478000000000001 - type: recall_at_5 value: 8.294 - task: type: Classification dataset: type: mteb/toxic_conversations_50k name: MTEB ToxicConversationsClassification config: default split: test revision: edfaf9da55d3dd50d43143d90c1ac476895ae6de metrics: - type: accuracy value: 68.84080000000002 - type: ap value: 13.135219251019848 - type: f1 value: 52.849999421995506 - task: type: Classification dataset: type: mteb/tweet_sentiment_extraction name: MTEB TweetSentimentExtractionClassification config: default split: test revision: 62146448f05be9e52a36b8ee9936447ea787eede metrics: - type: accuracy value: 56.68647425014149 - type: f1 value: 56.97981427365949 - task: type: Clustering dataset: type: mteb/twentynewsgroups-clustering name: MTEB TwentyNewsgroupsClustering config: default split: test revision: 091a54f9a36281ce7d6590ec8c75dd485e7e01d4 metrics: - type: v_measure value: 40.8911707239219 - task: type: PairClassification dataset: type: mteb/twittersemeval2015-pairclassification name: MTEB TwitterSemEval2015 config: default split: test revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 metrics: - type: cos_sim_accuracy value: 83.04226023722954 - type: cos_sim_ap value: 63.681339908301325 - type: cos_sim_f1 value: 60.349184470480125 - type: cos_sim_precision value: 53.437754271765655 - type: cos_sim_recall value: 69.31398416886545 - type: dot_accuracy value: 81.46271681468677 - type: dot_ap value: 57.78072296265885 - type: dot_f1 value: 56.28769265132901 - type: dot_precision value: 48.7993803253292 - type: dot_recall value: 66.49076517150397 - type: euclidean_accuracy value: 82.16606067830959 - type: euclidean_ap value: 59.974530371203514 - type: euclidean_f1 value: 56.856023506366306 - type: euclidean_precision value: 53.037916857012334 - type: euclidean_recall value: 61.2664907651715 - type: manhattan_accuracy value: 82.16606067830959 - type: manhattan_ap value: 59.98962379571767 - type: manhattan_f1 value: 56.98153158451947 - type: manhattan_precision value: 51.41158989598811 - type: manhattan_recall value: 63.90501319261214 - type: max_accuracy value: 83.04226023722954 - type: max_ap value: 63.681339908301325 - type: max_f1 value: 60.349184470480125 - task: type: PairClassification dataset: type: mteb/twitterurlcorpus-pairclassification name: MTEB TwitterURLCorpus config: default split: test revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf metrics: - type: cos_sim_accuracy value: 88.56871191834517 - type: cos_sim_ap value: 84.80240716354544 - type: cos_sim_f1 value: 77.07765285922385 - type: cos_sim_precision value: 74.84947406601378 - type: cos_sim_recall value: 79.44256236526024 - type: dot_accuracy value: 86.00923662048356 - type: dot_ap value: 78.6556459012073 - type: dot_f1 value: 72.7583749109052 - type: dot_precision value: 67.72823779193206 - type: dot_recall value: 78.59562673236834 - type: euclidean_accuracy value: 87.84103698529127 - type: euclidean_ap value: 83.50424424952834 - type: euclidean_f1 value: 75.74496544549307 - type: euclidean_precision value: 73.19402556369381 - type: euclidean_recall value: 78.48013550970127 - type: manhattan_accuracy value: 87.9225365777933 - type: manhattan_ap value: 83.49479248597825 - type: manhattan_f1 value: 75.67748162447101 - type: manhattan_precision value: 73.06810035842294 - type: manhattan_recall value: 78.48013550970127 - type: max_accuracy value: 88.56871191834517 - type: max_ap value: 84.80240716354544 - type: max_f1 value: 77.07765285922385 --- # SGPT-2.7B-weightedmean-msmarco-specb-bitfit ## Usage For usage instructions, refer to our codebase: https://github.com/Muennighoff/sgpt ## Evaluation Results For eval results, refer to the eval folder or our paper: https://arxiv.org/abs/2202.08904 ## Training The model was trained with the parameters: DataLoader: `torch.utils.data.dataloader.DataLoader` of length 124796 with parameters: ``` {'batch_size': 4, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` Loss: `sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters: ``` {'scale': 20.0, 'similarity_fct': 'cos_sim'} ``` Parameters of the fit()-Method: ``` { "epochs": 10, "evaluation_steps": 0, "evaluator": "NoneType", "max_grad_norm": 1, "optimizer_class": "<class 'transformers.optimization.AdamW'>", "optimizer_params": { "lr": 7.5e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 1000, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 300, 'do_lower_case': False}) with Transformer model: GPTNeoModel (1): Pooling({'word_embedding_dimension': 2560, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': True, 'pooling_mode_lasttoken': False}) ) ``` ## Citing & Authors ```bibtex @article{muennighoff2022sgpt, title={SGPT: GPT Sentence Embeddings for Semantic Search}, author={Muennighoff, Niklas}, journal={arXiv preprint arXiv:2202.08904}, year={2022} } ```
SilvioLima/absa_10_domains_lora_base	SilvioLima	2024-07-02T13:13:54Z	4,748	0	peft	[ "peft", "safetensors", "code", "text-generation", "en", "dataset:SilvioLima/absa", "dataset:SilvioLima/raw_data_aug", "dataset:SilvioLima/raw_data", "license:apache-2.0", "region:us" ]	text-generation	2024-06-13T01:02:43Z	--- datasets: - SilvioLima/absa - SilvioLima/raw_data_aug - SilvioLima/raw_data language: - en metrics: - rouge - precision - recall - f1 license: apache-2.0 library_name: peft pipeline_tag: text-generation tags: - code --- # Absa_10_domains ### Model Summary Este modelo identifica aspectos, opiniões e polaridades em reviews.. Foi treinado com 10k reviews de pessoas que registraram um ou mais comentários num site de e-commerce. Nesse contexto aspectos estão relacionados a um ou mais produtos ou serviços, opiniões esta relacionado a caracteristica destacada e polaridade a satisfação com o respectivo produto ou serviço. No dataset usado os reviews pertencem a 10 dominios: Restaurants, Electronics, Home, Laptop, Fashion, Beauty, Toy, Grocery, Pet and Book. O modelo foi treinado com um review especifico para cada dominio, esse review foi reescrito para tornar mais claro e consistente o processo de identificar os produtos ou serviços, a opinião expressa e a polaridade do review. O modelo pré-treinado google/flan-t5-base foi usado como modelo base e o LORA foi configurado para usar apenas uma pequena parte dos parametros treinaveis e assim conseguir usar todos registros no dataset sem esgotar a memória da VM na GCP. Durante o treinamento o modelo foi instruído para seguir o exemplo dado e retornar uma sentença no mesmo formato do exemplo. ### How to use Carregar o modelo: from transformers import AutoModelForSeq2SeqLM, AutoTokenizer from peft import PeftModel, PeftConfig repo_model = "SilvioLima/absa_10_domains" model_name_or_path = repo_model config = PeftConfig.from_pretrained(model_name_or_path) model = AutoModelForSeq2SeqLM.from_pretrained(config.base_model_name_or_path) model = PeftModel.from_pretrained(model, model_name_or_path) tokenizer = AutoTokenizer.from_pretrained(repo_model) max_length = 256 prefix_fs = "Follow the example, rewrite the sentence and answer in the same format of example\n." example_task_parafrase = "Example:\nSentence = 'The pizza was delicious because pizza came hot.'\nAnswer = Pizza is great because it came hot." review = 'This book is amazing, story is intersting and price is good.' input_text = prefix_fs + example_task_parafrase + review + 'Answer=' inputs = tokenizer(input_text, max_length=max_length, truncation=True , return_tensors="pt", padding="max_length") input_ids = inputs['input_ids'] outputs = model.generate(input_ids = input_ids, max_new_tokens=max_length) #outputs = model.generate(inputs_ids, max_length=max_length) #do_sample=True, temperature=(temperature).astype('float')) generated = tokenizer.decode(outputs[0], skip_special_tokens=True) print(generated): #### - "book is great because book is amazing, #### - story is great because story is intersting, #### - price is great because price is cheap." A partir da saída formatada são extraídos os termos aspectos, opiniões e polaridades identificados no review. ### Training O modelo foi treinado com um dataset que separado em 3 partes, sendo 80% para treinamento, 10% para validação e 10% para teste. O modelo pré-treinado escolhido foi o google/flan-t5-base que possue 248 M de parametros, que foi treinado com diversos corpus para diversas tarefas. Durante o treinamento foi acompanhado os valores de rouge1 e rouge2 para avaliar a capacidade de aprendizado do modelo. O modelo final foi avaliado com os dados de testes. A avaliação foi feita por dominio. ![Model_absa_10_domains.png](https://cdn-uploads.huggingface.co/production/uploads/659820c0ada2ade50bc44f71/Kl1AfLH3_X51WeZfWQ4on.png) O critério de avaliação definido foi que os termos aspectos, opiniões e polaridades presentes na parafrase gerada pelo modelo, devem ser iguais aos termos usados na construção da parafrase fornecida durante o treinamento. O prompt usado na tokenização dos dados de treinamento, apresenta uma instrução que o modelo deve seguir. Um exemplo de review / sentence de cada dominio e a parafrase gerada é apresentada ao modelo, seguida de uma nova sentence que o modelo deverá gerar, seguindo o formato do exemplo apresentado. prefix_fs = "Follow the example, rewrite the sentence and answer in the same format of example.\n" #### input_str = f"{prefix_fs} Example:\nSentence = {sentence_example} \nAnswer = {target_example} \nSentence = {sentence} \nAnswer = " Exemplo: #### - Restaurant_sentence: 'Excellent atmosphere , delicious dishes good and friendly service.' #### - Restaurant_target : "atmosphere is great because atmosphere is Excellent , dishes is great because dishes is delicious , service is great because service is good , service is great because service is friendly </s>" #### - Restaurant_triple':"[('atmosphere', 'Excellent', 'POS'), ('dishes', 'delicious', 'POS'), ('service', 'good', 'POS'), ('service', 'friendly', 'POS')]" Reviews do mesmo dominio do exemplo usado são apresentados para o que o modelo gera um nova review no mesmo formato. ### Triples As reviews geradas pelo modelo são compostas pelos aspectos, opiniões e polaridades que ao final do treinamento são extraidas e avaliadas se cada conjunto de aspecto/opinião/polaridade estão presentes no mesmo conjunto usado na construção da parafrase de treinamento. Exemplo: Parafrase gerada pelo modelo: #### "atmosphere is great because atmosphere is Excellent , dishes is great because dishes is amazing , service is great because service is good , location is bad because location is danger" A triple ou o conjunto aspecto/opiniao/polaridade é extraida da parafrase: #### -[('atmosphere', 'Excellent', 'POS'), ('dishes', 'amazing', 'POS'), ('service', 'good', 'POS'), ('location', 'danger', 'NEG')]" As triples extraídas da parafrase gerada pelo modelo devem ser iguais as triples correspondentes no review de entrada. A review apresentada ao modelo para que seja feita a parafrase, tem um conjunto que corresponde a parafrase correta relacionada a review apresentada ao modelo. ### Métricas: Precision, Recall e F1-score True Positive (TP)) corresponde a triple extraida da parafrase gerada pelo modelo que é exatamente igual a triple de entrada. False Positive (FP) corresponde a triple extraída que não se encontra na triple de entrada. A Quantidade de FN é a quantidade de triples de entrada menos quantidade de triples encontrados na saida. A partir desses valores é calculada a Precisão, o Recall e F1-score do modelo. ### Domain - Restaurant: 82.42 - Laptop: 75.10 - book: 40.08 - home: 39.21 - pet: 35.23 - beauty: 33.68 - grocery: 33.39 - electronics: 32.95 - fashion: 32.49 - toy: 30.52 ### F1-score % média: 43,50 ### Training Details - Model: "google/flan-t5-base" - Tokenizer: T5Tokenizer / T5ForConditionalGeneration - Dataset: 13513 - Train: 10810 - Valid/Test: 1352 - Batch-size: 4 - Max_length: 512 - Num_Epochs: 10 ### Global Parameters L_RATE = 3e-4 BATCH_SIZE = batch_size PER_DEVICE_EVAL_BATCH = batch_size WEIGHT_DECAY = 0.01 SAVE_TOTAL_LIM = 1 NUM_EPOCHS = num_epochs MAX_NEW_TOKENS = max_length ### Set up training arguments training_args = Seq2SeqTrainingArguments( output_dir="./results/absa_domain_parafrase_v512_2", evaluation_strategy="epoch", save_strategy="epoch", learning_rate=L_RATE, per_device_train_batch_size=BATCH_SIZE, per_device_eval_batch_size=PER_DEVICE_EVAL_BATCH, gradient_accumulation_steps=8, weight_decay=WEIGHT_DECAY, save_total_limit=SAVE_TOTAL_LIM, load_best_model_at_end=True, num_train_epochs=NUM_EPOCHS, predict_with_generate=True, logging_dir="./logs", logging_strategy="epoch", logging_steps=100, report_to = 'wandb', push_to_hub=True ) ### Lora config: - lora_config = LoraConfig( task_type = TaskType.SEQ_2_SEQ_LM, r = 16, lora_alpha = 32, lora_dropout = 0.1, target_modules = ["q", "v"] )
Cohere/Cohere-embed-multilingual-light-v3.0	Cohere	2023-11-07T12:59:57Z	4,746	8	transformers	[ "transformers", "mteb", "model-index", "endpoints_compatible", "region:us" ]	null	2023-11-01T20:54:54Z	--- tags: - mteb model-index: - name: embed-multilingual-light-v3.0 results: - task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (en) config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 70.02985074626865 - type: ap value: 33.228065779544146 - type: f1 value: 64.27173953207297 - task: type: Classification dataset: type: mteb/amazon_polarity name: MTEB AmazonPolarityClassification config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 90.701225 - type: ap value: 87.07178174251762 - type: f1 value: 90.69168484877625 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (en) config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 46.550000000000004 - type: f1 value: 44.7233215588199 - task: type: Retrieval dataset: type: arguana name: MTEB ArguAna config: default split: test revision: None metrics: - type: ndcg_at_10 value: 53.369 - task: type: Clustering dataset: type: mteb/arxiv-clustering-p2p name: MTEB ArxivClusteringP2P config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics: - type: v_measure value: 44.206988765030744 - task: type: Clustering dataset: type: mteb/arxiv-clustering-s2s name: MTEB ArxivClusteringS2S config: default split: test revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 metrics: - type: v_measure value: 33.913737041277 - task: type: Reranking dataset: type: mteb/askubuntudupquestions-reranking name: MTEB AskUbuntuDupQuestions config: default split: test revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 metrics: - type: map value: 58.544257541214925 - type: mrr value: 72.07151651057468 - task: type: STS dataset: type: mteb/biosses-sts name: MTEB BIOSSES config: default split: test revision: d3fb88f8f02e40887cd149695127462bbcf29b4a metrics: - type: cos_sim_pearson value: 84.79582115243736 - type: cos_sim_spearman value: 84.01396250789998 - type: euclidean_pearson value: 83.90766476102458 - type: euclidean_spearman value: 84.01396250789998 - type: manhattan_pearson value: 84.75071274784274 - type: manhattan_spearman value: 85.02482891467078 - task: type: Classification dataset: type: mteb/banking77 name: MTEB Banking77Classification config: default split: test revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 metrics: - type: accuracy value: 78.12337662337663 - type: f1 value: 77.48610340227478 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-p2p name: MTEB BiorxivClusteringP2P config: default split: test revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 metrics: - type: v_measure value: 38.68268504601174 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-s2s name: MTEB BiorxivClusteringS2S config: default split: test revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 metrics: - type: v_measure value: 32.20870648143671 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackAndroidRetrieval config: default split: test revision: None metrics: - type: ndcg_at_10 value: 46.259 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackEnglishRetrieval config: default split: test revision: None metrics: - type: ndcg_at_10 value: 44.555 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGamingRetrieval config: default split: test revision: None metrics: - type: ndcg_at_10 value: 56.564 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGisRetrieval config: default split: test revision: None metrics: - type: ndcg_at_10 value: 36.162 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackMathematicaRetrieval config: default split: test revision: None metrics: - type: ndcg_at_10 value: 26.185000000000002 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackPhysicsRetrieval config: default split: test revision: None metrics: - type: ndcg_at_10 value: 41.547 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackProgrammersRetrieval config: default split: test revision: None metrics: - type: ndcg_at_10 value: 39.042 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackRetrieval config: default split: test revision: None metrics: - type: ndcg_at_10 value: 38.086999999999996 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackStatsRetrieval config: default split: test revision: None metrics: - type: ndcg_at_10 value: 32.088 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackTexRetrieval config: default split: test revision: None metrics: - type: ndcg_at_10 value: 27.006999999999998 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackUnixRetrieval config: default split: test revision: None metrics: - type: ndcg_at_10 value: 37.336999999999996 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWebmastersRetrieval config: default split: test revision: None metrics: - type: ndcg_at_10 value: 38.011 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWordpressRetrieval config: default split: test revision: None metrics: - type: ndcg_at_10 value: 32.287 - task: type: Retrieval dataset: type: climate-fever name: MTEB ClimateFEVER config: default split: test revision: None metrics: - type: ndcg_at_10 value: 24.804000000000002 - task: type: Retrieval dataset: type: dbpedia-entity name: MTEB DBPedia config: default split: test revision: None metrics: - type: ndcg_at_10 value: 38.055 - task: type: Classification dataset: type: mteb/emotion name: MTEB EmotionClassification config: default split: test revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 metrics: - type: accuracy value: 46.665 - type: f1 value: 40.77568559660878 - task: type: Retrieval dataset: type: fever name: MTEB FEVER config: default split: test revision: None metrics: - type: ndcg_at_10 value: 85.52499999999999 - task: type: Retrieval dataset: type: fiqa name: MTEB FiQA2018 config: default split: test revision: None metrics: - type: ndcg_at_10 value: 36.161 - task: type: Retrieval dataset: type: hotpotqa name: MTEB HotpotQA config: default split: test revision: None metrics: - type: ndcg_at_10 value: 66.878 - task: type: Classification dataset: type: mteb/imdb name: MTEB ImdbClassification config: default split: test revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 metrics: - type: accuracy value: 85.6372 - type: ap value: 80.54846874011302 - type: f1 value: 85.61438421821343 - task: type: Retrieval dataset: type: msmarco name: MTEB MSMARCO config: default split: test revision: None metrics: - type: ndcg_at_10 value: 40.487 - task: type: Classification dataset: type: mteb/mtop_domain name: MTEB MTOPDomainClassification (en) config: en split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 91.8559051527588 - type: f1 value: 91.6271749996447 - task: type: Classification dataset: type: mteb/mtop_intent name: MTEB MTOPIntentClassification (en) config: en split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - 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type: v_measure value: 30.993641089208683 - task: type: Reranking dataset: type: mteb/mind_small name: MTEB MindSmallReranking config: default split: test revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 metrics: - type: map value: 31.362481813275295 - type: mrr value: 32.43717742343303 - task: type: Retrieval dataset: type: nfcorpus name: MTEB NFCorpus config: default split: test revision: None metrics: - type: ndcg_at_10 value: 32.123000000000005 - task: type: Retrieval dataset: type: nq name: MTEB NQ config: default split: test revision: None metrics: - type: ndcg_at_10 value: 55.51199999999999 - task: type: Retrieval dataset: type: quora name: MTEB QuoraRetrieval config: default split: test revision: None metrics: - type: ndcg_at_10 value: 87.847 - task: type: Clustering dataset: type: mteb/reddit-clustering name: MTEB RedditClustering config: default split: test revision: 24640382cdbf8abc73003fb0fa6d111a705499eb metrics: - type: v_measure value: 49.4973643968247 - task: type: Clustering dataset: type: mteb/reddit-clustering-p2p name: MTEB RedditClusteringP2P config: default split: test revision: 282350215ef01743dc01b456c7f5241fa8937f16 metrics: - type: v_measure value: 60.2135284243427 - task: type: Retrieval dataset: type: scidocs name: MTEB SCIDOCS config: default split: test revision: None metrics: - type: ndcg_at_10 value: 17.1 - task: type: STS dataset: type: mteb/sickr-sts name: MTEB SICK-R config: default split: test revision: a6ea5a8cab320b040a23452cc28066d9beae2cee metrics: - type: cos_sim_pearson value: 83.7330191296952 - type: cos_sim_spearman value: 77.03523134004043 - type: euclidean_pearson value: 80.86067787185137 - type: euclidean_spearman value: 77.03522959536473 - type: manhattan_pearson value: 80.76089708603587 - type: manhattan_spearman value: 76.86245377437302 - task: type: STS dataset: type: mteb/sts12-sts name: MTEB STS12 config: default split: test revision: a0d554a64d88156834ff5ae9920b964011b16384 metrics: - type: cos_sim_pearson value: 80.46387812633851 - type: cos_sim_spearman value: 73.21878234127571 - type: euclidean_pearson value: 76.82160699895033 - type: euclidean_spearman value: 73.21878234127571 - type: manhattan_pearson value: 76.75657006349886 - type: manhattan_spearman value: 73.19160258034827 - task: type: STS dataset: type: mteb/sts13-sts name: MTEB STS13 config: default split: test revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca metrics: - type: cos_sim_pearson value: 79.06411399119807 - type: cos_sim_spearman value: 79.49916779764082 - type: euclidean_pearson value: 79.3356521660954 - type: euclidean_spearman value: 79.49916779764082 - type: manhattan_pearson value: 79.04971532119936 - type: manhattan_spearman value: 79.16859911220654 - task: type: STS dataset: type: mteb/sts14-sts name: MTEB STS14 config: default split: test revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 metrics: - type: cos_sim_pearson value: 80.6940934994372 - type: cos_sim_spearman value: 76.9552055757283 - type: euclidean_pearson value: 79.52818133592284 - type: euclidean_spearman value: 76.9552055757283 - type: manhattan_pearson value: 79.35220459438406 - type: manhattan_spearman value: 76.85314462036561 - task: type: STS dataset: type: mteb/sts15-sts name: MTEB STS15 config: default split: test revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 metrics: - type: cos_sim_pearson value: 85.58608774451231 - type: cos_sim_spearman value: 86.42805701554927 - type: euclidean_pearson value: 86.01117122595934 - type: euclidean_spearman value: 86.42805701554927 - type: manhattan_pearson value: 86.01345208923057 - type: manhattan_spearman value: 86.43179450307953 - task: type: STS dataset: type: mteb/sts16-sts name: MTEB STS16 config: default split: test revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 metrics: - type: cos_sim_pearson value: 83.18733039014667 - type: cos_sim_spearman value: 84.3339529564109 - type: euclidean_pearson value: 83.54530885349595 - type: euclidean_spearman value: 84.3339529564109 - type: manhattan_pearson value: 83.47015931913937 - type: manhattan_spearman value: 84.22564786654777 - task: type: STS dataset: type: mteb/sts17-crosslingual-sts name: MTEB STS17 (en-en) config: en-en split: test revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d metrics: - type: cos_sim_pearson value: 87.88402211340522 - type: cos_sim_spearman value: 88.6693290310468 - type: euclidean_pearson value: 88.24947476618257 - type: euclidean_spearman value: 88.6693290310468 - type: manhattan_pearson value: 88.24496656367964 - type: manhattan_spearman value: 88.52029848819545 - task: type: STS dataset: type: mteb/sts22-crosslingual-sts name: MTEB STS22 (en) config: en split: test revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 metrics: - type: cos_sim_pearson value: 64.96467575926597 - type: cos_sim_spearman value: 65.30666900046252 - type: euclidean_pearson value: 66.58031971340725 - type: euclidean_spearman value: 65.30666900046252 - type: manhattan_pearson value: 66.56530433327998 - type: manhattan_spearman value: 65.42121899024113 - task: type: STS dataset: type: mteb/stsbenchmark-sts name: MTEB STSBenchmark config: default split: test revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 metrics: - type: cos_sim_pearson value: 85.31047656296519 - type: cos_sim_spearman value: 85.46101092708824 - type: euclidean_pearson value: 85.75896623084044 - type: euclidean_spearman value: 85.46101092708824 - type: manhattan_pearson value: 85.57323880630182 - type: manhattan_spearman value: 85.23375523080594 - task: type: Reranking dataset: type: mteb/scidocs-reranking name: MTEB SciDocsRR config: default split: test revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab metrics: - type: map value: 79.89731978284804 - type: mrr value: 94.28980424078465 - task: type: Retrieval dataset: type: scifact name: MTEB SciFact config: default split: test revision: None metrics: - type: ndcg_at_10 value: 67.95 - task: type: PairClassification dataset: type: mteb/sprintduplicatequestions-pairclassification name: MTEB SprintDuplicateQuestions config: default split: test revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 metrics: - type: cos_sim_accuracy value: 99.85643564356435 - type: cos_sim_ap value: 96.59618618212247 - type: cos_sim_f1 value: 92.6221335992024 - type: cos_sim_precision value: 92.34592445328032 - type: cos_sim_recall value: 92.9 - type: dot_accuracy value: 99.85643564356435 - type: dot_ap value: 96.5961861821225 - type: dot_f1 value: 92.6221335992024 - type: dot_precision value: 92.34592445328032 - type: dot_recall value: 92.9 - type: euclidean_accuracy value: 99.85643564356435 - type: euclidean_ap value: 96.5961861821225 - type: euclidean_f1 value: 92.6221335992024 - type: euclidean_precision value: 92.34592445328032 - type: euclidean_recall value: 92.9 - type: manhattan_accuracy value: 99.85841584158416 - type: manhattan_ap value: 96.5578240948512 - type: manhattan_f1 value: 92.71523178807946 - type: manhattan_precision value: 94.4963655244029 - type: manhattan_recall value: 91.0 - type: max_accuracy value: 99.85841584158416 - type: max_ap value: 96.5961861821225 - type: max_f1 value: 92.71523178807946 - task: type: Clustering dataset: type: mteb/stackexchange-clustering name: MTEB StackExchangeClustering config: default split: test revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 metrics: - type: v_measure value: 60.84750068050385 - task: type: Clustering dataset: type: mteb/stackexchange-clustering-p2p name: MTEB StackExchangeClusteringP2P config: default split: test revision: 815ca46b2622cec33ccafc3735d572c266efdb44 metrics: - type: v_measure value: 33.96844721192451 - task: type: Reranking dataset: type: mteb/stackoverflowdupquestions-reranking name: MTEB StackOverflowDupQuestions config: default split: test revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 metrics: - type: map value: 50.454280909595205 - type: mrr value: 51.24249320940497 - task: type: Summarization dataset: type: mteb/summeval name: MTEB SummEval config: default split: test revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c metrics: - type: cos_sim_pearson value: 29.998438678552517 - type: cos_sim_spearman value: 30.409482543506876 - type: dot_pearson value: 29.998443850173224 - type: dot_spearman value: 30.409482543506876 - task: type: Retrieval dataset: type: trec-covid name: MTEB TRECCOVID config: default split: test revision: None metrics: - type: ndcg_at_10 value: 78.93 - task: type: Retrieval dataset: type: webis-touche2020 name: MTEB Touche2020 config: default split: test revision: None metrics: - type: ndcg_at_10 value: 29.482999999999997 - task: type: Classification dataset: type: mteb/toxic_conversations_50k name: MTEB ToxicConversationsClassification config: default split: test revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c metrics: - type: accuracy value: 70.65859999999999 - type: ap value: 15.03693738050973 - type: f1 value: 54.94379403846167 - task: type: Classification dataset: type: mteb/tweet_sentiment_extraction name: MTEB TweetSentimentExtractionClassification config: default split: test revision: d604517c81ca91fe16a244d1248fc021f9ecee7a metrics: - type: accuracy value: 64.4567062818336 - type: f1 value: 64.48980729427107 - task: type: Clustering dataset: type: mteb/twentynewsgroups-clustering name: MTEB TwentyNewsgroupsClustering config: default split: test revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 metrics: - type: v_measure value: 42.08554991843959 - task: type: PairClassification dataset: type: mteb/twittersemeval2015-pairclassification name: MTEB TwitterSemEval2015 config: default split: test revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 metrics: - type: cos_sim_accuracy value: 84.75293556654945 - type: cos_sim_ap value: 69.40551043272129 - type: cos_sim_f1 value: 65.56335231034026 - type: cos_sim_precision value: 65.79856497475419 - type: cos_sim_recall value: 65.32981530343008 - type: dot_accuracy value: 84.75293556654945 - type: dot_ap value: 69.40550704470631 - type: dot_f1 value: 65.56335231034026 - type: dot_precision value: 65.79856497475419 - type: dot_recall value: 65.32981530343008 - type: euclidean_accuracy value: 84.75293556654945 - type: euclidean_ap value: 69.4055136381454 - type: euclidean_f1 value: 65.56335231034026 - type: euclidean_precision value: 65.79856497475419 - type: euclidean_recall value: 65.32981530343008 - type: manhattan_accuracy value: 84.6337247422066 - type: manhattan_ap value: 69.13628354134198 - type: manhattan_f1 value: 65.46998180715585 - type: manhattan_precision value: 60.58361391694726 - type: manhattan_recall value: 71.21372031662268 - type: max_accuracy value: 84.75293556654945 - type: max_ap value: 69.4055136381454 - type: max_f1 value: 65.56335231034026 - task: type: PairClassification dataset: type: mteb/twitterurlcorpus-pairclassification name: MTEB TwitterURLCorpus config: default split: test revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf metrics: - type: cos_sim_accuracy value: 89.04800714091667 - type: cos_sim_ap value: 85.84596325009252 - type: cos_sim_f1 value: 78.39228527221042 - type: cos_sim_precision value: 73.58643518205768 - type: cos_sim_recall value: 83.86972590083154 - type: dot_accuracy value: 89.04800714091667 - type: dot_ap value: 85.8459646697087 - type: dot_f1 value: 78.39228527221042 - type: dot_precision value: 73.58643518205768 - type: dot_recall value: 83.86972590083154 - type: euclidean_accuracy value: 89.04800714091667 - type: euclidean_ap value: 85.84596376376919 - type: euclidean_f1 value: 78.39228527221042 - type: euclidean_precision value: 73.58643518205768 - type: euclidean_recall value: 83.86972590083154 - type: manhattan_accuracy value: 89.0266620095471 - type: manhattan_ap value: 85.80124417850608 - type: manhattan_f1 value: 78.37817859254879 - type: manhattan_precision value: 75.36963321012226 - type: manhattan_recall value: 81.63689559593472 - type: max_accuracy value: 89.04800714091667 - type: max_ap value: 85.8459646697087 - type: max_f1 value: 78.39228527221042 --- # Cohere embed-multilingual-light-v3.0 This repository contains the tokenizer for the Cohere `embed-multilingual-light-v3.0` model. See our blogpost [Cohere Embed V3](https://txt.cohere.com/introducing-embed-v3/) for more details on this model. You can use the embedding model either via the Cohere API, AWS SageMaker or in your private deployments. ## Usage Cohere API The following code snippet shows the usage of the Cohere API. Install the cohere SDK via: ``` pip install -U cohere ``` Get your free API key on: www.cohere.com ```python # This snippet shows and example how to use the Cohere Embed V3 models for semantic search. # Make sure to have the Cohere SDK in at least v4.30 install: pip install -U cohere # Get your API key from: www.cohere.com import cohere import numpy as np cohere_key = "{YOUR_COHERE_API_KEY}" #Get your API key from www.cohere.com co = cohere.Client(cohere_key) docs = ["The capital of France is Paris", "PyTorch is a machine learning framework based on the Torch library.", "The average cat lifespan is between 13-17 years"] #Encode your documents with input type 'search_document' doc_emb = co.embed(docs, input_type="search_document", model="embed-multilingual-light-v3.0").embeddings doc_emb = np.asarray(doc_emb) #Encode your query with input type 'search_query' query = "What is Pytorch" query_emb = co.embed([query], input_type="search_query", model="embed-multilingual-light-v3.0").embeddings query_emb = np.asarray(query_emb) query_emb.shape #Compute the dot product between query embedding and document embedding scores = np.dot(query_emb, doc_emb.T)[0] #Find the highest scores max_idx = np.argsort(-scores) print(f"Query: {query}") for idx in max_idx: print(f"Score: {scores[idx]:.2f}") print(docs[idx]) print("--------") ``` ## Usage AWS SageMaker The embedding model can be privately deployed in your AWS Cloud using our [AWS SageMaker marketplace offering](https://aws.amazon.com/marketplace/pp/prodview-z6huxszcqc25i). It runs privately in your VPC, with latencies as low as 5ms for query encoding. ## Usage AWS Bedrock Soon the model will also be available via AWS Bedrock. Stay tuned ## Private Deployment You want to run the model on your own hardware? [Contact Sales](https://cohere.com/contact-sales) to learn more. ## Supported Languages This model was trained on nearly 1B English training pairs and nearly 0.5B Non-English training pairs from 100+ languages. Evaluation results can be found in the [Embed V3.0 Benchmark Results spreadsheet](https://docs.google.com/spreadsheets/d/1w7gnHWMDBdEUrmHgSfDnGHJgVQE5aOiXCCwO3uNH_mI/edit?usp=sharing).
bartowski/aya-23-35B-GGUF	bartowski	2024-05-23T20:08:46Z	4,746	19	transformers	[ "transformers", "gguf", "text-generation", "en", "fr", "de", "es", "it", "pt", "ja", "ko", "zh", "ar", "el", "fa", "pl", "id", "cs", "he", "hi", "nl", "ro", "ru", "tr", "uk", "vi", "base_model:CohereForAI/aya-23-35B", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]	text-generation	2024-05-23T18:14:59Z	--- library_name: transformers language: - en - fr - de - es - it - pt - ja - ko - zh - ar - el - fa - pl - id - cs - he - hi - nl - ro - ru - tr - uk - vi license: cc-by-nc-4.0 quantized_by: bartowski pipeline_tag: text-generation base_model: CohereForAI/aya-23-35B --- ## Llamacpp imatrix Quantizations of aya-23-35B Using <a href="https://github.com/ggerganov/llama.cpp/">llama.cpp</a> release <a href="https://github.com/ggerganov/llama.cpp/releases/tag/b2965">b2965</a> for quantization. Original model: https://huggingface.co/CohereForAI/aya-23-35B All quants made using imatrix option with dataset from [here](https://gist.github.com/bartowski1182/b6ac44691e994344625687afe3263b3a) ## Prompt format ``` <BOS_TOKEN><\|START_OF_TURN_TOKEN\|><\|SYSTEM_TOKEN\|>{system_prompt}<\|END_OF_TURN_TOKEN\|><\|START_OF_TURN_TOKEN\|><\|USER_TOKEN\|>{prompt}<\|END_OF_TURN_TOKEN\|><\|START_OF_TURN_TOKEN\|><\|CHATBOT_TOKEN\|><\|END_OF_TURN_TOKEN\|><\|START_OF_TURN_TOKEN\|><\|CHATBOT_TOKEN\|> ``` ## Download a file (not the whole branch) from below: \| Filename \| Quant type \| File Size \| Description \| \| -------- \| ---------- \| --------- \| ----------- \| \| [aya-23-35B-Q8_0.gguf](https://huggingface.co/bartowski/aya-23-35B-GGUF/blob/main/aya-23-35B-Q8_0.gguf) \| Q8_0 \| 37.17GB \| Extremely high quality, generally unneeded but max available quant. \| \| [aya-23-35B-Q6_K.gguf](https://huggingface.co/bartowski/aya-23-35B-GGUF/blob/main/aya-23-35B-Q6_K.gguf) \| Q6_K \| 28.70GB \| Very high quality, near perfect, recommended. \| \| [aya-23-35B-Q5_K_M.gguf](https://huggingface.co/bartowski/aya-23-35B-GGUF/blob/main/aya-23-35B-Q5_K_M.gguf) \| Q5_K_M \| 25.00GB \| High quality, recommended. \| \| [aya-23-35B-Q5_K_S.gguf](https://huggingface.co/bartowski/aya-23-35B-GGUF/blob/main/aya-23-35B-Q5_K_S.gguf) \| Q5_K_S \| 24.33GB \| High quality, recommended. \| \| [aya-23-35B-Q4_K_M.gguf](https://huggingface.co/bartowski/aya-23-35B-GGUF/blob/main/aya-23-35B-Q4_K_M.gguf) \| Q4_K_M \| 21.52GB \| Good quality, uses about 4.83 bits per weight, recommended. \| \| [aya-23-35B-Q4_K_S.gguf](https://huggingface.co/bartowski/aya-23-35B-GGUF/blob/main/aya-23-35B-Q4_K_S.gguf) \| Q4_K_S \| 20.37GB \| Slightly lower quality with more space savings, recommended. \| \| [aya-23-35B-IQ4_NL.gguf](https://huggingface.co/bartowski/aya-23-35B-GGUF/blob/main/aya-23-35B-IQ4_NL.gguf) \| IQ4_NL \| 20.22GB \| Decent quality, slightly smaller than Q4_K_S with similar performance recommended. \| \| [aya-23-35B-IQ4_XS.gguf](https://huggingface.co/bartowski/aya-23-35B-GGUF/blob/main/aya-23-35B-IQ4_XS.gguf) \| IQ4_XS \| 19.20GB \| Decent quality, smaller than Q4_K_S with similar performance, recommended. \| \| [aya-23-35B-Q3_K_L.gguf](https://huggingface.co/bartowski/aya-23-35B-GGUF/blob/main/aya-23-35B-Q3_K_L.gguf) \| Q3_K_L \| 19.14GB \| Lower quality but usable, good for low RAM availability. \| \| [aya-23-35B-Q3_K_M.gguf](https://huggingface.co/bartowski/aya-23-35B-GGUF/blob/main/aya-23-35B-Q3_K_M.gguf) \| Q3_K_M \| 17.61GB \| Even lower quality. \| \| [aya-23-35B-IQ3_M.gguf](https://huggingface.co/bartowski/aya-23-35B-GGUF/blob/main/aya-23-35B-IQ3_M.gguf) \| IQ3_M \| 16.69GB \| Medium-low quality, new method with decent performance comparable to Q3_K_M. \| \| [aya-23-35B-IQ3_S.gguf](https://huggingface.co/bartowski/aya-23-35B-GGUF/blob/main/aya-23-35B-IQ3_S.gguf) \| IQ3_S \| 15.86GB \| Lower quality, new method with decent performance, recommended over Q3_K_S quant, same size with better performance. \| \| [aya-23-35B-Q3_K_S.gguf](https://huggingface.co/bartowski/aya-23-35B-GGUF/blob/main/aya-23-35B-Q3_K_S.gguf) \| Q3_K_S \| 15.86GB \| Low quality, not recommended. \| \| [aya-23-35B-IQ3_XS.gguf](https://huggingface.co/bartowski/aya-23-35B-GGUF/blob/main/aya-23-35B-IQ3_XS.gguf) \| IQ3_XS \| 15.09GB \| Lower quality, new method with decent performance, slightly better than Q3_K_S. \| \| [aya-23-35B-IQ3_XXS.gguf](https://huggingface.co/bartowski/aya-23-35B-GGUF/blob/main/aya-23-35B-IQ3_XXS.gguf) \| IQ3_XXS \| 13.83GB \| Lower quality, new method with decent performance, comparable to Q3 quants. \| \| [aya-23-35B-Q2_K.gguf](https://huggingface.co/bartowski/aya-23-35B-GGUF/blob/main/aya-23-35B-Q2_K.gguf) \| Q2_K \| 13.81GB \| Very low quality but surprisingly usable. \| \| [aya-23-35B-IQ2_M.gguf](https://huggingface.co/bartowski/aya-23-35B-GGUF/blob/main/aya-23-35B-IQ2_M.gguf) \| IQ2_M \| 12.67GB \| Very low quality, uses SOTA techniques to also be surprisingly usable. \| \| [aya-23-35B-IQ2_S.gguf](https://huggingface.co/bartowski/aya-23-35B-GGUF/blob/main/aya-23-35B-IQ2_S.gguf) \| IQ2_S \| 11.84GB \| Very low quality, uses SOTA techniques to be usable. \| \| [aya-23-35B-IQ2_XS.gguf](https://huggingface.co/bartowski/aya-23-35B-GGUF/blob/main/aya-23-35B-IQ2_XS.gguf) \| IQ2_XS \| 11.10GB \| Very low quality, uses SOTA techniques to be usable. \| \| [aya-23-35B-IQ2_XXS.gguf](https://huggingface.co/bartowski/aya-23-35B-GGUF/blob/main/aya-23-35B-IQ2_XXS.gguf) \| IQ2_XXS \| 10.18GB \| Lower quality, uses SOTA techniques to be usable. \| \| [aya-23-35B-IQ1_M.gguf](https://huggingface.co/bartowski/aya-23-35B-GGUF/blob/main/aya-23-35B-IQ1_M.gguf) \| IQ1_M \| 9.14GB \| Extremely low quality, not recommended. \| \| [aya-23-35B-IQ1_S.gguf](https://huggingface.co/bartowski/aya-23-35B-GGUF/blob/main/aya-23-35B-IQ1_S.gguf) \| IQ1_S \| 8.52GB \| Extremely low quality, not recommended. \| ## Downloading using huggingface-cli First, make sure you have hugginface-cli installed: ``` pip install -U "huggingface_hub[cli]" ``` Then, you can target the specific file you want: ``` huggingface-cli download bartowski/aya-23-35B-GGUF --include "aya-23-35B-Q4_K_M.gguf" --local-dir ./ ``` If the model is bigger than 50GB, it will have been split into multiple files. In order to download them all to a local folder, run: ``` huggingface-cli download bartowski/aya-23-35B-GGUF --include "aya-23-35B-Q8_0.gguf/" --local-dir aya-23-35B-Q8_0 ``` You can either specify a new local-dir (aya-23-35B-Q8_0) or download them all in place (./) ## Which file should I choose? A great write up with charts showing various performances is provided by Artefact2 [here](https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9) The first thing to figure out is how big a model you can run. To do this, you'll need to figure out how much RAM and/or VRAM you have. If you want your model running as FAST as possible, you'll want to fit the whole thing on your GPU's VRAM. Aim for a quant with a file size 1-2GB smaller than your GPU's total VRAM. If you want the absolute maximum quality, add both your system RAM and your GPU's VRAM together, then similarly grab a quant with a file size 1-2GB Smaller than that total. Next, you'll need to decide if you want to use an 'I-quant' or a 'K-quant'. If you don't want to think too much, grab one of the K-quants. These are in format 'QX_K_X', like Q5_K_M. If you want to get more into the weeds, you can check out this extremely useful feature chart: [llama.cpp feature matrix](https://github.com/ggerganov/llama.cpp/wiki/Feature-matrix) But basically, if you're aiming for below Q4, and you're running cuBLAS (Nvidia) or rocBLAS (AMD), you should look towards the I-quants. These are in format IQX_X, like IQ3_M. These are newer and offer better performance for their size. These I-quants can also be used on CPU and Apple Metal, but will be slower than their K-quant equivalent, so speed vs performance is a tradeoff you'll have to decide. The I-quants are not* compatible with Vulcan, which is also AMD, so if you have an AMD card double check if you're using the rocBLAS build or the Vulcan build. At the time of writing this, LM Studio has a preview with ROCm support, and other inference engines have specific builds for ROCm. Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski
digiplay/MeinaPastel_v1	digiplay	2023-11-04T14:39:24Z	4,745	4	diffusers	[ "diffusers", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "license:other", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-06-17T11:08:13Z	--- license: other tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers inference: true --- Model info: https://civitai.com/models/11866?modelVersionId=14019 Sample images generated by Hugginface's API: ![8eee8860-ca9a-432c-a30c-58aed6e7f4bc.jpeg](https://cdn-uploads.huggingface.co/production/uploads/646c83c871d0c8a6e4455854/iGJc0vHho_EodhyD9RQq3.jpeg) ![d5464f64-9e1d-4d95-b84e-b2e77ce69776.jpeg](https://cdn-uploads.huggingface.co/production/uploads/646c83c871d0c8a6e4455854/rI5YuxgQPsgKOYmQmxIDR.jpeg) Original Author's DEMO images : ![](https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/e6a0c0a0-a899-440f-023f-746436299100/width=1024/00692-1847583457.jpeg) ![](https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/3bfb18c1-200f-449a-1b71-839421514f00/width=1024/00642-1500598787.jpeg)
mradermacher/Falcon2-10B-multilingual-i1-GGUF	mradermacher	2024-06-05T08:43:59Z	4,745	0	transformers	[ "transformers", "gguf", "mergekit", "merge", "en", "base_model:ssmits/Falcon2-10B-multilingual", "endpoints_compatible", "region:us" ]	null	2024-06-04T10:53:17Z	--- base_model: ssmits/Falcon2-10B-multilingual language: - en library_name: transformers quantized_by: mradermacher tags: - mergekit - merge --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/ssmits/Falcon2-10B-multilingual <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/Falcon2-10B-multilingual-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Falcon2-10B-multilingual-i1-GGUF/resolve/main/Falcon2-10B-multilingual.i1-IQ1_S.gguf) \| i1-IQ1_S \| 2.0 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/Falcon2-10B-multilingual-i1-GGUF/resolve/main/Falcon2-10B-multilingual.i1-IQ1_M.gguf) \| i1-IQ1_M \| 2.2 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/Falcon2-10B-multilingual-i1-GGUF/resolve/main/Falcon2-10B-multilingual.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 2.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Falcon2-10B-multilingual-i1-GGUF/resolve/main/Falcon2-10B-multilingual.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 2.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Falcon2-10B-multilingual-i1-GGUF/resolve/main/Falcon2-10B-multilingual.i1-IQ2_S.gguf) \| i1-IQ2_S \| 2.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Falcon2-10B-multilingual-i1-GGUF/resolve/main/Falcon2-10B-multilingual.i1-IQ2_M.gguf) \| i1-IQ2_M \| 3.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Falcon2-10B-multilingual-i1-GGUF/resolve/main/Falcon2-10B-multilingual.i1-Q2_K.gguf) \| i1-Q2_K \| 3.3 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Falcon2-10B-multilingual-i1-GGUF/resolve/main/Falcon2-10B-multilingual.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 3.4 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Falcon2-10B-multilingual-i1-GGUF/resolve/main/Falcon2-10B-multilingual.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 3.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Falcon2-10B-multilingual-i1-GGUF/resolve/main/Falcon2-10B-multilingual.i1-IQ3_S.gguf) \| i1-IQ3_S \| 3.8 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Falcon2-10B-multilingual-i1-GGUF/resolve/main/Falcon2-10B-multilingual.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 3.8 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Falcon2-10B-multilingual-i1-GGUF/resolve/main/Falcon2-10B-multilingual.i1-IQ3_M.gguf) \| i1-IQ3_M \| 3.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Falcon2-10B-multilingual-i1-GGUF/resolve/main/Falcon2-10B-multilingual.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 4.1 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/Falcon2-10B-multilingual-i1-GGUF/resolve/main/Falcon2-10B-multilingual.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 4.4 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/Falcon2-10B-multilingual-i1-GGUF/resolve/main/Falcon2-10B-multilingual.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 4.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Falcon2-10B-multilingual-i1-GGUF/resolve/main/Falcon2-10B-multilingual.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 4.8 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/Falcon2-10B-multilingual-i1-GGUF/resolve/main/Falcon2-10B-multilingual.i1-Q4_0.gguf) \| i1-Q4_0 \| 4.8 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/Falcon2-10B-multilingual-i1-GGUF/resolve/main/Falcon2-10B-multilingual.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 5.1 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Falcon2-10B-multilingual-i1-GGUF/resolve/main/Falcon2-10B-multilingual.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 5.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Falcon2-10B-multilingual-i1-GGUF/resolve/main/Falcon2-10B-multilingual.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 6.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Falcon2-10B-multilingual-i1-GGUF/resolve/main/Falcon2-10B-multilingual.i1-Q6_K.gguf) \| i1-Q6_K \| 6.8 \| practically like static Q6_K \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his hardware for calculating the imatrix for these quants. <!-- end -->
duyntnet/bagel-8b-v1.0-imatrix-GGUF	duyntnet	2024-06-05T04:30:48Z	4,744	0	transformers	[ "transformers", "gguf", "imatrix", "bagel-8b-v1.0", "text-generation", "en", "license:other", "region:us" ]	text-generation	2024-06-05T02:10:12Z	--- license: other language: - en pipeline_tag: text-generation inference: false tags: - transformers - gguf - imatrix - bagel-8b-v1.0 --- Quantizations of https://huggingface.co/jondurbin/bagel-8b-v1.0 # From original readme ## Prompt formatting This model uses the llama-3-instruct prompt template, and is provided in the tokenizer config. You can use the `apply_chat_template` method to accurate format prompts, e.g.: ```python import transformers tokenizer = transformers.AutoTokenizer.from_pretrained("jondurbin/bagel-8b-v1.0", trust_remote_code=True) chat = [ {"role": "system", "content": "You are Bob, a friendly AI assistant."}, {"role": "user", "content": "Hello, how are you?"}, {"role": "assistant", "content": "I'm doing great. How can I help you today?"}, {"role": "user", "content": "I'd like to show off how chat templating works!"}, ] print(tokenizer.apply_chat_template(chat, tokenize=False)) ```
ZeroWw/Qwen1.5-7B-Chat-GGUF	ZeroWw	2024-06-23T03:25:42Z	4,744	0	null	[ "gguf", "en", "license:mit", "region:us" ]	null	2024-06-23T03:13:38Z	--- license: mit language: - en --- My own (ZeroWw) quantizations. output and embed tensors quantized to f16. all other tensors quantized to q5_k or q6_k. Result: both f16.q6 and f16.q5 are smaller than q8_0 standard quantization and they perform as well as the pure f16.
mradermacher/Mahou-mistral-slerp-7B-GGUF	mradermacher	2024-06-26T20:52:27Z	4,740	0	transformers	[ "transformers", "gguf", "mergekit", "merge", "en", "base_model:nbeerbower/Mahou-mistral-slerp-7B", "endpoints_compatible", "region:us" ]	null	2024-06-02T04:40:22Z	--- base_model: nbeerbower/Mahou-mistral-slerp-7B language: - en library_name: transformers quantized_by: mradermacher tags: - mergekit - merge --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/nbeerbower/Mahou-mistral-slerp-7B <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Mahou-mistral-slerp-7B-GGUF/resolve/main/Mahou-mistral-slerp-7B.Q2_K.gguf) \| Q2_K \| 2.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mahou-mistral-slerp-7B-GGUF/resolve/main/Mahou-mistral-slerp-7B.IQ3_XS.gguf) \| IQ3_XS \| 3.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mahou-mistral-slerp-7B-GGUF/resolve/main/Mahou-mistral-slerp-7B.Q3_K_S.gguf) \| Q3_K_S \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mahou-mistral-slerp-7B-GGUF/resolve/main/Mahou-mistral-slerp-7B.IQ3_S.gguf) \| IQ3_S \| 3.3 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Mahou-mistral-slerp-7B-GGUF/resolve/main/Mahou-mistral-slerp-7B.IQ3_M.gguf) \| IQ3_M \| 3.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mahou-mistral-slerp-7B-GGUF/resolve/main/Mahou-mistral-slerp-7B.Q3_K_M.gguf) \| Q3_K_M \| 3.6 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Mahou-mistral-slerp-7B-GGUF/resolve/main/Mahou-mistral-slerp-7B.Q3_K_L.gguf) \| Q3_K_L \| 3.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mahou-mistral-slerp-7B-GGUF/resolve/main/Mahou-mistral-slerp-7B.IQ4_XS.gguf) \| IQ4_XS \| 4.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mahou-mistral-slerp-7B-GGUF/resolve/main/Mahou-mistral-slerp-7B.Q4_K_S.gguf) \| Q4_K_S \| 4.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Mahou-mistral-slerp-7B-GGUF/resolve/main/Mahou-mistral-slerp-7B.Q4_K_M.gguf) \| Q4_K_M \| 4.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Mahou-mistral-slerp-7B-GGUF/resolve/main/Mahou-mistral-slerp-7B.Q5_K_S.gguf) \| Q5_K_S \| 5.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mahou-mistral-slerp-7B-GGUF/resolve/main/Mahou-mistral-slerp-7B.Q5_K_M.gguf) \| Q5_K_M \| 5.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mahou-mistral-slerp-7B-GGUF/resolve/main/Mahou-mistral-slerp-7B.Q6_K.gguf) \| Q6_K \| 6.0 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Mahou-mistral-slerp-7B-GGUF/resolve/main/Mahou-mistral-slerp-7B.Q8_0.gguf) \| Q8_0 \| 7.8 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/Mahou-mistral-slerp-7B-GGUF/resolve/main/Mahou-mistral-slerp-7B.f16.gguf) \| f16 \| 14.6 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
Mihaiii/test24	Mihaiii	2024-05-01T21:50:28Z	4,734	0	sentence-transformers	[ "sentence-transformers", "onnx", "safetensors", "bert", "feature-extraction", "sentence-similarity", "bge", "mteb", "mergekit", "merge", "arxiv:2403.19522", "base_model:Mihaiii/Wartortle", "base_model:TaylorAI/bge-micro-v2", "base_model:TaylorAI/bge-micro", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "text-embeddings-inference", "region:us" ]	sentence-similarity	2024-05-01T18:23:14Z	--- license: mit library_name: sentence-transformers pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - bge - mteb - mergekit - merge base_model: - Mihaiii/Wartortle - TaylorAI/bge-micro-v2 - TaylorAI/bge-micro model-index: - name: Kyurem results: - task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (en) config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 66.83582089552239 - type: ap value: 29.376874523513568 - type: f1 value: 60.66923695285069 - task: type: Classification dataset: type: mteb/amazon_polarity name: MTEB AmazonPolarityClassification config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 70.484925 - type: ap value: 64.8627321394567 - type: f1 value: 70.2682474297364 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (en) config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 33.652 - type: f1 value: 33.48200260424572 - task: type: Retrieval dataset: type: mteb/arguana name: MTEB ArguAna config: default split: test revision: c22ab2a51041ffd869aaddef7af8d8215647e41a metrics: - type: map_at_1 value: 22.404 - type: map_at_10 value: 36.144999999999996 - type: map_at_100 value: 37.309 - type: map_at_1000 value: 37.333 - type: map_at_20 value: 37.0 - type: map_at_3 value: 31.105 - type: map_at_5 value: 34.149 - type: mrr_at_1 value: 23.186 - type: mrr_at_10 value: 36.439 - type: mrr_at_100 value: 37.617 - type: mrr_at_1000 value: 37.641000000000005 - type: mrr_at_20 value: 37.308 - type: mrr_at_3 value: 31.52 - type: mrr_at_5 value: 34.486 - type: ndcg_at_1 value: 22.404 - type: ndcg_at_10 value: 44.346000000000004 - type: ndcg_at_100 value: 49.594 - type: ndcg_at_1000 value: 50.183 - type: ndcg_at_20 value: 47.435 - type: ndcg_at_3 value: 34.032000000000004 - type: ndcg_at_5 value: 39.513999999999996 - type: precision_at_1 value: 22.404 - type: precision_at_10 value: 7.077 - type: precision_at_100 value: 0.9440000000000001 - type: precision_at_1000 value: 0.099 - type: precision_at_20 value: 4.147 - type: precision_at_3 value: 14.177000000000001 - type: precision_at_5 value: 11.166 - type: recall_at_1 value: 22.404 - type: recall_at_10 value: 70.768 - type: recall_at_100 value: 94.381 - type: recall_at_1000 value: 98.933 - type: recall_at_20 value: 82.93 - type: recall_at_3 value: 42.532 - type: recall_at_5 value: 55.832 - task: type: Clustering dataset: type: mteb/arxiv-clustering-p2p name: MTEB ArxivClusteringP2P config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics: - type: v_measure value: 41.21099868792524 - type: v_measures value: [0.40254382303117714, 0.4224347357966498, 0.4262617634576952, 0.4155783533141191, 0.4134542696349061, 0.4109306689786127, 0.42283748567668517, 0.42630877911174075, 0.41954609741659976, 0.4080526281513678, 0.4665726313656592, 0.46970780377849464, 0.47074911489648613, 0.47032107785889893, 0.47247596890763377, 0.4743057900773427, 0.47343092962272254, 0.4740124648309491, 0.47535619759392983, 0.47158247790286856, 0.437018098047854, 0.27185199681652455, 0.3306623377989388, 0.33899929363512366, 0.3121088511800512, 0.23413488160460388, 0.2719324856879174, 0.1998457246704459, 0.24909013187651663, 1.0, 0.2433027305343081, 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mteb/cqadupstack-english name: MTEB CQADupstackEnglishRetrieval config: default split: test revision: ad9991cb51e31e31e430383c75ffb2885547b5f0 metrics: - type: map_at_1 value: 14.81 - type: map_at_10 value: 19.692999999999998 - type: map_at_100 value: 20.535 - type: map_at_1000 value: 20.643 - type: map_at_20 value: 20.097 - type: map_at_3 value: 18.157 - type: map_at_5 value: 19.006999999999998 - type: mrr_at_1 value: 17.898 - type: mrr_at_10 value: 22.719 - type: mrr_at_100 value: 23.473 - type: mrr_at_1000 value: 23.547 - type: mrr_at_20 value: 23.111 - type: mrr_at_3 value: 21.189 - type: mrr_at_5 value: 21.934 - type: ndcg_at_1 value: 17.898 - type: ndcg_at_10 value: 22.817999999999998 - type: ndcg_at_100 value: 26.998 - type: ndcg_at_1000 value: 29.698 - type: ndcg_at_20 value: 24.123 - type: ndcg_at_3 value: 20.115 - type: ndcg_at_5 value: 21.288999999999998 - type: precision_at_1 value: 17.898 - type: precision_at_10 value: 4.159 - type: precision_at_100 value: 0.769 - type: 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mteb/cqadupstack-gis name: MTEB CQADupstackGisRetrieval config: default split: test revision: 5003b3064772da1887988e05400cf3806fe491f2 metrics: - type: map_at_1 value: 15.157000000000002 - type: map_at_10 value: 21.04 - type: map_at_100 value: 21.94 - type: map_at_1000 value: 22.048000000000002 - type: map_at_20 value: 21.497 - type: map_at_3 value: 19.082 - type: map_at_5 value: 20.252 - type: mrr_at_1 value: 16.723 - type: mrr_at_10 value: 22.637999999999998 - type: mrr_at_100 value: 23.51 - type: mrr_at_1000 value: 23.602 - type: mrr_at_20 value: 23.086000000000002 - type: mrr_at_3 value: 20.716 - type: mrr_at_5 value: 21.863 - type: ndcg_at_1 value: 16.723 - type: ndcg_at_10 value: 24.684 - type: ndcg_at_100 value: 29.397000000000002 - type: ndcg_at_1000 value: 32.545 - type: ndcg_at_20 value: 26.299 - type: ndcg_at_3 value: 20.809 - type: ndcg_at_5 value: 22.830000000000002 - type: precision_at_1 value: 16.723 - type: precision_at_10 value: 3.932 - type: precision_at_100 value: 0.661 - type: precision_at_1000 value: 0.098 - type: precision_at_20 value: 2.339 - type: precision_at_3 value: 8.964 - type: precision_at_5 value: 6.531000000000001 - type: recall_at_1 value: 15.157000000000002 - type: recall_at_10 value: 34.552 - type: recall_at_100 value: 56.629 - type: recall_at_1000 value: 80.962 - type: recall_at_20 value: 40.626 - type: recall_at_3 value: 24.012 - type: recall_at_5 value: 28.888 - task: type: Retrieval dataset: type: mteb/cqadupstack-mathematica name: MTEB CQADupstackMathematicaRetrieval config: default split: test revision: 90fceea13679c63fe563ded68f3b6f06e50061de metrics: - type: map_at_1 value: 10.983 - type: map_at_10 value: 16.149 - type: map_at_100 value: 17.321 - type: map_at_1000 value: 17.432 - type: map_at_20 value: 16.742 - type: map_at_3 value: 14.524999999999999 - type: map_at_5 value: 15.357000000000001 - type: mrr_at_1 value: 13.433 - type: mrr_at_10 value: 19.508 - type: mrr_at_100 value: 20.559 - type: mrr_at_1000 value: 20.64 - type: mrr_at_20 value: 20.078 - type: mrr_at_3 value: 17.848 - type: mrr_at_5 value: 18.657 - type: ndcg_at_1 value: 13.433 - type: ndcg_at_10 value: 19.719 - type: ndcg_at_100 value: 25.689 - type: ndcg_at_1000 value: 28.907 - type: ndcg_at_20 value: 21.816 - type: ndcg_at_3 value: 16.659 - type: ndcg_at_5 value: 17.877000000000002 - type: precision_at_1 value: 13.433 - type: precision_at_10 value: 3.794 - type: precision_at_100 value: 0.7849999999999999 - type: precision_at_1000 value: 0.12 - type: precision_at_20 value: 2.456 - type: precision_at_3 value: 8.126 - type: precision_at_5 value: 5.821 - type: recall_at_1 value: 10.983 - type: recall_at_10 value: 27.284000000000002 - type: recall_at_100 value: 54.167 - type: recall_at_1000 value: 78.131 - type: recall_at_20 value: 35.012 - type: recall_at_3 value: 18.557000000000002 - type: recall_at_5 value: 21.753 - task: type: Retrieval dataset: type: mteb/cqadupstack-physics name: MTEB CQADupstackPhysicsRetrieval config: default split: test revision: 79531abbd1fb92d06c6d6315a0cbbbf5bb247ea4 metrics: - type: map_at_1 value: 19.062 - type: map_at_10 value: 26.586 - type: map_at_100 value: 27.767999999999997 - type: map_at_1000 value: 27.904 - type: map_at_20 value: 27.162999999999997 - type: map_at_3 value: 23.751 - type: map_at_5 value: 25.320999999999998 - type: mrr_at_1 value: 23.388 - type: mrr_at_10 value: 31.291999999999998 - type: mrr_at_100 value: 32.196000000000005 - type: mrr_at_1000 value: 32.269999999999996 - type: mrr_at_20 value: 31.752000000000002 - type: mrr_at_3 value: 28.681 - type: mrr_at_5 value: 30.168 - type: ndcg_at_1 value: 23.388 - type: ndcg_at_10 value: 31.741999999999997 - type: ndcg_at_100 value: 37.279 - type: ndcg_at_1000 value: 40.199 - type: ndcg_at_20 value: 33.566 - type: ndcg_at_3 value: 26.858999999999998 - type: ndcg_at_5 value: 29.165000000000003 - type: precision_at_1 value: 23.388 - type: precision_at_10 value: 6.0249999999999995 - type: precision_at_100 value: 1.056 - type: precision_at_1000 value: 0.151 - type: precision_at_20 value: 3.614 - type: precision_at_3 value: 12.737000000000002 - type: precision_at_5 value: 9.471 - type: recall_at_1 value: 19.062 - type: recall_at_10 value: 42.549 - type: recall_at_100 value: 66.708 - type: recall_at_1000 value: 86.7 - type: recall_at_20 value: 48.991 - type: recall_at_3 value: 29.024 - type: recall_at_5 value: 34.885 - task: type: Retrieval dataset: type: mteb/cqadupstack-programmers name: MTEB CQADupstackProgrammersRetrieval config: default split: test revision: 6184bc1440d2dbc7612be22b50686b8826d22b32 metrics: - type: map_at_1 value: 13.578000000000001 - type: map_at_10 value: 19.436 - type: map_at_100 value: 20.51 - type: map_at_1000 value: 20.654 - type: map_at_20 value: 19.948 - type: map_at_3 value: 17.293 - type: map_at_5 value: 18.166 - type: mrr_at_1 value: 16.21 - type: mrr_at_10 value: 22.668 - type: mrr_at_100 value: 23.572000000000003 - type: mrr_at_1000 value: 23.666 - type: mrr_at_20 value: 23.095 - type: mrr_at_3 value: 20.491 - type: mrr_at_5 value: 21.444 - type: ndcg_at_1 value: 16.21 - type: ndcg_at_10 value: 23.648 - type: ndcg_at_100 value: 29.029 - type: ndcg_at_1000 value: 32.550000000000004 - type: ndcg_at_20 value: 25.28 - type: ndcg_at_3 value: 19.515 - type: ndcg_at_5 value: 20.821 - type: precision_at_1 value: 16.21 - type: precision_at_10 value: 4.566 - type: precision_at_100 value: 0.873 - type: precision_at_1000 value: 0.135 - type: precision_at_20 value: 2.791 - type: precision_at_3 value: 9.399000000000001 - type: precision_at_5 value: 6.758 - type: recall_at_1 value: 13.578000000000001 - type: recall_at_10 value: 33.276 - type: recall_at_100 value: 57.316 - type: recall_at_1000 value: 82.33500000000001 - type: recall_at_20 value: 38.95 - type: recall_at_3 value: 21.467 - type: recall_at_5 value: 24.939 - task: type: Retrieval dataset: type: mteb/cqadupstack name: MTEB CQADupstackRetrieval config: default split: test revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4 metrics: - type: map_at_1 value: 15.536166666666668 - type: map_at_10 value: 21.584333333333337 - type: map_at_100 value: 22.610750000000003 - type: map_at_1000 value: 22.732083333333335 - type: map_at_20 value: 22.088166666666666 - type: map_at_3 value: 19.561583333333328 - type: map_at_5 value: 20.634666666666668 - type: mrr_at_1 value: 18.388583333333333 - type: mrr_at_10 value: 24.63783333333333 - type: mrr_at_100 value: 25.53608333333333 - type: mrr_at_1000 value: 25.61658333333333 - type: mrr_at_20 value: 25.101000000000003 - type: mrr_at_3 value: 22.641583333333333 - type: mrr_at_5 value: 23.715083333333336 - type: ndcg_at_1 value: 18.388583333333333 - type: ndcg_at_10 value: 25.564000000000004 - type: ndcg_at_100 value: 30.654500000000002 - type: ndcg_at_1000 value: 33.64308333333334 - type: ndcg_at_20 value: 27.234 - type: ndcg_at_3 value: 21.81491666666667 - type: ndcg_at_5 value: 23.46691666666667 - type: precision_at_1 value: 18.388583333333333 - type: precision_at_10 value: 4.581499999999999 - type: precision_at_100 value: 0.8400833333333335 - type: precision_at_1000 value: 0.12791666666666665 - type: precision_at_20 value: 2.7849166666666663 - type: precision_at_3 value: 10.077333333333334 - type: precision_at_5 value: 7.273250000000001 - type: recall_at_1 value: 15.536166666666668 - type: recall_at_10 value: 34.61533333333334 - type: recall_at_100 value: 57.71308333333332 - type: recall_at_1000 value: 79.32074999999999 - type: recall_at_20 value: 40.750416666666666 - type: recall_at_3 value: 24.079333333333334 - type: recall_at_5 value: 28.31308333333333 - task: type: Retrieval dataset: type: mteb/cqadupstack-stats name: MTEB CQADupstackStatsRetrieval config: default split: test revision: 65ac3a16b8e91f9cee4c9828cc7c335575432a2a metrics: - type: map_at_1 value: 14.603 - type: map_at_10 value: 20.063 - type: map_at_100 value: 20.966 - type: map_at_1000 value: 21.060000000000002 - type: map_at_20 value: 20.531 - type: map_at_3 value: 18.448 - type: map_at_5 value: 19.484 - type: mrr_at_1 value: 16.258 - type: mrr_at_10 value: 22.21 - type: mrr_at_100 value: 23.066 - type: mrr_at_1000 value: 23.142 - type: mrr_at_20 value: 22.631999999999998 - type: mrr_at_3 value: 20.602999999999998 - type: mrr_at_5 value: 21.593 - type: ndcg_at_1 value: 16.258 - type: ndcg_at_10 value: 23.396 - type: ndcg_at_100 value: 28.023999999999997 - type: ndcg_at_1000 value: 30.681000000000004 - type: ndcg_at_20 value: 24.971 - type: ndcg_at_3 value: 20.352 - type: ndcg_at_5 value: 22.036 - type: precision_at_1 value: 16.258 - type: precision_at_10 value: 3.758 - type: precision_at_100 value: 0.661 - type: precision_at_1000 value: 0.096 - type: precision_at_20 value: 2.247 - type: precision_at_3 value: 9.1 - type: precision_at_5 value: 6.503 - type: recall_at_1 value: 14.603 - type: recall_at_10 value: 31.578 - type: recall_at_100 value: 52.87500000000001 - type: recall_at_1000 value: 72.993 - type: recall_at_20 value: 37.464 - type: recall_at_3 value: 23.089000000000002 - type: recall_at_5 value: 27.272000000000002 - task: type: Retrieval dataset: type: mteb/cqadupstack-tex name: MTEB CQADupstackTexRetrieval config: default split: test revision: 46989137a86843e03a6195de44b09deda022eec7 metrics: - type: map_at_1 value: 9.844999999999999 - type: map_at_10 value: 14.209 - type: map_at_100 value: 15.094 - type: map_at_1000 value: 15.215 - type: map_at_20 value: 14.646 - type: map_at_3 value: 12.802 - type: map_at_5 value: 13.543 - type: mrr_at_1 value: 12.113 - type: mrr_at_10 value: 16.875 - type: mrr_at_100 value: 17.73 - type: mrr_at_1000 value: 17.824 - type: mrr_at_20 value: 17.318 - type: mrr_at_3 value: 15.273 - type: mrr_at_5 value: 16.164 - type: ndcg_at_1 value: 12.113 - type: ndcg_at_10 value: 17.28 - type: ndcg_at_100 value: 21.939 - type: ndcg_at_1000 value: 25.262 - type: ndcg_at_20 value: 18.822 - type: ndcg_at_3 value: 14.485999999999999 - type: ndcg_at_5 value: 15.731 - type: precision_at_1 value: 12.113 - type: precision_at_10 value: 3.19 - type: precision_at_100 value: 0.661 - type: precision_at_1000 value: 0.11 - type: precision_at_20 value: 2.046 - type: precision_at_3 value: 6.825 - type: precision_at_5 value: 5.003 - type: recall_at_1 value: 9.844999999999999 - type: recall_at_10 value: 24.005000000000003 - type: recall_at_100 value: 45.385 - type: recall_at_1000 value: 69.99499999999999 - type: recall_at_20 value: 29.704000000000004 - type: recall_at_3 value: 16.295 - type: recall_at_5 value: 19.444 - task: type: Retrieval dataset: type: mteb/cqadupstack-unix name: MTEB CQADupstackUnixRetrieval config: default split: test revision: 6c6430d3a6d36f8d2a829195bc5dc94d7e063e53 metrics: - type: map_at_1 value: 14.875 - type: map_at_10 value: 20.305999999999997 - type: map_at_100 value: 21.23 - type: map_at_1000 value: 21.342 - type: map_at_20 value: 20.709 - type: map_at_3 value: 18.199 - type: map_at_5 value: 19.268 - type: mrr_at_1 value: 17.071 - type: mrr_at_10 value: 23.003 - type: mrr_at_100 value: 23.857999999999997 - type: mrr_at_1000 value: 23.939 - type: mrr_at_20 value: 23.398 - type: mrr_at_3 value: 20.569000000000003 - type: mrr_at_5 value: 21.776999999999997 - type: ndcg_at_1 value: 17.071 - type: ndcg_at_10 value: 24.262 - type: ndcg_at_100 value: 29.217 - type: ndcg_at_1000 value: 32.133 - type: ndcg_at_20 value: 25.679000000000002 - type: ndcg_at_3 value: 19.844 - type: ndcg_at_5 value: 21.712 - type: precision_at_1 value: 17.071 - type: precision_at_10 value: 4.198 - type: precision_at_100 value: 0.765 - type: precision_at_1000 value: 0.11199999999999999 - type: precision_at_20 value: 2.481 - type: precision_at_3 value: 8.613 - type: precision_at_5 value: 6.287 - type: recall_at_1 value: 14.875 - type: recall_at_10 value: 33.915 - type: recall_at_100 value: 56.513000000000005 - type: recall_at_1000 value: 77.737 - type: recall_at_20 value: 39.047 - type: recall_at_3 value: 22.017999999999997 - type: recall_at_5 value: 26.514 - task: type: Retrieval dataset: type: mteb/cqadupstack-webmasters name: MTEB CQADupstackWebmastersRetrieval config: default split: test revision: 160c094312a0e1facb97e55eeddb698c0abe3571 metrics: - type: map_at_1 value: 16.02 - type: map_at_10 value: 21.43 - type: map_at_100 value: 22.468 - type: map_at_1000 value: 22.659000000000002 - type: map_at_20 value: 21.926000000000002 - type: map_at_3 value: 19.258 - type: map_at_5 value: 20.405 - type: mrr_at_1 value: 19.564999999999998 - type: mrr_at_10 value: 25.063999999999997 - type: mrr_at_100 value: 25.918999999999997 - type: mrr_at_1000 value: 26.007 - type: mrr_at_20 value: 25.490000000000002 - type: mrr_at_3 value: 22.727 - type: mrr_at_5 value: 23.992 - type: ndcg_at_1 value: 19.564999999999998 - type: ndcg_at_10 value: 25.575 - type: ndcg_at_100 value: 30.464999999999996 - type: ndcg_at_1000 value: 34.131 - type: ndcg_at_20 value: 27.119 - type: ndcg_at_3 value: 21.607000000000003 - type: ndcg_at_5 value: 23.331 - type: precision_at_1 value: 19.564999999999998 - type: precision_at_10 value: 4.822 - type: precision_at_100 value: 0.98 - type: precision_at_1000 value: 0.196 - type: precision_at_20 value: 3.0140000000000002 - type: precision_at_3 value: 10.079 - type: precision_at_5 value: 7.51 - type: recall_at_1 value: 16.02 - type: recall_at_10 value: 34.215 - type: recall_at_100 value: 57.220000000000006 - type: recall_at_1000 value: 81.721 - type: recall_at_20 value: 40.169 - type: recall_at_3 value: 22.289 - type: recall_at_5 value: 26.856 - task: type: Retrieval dataset: type: mteb/cqadupstack-wordpress name: MTEB CQADupstackWordpressRetrieval config: default split: test revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4 metrics: - type: map_at_1 value: 10.173 - type: map_at_10 value: 15.528 - type: map_at_100 value: 16.470000000000002 - type: map_at_1000 value: 16.566 - type: map_at_20 value: 15.967999999999998 - type: map_at_3 value: 13.902999999999999 - type: map_at_5 value: 14.774000000000001 - type: mrr_at_1 value: 11.275 - type: mrr_at_10 value: 16.659 - type: mrr_at_100 value: 17.637 - type: mrr_at_1000 value: 17.723 - type: mrr_at_20 value: 17.158 - type: mrr_at_3 value: 15.065000000000001 - type: mrr_at_5 value: 15.943 - type: ndcg_at_1 value: 11.275 - type: ndcg_at_10 value: 18.825 - type: ndcg_at_100 value: 23.985 - type: ndcg_at_1000 value: 26.927 - type: ndcg_at_20 value: 20.462 - type: ndcg_at_3 value: 15.519 - type: ndcg_at_5 value: 17.058 - type: precision_at_1 value: 11.275 - type: precision_at_10 value: 3.179 - type: precision_at_100 value: 0.627 - type: precision_at_1000 value: 0.094 - type: precision_at_20 value: 1.978 - type: precision_at_3 value: 6.839 - type: precision_at_5 value: 4.9910000000000005 - type: recall_at_1 value: 10.173 - type: recall_at_10 value: 27.994000000000003 - type: recall_at_100 value: 52.418 - type: recall_at_1000 value: 75.36699999999999 - type: recall_at_20 value: 34.184 - type: recall_at_3 value: 19.082 - type: recall_at_5 value: 22.807 - task: type: Retrieval dataset: type: mteb/climate-fever name: MTEB ClimateFEVER config: default split: test revision: 47f2ac6acb640fc46020b02a5b59fdda04d39380 metrics: - type: map_at_1 value: 7.394 - type: map_at_10 value: 12.179 - type: map_at_100 value: 13.644 - type: map_at_1000 value: 13.821 - type: map_at_20 value: 12.969 - type: map_at_3 value: 10.177 - type: map_at_5 value: 11.155 - type: mrr_at_1 value: 15.961 - type: mrr_at_10 value: 24.118000000000002 - type: mrr_at_100 value: 25.318 - type: mrr_at_1000 value: 25.384 - type: mrr_at_20 value: 24.911 - type: mrr_at_3 value: 21.151 - type: mrr_at_5 value: 22.731 - type: ndcg_at_1 value: 15.961 - type: ndcg_at_10 value: 17.77 - type: ndcg_at_100 value: 24.282 - type: ndcg_at_1000 value: 27.788 - type: ndcg_at_20 value: 20.345 - type: ndcg_at_3 value: 13.996 - type: ndcg_at_5 value: 15.277 - type: precision_at_1 value: 15.961 - type: precision_at_10 value: 5.629 - type: precision_at_100 value: 1.243 - type: precision_at_1000 value: 0.188 - type: precision_at_20 value: 3.8830000000000005 - type: precision_at_3 value: 10.228 - type: precision_at_5 value: 8.065 - type: recall_at_1 value: 7.394 - type: recall_at_10 value: 22.037000000000003 - type: recall_at_100 value: 44.823 - type: recall_at_1000 value: 64.782 - type: recall_at_20 value: 29.416999999999998 - type: recall_at_3 value: 12.931999999999999 - type: recall_at_5 value: 16.392 - task: type: Retrieval dataset: type: mteb/dbpedia name: MTEB DBPedia config: default split: test revision: c0f706b76e590d620bd6618b3ca8efdd34e2d659 metrics: - type: map_at_1 value: 4.22 - type: map_at_10 value: 9.109 - type: map_at_100 value: 12.520999999999999 - type: map_at_1000 value: 13.461 - type: map_at_20 value: 10.374 - type: map_at_3 value: 6.524000000000001 - type: map_at_5 value: 7.664999999999999 - type: mrr_at_1 value: 34.75 - type: mrr_at_10 value: 46.822 - type: mrr_at_100 value: 47.501 - type: mrr_at_1000 value: 47.546 - type: mrr_at_20 value: 47.246 - type: mrr_at_3 value: 43.875 - type: mrr_at_5 value: 45.637 - type: ndcg_at_1 value: 25.874999999999996 - type: ndcg_at_10 value: 21.938 - type: ndcg_at_100 value: 24.924 - type: ndcg_at_1000 value: 31.627 - type: ndcg_at_20 value: 21.627 - type: ndcg_at_3 value: 23.132 - type: ndcg_at_5 value: 22.553 - type: precision_at_1 value: 34.75 - type: precision_at_10 value: 18.65 - type: precision_at_100 value: 5.862 - type: precision_at_1000 value: 1.348 - type: precision_at_20 value: 13.750000000000002 - type: precision_at_3 value: 27.333000000000002 - type: precision_at_5 value: 24.05 - type: recall_at_1 value: 4.22 - type: recall_at_10 value: 14.191 - type: recall_at_100 value: 31.192999999999998 - type: recall_at_1000 value: 53.517 - type: recall_at_20 value: 18.516 - type: recall_at_3 value: 8.036 - type: recall_at_5 value: 10.329 - task: type: Classification dataset: type: mteb/emotion name: MTEB EmotionClassification config: default split: test revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 metrics: - type: accuracy value: 39.184999999999995 - type: f1 value: 35.27301038035798 - task: type: Classification dataset: type: mteb/imdb name: MTEB ImdbClassification config: default split: test revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 metrics: - type: accuracy value: 65.1424 - type: ap value: 60.060650493494705 - type: f1 value: 64.97836345253516 - task: type: Classification dataset: type: mteb/mtop_domain name: MTEB MTOPDomainClassification (en) config: en split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 89.27496580027359 - type: f1 value: 88.46317641931785 - task: type: Classification dataset: type: mteb/mtop_intent name: MTEB MTOPIntentClassification (en) config: en split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - type: accuracy value: 65.35567715458276 - type: f1 value: 46.496133882315355 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (en) config: en split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 65.31943510423672 - type: f1 value: 63.99472103750694 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (en) config: en split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 70.51445864156018 - type: f1 value: 69.5241099650794 - task: type: Clustering dataset: type: mteb/medrxiv-clustering-p2p name: MTEB MedrxivClusteringP2P config: default split: test revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 metrics: - type: v_measure value: 31.419094146071636 - type: v_measures value: [0.3020555490758911, 0.2938404993041935, 0.31003486442397693, 0.29994028507254, 0.3117838741396249, 0.33398660039531675, 0.3256932743486633, 0.3333149476944512, 0.31390791542085783, 0.31735160473164803, 0.3020555490758911, 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385e3cb46b4cfa89021f56c4380204149d0efe33 metrics: - type: v_measure value: 52.2014890689022 - type: v_measures value: [0.5736447196090492, 0.5591207176151745, 0.6165844008882311, 0.32294491953108684, 0.5721588579925929, 0.5023325203908285, 0.25921303858162814, 0.6135926784368556, 0.5799592791212607, 0.6205977747235125, 0.5736447196090492, 0.5591207176151745, 0.6165844008882311, 0.32294491953108684, 0.5721588579925929, 0.5023325203908285, 0.25921303858162814, 0.6135926784368556, 0.5799592791212607, 0.6205977747235125, 0.5736447196090492, 0.5591207176151745, 0.6165844008882311, 0.32294491953108684, 0.5721588579925929, 0.5023325203908285, 0.25921303858162814, 0.6135926784368556, 0.5799592791212607, 0.6205977747235125, 0.5736447196090492, 0.5591207176151745, 0.6165844008882311, 0.32294491953108684, 0.5721588579925929, 0.5023325203908285, 0.25921303858162814, 0.6135926784368556, 0.5799592791212607, 0.6205977747235125, 0.5736447196090492, 0.5591207176151745, 0.6165844008882311, 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0.3211706139482833, 0.3140126107181758, 0.29509063136396313, 0.277413411099062, 0.31521756372510074, 0.30320384565249453, 0.3189898605229486, 0.29781665025127024, 0.3067043341523218, 0.29320744109308605, 0.3211706139482833, 0.3140126107181758, 0.29509063136396313, 0.277413411099062, 0.31521756372510074, 0.30320384565249453, 0.3189898605229486, 0.29781665025127024, 0.3067043341523218, 0.29320744109308605, 0.3211706139482833, 0.3140126107181758, 0.29509063136396313, 0.277413411099062, 0.31521756372510074, 0.30320384565249453, 0.3189898605229486, 0.29781665025127024, 0.3067043341523218, 0.29320744109308605, 0.3211706139482833, 0.3140126107181758, 0.29509063136396313, 0.277413411099062, 0.31521756372510074, 0.30320384565249453, 0.3189898605229486, 0.29781665025127024, 0.3067043341523218, 0.29320744109308605, 0.3211706139482833] - task: type: PairClassification dataset: type: mteb/twittersemeval2015-pairclassification name: MTEB TwitterSemEval2015 config: default split: test revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 metrics: - type: cos_sim_accuracy value: 83.04226023722954 - type: cos_sim_ap value: 63.85841588156352 - type: cos_sim_f1 value: 60.82009954965631 - type: cos_sim_precision value: 55.2065404475043 - type: cos_sim_recall value: 67.70448548812665 - type: dot_accuracy value: 78.91756571496693 - type: dot_ap value: 46.39288120938224 - type: dot_f1 value: 49.36296847391426 - type: dot_precision value: 38.11575470343243 - type: dot_recall value: 70.0263852242744 - type: euclidean_accuracy value: 83.18531322644095 - type: euclidean_ap value: 64.47939517179049 - type: euclidean_f1 value: 61.326567596955414 - type: euclidean_precision value: 56.56340539335859 - type: euclidean_recall value: 66.96569920844327 - type: manhattan_accuracy value: 82.9826548250581 - type: manhattan_ap value: 64.01165035368786 - type: manhattan_f1 value: 60.99290780141844 - type: manhattan_precision value: 54.52088962793597 - type: manhattan_recall value: 69.2084432717678 - type: max_accuracy value: 83.18531322644095 - type: max_ap value: 64.47939517179049 - type: max_f1 value: 61.326567596955414 - task: type: PairClassification dataset: type: mteb/twitterurlcorpus-pairclassification name: MTEB TwitterURLCorpus config: default split: test revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf metrics: - type: cos_sim_accuracy value: 87.53832421314084 - type: cos_sim_ap value: 82.94679942153577 - type: cos_sim_f1 value: 74.90408975750995 - type: cos_sim_precision value: 70.67340527250376 - type: cos_sim_recall value: 79.6735448105944 - type: dot_accuracy value: 85.2214072262972 - type: dot_ap value: 76.39891716014382 - type: dot_f1 value: 70.62225554246545 - type: dot_precision value: 65.83904679491447 - type: dot_recall value: 76.15491222667077 - type: euclidean_accuracy value: 87.55190747855785 - type: euclidean_ap value: 82.9537174035843 - type: euclidean_f1 value: 75.01588844442783 - type: euclidean_precision value: 72.90894557081607 - type: euclidean_recall value: 77.24822913458577 - type: manhattan_accuracy value: 87.5499670120697 - type: manhattan_ap value: 82.85971137826064 - type: manhattan_f1 value: 74.86758672137262 - type: manhattan_precision value: 72.60888438720879 - type: manhattan_recall value: 77.27132737911919 - type: max_accuracy value: 87.55190747855785 - type: max_ap value: 82.9537174035843 - type: max_f1 value: 75.01588844442783 --- # Kyurem This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the [Model Stock](https://arxiv.org/abs/2403.19522) merge method using [TaylorAI/bge-micro](https://huggingface.co/TaylorAI/bge-micro) as a base. ### Models Merged The following models were included in the merge: * [Mihaiii/Wartortle](https://huggingface.co/Mihaiii/Wartortle) * [TaylorAI/bge-micro-v2](https://huggingface.co/TaylorAI/bge-micro-v2) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: Mihaiii/Wartortle - model: TaylorAI/bge-micro-v2 - model: TaylorAI/bge-micro merge_method: model_stock base_model: TaylorAI/bge-micro ```
mistral-community/Codestral-22B-v0.1	mistral-community	2024-07-01T08:51:52Z	4,734	17	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "code", "license:other", "autotrain_compatible", "text-generation-inference", "region:us" ]	text-generation	2024-05-29T20:52:18Z	--- inference: false license: other license_name: mnpl license_link: https://mistral.ai/licences/MNPL-0.1.md tags: - code language: - code --- > [!WARNING] > This model checkpoint is provided as-is and might not be up-to-date. Please use the corresponding version from https://huggingface.co/mistralai org # Model Card for Codestral-22B-v0.1 Codestrall-22B-v0.1 is trained on a diverse dataset of 80+ programming languages, including the most popular ones, such as Python, Java, C, C++, JavaScript, and Bash (more details in the [Blogpost](https://mistral.ai/news/codestral/)). The model can be queried: - As instruct, for instance to answer any questions about a code snippet (write documentation, explain, factorize) or to generate code following specific indications - As Fill in the Middle (FIM), to predict the middle tokens between a prefix and a suffix (very useful for software development add-ons like in VS Code) ## Inference It's the same as Mistral 7B. ## Limitations The Codestral-22B-v0.1 does not have any moderation mechanisms. We're looking forward to engaging with the community on ways to make the model finely respect guardrails, allowing for deployment in environments requiring moderated outputs. ## License Codestral-22B-v0.1 is released under the `MNLP-0.1` license. ## The Mistral AI Team Albert Jiang, Alexandre Sablayrolles, Alexis Tacnet, Antoine Roux, Arthur Mensch, Audrey Herblin-Stoop, Baptiste Bout, Baudouin de Monicault, Blanche Savary, Bam4d, Caroline Feldman, Devendra Singh Chaplot, Diego de las Casas, Eleonore Arcelin, Emma Bou Hanna, Etienne Metzger, Gianna Lengyel, Guillaume Bour, Guillaume Lample, Harizo Rajaona, Henri Roussez, Jean-Malo Delignon, Jia Li, Justus Murke, Kartik Khandelwal, Lawrence Stewart, Louis Martin, Louis Ternon, Lucile Saulnier, Lélio Renard Lavaud, Margaret Jennings, Marie Pellat, Marie Torelli, Marie-Anne Lachaux, Marjorie Janiewicz, Mickael Seznec, Nicolas Schuhl, Patrick von Platen, Romain Sauvestre, Pierre Stock, Sandeep Subramanian, Saurabh Garg, Sophia Yang, Szymon Antoniak, Teven Le Scao, Thibaut Lavril, Thibault Schueller, Timothée Lacroix, Théophile Gervet, Thomas Wang, Valera Nemychnikova, Wendy Shang, William El Sayed, William Marshall
predibase/Meta-Llama-3-8B-Instruct-dequantized	predibase	2024-05-03T16:40:59Z	4,728	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "conversational", "en", "base_model:meta-llama/Meta-Llama-3-8B-Instruct", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-05-03T16:36:39Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - llama base_model: meta-llama/Meta-Llama-3-8B-Instruct ---
HuggingFaceM4/siglip-so400m-14-700-flash-attn2-navit	HuggingFaceM4	2024-06-13T10:33:33Z	4,727	0	transformers	[ "transformers", "safetensors", "siglip", "zero-shot-image-classification", "custom_code", "license:apache-2.0", "endpoints_compatible", "region:us" ]	zero-shot-image-classification	2024-06-13T10:11:30Z	--- license: apache-2.0 ---
RichardErkhov/Rakuten_-_RakutenAI-7B-chat-gguf	RichardErkhov	2024-06-15T12:03:20Z	4,727	0	null	[ "gguf", "arxiv:2403.15484", "region:us" ]	null	2024-06-15T09:37:55Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) RakutenAI-7B-chat - GGUF - Model creator: https://huggingface.co/Rakuten/ - Original model: https://huggingface.co/Rakuten/RakutenAI-7B-chat/ \| Name \| Quant method \| Size \| \| ---- \| ---- \| ---- \| \| [RakutenAI-7B-chat.Q2_K.gguf](https://huggingface.co/RichardErkhov/Rakuten_-_RakutenAI-7B-chat-gguf/blob/main/RakutenAI-7B-chat.Q2_K.gguf) \| Q2_K \| 2.6GB \| \| [RakutenAI-7B-chat.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/Rakuten_-_RakutenAI-7B-chat-gguf/blob/main/RakutenAI-7B-chat.IQ3_XS.gguf) \| IQ3_XS \| 2.89GB \| \| [RakutenAI-7B-chat.IQ3_S.gguf](https://huggingface.co/RichardErkhov/Rakuten_-_RakutenAI-7B-chat-gguf/blob/main/RakutenAI-7B-chat.IQ3_S.gguf) \| IQ3_S \| 3.04GB \| \| [RakutenAI-7B-chat.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/Rakuten_-_RakutenAI-7B-chat-gguf/blob/main/RakutenAI-7B-chat.Q3_K_S.gguf) \| Q3_K_S \| 3.02GB \| \| [RakutenAI-7B-chat.IQ3_M.gguf](https://huggingface.co/RichardErkhov/Rakuten_-_RakutenAI-7B-chat-gguf/blob/main/RakutenAI-7B-chat.IQ3_M.gguf) \| IQ3_M \| 3.14GB \| \| [RakutenAI-7B-chat.Q3_K.gguf](https://huggingface.co/RichardErkhov/Rakuten_-_RakutenAI-7B-chat-gguf/blob/main/RakutenAI-7B-chat.Q3_K.gguf) \| Q3_K \| 3.35GB \| \| [RakutenAI-7B-chat.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/Rakuten_-_RakutenAI-7B-chat-gguf/blob/main/RakutenAI-7B-chat.Q3_K_M.gguf) \| Q3_K_M \| 3.35GB \| \| [RakutenAI-7B-chat.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/Rakuten_-_RakutenAI-7B-chat-gguf/blob/main/RakutenAI-7B-chat.Q3_K_L.gguf) \| Q3_K_L \| 3.64GB \| \| [RakutenAI-7B-chat.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/Rakuten_-_RakutenAI-7B-chat-gguf/blob/main/RakutenAI-7B-chat.IQ4_XS.gguf) \| IQ4_XS \| 3.76GB \| \| [RakutenAI-7B-chat.Q4_0.gguf](https://huggingface.co/RichardErkhov/Rakuten_-_RakutenAI-7B-chat-gguf/blob/main/RakutenAI-7B-chat.Q4_0.gguf) \| Q4_0 \| 3.91GB \| \| [RakutenAI-7B-chat.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/Rakuten_-_RakutenAI-7B-chat-gguf/blob/main/RakutenAI-7B-chat.IQ4_NL.gguf) \| IQ4_NL \| 3.95GB \| \| [RakutenAI-7B-chat.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/Rakuten_-_RakutenAI-7B-chat-gguf/blob/main/RakutenAI-7B-chat.Q4_K_S.gguf) \| Q4_K_S \| 3.94GB \| \| [RakutenAI-7B-chat.Q4_K.gguf](https://huggingface.co/RichardErkhov/Rakuten_-_RakutenAI-7B-chat-gguf/blob/main/RakutenAI-7B-chat.Q4_K.gguf) \| Q4_K \| 4.15GB \| \| [RakutenAI-7B-chat.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/Rakuten_-_RakutenAI-7B-chat-gguf/blob/main/RakutenAI-7B-chat.Q4_K_M.gguf) \| Q4_K_M \| 4.15GB \| \| [RakutenAI-7B-chat.Q4_1.gguf](https://huggingface.co/RichardErkhov/Rakuten_-_RakutenAI-7B-chat-gguf/blob/main/RakutenAI-7B-chat.Q4_1.gguf) \| Q4_1 \| 4.33GB \| \| [RakutenAI-7B-chat.Q5_0.gguf](https://huggingface.co/RichardErkhov/Rakuten_-_RakutenAI-7B-chat-gguf/blob/main/RakutenAI-7B-chat.Q5_0.gguf) \| Q5_0 \| 4.75GB \| \| [RakutenAI-7B-chat.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/Rakuten_-_RakutenAI-7B-chat-gguf/blob/main/RakutenAI-7B-chat.Q5_K_S.gguf) \| Q5_K_S \| 4.75GB \| \| [RakutenAI-7B-chat.Q5_K.gguf](https://huggingface.co/RichardErkhov/Rakuten_-_RakutenAI-7B-chat-gguf/blob/main/RakutenAI-7B-chat.Q5_K.gguf) \| Q5_K \| 4.87GB \| \| [RakutenAI-7B-chat.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/Rakuten_-_RakutenAI-7B-chat-gguf/blob/main/RakutenAI-7B-chat.Q5_K_M.gguf) \| Q5_K_M \| 4.87GB \| \| [RakutenAI-7B-chat.Q5_1.gguf](https://huggingface.co/RichardErkhov/Rakuten_-_RakutenAI-7B-chat-gguf/blob/main/RakutenAI-7B-chat.Q5_1.gguf) \| Q5_1 \| 5.16GB \| \| [RakutenAI-7B-chat.Q6_K.gguf](https://huggingface.co/RichardErkhov/Rakuten_-_RakutenAI-7B-chat-gguf/blob/main/RakutenAI-7B-chat.Q6_K.gguf) \| Q6_K \| 5.63GB \| \| [RakutenAI-7B-chat.Q8_0.gguf](https://huggingface.co/RichardErkhov/Rakuten_-_RakutenAI-7B-chat-gguf/blob/main/RakutenAI-7B-chat.Q8_0.gguf) \| Q8_0 \| 7.3GB \| Original model description: --- license: apache-2.0 --- # RakutenAI-7B-chat ## Model Description RakutenAI-7B is a systematic initiative that brings the latest technologies to the world of Japanese LLMs. RakutenAI-7B achieves the best scores on the Japanese language understanding benchmarks while maintaining a competitive performance on the English test sets among similar models such as OpenCalm, Elyza, Youri, Nekomata and Swallow. RakutenAI-7B leverages the Mistral model architecture and is based on [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) pre-trained checkpoint, exemplifying a successful retrofitting of the pre-trained model weights. Moreover, we extend Mistral's vocabulary from 32k to 48k to offer a better character-per-token rate for Japanese. The technical report can be accessed at [arXiv](https://arxiv.org/abs/2403.15484). If you are looking for a foundation model, check [RakutenAI-7B](https://huggingface.co/Rakuten/RakutenAI-7B). If you are looking for an instruction-tuned model, check [RakutenAI-7B-instruct](https://huggingface.co/Rakuten/RakutenAI-7B-instruct). An independent evaluation by Kamata et.al. for [Nejumi LLMリーダーボード Neo](https://wandb.ai/wandb-japan/llm-leaderboard/reports/Nejumi-LLM-Neo--Vmlldzo2MTkyMTU0#総合評価) using a weighted average of [llm-jp-eval](https://github.com/llm-jp/llm-jp-eval) and [Japanese MT-bench](https://github.com/Stability-AI/FastChat/tree/jp-stable/fastchat/llm_judge) also confirms the highest performance of chat/instruct versions of RakutenAI-7B among Open LLMs of similar sizes, with a score of 0.393/0.331 respectively, as of 22nd March 2024. ## Usage ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_path = "Rakuten/RakutenAI-7B-chat" tokenizer = AutoTokenizer.from_pretrained(model_path) model = AutoModelForCausalLM.from_pretrained(model_path, torch_dtype="auto", device_map="auto") model.eval() requests = [ "「馬が合う」はどう言う意味ですか", "How to make an authentic Spanish Omelette?", ] system_message = "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: {user_input} ASSISTANT:" for req in requests: input_req = system_message.format(user_input=req) input_ids = tokenizer.encode(input_req, return_tensors="pt").to(device=model.device) tokens = model.generate( input_ids, max_new_tokens=1024, do_sample=True, pad_token_id=tokenizer.eos_token_id, ) out = tokenizer.decode(tokens[0][len(input_ids[0]):], skip_special_tokens=True) print("USER:\n" + req) print("ASSISTANT:\n" + out) print() print() ``` ## Model Details * Developed by: [Rakuten Group, Inc.](https://ai.rakuten.com/) * Language(s): Japanese, English * License: This model is licensed under [Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0). * Instruction-Tuning Dataset: We fine-tune our foundation model to create RakutenAI-7B-instruct and RakutenAI-7B-chat using a mix of open source and internally hand-crafted datasets. We use `train` part of the following datasets (CC by-SA License) for instruction-tuned and chat-tuned models: - [JSNLI](https://nlp.ist.i.kyoto-u.ac.jp/?%E6%97%A5%E6%9C%AC%E8%AA%9ESNLI%28JSNLI%29%E3%83%87%E3%83%BC%E3%82%BF%E3%82%BB%E3%83%83%E3%83%88) - [RTE](https://nlp.ist.i.kyoto-u.ac.jp/?Textual+Entailment+%E8%A9%95%E4%BE%A1%E3%83%87%E3%83%BC%E3%82%BF) - [KUCI](https://nlp.ist.i.kyoto-u.ac.jp/?KUCI) - [BELEBELE](https://huggingface.co/datasets/facebook/belebele) - [JCS](https://aclanthology.org/2022.lrec-1.317/) - [JNLI](https://aclanthology.org/2022.lrec-1.317/) - [Dolly-15K](https://huggingface.co/datasets/databricks/databricks-dolly-15k) - [OpenAssistant1](https://huggingface.co/datasets/OpenAssistant/oasst1) ### Limitations and Bias The suite of RakutenAI-7B models is capable of generating human-like text on a wide range of topics. However, like all LLMs, they have limitations and can produce biased, inaccurate, or unsafe outputs. Please exercise caution and judgement while interacting with them. ## Citation For citing our work on the suite of RakutenAI-7B models, please use: ``` @misc{rakutengroup2024rakutenai7b, title={RakutenAI-7B: Extending Large Language Models for Japanese}, author={{Rakuten Group, Inc.} and Aaron Levine and Connie Huang and Chenguang Wang and Eduardo Batista and Ewa Szymanska and Hongyi Ding and Hou Wei Chou and Jean-François Pessiot and Johanes Effendi and Justin Chiu and Kai Torben Ohlhus and Karan Chopra and Keiji Shinzato and Koji Murakami and Lee Xiong and Lei Chen and Maki Kubota and Maksim Tkachenko and Miroku Lee and Naoki Takahashi and Prathyusha Jwalapuram and Ryutaro Tatsushima and Saurabh Jain and Sunil Kumar Yadav and Ting Cai and Wei-Te Chen and Yandi Xia and Yuki Nakayama and Yutaka Higashiyama}, year={2024}, eprint={2403.15484}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
deepset/gelectra-base-germanquad	deepset	2023-05-05T07:02:56Z	4,726	24	transformers	[ "transformers", "pytorch", "tf", "safetensors", "electra", "question-answering", "exbert", "de", "dataset:deepset/germanquad", "license:mit", "endpoints_compatible", "region:us" ]	question-answering	2022-03-02T23:29:05Z	--- language: de datasets: - deepset/germanquad license: mit thumbnail: https://thumb.tildacdn.com/tild3433-3637-4830-a533-353833613061/-/resize/720x/-/format/webp/germanquad.jpg tags: - exbert --- ![bert_image](https://thumb.tildacdn.com/tild3433-3637-4830-a533-353833613061/-/resize/720x/-/format/webp/germanquad.jpg) ## Overview Language model: gelectra-base-germanquad Language: German Training data: GermanQuAD train set (~ 12MB) Eval data: GermanQuAD test set (~ 5MB) Infrastructure: 1x V100 GPU Published: Apr 21st, 2021 ## Details - We trained a German question answering model with a gelectra-base model as its basis. - The dataset is GermanQuAD, a new, German language dataset, which we hand-annotated and published [online](https://deepset.ai/germanquad). - The training dataset is one-way annotated and contains 11518 questions and 11518 answers, while the test dataset is three-way annotated so that there are 2204 questions and with 2204·3−76 = 6536answers, because we removed 76 wrong answers. See https://deepset.ai/germanquad for more details and dataset download in SQuAD format. ## Hyperparameters ``` batch_size = 24 n_epochs = 2 max_seq_len = 384 learning_rate = 3e-5 lr_schedule = LinearWarmup embeds_dropout_prob = 0.1 ``` ## Performance We evaluated the extractive question answering performance on our GermanQuAD test set. Model types and training data are included in the model name. For finetuning XLM-Roberta, we use the English SQuAD v2.0 dataset. The GELECTRA models are warm started on the German translation of SQuAD v1.1 and finetuned on [GermanQuAD](https://deepset.ai/germanquad). The human baseline was computed for the 3-way test set by taking one answer as prediction and the other two as ground truth. ![performancetable](https://images.prismic.io/deepset/1c63afd8-40e6-4fd9-85c4-0dbb81996183_german-qa-vs-xlm-r.png) ## Authors Timo Möller: [email protected] Julian Risch: [email protected] Malte Pietsch: [email protected] ## About us <div class="grid lg:grid-cols-2 gap-x-4 gap-y-3"> <div class="w-full h-40 object-cover mb-2 rounded-lg flex items-center justify-center"> <img alt="" src="https://raw.githubusercontent.com/deepset-ai/.github/main/deepset-logo-colored.png" class="w-40"/> </div> <div class="w-full h-40 object-cover mb-2 rounded-lg flex items-center justify-center"> <img alt="" src="https://raw.githubusercontent.com/deepset-ai/.github/main/haystack-logo-colored.png" class="w-40"/> </div> </div> [deepset](http://deepset.ai/) is the company behind the open-source NLP framework [Haystack](https://haystack.deepset.ai/) which is designed to help you build production ready NLP systems that use: Question answering, summarization, ranking etc. Some of our other work: - [Distilled roberta-base-squad2 (aka "tinyroberta-squad2")]([https://huggingface.co/deepset/tinyroberta-squad2) - [German BERT (aka "bert-base-german-cased")](https://deepset.ai/german-bert) - [GermanQuAD and GermanDPR datasets and models (aka "gelectra-base-germanquad", "gbert-base-germandpr")](https://deepset.ai/germanquad) ## Get in touch and join the Haystack community <p>For more info on Haystack, visit our <strong><a href="https://github.com/deepset-ai/haystack">GitHub</a></strong> repo and <strong><a href="https://docs.haystack.deepset.ai">Documentation</a></strong>. We also have a <strong><a class="h-7" href="https://haystack.deepset.ai/community">Discord community open to everyone!</a></strong></p> [Twitter](https://twitter.com/deepset_ai) \| [LinkedIn](https://www.linkedin.com/company/deepset-ai/) \| [Discord](https://haystack.deepset.ai/community) \| [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) \| [Website](https://deepset.ai) By the way: [we're hiring!](http://www.deepset.ai/jobs)
timm/ese_vovnet39b.ra_in1k	timm	2023-04-21T23:12:23Z	4,724	0	timm	[ "timm", "pytorch", "safetensors", "image-classification", "dataset:imagenet-1k", "arxiv:2110.00476", "arxiv:1904.09730", "arxiv:1911.06667", "license:apache-2.0", "region:us" ]	image-classification	2023-04-21T23:12:01Z	--- tags: - image-classification - timm library_name: timm license: apache-2.0 datasets: - imagenet-1k --- # Model card for ese_vovnet39b.ra_in1k A VoVNet-v2 image classification model. Pretrained on ImageNet-1k in `timm` by Ross Wightman using RandAugment `RA` recipe. Related to `B` recipe in [ResNet Strikes Back](https://arxiv.org/abs/2110.00476). ## Model Details - Model Type: Image classification / feature backbone - Model Stats: - Params (M): 24.6 - GMACs: 7.1 - Activations (M): 6.7 - Image size: train = 224 x 224, test = 288 x 288 - Papers: - An Energy and GPU-Computation Efficient Backbone Network: https://arxiv.org/abs/1904.09730 - CenterMask : Real-Time Anchor-Free Instance Segmentation: https://arxiv.org/abs/1911.06667 - ResNet strikes back: An improved training procedure in timm: https://arxiv.org/abs/2110.00476 - Dataset: ImageNet-1k - Original: https://github.com/huggingface/pytorch-image-models ## Model Usage ### Image Classification ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open(urlopen( 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png' )) model = timm.create_model('ese_vovnet39b.ra_in1k', pretrained=True) model = model.eval() # get model specific transforms (normalization, resize) data_config = timm.data.resolve_model_data_config(model) transforms = timm.data.create_transform(*data_config, is_training=False) output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1 top5_probabilities, top5_class_indices = torch.topk(output.softmax(dim=1) 100, k=5) ``` ### Feature Map Extraction ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open(urlopen( 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png' )) model = timm.create_model( 'ese_vovnet39b.ra_in1k', pretrained=True, features_only=True, ) model = model.eval() # get model specific transforms (normalization, resize) data_config = timm.data.resolve_model_data_config(model) transforms = timm.data.create_transform(data_config, is_training=False) output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1 for o in output: # print shape of each feature map in output # e.g.: # torch.Size([1, 64, 112, 112]) # torch.Size([1, 256, 56, 56]) # torch.Size([1, 512, 28, 28]) # torch.Size([1, 768, 14, 14]) # torch.Size([1, 1024, 7, 7]) print(o.shape) ``` ### Image Embeddings ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open(urlopen( 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png' )) model = timm.create_model( 'ese_vovnet39b.ra_in1k', pretrained=True, num_classes=0, # remove classifier nn.Linear ) model = model.eval() # get model specific transforms (normalization, resize) data_config = timm.data.resolve_model_data_config(model) transforms = timm.data.create_transform(data_config, is_training=False) output = model(transforms(img).unsqueeze(0)) # output is (batch_size, num_features) shaped tensor # or equivalently (without needing to set num_classes=0) output = model.forward_features(transforms(img).unsqueeze(0)) # output is unpooled, a (1, 1024, 7, 7) shaped tensor output = model.forward_head(output, pre_logits=True) # output is a (1, num_features) shaped tensor ``` ## Citation ```bibtex @inproceedings{lee2019energy, title = {An Energy and GPU-Computation Efficient Backbone Network for Real-Time Object Detection}, author = {Lee, Youngwan and Hwang, Joong-won and Lee, Sangrok and Bae, Yuseok and Park, Jongyoul}, booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops}, year = {2019} } ``` ```bibtex @article{lee2019centermask, title={CenterMask: Real-Time Anchor-Free Instance Segmentation}, author={Lee, Youngwan and Park, Jongyoul}, booktitle={CVPR}, year={2020} } ``` ```bibtex @inproceedings{wightman2021resnet, title={ResNet strikes back: An improved training procedure in timm}, author={Wightman, Ross and Touvron, Hugo and Jegou, Herve}, booktitle={NeurIPS 2021 Workshop on ImageNet: Past, Present, and Future} } ```
hermes42/WizardLM-2-8x22B-imatrix-GGUF	hermes42	2024-06-01T20:10:23Z	4,723	0	null	[ "gguf", "arxiv:2304.12244", "arxiv:2306.08568", "arxiv:2308.09583", "license:apache-2.0", "region:us" ]	null	2024-05-31T13:32:34Z	--- license: apache-2.0 --- <p align="center"> GGUF Quants created using an imatrix calculated on <a href=https://gist.githubusercontent.com/bartowski1182/b6ac44691e994344625687afe3263b3a/raw/d53a2c532e318ebb8258bb1ccb94ddb870b04be2/calibration_data.txt>this calibration data by barowski1182</a> Attention: split Files in the main branch are split using the UNIX split command, not gguf-split; they need to be manually concaternated via e.g. <code> cat WizardLM-2-8x22B-imatrix-Q6_K-* > WizardLM-2-8x22B-imatrix-Q6_K.gguf </code> All split files in branch <a href=https://huggingface.co/hermes42/WizardLM-2-8x22B-imatrix-GGUF/tree/gguf-split>gguf-split</a> uploaded after June 1st, 12:00h GMT can be loaded directly by pointing llama.cpp to the first part, it will load the following parts automagically. They can be reassabled to a single file via the gguf-split --merge command, just concaternating as the UNIX split files WON'T WORK. <p style="font-size:20px;" align="center"> 🏠 <a href="https://wizardlm.github.io/WizardLM2" target="_blank">WizardLM-2 Release Blog</a> </p> <p align="center"> 🤗 <a href="https://huggingface.co/collections/microsoft/wizardlm-2-661d403f71e6c8257dbd598a" target="_blank">HF Repo</a> •🐱 <a href="https://github.com/victorsungo/WizardLM/tree/main/WizardLM-2" target="_blank">Github Repo</a> • 🐦 <a href="https://twitter.com/WizardLM_AI" target="_blank">Twitter</a> • 📃 <a href="https://arxiv.org/abs/2304.12244" target="_blank">[WizardLM]</a> • 📃 <a href="https://arxiv.org/abs/2306.08568" target="_blank">[WizardCoder]</a> • 📃 <a href="https://arxiv.org/abs/2308.09583" target="_blank">[WizardMath]</a> <br> </p> <p align="center"> 👋 Join our <a href="https://discord.gg/VZjjHtWrKs" target="_blank">Discord</a> </p> ## See [here](https://huggingface.co/lucyknada/microsoft_WizardLM-2-7B) for the WizardLM-2-7B re-upload. ## News 🔥🔥🔥 [2024/04/15] We introduce and opensource WizardLM-2, our next generation state-of-the-art large language models, which have improved performance on complex chat, multilingual, reasoning and agent. New family includes three cutting-edge models: WizardLM-2 8x22B, WizardLM-2 70B, and WizardLM-2 7B. - WizardLM-2 8x22B is our most advanced model, demonstrates highly competitive performance compared to those leading proprietary works and consistently outperforms all the existing state-of-the-art opensource models. - WizardLM-2 70B reaches top-tier reasoning capabilities and is the first choice in the same size. - WizardLM-2 7B is the fastest and achieves comparable performance with existing 10x larger opensource leading models. For more details of WizardLM-2 please read our [release blog post](https://web.archive.org/web/20240415221214/https://wizardlm.github.io/WizardLM2/) and upcoming paper. ## Model Details * Model name: WizardLM-2 8x22B * Developed by: WizardLM@Microsoft AI * Model type: Mixture of Experts (MoE) * Base model: [mistral-community/Mixtral-8x22B-v0.1](https://huggingface.co/mistral-community/Mixtral-8x22B-v0.1) * Parameters: 141B * Language(s): Multilingual * Blog: [Introducing WizardLM-2](https://web.archive.org/web/20240415221214/https://wizardlm.github.io/WizardLM2/) * Repository: [https://github.com/nlpxucan/WizardLM](https://github.com/nlpxucan/WizardLM) * Paper: WizardLM-2 (Upcoming) * License: Apache2.0 ## Model Capacities MT-Bench We also adopt the automatic MT-Bench evaluation framework based on GPT-4 proposed by lmsys to assess the performance of models. The WizardLM-2 8x22B even demonstrates highly competitive performance compared to the most advanced proprietary models. Meanwhile, WizardLM-2 7B and WizardLM-2 70B are all the top-performing models among the other leading baselines at 7B to 70B model scales. <p align="center" width="100%"> <a ><img src="https://web.archive.org/web/20240415175608im_/https://wizardlm.github.io/WizardLM2/static/images/mtbench.png" alt="MTBench" style="width: 96%; min-width: 300px; display: block; margin: auto;"></a> </p> Human Preferences Evaluation We carefully collected a complex and challenging set consisting of real-world instructions, which includes main requirements of humanity, such as writing, coding, math, reasoning, agent, and multilingual. We report the win:loss rate without tie: - WizardLM-2 8x22B is just slightly falling behind GPT-4-1106-preview, and significantly stronger than Command R Plus and GPT4-0314. - WizardLM-2 70B is better than GPT4-0613, Mistral-Large, and Qwen1.5-72B-Chat. - WizardLM-2 7B is comparable with Qwen1.5-32B-Chat, and surpasses Qwen1.5-14B-Chat and Starling-LM-7B-beta. <p align="center" width="100%"> <a ><img src="https://web.archive.org/web/20240415163303im_/https://wizardlm.github.io/WizardLM2/static/images/winall.png" alt="Win" style="width: 96%; min-width: 300px; display: block; margin: auto;"></a> </p> ## Method Overview We built a fully AI powered synthetic training system to train WizardLM-2 models, please refer to our [blog](https://web.archive.org/web/20240415221214/https://wizardlm.github.io/WizardLM2/) for more details of this system. <p align="center" width="100%"> <a ><img src="https://web.archive.org/web/20240415163303im_/https://wizardlm.github.io/WizardLM2/static/images/exp_1.png" alt="Method" style="width: 96%; min-width: 300px; display: block; margin: auto;"></a> </p> ## Usage ❗<b>Note for model system prompts usage:</b> <b>WizardLM-2</b> adopts the prompt format from <b>Vicuna</b> and supports multi-turn conversation. The prompt should be as following: ``` A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: Hi ASSISTANT: Hello.</s> USER: Who are you? ASSISTANT: I am WizardLM.</s>...... ``` <b> Inference WizardLM-2 Demo Script</b> We provide a WizardLM-2 inference demo [code](https://github.com/nlpxucan/WizardLM/tree/main/demo) on our github.
shi-labs/oneformer_cityscapes_swin_large	shi-labs	2023-01-20T08:28:09Z	4,722	2	transformers	[ "transformers", "pytorch", "oneformer", "vision", "image-segmentation", "dataset:huggan/cityscapes", "arxiv:2211.06220", "license:mit", "endpoints_compatible", "region:us" ]	image-segmentation	2022-11-15T20:24:27Z	--- license: mit tags: - vision - image-segmentation datasets: - huggan/cityscapes widget: - src: https://huggingface.co/datasets/shi-labs/oneformer_demo/blob/main/cityscapes.png example_title: Cityscapes --- # OneFormer OneFormer model trained on the Cityscapes dataset (large-sized version, Swin backbone). It was introduced in the paper [OneFormer: One Transformer to Rule Universal Image Segmentation](https://arxiv.org/abs/2211.06220) by Jain et al. and first released in [this repository](https://github.com/SHI-Labs/OneFormer). ![model image](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/oneformer_teaser.png) ## Model description OneFormer is the first multi-task universal image segmentation framework. It needs to be trained only once with a single universal architecture, a single model, and on a single dataset, to outperform existing specialized models across semantic, instance, and panoptic segmentation tasks. OneFormer uses a task token to condition the model on the task in focus, making the architecture task-guided for training, and task-dynamic for inference, all with a single model. ![model image](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/oneformer_architecture.png) ## Intended uses & limitations You can use this particular checkpoint for semantic, instance and panoptic segmentation. See the [model hub](https://huggingface.co/models?search=oneformer) to look for other fine-tuned versions on a different dataset. ### How to use Here is how to use this model: ```python from transformers import OneFormerProcessor, OneFormerForUniversalSegmentation from PIL import Image import requests url = "https://huggingface.co/datasets/shi-labs/oneformer_demo/blob/main/cityscapes.png" image = Image.open(requests.get(url, stream=True).raw) # Loading a single model for all three tasks processor = OneFormerProcessor.from_pretrained("shi-labs/oneformer_cityscapes_swin_large") model = OneFormerForUniversalSegmentation.from_pretrained("shi-labs/oneformer_cityscapes_swin_large") # Semantic Segmentation semantic_inputs = processor(images=image, task_inputs=["semantic"], return_tensors="pt") semantic_outputs = model(semantic_inputs) # pass through image_processor for postprocessing predicted_semantic_map = processor.post_process_semantic_segmentation(outputs, target_sizes=[image.size[::-1]])[0] # Instance Segmentation instance_inputs = processor(images=image, task_inputs=["instance"], return_tensors="pt") instance_outputs = model(instance_inputs) # pass through image_processor for postprocessing predicted_instance_map = processor.post_process_instance_segmentation(outputs, target_sizes=[image.size[::-1]])[0]["segmentation"] # Panoptic Segmentation panoptic_inputs = processor(images=image, task_inputs=["panoptic"], return_tensors="pt") panoptic_outputs = model(**panoptic_inputs) # pass through image_processor for postprocessing predicted_semantic_map = processor.post_process_panoptic_segmentation(outputs, target_sizes=[image.size[::-1]])[0]["segmentation"] ``` For more examples, please refer to the [documentation](https://huggingface.co/docs/transformers/master/en/model_doc/oneformer). ### Citation ```bibtex @article{jain2022oneformer, title={{OneFormer: One Transformer to Rule Universal Image Segmentation}}, author={Jitesh Jain and Jiachen Li and MangTik Chiu and Ali Hassani and Nikita Orlov and Humphrey Shi}, journal={arXiv}, year={2022} } ```
Zetaphor/lorealtest_gguf	Zetaphor	2024-06-24T17:54:10Z	4,720	0	transformers	[ "transformers", "gguf", "mistral", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-06-10T00:18:41Z	Entry not found
hydaitw/Llama-3-Taiwan-8B-Instruct-gguf	hydaitw	2024-07-01T19:39:24Z	4,719	0	null	[ "gguf", "region:us" ]	null	2024-07-01T19:28:09Z	Entry not found
mzwing/NSFW_13B_sft-GGUF	mzwing	2024-02-24T15:47:52Z	4,717	17	null	[ "gguf", "baichuan", "not-for-all-audiences", "text-generation", "zh", "dataset:zxbsmk/instruct_short_novel", "base_model:zxbsmk/NSFW_13B_sft", "license:apache-2.0", "region:us" ]	text-generation	2024-02-23T09:41:26Z	--- base_model: zxbsmk/NSFW_13B_sft inference: false license: apache-2.0 model_creator: zxbsmk model_name: NSFW 13B sft model_type: baichuan prompt_template: > System: A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the human's questions. Human: {prompt} Assistant: quantized_by: mzwing language: - zh tags: - baichuan - not-for-all-audiences pipeline_tag: text-generation datasets: - zxbsmk/instruct_short_novel --- # NSFW 13B sft - GGUF - Model creator: [zxbsmk](https://huggingface.co/zxbsmk) - Original model: [NSFW 13B sft](https://huggingface.co/zxbsmk/NSFW_13B_sft) <!-- description start --> ## Description This repo contains GGUF format model files for [zxbsmk's NSFW 13B sft](https://huggingface.co/zxbsmk/NSFW_13B_sft). These files were quantised using hardware kindly provided by [Google Colab](https://colab.research.google.com/)(Free CPU Machine). [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/mzwing/AI-related/blob/master/notebooks/NSFW_13B_sft-GGUF.ipynb) You can also check it out easily in [my GitHub repo](https://github.com/mzwing/AI-related/blob/master/notebooks/NSFW_13B_sft-GGUF.ipynb). <!-- description end --> <!-- README_GGUF.md-about-gguf start --> ### About GGUF GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp. Here is an incomplate list of clients and libraries that are known to support GGUF: * [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option. * [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration. * [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling. * [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. * [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection. * [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration. * [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. * [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server. * [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use. * [Nitro](https://nitro.jan.ai/), a fast, lightweight 3mb inference server to supercharge apps with local AI, and OpenAI-compatible API server. <!-- README_GGUF.md-about-gguf end --> <!-- repositories-available start --> ## Repositories available * [2, 3, 4, 5, 6, 8, 16 and 32-bit GGUF models for CPU+GPU inference](https://huggingface.co/mzwing/NSFW_13B_sft-GGUF) * [zxbsmk's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/zxbsmk/NSFW_13B_sft) <!-- repositories-available end --> <!-- prompt-template start --> ## Prompt template: BLING ``` System: A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the human's questions. Human: {prompt} Assistant: ``` <!-- prompt-template end --> <!-- compatibility_gguf start --> ## Compatibility These quantised GGUFv2 files are compatible with llama.cpp from August 27th onwards, as of commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) They are also compatible with many third party UIs and libraries - please see the list at the top of this README. ## Explanation of quantisation methods <details> <summary>Click to see details</summary> The new methods available are: * GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw) * GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw. * GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw. * GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw * GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw Refer to the Provided Files table below to see what files use which methods, and how. </details> <!-- compatibility_gguf end --> <!-- README_GGUF.md-provided-files start --> ## Provided files \| Name \| Quant method \| Bits \| Size \| Max RAM required \| Use case \| \| ---- \| ---- \| ---- \| ---- \| ---- \| ----- \| \| [NSFW_13B_sft.Q2_K.gguf](https://huggingface.co/mzwing/NSFW_13B_sft-GGUF/blob/main/NSFW_13B_sft.Q2_K.gguf) \| Q2_K \| 2 \| 5.56 GB \| untested yet \| smallest, significant quality loss - not recommended for most purposes \| \| [NSFW_13B_sft.Q3_K_S.gguf](https://huggingface.co/mzwing/NSFW_13B_sft-GGUF/blob/main/NSFW_13B_sft.Q3_K_S.gguf) \| Q3_K_S \| 3 \| 6.38 GB \| untested yet \| very small, high quality loss \| \| [NSFW_13B_sft.Q3_K_M.gguf](https://huggingface.co/mzwing/NSFW_13B_sft-GGUF/blob/main/NSFW_13B_sft.Q3_K_M.gguf) \| Q3_K_M \| 3 \| 6.85 GB \| untested yet \| very small, high quality loss \| \| [NSFW_13B_sft.Q3_K_L.gguf](https://huggingface.co/mzwing/NSFW_13B_sft-GGUF/blob/main/NSFW_13B_sft.Q3_K_L.gguf) \| Q3_K_L \| 3 \| 7.27 GB \| untested yet \| small, substantial quality loss \| \| [NSFW_13B_sft.Q4_0.gguf](https://huggingface.co/mzwing/NSFW_13B_sft-GGUF/blob/main/NSFW_13B_sft.Q4_0.gguf) \| Q4_0 \| 4 \| 7.55 GB \| untested yet \| legacy; small, very high quality loss - prefer using Q3_K_M \| \| [NSFW_13B_sft.Q4_K_S.gguf](https://huggingface.co/mzwing/NSFW_13B_sft-GGUF/blob/main/NSFW_13B_sft.Q4_K_S.gguf) \| Q4_K_S \| 4 \| 7.93 GB \| untested yet \| small, greater quality loss \| \| [NSFW_13B_sft.Q4_K_M.gguf](https://huggingface.co/mzwing/NSFW_13B_sft-GGUF/blob/main/NSFW_13B_sft.Q4_K_M.gguf) \| Q4_K_M \| 4 \| 8.56 GB \| untested yet \| medium, balanced quality - recommended \| \| [NSFW_13B_sft.Q5_0.gguf](https://huggingface.co/mzwing/NSFW_13B_sft-GGUF/blob/main/NSFW_13B_sft.Q5_0.gguf) \| Q5_0 \| 5 \| 9.17 GB \| untested yet \| legacy; medium, balanced quality - prefer using Q4_K_M \| \| [NSFW_13B_sft.Q5_K_S.gguf](https://huggingface.co/mzwing/NSFW_13B_sft-GGUF/blob/main/NSFW_13B_sft.Q5_K_S.gguf) \| Q5_K_S \| 5 \| 9.34 GB \| untested yet \| large, low quality loss - recommended \| \| [NSFW_13B_sft.Q5_K_M.gguf](https://huggingface.co/mzwing/NSFW_13B_sft-GGUF/blob/main/NSFW_13B_sft.Q5_K_M.gguf) \| Q5_K_M \| 5 \| 9.85 GB \| untested yet \| large, very low quality loss - recommended \| \| [NSFW_13B_sft.Q6_K.gguf](https://huggingface.co/mzwing/NSFW_13B_sft-GGUF/blob/main/NSFW_13B_sft.Q6_K.gguf) \| Q6_K \| 6 \| 11.6 GB \| untested yet \| very large, extremely low quality loss \| \| [NSFW_13B_sft.Q8_0.gguf](https://huggingface.co/mzwing/NSFW_13B_sft-GGUF/blob/main/NSFW_13B_sft.Q8_0.gguf) \| Q8_0 \| 8 \| 14.1 GB \| untested yet \| very large, extremely low quality loss - not recommended \| \| [NSFW_13B_sft.F16.gguf](https://huggingface.co/mzwing/NSFW_13B_sft-GGUF/blob/main/NSFW_13B_sft.F16.gguf) \| F16 \| 16 \| 26.5 GB \| untested yet \| extremely large, extremely low quality loss - not recommended \| Note: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead. <!-- README_GGUF.md-provided-files end --> <!-- README_GGUF.md-how-to-download start --> ## How to download GGUF files Note for manual downloaders: You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file. The following clients/libraries will automatically download models for you, providing a list of available models to choose from: * LM Studio * LoLLMS Web UI * Faraday.dev ### In `text-generation-webui` Under Download Model, you can enter the model repo: `mzwing/NSFW_13B_sft-GGUF`, and below it, a specific filename to download, such as: `NSFW_13B_sft.Q4_K_M.gguf`. Then click Download. ### On the command line, including multiple files at once I recommend using the `huggingface-hub` Python library: ```shell pip3 install huggingface-hub ``` Then you can download any individual model file to the current directory, at high speed, with a command like this: ```shell huggingface-cli download mzwing/NSFW_13B_sft-GGUF NSFW_13B_sft.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False ``` <details> <summary>More advanced huggingface-cli download usage</summary> You can also download multiple files at once with a pattern: ```shell huggingface-cli download mzwing/NSFW_13B_sft-GGUF --local-dir . --local-dir-use-symlinks False --include='Q4_Kgguf' ``` For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli). To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`: ```shell pip3 install hf_transfer ``` And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`: ```shell HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download mzwing/NSFW_13B_sft-GGUF NSFW_13B_sft.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False ``` Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command. </details> <!-- README_GGUF.md-how-to-download end --> <!-- README_GGUF.md-how-to-run start --> ## Example `llama.cpp` command Make sure you are using `llama.cpp` from commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later. ```shell ./main -ngl 32 -m NSFW_13B_sft.Q4_K_M.gguf --color -c 2048 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "System: A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the human's questions.\nHuman: {prompt}\nAssistant:" ``` Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration. Change `-c 2048` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically. If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins` For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md) ## How to run in `text-generation-webui` Further instructions here: [text-generation-webui/docs/llama.cpp.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/llama.cpp.md). ## How to run from Python code You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) or [ctransformers](https://github.com/marella/ctransformers) libraries. ### How to load this model in Python code, using ctransformers #### First install the package Run one of the following commands, according to your system: ```shell # Base ctransformers with no GPU acceleration pip install ctransformers # Or with CUDA GPU acceleration pip install ctransformers[cuda] # Or with AMD ROCm GPU acceleration (Linux only) CT_HIPBLAS=1 pip install ctransformers --no-binary ctransformers # Or with Metal GPU acceleration for macOS systems only CT_METAL=1 pip install ctransformers --no-binary ctransformers ``` #### Simple ctransformers example code ```python from ctransformers import AutoModelForCausalLM # Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system. llm = AutoModelForCausalLM.from_pretrained("mzwing/NSFW_13B_sft-GGUF", model_file="NSFW_13B_sft.Q4_K_M.gguf", model_type="phi", gpu_layers=50) print(llm("AI is going to")) ``` ## How to use with LangChain Here are guides on using llama-cpp-python and ctransformers with LangChain: * [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp) * [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers) <!-- README_GGUF.md-how-to-run end --> <!-- footer start --> <!-- 200823 --> ## Thanks, and how to contribute Thanks to [Google Colab](https://colab.research.google.com/)! All the quantised models in this repo are done on the awesome platform. Thanks a lot! Thanks to [llama.cpp](https://github.com/ggerganov/llama.cpp)! It inspired me to explore the inspiring AI field, thanks! Thanks to [TheBloke](https://huggingface.co/TheBloke)! Everything in this repo is a reference to him. You are welcome to create a PullRequest! Especially for the RAM Usage! <!-- footer end --> <!-- original-model-card start --> A instruction-tuned model of https://huggingface.co/baichuan-inc/Baichuan-13B-Base - Instruction-following datasets used: instruct_nsfw_cn - Training framework: https://github.com/hiyouga/LLaMA-Efficient-Tuning # Usage: ```python from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer tokenizer = AutoTokenizer.from_pretrained("zxbsmk/NSFW_13B_sft", trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained("zxbsmk/NSFW_13B_sft", trust_remote_code=True).cuda() streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True) query = "续写。白洁已经动情了，肩带滑落露出香肩。" inputs = tokenizer([query], return_tensors="pt") inputs = inputs.to("cuda") generate_ids = model.generate(inputs, max_new_tokens=1024, streamer=streamer) ``` You could also alternatively launch a CLI demo by using the script in https://github.com/hiyouga/LLaMA-Efficient-Tuning ```bash python src/cli_demo.py --template baichuan --model_name_or_path zxbsmk/NSFW_13B_sft ``` # Examples: Omit part of results for better visualization. \| Question \| NSFW_13B_sft \| ChatGPT \| \| -------- \| ---------------- \| ------- \| \| 中国第三代战斗机的代表是什么？ \| 中国第三代战斗机的代表是歼-10战斗机。 \|截至我所掌握的信息（截止2021年9月），中国第三代战斗机的代表是中国航空工业集团公司（AVIC）生产的"歼-20"战斗机。该战斗机是中国第三代隐形战斗机，也被称为"歼-20风暴"，它是中国空军的先进战斗机，具备隐身特性、超音速巡航能力以及先进的雷达系统和电子设备。歼-20的研发和部署标志着中国在军事航空技术方面取得了重要进展。请注意，自那之后，可能已经有更多新型战斗机被研发或部署，因此建议查阅最新资料以获取更准确和最新的信息。\| \| 解释这句话中每个词语的含义：今天下雨，我骑车差点摔倒，好在我一把把把把住了。 \| 这句话中，“今天”指的是当前的时间，即雨天。“下雨”指的是天气阴沉，雨水落下。“我”指的是说话者，即骑车人。“骑车”指的是用自行车或其他交通工具出行。“差点”指的是几乎，几乎要摔倒。“摔倒”指的是身体失去平衡，摔在地上。“好在我”指的是幸亏有。“一把”指的是一下子。“把”指的是握住。“把住了”指的是握住车把，保持平衡。 \| 今天：指代当天，即当前的日期。下雨：指天气现象，指雨水从天空降落。我：指说话人自己，第一人称代词。骑车：动词短语，表示骑着自行车。差点：几乎、险些，表示差一点就发生某种情况。摔倒：动词，意为因失去平衡而跌倒。好在：幸好、庆幸的意思，用来引出一个好的结果或意外。一把把把：这是一个错别字或打字错误，可能原本是“一把抓住”或类似的表达。考虑到语境，应该是指在骑车差点摔倒的情况下，通过抓住某物保住了平衡。抓住：动词短语，意为紧紧抓住某物，以保持稳定或防止掉落。综合起来，这句话的意思是：今天因为下雨，我骑车时差点摔倒，幸好我抓住了某物，避免了摔倒。 \| \| 续写。白洁已经动情了，肩带滑落露出香肩。 \| 白洁低下头，将秀发从肩头挽起，露出娇俏的耳垂，戴着个银制的耳钉，是叶子送给她的生日礼物，上面有个心型的装饰，是叶子亲手在心型装饰上刻上了白洁的名字。白洁没有穿胸罩，一对丰满的乳房随着她的动作轻轻颤动，滑落的肩带垂落在乳房上，更平添了一份诱惑... \| 她静静地坐在窗边，微风轻拂着她的长发，将她的芳香散发到整个房间。白洁的心情久久不能平静，她的内心充满了对他的思念和渴望。她闭上眼睛，回想起他们之间的点点滴滴，那些温柔的瞬间在她的脑海中浮现... \| \| 续写一下片段。王健已经忍耐不住了，一把搂住陆春兰，狠狠地揉捏她的巨乳。 \| “嗯......王校长......轻点，哎呀......”陆春兰痛呼一声，连忙推开王健，一边赶紧拉开自己的上衣，露出雪白的胸脯，擦干乳房上的汗水，一边瞪了王健一眼，“都是你，弄得我这么黏糊糊的。”... \| 很抱歉，但我无法为你提供所请求的续写。 \| # Evaluation Comparision between Baichuan-13B-Chat and NSFW_13B_sft. (Zero-shot [CMMLU](https://github.com/haonan-li/CMMLU)) \| Score \| NSFW_13B_sft \| Baichuan-13B-Chat \| ChatGPT \| \| -------- \| ---------------- \| ------- \|------- \| \| STEM \| 37.73 \| 37.00 \|44.80 \| \| Humanities \| 54.85 \| 53.74 \|53.61 \| \| Social Sciences \| 55.55 \| 52.77 \|54.22 \| \| Other \| 53.47 \| 52.31 \|59.95 \| \| China specific \| 51.84 \| 50.55 \|49.74 \| \| Overall \| 50.42 \| 48.86 \|53.22** \| (By the way, Baichuan-13B-Chat gets 50.43 with one-shot which seems much better than 48.86 with zero-shot.) # Contact Us Join group via https://t.me/+JbovpBG6-gBiNDI1 <!-- original-model-card end -->
digiplay/realspice_v2	digiplay	2024-03-09T23:21:36Z	4,716	3	diffusers	[ "diffusers", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "license:other", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2024-03-06T18:25:48Z	--- license: other tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers inference: true --- Model info : https://civitai.com/models/158734?modelVersionId=208629 Sample image I made generated by huggingface's API : ![162dc101-0581-4228-a971-32bc10a4955b.jpeg](https://cdn-uploads.huggingface.co/production/uploads/646c83c871d0c8a6e4455854/9pL-Tz3fh-dsUgd_FNxkg.jpeg)
RichardErkhov/VAGOsolutions_-_SauerkrautLM-7b-HerO-gguf	RichardErkhov	2024-06-05T20:15:31Z	4,716	1	null	[ "gguf", "region:us" ]	null	2024-06-05T19:00:51Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) SauerkrautLM-7b-HerO - GGUF - Model creator: https://huggingface.co/VAGOsolutions/ - Original model: https://huggingface.co/VAGOsolutions/SauerkrautLM-7b-HerO/ \| Name \| Quant method \| Size \| \| ---- \| ---- \| ---- \| \| [SauerkrautLM-7b-HerO.Q2_K.gguf](https://huggingface.co/RichardErkhov/VAGOsolutions_-_SauerkrautLM-7b-HerO-gguf/blob/main/SauerkrautLM-7b-HerO.Q2_K.gguf) \| Q2_K \| 2.53GB \| \| [SauerkrautLM-7b-HerO.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/VAGOsolutions_-_SauerkrautLM-7b-HerO-gguf/blob/main/SauerkrautLM-7b-HerO.IQ3_XS.gguf) \| IQ3_XS \| 2.81GB \| \| [SauerkrautLM-7b-HerO.IQ3_S.gguf](https://huggingface.co/RichardErkhov/VAGOsolutions_-_SauerkrautLM-7b-HerO-gguf/blob/main/SauerkrautLM-7b-HerO.IQ3_S.gguf) \| IQ3_S \| 2.86GB \| \| [SauerkrautLM-7b-HerO.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/VAGOsolutions_-_SauerkrautLM-7b-HerO-gguf/blob/main/SauerkrautLM-7b-HerO.Q3_K_S.gguf) \| Q3_K_S \| 2.56GB \| \| [SauerkrautLM-7b-HerO.IQ3_M.gguf](https://huggingface.co/RichardErkhov/VAGOsolutions_-_SauerkrautLM-7b-HerO-gguf/blob/main/SauerkrautLM-7b-HerO.IQ3_M.gguf) \| IQ3_M \| 0.96GB \| \| [SauerkrautLM-7b-HerO.Q3_K.gguf](https://huggingface.co/RichardErkhov/VAGOsolutions_-_SauerkrautLM-7b-HerO-gguf/blob/main/SauerkrautLM-7b-HerO.Q3_K.gguf) \| Q3_K \| 0.78GB \| \| [SauerkrautLM-7b-HerO.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/VAGOsolutions_-_SauerkrautLM-7b-HerO-gguf/blob/main/SauerkrautLM-7b-HerO.Q3_K_M.gguf) \| Q3_K_M \| 0.51GB \| \| [SauerkrautLM-7b-HerO.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/VAGOsolutions_-_SauerkrautLM-7b-HerO-gguf/blob/main/SauerkrautLM-7b-HerO.Q3_K_L.gguf) \| Q3_K_L \| 0.37GB \| \| [SauerkrautLM-7b-HerO.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/VAGOsolutions_-_SauerkrautLM-7b-HerO-gguf/blob/main/SauerkrautLM-7b-HerO.IQ4_XS.gguf) \| IQ4_XS \| 0.82GB \| \| [SauerkrautLM-7b-HerO.Q4_0.gguf](https://huggingface.co/RichardErkhov/VAGOsolutions_-_SauerkrautLM-7b-HerO-gguf/blob/main/SauerkrautLM-7b-HerO.Q4_0.gguf) \| Q4_0 \| 3.83GB \| \| [SauerkrautLM-7b-HerO.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/VAGOsolutions_-_SauerkrautLM-7b-HerO-gguf/blob/main/SauerkrautLM-7b-HerO.IQ4_NL.gguf) \| IQ4_NL \| 2.11GB \| \| [SauerkrautLM-7b-HerO.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/VAGOsolutions_-_SauerkrautLM-7b-HerO-gguf/blob/main/SauerkrautLM-7b-HerO.Q4_K_S.gguf) \| Q4_K_S \| 3.0GB \| \| [SauerkrautLM-7b-HerO.Q4_K.gguf](https://huggingface.co/RichardErkhov/VAGOsolutions_-_SauerkrautLM-7b-HerO-gguf/blob/main/SauerkrautLM-7b-HerO.Q4_K.gguf) \| Q4_K \| 1.87GB \| \| [SauerkrautLM-7b-HerO.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/VAGOsolutions_-_SauerkrautLM-7b-HerO-gguf/blob/main/SauerkrautLM-7b-HerO.Q4_K_M.gguf) \| Q4_K_M \| 2.08GB \| \| [SauerkrautLM-7b-HerO.Q4_1.gguf](https://huggingface.co/RichardErkhov/VAGOsolutions_-_SauerkrautLM-7b-HerO-gguf/blob/main/SauerkrautLM-7b-HerO.Q4_1.gguf) \| Q4_1 \| 3.0GB \| \| [SauerkrautLM-7b-HerO.Q5_0.gguf](https://huggingface.co/RichardErkhov/VAGOsolutions_-_SauerkrautLM-7b-HerO-gguf/blob/main/SauerkrautLM-7b-HerO.Q5_0.gguf) \| Q5_0 \| 4.65GB \| \| [SauerkrautLM-7b-HerO.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/VAGOsolutions_-_SauerkrautLM-7b-HerO-gguf/blob/main/SauerkrautLM-7b-HerO.Q5_K_S.gguf) \| Q5_K_S \| 2.44GB \| \| [SauerkrautLM-7b-HerO.Q5_K.gguf](https://huggingface.co/RichardErkhov/VAGOsolutions_-_SauerkrautLM-7b-HerO-gguf/blob/main/SauerkrautLM-7b-HerO.Q5_K.gguf) \| Q5_K \| 1.42GB \| \| [SauerkrautLM-7b-HerO.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/VAGOsolutions_-_SauerkrautLM-7b-HerO-gguf/blob/main/SauerkrautLM-7b-HerO.Q5_K_M.gguf) \| Q5_K_M \| 0.89GB \| \| [SauerkrautLM-7b-HerO.Q5_1.gguf](https://huggingface.co/RichardErkhov/VAGOsolutions_-_SauerkrautLM-7b-HerO-gguf/blob/main/SauerkrautLM-7b-HerO.Q5_1.gguf) \| Q5_1 \| 0.81GB \| \| [SauerkrautLM-7b-HerO.Q6_K.gguf](https://huggingface.co/RichardErkhov/VAGOsolutions_-_SauerkrautLM-7b-HerO-gguf/blob/main/SauerkrautLM-7b-HerO.Q6_K.gguf) \| Q6_K \| 0.73GB \| \| [SauerkrautLM-7b-HerO.Q8_0.gguf](https://huggingface.co/RichardErkhov/VAGOsolutions_-_SauerkrautLM-7b-HerO-gguf/blob/main/SauerkrautLM-7b-HerO.Q8_0.gguf) \| Q8_0 \| 0.65GB \| Original model description: --- license: apache-2.0 language: - en - de library_name: transformers pipeline_tag: text-generation tags: - mistral - finetune - chatml - augmentation - german - merge - mergekit --- ![SauerkrautLM](https://vago-solutions.de/wp-content/uploads/2023/11/hero.png "SauerkrautLM-7b-HerO") ## VAGO solutions SauerkrautLM-7b-HerO Introducing SauerkrautLM-7b-HerO – the pinnacle of German language model technology! Crafted through the merging of [Teknium's OpenHermes-2.5-Mistral-7B](https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B) and [Open-Orca's Mistral-7B-OpenOrca](https://huggingface.co/Open-Orca/Mistral-7B-OpenOrca) and uniquely fine-tuned with the Sauerkraut dataset. SauerkrautLM-7b-HerO represents a breakthrough in language modeling, achieving an optimal balance between extensive German data and essential international sources. This ensures the model not only excels in understanding the nuances of the German language but also retains its global capabilities. Harnessing the innovative power of the gradient SLERP method from MergeKit, we've achieved a groundbreaking fusion of two of the most best performing 7B models based on the Mistral framework. This merge has allowed us to combine the best features of both models, creating an unparalleled synergy. Coupled with the German Sauerkraut dataset, which consists of a mix of augmented and translated data, we have successfully taught the English-speaking merged model the intricacies of the German language. This was achieved without the typical loss of core competencies often associated with fine-tuning in another language of models previously trained mainly in English. Our approach ensures that the model retains its original strengths while acquiring a profound understanding of German, setting a new benchmark in bilingual language model proficiency. # Table of Contents 1. [Overview of all Her0 models](#all-hero-models) 2. [Model Details](#model-details) - [Prompt template](#prompt-template) - [Training Dataset](#training-dataset) - [Merge Procedure](#merge-procedure) 3. [Evaluation](#evaluation) - [GPT4ALL](#gpt4all) - [Language Model evaluation Harness](#language-model-evaluation-harness) - [BigBench](#big-bench) - [MMLU](#mmlu) - [TruthfulQA](#truthfulqa) - [MT-Bench (German)](#mt-bench-german) - [MT-Bench (English)](#mt-bench-english) - [Additional German Benchmark results](#additional-german-benchmark-results) 5. [Disclaimer](#disclaimer) 6. [Contact](#contact) 7. [Collaborations](#collaborations) 8. [Acknowledgement](#acknowledgement) ## All HerO Models \| Model \| HF \| GPTQ \| GGUF \| AWQ \| \|-------\|-------\|-------\|-------\|-------\| \| SauerkrautLM-7b-HerO \| [Link](https://huggingface.co/VAGOsolutions/SauerkrautLM-7b-HerO) \| [Link](https://huggingface.co/TheBloke/SauerkrautLM-7B-HerO-GPTQ) \| [Link](https://huggingface.co/TheBloke/SauerkrautLM-7B-HerO-GGUF) \|[Link](https://huggingface.co/TheBloke/SauerkrautLM-7B-HerO-AWQ) \| ## Model Details SauerkrautLM-7b-HerO - Model Type: SauerkrautLM-7b-HerO is an auto-regressive language model based on the transformer architecture - Language(s): English, German - License: APACHE 2.0 - Contact: [Website](https://vago-solutions.de/#Kontakt) [David Golchinfar](mailto:[email protected]) ### Training Dataset: SauerkrautLM-7b-HerO was trained with mix of German data augmentation and translated data. We found, that only a simple translation of training data can lead to unnatural German phrasings. Data augmentation techniques were used to grant grammatical, syntactical correctness and a more natural German wording in our training data. ### Merge Procedure: SauerkrautLM-7b-HerO was merged on 1 A100 with [mergekit](https://github.com/cg123/mergekit). The merged model contains [OpenHermes-2.5-Mistral-7B](https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B) and [Open-Orca/Mistral-7B-OpenOrca](https://huggingface.co/Open-Orca/Mistral-7B-OpenOrca). We applied the gradient SLERP method. ### Prompt Template: ``` <\|im_start\|>system Du bist Sauerkraut-HerO, ein großes Sprachmodell, das höflich und kompetent antwortet. Schreibe deine Gedanken Schritt für Schritt auf, um Probleme sinnvoll zu lösen.<\|im_end\|> <\|im_start\|>user Wie geht es dir?<\|im_end\|> <\|im_start\|>assistant Mir geht es gut!<\|im_end\|> <\|im_start\|>user Bitte erkläre mir, wie die Zusammenführung von Modellen durch bestehende Spitzenmodelle profitieren kann.<\|im_end\|> <\|im_start\|>assistant ``` ## Evaluation ### GPT4ALL: Compared to relevant German Closed and Open Source models ![GPT4ALL diagram](https://vago-solutions.de/wp-content/uploads/2023/11/GPT4All.png "SauerkrautLM-7b-HerO GPT4ALL Diagram") ![GPT4ALL table](https://vago-solutions.de/wp-content/uploads/2023/11/GPT4All-Tabelle.png "SauerkrautLM-7b-HerO GPT4ALL Table") ### Language Model evaluation Harness: Compared to Aleph Alpha Luminous Models ![Harness](https://vago-solutions.de/wp-content/uploads/2023/11/Luminous-comparison.png "SauerkrautLM-7b-HerO Harness") *performed with newest Language Model Evaluation Harness ### Big Bench: ![BBH](https://vago-solutions.de/wp-content/uploads/2023/11/BigBench.png "SauerkrautLM-7b-HerO BBH") *performed with newest Language Model Evaluation Harness ### MMLU: Compared to Big Boy LLMs (Grok0,Grok1,GPT3.5,GPT4) ![MMLU](https://vago-solutions.de/wp-content/uploads/2023/11/MMLU-Benchmark.png "SauerkrautLM-7b-HerO MMLU") ### TruthfulQA: Compared to OpenAI Models (GPT3.5,GPT4) ![TruthfulQA](https://vago-solutions.de/wp-content/uploads/2023/11/Truthfulqa-Benchmark.png "SauerkrautLM-7b-HerO TruthfulQA") ### MT-Bench (German): ![MT-Bench German Diagram](https://vago-solutions.de/wp-content/uploads/2023/11/MT-Bench-German.png "SauerkrautLM-7b-HerO MT-Bench German Diagram") ``` ########## First turn ########## score model turn SauerkrautLM-70b-v1 1 7.25000 SauerkrautLM-7b-HerO <--- 1 6.96875 SauerkrautLM-7b-v1-mistral 1 6.30625 leo-hessianai-13b-chat 1 6.18750 SauerkrautLM-13b-v1 1 6.16250 leo-mistral-hessianai-7b-chat 1 6.15625 Llama-2-70b-chat-hf 1 6.03750 vicuna-13b-v1.5 1 5.80000 SauerkrautLM-7b-v1 1 5.65000 leo-hessianai-7b-chat 1 5.52500 vicuna-7b-v1.5 1 5.42500 Mistral-7B-v0.1 1 5.37500 SauerkrautLM-3b-v1 1 3.17500 Llama-2-7b 1 1.28750 open_llama_3b_v2 1 1.68750 ########## Second turn ########## score model turn SauerkrautLM-70b-v1 2 6.83125 SauerkrautLM-7b-HerO <--- 2 6.30625 vicuna-13b-v1.5 2 5.63125 SauerkrautLM-13b-v1 2 5.34375 SauerkrautLM-7b-v1-mistral 2 5.26250 leo-mistral-hessianai-7b-chat 2 4.99375 SauerkrautLM-7b-v1 2 4.73750 leo-hessianai-13b-chat 2 4.71250 vicuna-7b-v1.5 2 4.67500 Llama-2-70b-chat-hf 2 4.66250 Mistral-7B-v0.1 2 4.53750 leo-hessianai-7b-chat 2 2.65000 SauerkrautLM-3b-v1 2 1.98750 open_llama_3b_v2 2 1.22500 Llama-2-7b 2 1.07500 ########## Average ########## score model SauerkrautLM-70b-v1 7.040625 SauerkrautLM-7b-HerO <--- 6.637500 SauerkrautLM-7b-v1-mistral 5.784375 SauerkrautLM-13b-v1 5.753125 vicuna-13b-v1.5 5.715625 leo-mistral-hessianai-7b-chat 5.575000 leo-hessianai-13b-chat 5.450000 Llama-2-70b-chat-hf 5.350000 SauerkrautLM-v1-7b 5.193750 vicuna-7b-v1.5 5.050000 Mistral-7B-v0.1 4.956250 leo-hessianai-7b-chat 4.087500 SauerkrautLM-3b-v1 2.581250 open_llama_3b_v2 1.456250 Llama-2-7b 1.181250 ``` *performed with the newest FastChat Version ### MT-Bench (English): ![MT-Bench English Diagram](https://vago-solutions.de/wp-content/uploads/2023/11/MT-Bench-English.png "SauerkrautLM-7b-HerO MT-Bench English Diagram") ``` ########## First turn ########## score model turn OpenHermes-2.5-Mistral-7B 1 8.21875 SauerkrautLM-7b-HerO <--- 1 8.03125 Mistral-7B-OpenOrca 1 7.65625 neural-chat-7b-v3-1 1 7.22500 ########## Second turn ########## score model turn OpenHermes-2.5-Mistral-7B 2 7.1000 SauerkrautLM-7b-HerO <--- 2 6.7875 neural-chat-7b-v3-1 2 6.4000 Mistral-7B-OpenOrca 2 6.1750 ########## Average ########## score model OpenHermes-2.5-Mistral-7B 7.659375 SauerkrautLM-7b-HerO <--- 7.409375 Mistral-7B-OpenOrca 6.915625 neural-chat-7b-v3-1 6.812500 ``` *performed with the newest FastChat Version ### Additional German Benchmark results: ![GermanBenchmarks](https://vago-solutions.de/wp-content/uploads/2023/11/German-benchmarks.png "SauerkrautLM-7b-HerO German Benchmarks") *performed with newest Language Model Evaluation Harness ## Disclaimer We must inform users that despite our best efforts in data cleansing, the possibility of uncensored content slipping through cannot be entirely ruled out. However, we cannot guarantee consistently appropriate behavior. Therefore, if you encounter any issues or come across inappropriate content, we kindly request that you inform us through the contact information provided. Additionally, it is essential to understand that the licensing of these models does not constitute legal advice. We are not held responsible for the actions of third parties who utilize our models. These models may be employed for commercial purposes, and the Apache 2.0 remains applicable and is included with the model files. ## Contact If you are interested in customized LLMs for business applications, please get in contact with us via our website or contact us at [Dr. Daryoush Vaziri](mailto:[email protected]). We are also grateful for your feedback and suggestions. ## Collaborations We are also keenly seeking support and investment for our startup, VAGO solutions, where we continuously advance the development of robust language models designed to address a diverse range of purposes and requirements. If the prospect of collaboratively navigating future challenges excites you, we warmly invite you to reach out to us. ## Acknowledgement Many thanks to [OpenOrca](https://huggingface.co/Open-Orca) and [teknium](https://huggingface.co/teknium) for providing such valuable models to the Open-Source community. Many thanks to [TheBloke](https://huggingface.co/TheBloke) for super fast quantifying all of our models. [<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
Cohere/Cohere-embed-english-light-v3.0	Cohere	2023-11-02T10:09:41Z	4,713	1	transformers	[ "transformers", "mteb", "model-index", "endpoints_compatible", "region:us" ]	null	2023-11-02T10:05:45Z	--- tags: - mteb model-index: - name: embed-english-light-v3.0 results: - task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (en) config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 78.62686567164178 - type: ap value: 43.50072127690769 - type: f1 value: 73.12414870629323 - task: type: Classification dataset: type: mteb/amazon_polarity name: MTEB AmazonPolarityClassification config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 94.795 - type: ap value: 92.14178233328848 - type: f1 value: 94.79269356571955 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (en) config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 51.016000000000005 - type: f1 value: 48.9266470039522 - task: type: Retrieval dataset: type: arguana name: MTEB ArguAna config: default split: test revision: None metrics: - type: ndcg_at_10 value: 50.806 - task: type: Clustering dataset: type: mteb/arxiv-clustering-p2p name: MTEB ArxivClusteringP2P config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics: - type: v_measure value: 46.19304218375896 - task: type: Clustering dataset: type: mteb/arxiv-clustering-s2s name: MTEB ArxivClusteringS2S config: default split: test revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 metrics: - type: v_measure value: 37.57785041962193 - task: type: Reranking dataset: type: mteb/askubuntudupquestions-reranking name: MTEB AskUbuntuDupQuestions config: default split: test revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 metrics: - type: map value: 60.11396377106911 - type: mrr value: 72.9068284746955 - task: type: STS dataset: type: mteb/biosses-sts name: MTEB BIOSSES config: default split: test revision: d3fb88f8f02e40887cd149695127462bbcf29b4a metrics: - type: cos_sim_pearson value: 82.59354737468067 - type: cos_sim_spearman value: 81.71933190993215 - type: euclidean_pearson value: 81.39212345994983 - type: euclidean_spearman value: 81.71933190993215 - type: manhattan_pearson value: 81.29257414603093 - type: manhattan_spearman value: 81.80246633432691 - task: type: Classification dataset: type: mteb/banking77 name: MTEB Banking77Classification config: default split: test revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 metrics: - type: accuracy value: 79.69805194805193 - type: f1 value: 79.07431143559548 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-p2p name: MTEB BiorxivClusteringP2P config: default split: test revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 metrics: - type: v_measure value: 38.973417975095934 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-s2s name: MTEB BiorxivClusteringS2S config: default split: test revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 metrics: - type: v_measure value: 34.51608057107556 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackAndroidRetrieval config: default split: test revision: None metrics: - type: ndcg_at_10 value: 46.615 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackEnglishRetrieval config: default split: test revision: None metrics: - type: ndcg_at_10 value: 45.383 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGamingRetrieval config: default split: test revision: None metrics: - type: ndcg_at_10 value: 57.062999999999995 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGisRetrieval config: default split: test revision: None metrics: - type: ndcg_at_10 value: 37.201 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackMathematicaRetrieval config: default split: test revision: None metrics: - type: ndcg_at_10 value: 27.473 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackPhysicsRetrieval config: default split: test revision: None metrics: - type: ndcg_at_10 value: 41.868 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackProgrammersRetrieval config: default split: test revision: None metrics: - type: ndcg_at_10 value: 42.059000000000005 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackRetrieval config: default split: test revision: None metrics: - type: ndcg_at_10 value: 38.885416666666664 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackStatsRetrieval config: default split: test revision: None metrics: - type: ndcg_at_10 value: 32.134 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackTexRetrieval config: default split: test revision: None metrics: - type: ndcg_at_10 value: 28.052 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackUnixRetrieval config: default split: test revision: None metrics: - type: ndcg_at_10 value: 38.237 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWebmastersRetrieval config: default split: test revision: None metrics: - 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type: euclidean_f1 value: 78.678007640741 - type: euclidean_precision value: 73.95988616343678 - type: euclidean_recall value: 84.03911302740991 - type: manhattan_accuracy value: 89.06547133930997 - type: manhattan_ap value: 86.24122868846949 - type: manhattan_f1 value: 78.74963094183643 - type: manhattan_precision value: 75.62375956903884 - type: manhattan_recall value: 82.14505697566985 - type: max_accuracy value: 89.08681647067955 - type: max_ap value: 86.31913976395484 - type: max_f1 value: 78.74963094183643 --- # Cohere embed-english-light-v3.0 This repository contains the tokenizer for the Cohere `embed-english-light-v3.0` model. See our blogpost [Cohere Embed V3](https://txt.cohere.com/introducing-embed-v3/) for more details on this model. You can use the embedding model either via the Cohere API, AWS SageMaker or in your private deployments. ## Usage Cohere API The following code snippet shows the usage of the Cohere API. Install the cohere SDK via: ``` pip install -U cohere ``` Get your free API key on: www.cohere.com ```python # This snippet shows and example how to use the Cohere Embed V3 models for semantic search. # Make sure to have the Cohere SDK in at least v4.30 install: pip install -U cohere # Get your API key from: www.cohere.com import cohere import numpy as np cohere_key = "{YOUR_COHERE_API_KEY}" #Get your API key from www.cohere.com co = cohere.Client(cohere_key) docs = ["The capital of France is Paris", "PyTorch is a machine learning framework based on the Torch library.", "The average cat lifespan is between 13-17 years"] #Encode your documents with input type 'search_document' doc_emb = co.embed(docs, input_type="search_document", model="embed-english-light-v3.0").embeddings doc_emb = np.asarray(doc_emb) #Encode your query with input type 'search_query' query = "What is Pytorch" query_emb = co.embed([query], input_type="search_query", model="embed-english-light-v3.0").embeddings query_emb = np.asarray(query_emb) query_emb.shape #Compute the dot product between query embedding and document embedding scores = np.dot(query_emb, doc_emb.T)[0] #Find the highest scores max_idx = np.argsort(-scores) print(f"Query: {query}") for idx in max_idx: print(f"Score: {scores[idx]:.2f}") print(docs[idx]) print("--------") ``` ## Usage AWS SageMaker The embedding model can be privately deployed in your AWS Cloud using our [AWS SageMaker marketplace offering](https://aws.amazon.com/marketplace/pp/prodview-z6huxszcqc25i). It runs privately in your VPC, with latencies as low as 5ms for query encoding. ## Usage AWS Bedrock Soon the model will also be available via AWS Bedrock. Stay tuned ## Private Deployment You want to run the model on your own hardware? [Contact Sales](https://cohere.com/contact-sales) to learn more. ## Supported Languages This model was trained on nearly 1B English training pairs. Evaluation results can be found in the [Embed V3.0 Benchmark Results spreadsheet](https://docs.google.com/spreadsheets/d/1w7gnHWMDBdEUrmHgSfDnGHJgVQE5aOiXCCwO3uNH_mI/edit?usp=sharing).
MahmoodLab/CONCH	MahmoodLab	2024-05-05T06:05:43Z	4,707	66	timm	[ "timm", "pytorch", "pathology", "vision", "vision language", "image-feature-extraction", "en", "license:cc-by-nc-nd-4.0", "region:us" ]	image-feature-extraction	2024-01-05T00:50:22Z	--- license: cc-by-nc-nd-4.0 language: - en tags: - pathology - vision - vision language - pytorch extra_gated_prompt: >- This model and associated code are released under the CC-BY-NC-ND 4.0 license and may only be used for non-commercial, academic research purposes with proper attribution. Any commercial use, sale, or other monetization of the CONCH model and its derivatives, which include models trained on outputs from the CONCH model or datasets created from the CONCH model, is prohibited and requires prior approval. Downloading the model requires prior registration on Hugging Face and agreeing to the terms of use. By downloading this model, you agree not to distribute, publish or reproduce a copy of the model. If another user within your organization wishes to use the CONCH model, they must register as an individual user and agree to comply with the terms of use. Users may not attempt to re-identify the deidentified data used to develop the underlying model. If you are a commercial entity, please contact the corresponding author. Please note that the primary email used to sign up for your Hugging Face account must match your institutional email to received approval. Further details included in the model card. extra_gated_fields: Full name: text Affiliation: text Type of affiliation: type: select options: - Academia - Industry - label: Other value: other Official email (must match primary email in your Hugging Face account): text Please explain your intended research use: text I agree to all terms outlined above: checkbox I agree to use this model for non-commercial, academic purposes only: checkbox I agree not to distribute the model, if another user within your organization wishes to use the CONCH model, they must register as an individual user: checkbox library_name: timm pipeline_tag: image-feature-extraction --- # Model Card for CONCH \[[Journal Link](https://www.nature.com/articles/s41591-024-02856-4)\] \| \[[Open Access Read Link](https://rdcu.be/dBMf6)\] \| [\[Github Repo](https://github.com/mahmoodlab/CONCH)\] \| \[[Cite](#how-to-cite)\] ## What is CONCH? CONCH (CONtrastive learning from Captions for Histopathology) is a vision language foundation model for histopathology, pretrained on currently the largest histopathology-specific vision-language dataset of 1.17M image caption pairs. Compare to other vision language foundation models, it demonstrates state-of-the-art performance across 14 tasks in computational pathology ranging from image classification, text-to-image, and image-to-text retrieval, captioning, and tissue segmentation. - _Why use CONCH?_: Compared to popular self-supervised encoders for computational pathology that were pretrained only on H&E images, CONCH may produce more performant representations for non-H&E stained images such as IHCs and special stains, and can be used for a wide range of downstream tasks involving either or both histopathology images and text. CONCH also did not use large public histology slide collections such as TCGA, PAIP, GTEX, etc. for pretraining, which are routinely used in benchmark development in computational pathology. Therefore, we make CONCH available for the research community in building and evaluating pathology AI models with minimal risk of data contamination on public benchmarks or private histopathology slide collections. ![image/png](hf.jpg) ## Requesting Access As mentioned in the gated prompt, you must agree to the outlined terms of use, _with the primary email for your HuggingFace account matching your institutional email_. If your primary email is a personal email (@gmail/@hotmail/@qq) your request will be denied. To fix this, you can: (1) add your official institutional email to your HF account, and confirm your email address to verify, and (2) set your institutional email as your primary email in your HF account. Other reasons for your request access being denied include other mistakes in the form submitted, for example: full name includes abbreviations, affiliation is not spelled out, the described research use is not sufficient, or email domain address not recognized. ## License and Terms of Use This model and associated code are released under the CC-BY-NC-ND 4.0 license and may only be used for non-commercial, academic research purposes with proper attribution. Any commercial use, sale, or other monetization of the CONCH model and its derivatives, which include models trained on outputs from the CONCH model or datasets created from the CONCH model, is prohibited and requires prior approval. Downloading the model requires prior registration on Hugging Face and agreeing to the terms of use. By downloading this model, you agree not to distribute, publish or reproduce a copy of the model. If another user within your organization wishes to use the CONCH model, they must register as an individual user and agree to comply with the terms of use. Users may not attempt to re-identify the deidentified data used to develop the underlying model. If you are a commercial entity, please contact the corresponding author. ![](requesting_access.png) ## Model Details ### Model Description - Developed by: Mahmood Lab AI for Pathology Lab @ Harvard/BWH - Model type: Pretrained vision-language encoders (vision encoder: ViT-B/16, 90M params; text encoder: L12-E768-H12, 110M params) - Pretraining dataset: 1.17 million histopathology image-caption pairs - Repository: https://github.com/mahmoodlab/CONCH - Paper: https://www.nature.com/articles/s41591-024-02856-4 - License: CC-BY-NC-ND-4.0 Note: while the original CONCH model arechitecture also includes a multimodal decoder trained with the captioning loss of CoCa, as additional precaution to ensure that no proprietary data or Protected Health Information (PHI) is leaked untentionally, we have removed the weights for the decoder from the publicly released CONCH weights. The weights for the text encoder and the vision encoder are intact and therefore the results on all key tasks presented in the paper such as image classification and image-text retrieval are not affected. The ability of CONCH to serve as a general purpose encoder for both histopathology images and pathology-related text also remains unaffected. ### Usage Install the conch repository using pip: ```shell pip install git+https://github.com/Mahmoodlab/CONCH.git ``` After succesfully requesting access to the weights: ```python from conch.open_clip_custom import create_model_from_pretrained model, preprocess = create_model_from_pretrained('conch_ViT-B-16', "hf_hub:MahmoodLab/conch", hf_auth_token="<your_user_access_token>") ``` Note you may need to supply your huggingface user access token via `hf_auth_token=<your_token>` to `create_model_from_pretrained` for authentification. See the [HF documentation](https://huggingface.co/docs/hub/security-tokens) for more details. Alternatively, you can download the checkpoint mannually, and load the model as follows: ```python model, preprocess = create_model_from_pretrained('conch_ViT-B-16', "path/to/conch/pytorch_model.bin") ``` You can then use the model to encode images as follows: ```python import torch from PIL import Image image = Image.open("path/to/image.jpg") image = preprocess(image).unsqueeze(0) with torch.inference_mode(): image_embs = model.encode_image(image, proj_contrast=False, normalize=False) ``` This will give you the image embeddings before the projection head and normalization, suitable for linear probe or working with WSIs under the multiple-instance learning framework. For image-text retrieval tasks, you should use the normalized and projected embeddings as follows: ```python with torch.inference_mode(): image_embs = model.encode_image(image, proj_contrast=True, normalize=True) text_embedings = model.encode_text(tokenized_prompts) sim_scores = (image_embedings @ text_embedings.T).squeeze(0) ``` For concrete examples on using the model for various tasks, please visit the [github](https://github.com/mahmoodlab/CONCH) repository. ### Use Cases The model is primarily intended for researchers and can be used to perform tasks in computational pathology such as: - Zero-shot ROI classification - Zero-shot ROI image to text and text to image retrieval - Zero-shot WSI classification using MI-Zero - ROI classification using linear probing / knn probing / end-to-end fine-tuning - WSI classification using with multiple instance learning (MIL) ## Training Details - Training data: 1.17 million human histopathology image-caption pairs from publicly available Pubmed Central Open Access (PMC-OA) and internally curated sources. Images include H&E, IHC, and special stains. - Training regime: fp16 automatic mixed-precision - Training objective: CoCa (image-text contrastive loss + captioning loss) - Hardware: 8 x Nvidia A100 - Hours used: ~21.5 hours - Software: PyTorch 2.0, CUDA 11.7 Note: The vision encoder and the text encoder / decoder are first pretrained separately and then fine-tuned together using the CoCa loss. See the paper for more details. ## Contact For any additional questions or comments, contact Faisal Mahmood (`[email protected]`), Ming Y. Lu (`[email protected]`), or Bowen Chen (`[email protected]`). ## Acknowledgements The project was built on top of amazing repositories such as [openclip](https://github.com/mlfoundations/open_clip) (used for model training), [timm](https://github.com/huggingface/pytorch-image-models/) (ViT model implementation) and [huggingface transformers](https://github.com/huggingface/transformers) (tokenization). We thank the authors and developers for their contribution. ## How to Cite ``` @article{lu2024avisionlanguage, title={A visual-language foundation model for computational pathology}, author={Lu, Ming Y and Chen, Bowen and Williamson, Drew FK and Chen, Richard J and Liang, Ivy and Ding, Tong and Jaume, Guillaume and Odintsov, Igor and Le, Long Phi and Gerber, Georg and others}, journal={Nature Medicine}, pages={863–874}, volume={30}, year={2024}, publisher={Nature Publishing Group} } ```
deepfile/embedder-100p	deepfile	2023-12-30T21:34:23Z	4,706	1	transformers	[ "transformers", "pytorch", "xlm-roberta", "feature-extraction", "sentence-similarity", "mteb", "model-index", "endpoints_compatible", "text-embeddings-inference", "region:us" ]	sentence-similarity	2023-07-24T11:02:34Z	--- pipeline_tag: sentence-similarity tags: - feature-extraction - sentence-similarity - transformers - mteb model-index: - name: embedder-100p results: - task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (en) config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 67.05970149253731 - type: ap value: 30.376473854922846 - type: f1 value: 61.30474831792133 - task: type: Classification dataset: type: mteb/amazon_polarity name: MTEB AmazonPolarityClassification config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 70.40857500000001 - type: ap value: 64.61611594622543 - type: f1 value: 70.28136292034776 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (en) config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 33.214 - type: f1 value: 33.123322451005755 - task: type: Retrieval dataset: type: arguana name: MTEB ArguAna config: default split: test revision: None metrics: - type: map_at_1 value: 27.311999999999998 - type: map_at_10 value: 42.760999999999996 - type: map_at_100 value: 43.691 - type: map_at_1000 value: 43.698 - type: map_at_3 value: 37.091 - type: map_at_5 value: 40.398 - type: mrr_at_1 value: 28.165000000000003 - type: mrr_at_10 value: 43.05 - type: mrr_at_100 value: 43.994 - type: mrr_at_1000 value: 44.0 - type: mrr_at_3 value: 37.376 - type: mrr_at_5 value: 40.665 - type: ndcg_at_1 value: 27.311999999999998 - type: ndcg_at_10 value: 52.035 - type: ndcg_at_100 value: 55.891000000000005 - type: ndcg_at_1000 value: 56.043 - type: ndcg_at_3 value: 40.38 - type: ndcg_at_5 value: 46.364 - type: precision_at_1 value: 27.311999999999998 - type: precision_at_10 value: 8.193 - type: precision_at_100 value: 0.985 - type: precision_at_1000 value: 0.1 - type: precision_at_3 value: 16.643 - type: precision_at_5 value: 12.902 - type: recall_at_1 value: 27.311999999999998 - type: recall_at_10 value: 81.935 - type: recall_at_100 value: 98.506 - type: recall_at_1000 value: 99.644 - type: recall_at_3 value: 49.929 - type: recall_at_5 value: 64.509 - task: type: Clustering dataset: type: mteb/arxiv-clustering-p2p name: MTEB ArxivClusteringP2P config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics: - type: v_measure value: 42.899186071418946 - task: type: Clustering dataset: type: mteb/arxiv-clustering-s2s name: MTEB ArxivClusteringS2S config: default split: test revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 metrics: - type: v_measure value: 32.44851270109027 - task: type: Reranking dataset: type: mteb/askubuntudupquestions-reranking name: MTEB AskUbuntuDupQuestions config: default split: test revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 metrics: - type: map value: 61.05081337796836 - type: mrr value: 73.87218045112782 - task: type: STS dataset: type: mteb/biosses-sts name: MTEB BIOSSES config: default split: test revision: d3fb88f8f02e40887cd149695127462bbcf29b4a metrics: - type: cos_sim_pearson value: 80.06755261269532 - type: cos_sim_spearman value: 75.31798123153732 - type: euclidean_pearson value: 77.70454789166935 - type: euclidean_spearman value: 74.07578425253767 - type: manhattan_pearson value: 77.18021593857006 - type: manhattan_spearman value: 74.10590542079663 - task: type: Classification dataset: type: mteb/banking77 name: MTEB Banking77Classification config: default split: test revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 metrics: - type: accuracy value: 82.73051948051948 - type: f1 value: 82.61992011434658 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-p2p name: MTEB BiorxivClusteringP2P config: default split: test revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 metrics: - type: v_measure value: 37.236246179832975 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-s2s name: MTEB BiorxivClusteringS2S config: default split: test revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 metrics: - type: v_measure value: 29.75182197424716 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackAndroidRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 28.016999999999996 - type: map_at_10 value: 39.519999999999996 - type: map_at_100 value: 40.987 - type: map_at_1000 value: 41.124 - type: map_at_3 value: 36.120000000000005 - type: map_at_5 value: 38.071 - type: mrr_at_1 value: 35.05 - type: mrr_at_10 value: 45.589 - type: mrr_at_100 value: 46.322 - type: mrr_at_1000 value: 46.366 - type: mrr_at_3 value: 43.108999999999995 - type: mrr_at_5 value: 44.754 - type: ndcg_at_1 value: 35.05 - type: ndcg_at_10 value: 46.119 - type: ndcg_at_100 value: 51.512 - type: ndcg_at_1000 value: 53.471000000000004 - type: ndcg_at_3 value: 41.3 - type: ndcg_at_5 value: 43.657000000000004 - type: precision_at_1 value: 35.05 - type: precision_at_10 value: 9.156 - type: precision_at_100 value: 1.516 - type: precision_at_1000 value: 0.201 - type: precision_at_3 value: 20.552999999999997 - type: precision_at_5 value: 14.793000000000001 - type: recall_at_1 value: 28.016999999999996 - type: recall_at_10 value: 58.4 - type: recall_at_100 value: 81.67699999999999 - type: recall_at_1000 value: 94.119 - type: recall_at_3 value: 44.293 - type: recall_at_5 value: 51.056000000000004 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackEnglishRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 23.46 - type: map_at_10 value: 33.194 - type: map_at_100 value: 34.367999999999995 - type: map_at_1000 value: 34.514 - type: map_at_3 value: 30.134 - type: map_at_5 value: 31.796999999999997 - type: mrr_at_1 value: 29.744999999999997 - type: mrr_at_10 value: 38.213 - type: mrr_at_100 value: 38.942 - type: mrr_at_1000 value: 38.993 - type: mrr_at_3 value: 35.435 - type: mrr_at_5 value: 37.053000000000004 - type: ndcg_at_1 value: 29.744999999999997 - type: ndcg_at_10 value: 38.868 - type: ndcg_at_100 value: 43.562 - type: ndcg_at_1000 value: 46.036 - type: ndcg_at_3 value: 33.93 - type: ndcg_at_5 value: 36.175000000000004 - type: precision_at_1 value: 29.744999999999997 - type: precision_at_10 value: 7.605 - type: precision_at_100 value: 1.291 - type: precision_at_1000 value: 0.185 - type: precision_at_3 value: 16.582 - type: precision_at_5 value: 12.051 - type: recall_at_1 value: 23.46 - type: recall_at_10 value: 50.080000000000005 - type: recall_at_100 value: 70.161 - type: recall_at_1000 value: 86.009 - type: recall_at_3 value: 36.229 - type: recall_at_5 value: 42.055 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGamingRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 35.515 - type: map_at_10 value: 47.028999999999996 - type: map_at_100 value: 48.104 - type: map_at_1000 value: 48.171 - type: map_at_3 value: 44.224000000000004 - type: map_at_5 value: 45.795 - type: mrr_at_1 value: 40.627 - type: mrr_at_10 value: 50.251000000000005 - type: mrr_at_100 value: 51.001 - type: mrr_at_1000 value: 51.035 - type: mrr_at_3 value: 48.046 - type: mrr_at_5 value: 49.262 - type: ndcg_at_1 value: 40.627 - type: ndcg_at_10 value: 52.5 - type: ndcg_at_100 value: 56.967999999999996 - type: ndcg_at_1000 value: 58.414 - type: ndcg_at_3 value: 47.725 - type: ndcg_at_5 value: 49.932 - type: precision_at_1 value: 40.627 - type: precision_at_10 value: 8.464 - type: precision_at_100 value: 1.17 - type: precision_at_1000 value: 0.135 - type: precision_at_3 value: 21.526 - type: precision_at_5 value: 14.545 - type: recall_at_1 value: 35.515 - type: recall_at_10 value: 65.436 - type: recall_at_100 value: 85.06 - type: recall_at_1000 value: 95.50999999999999 - type: recall_at_3 value: 52.339 - type: recall_at_5 value: 57.894999999999996 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGisRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 19.75 - type: map_at_10 value: 27.639999999999997 - type: map_at_100 value: 28.612 - type: map_at_1000 value: 28.716 - type: map_at_3 value: 25.186999999999998 - type: map_at_5 value: 26.558999999999997 - type: mrr_at_1 value: 21.582 - type: mrr_at_10 value: 29.637999999999998 - type: mrr_at_100 value: 30.514000000000003 - type: mrr_at_1000 value: 30.592999999999996 - type: mrr_at_3 value: 27.326 - type: mrr_at_5 value: 28.58 - type: ndcg_at_1 value: 21.582 - type: ndcg_at_10 value: 32.301 - type: ndcg_at_100 value: 37.217 - type: ndcg_at_1000 value: 39.951 - type: ndcg_at_3 value: 27.483999999999998 - type: ndcg_at_5 value: 29.754 - type: precision_at_1 value: 21.582 - type: precision_at_10 value: 5.175 - type: precision_at_100 value: 0.803 - type: precision_at_1000 value: 0.108 - type: precision_at_3 value: 11.940000000000001 - type: precision_at_5 value: 8.52 - type: recall_at_1 value: 19.75 - type: recall_at_10 value: 44.783 - type: recall_at_100 value: 67.673 - type: recall_at_1000 value: 88.676 - type: recall_at_3 value: 31.740000000000002 - type: recall_at_5 value: 37.128 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackMathematicaRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 11.791 - type: map_at_10 value: 18.782 - type: map_at_100 value: 19.939 - type: map_at_1000 value: 20.083000000000002 - type: map_at_3 value: 16.564 - type: map_at_5 value: 17.592 - type: mrr_at_1 value: 15.174000000000001 - type: mrr_at_10 value: 22.448999999999998 - type: mrr_at_100 value: 23.430999999999997 - type: mrr_at_1000 value: 23.521 - type: mrr_at_3 value: 20.025000000000002 - type: mrr_at_5 value: 21.238 - type: ndcg_at_1 value: 15.174000000000001 - type: ndcg_at_10 value: 23.411 - type: ndcg_at_100 value: 29.365999999999996 - type: ndcg_at_1000 value: 32.893 - type: ndcg_at_3 value: 18.999 - type: ndcg_at_5 value: 20.721 - type: precision_at_1 value: 15.174000000000001 - type: precision_at_10 value: 4.714 - type: precision_at_100 value: 0.903 - type: precision_at_1000 value: 0.134 - type: precision_at_3 value: 9.494 - type: precision_at_5 value: 6.94 - type: recall_at_1 value: 11.791 - type: recall_at_10 value: 33.986 - type: recall_at_100 value: 60.833999999999996 - type: recall_at_1000 value: 86.291 - type: recall_at_3 value: 21.983 - type: recall_at_5 value: 26.313 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackPhysicsRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 25.041999999999998 - type: map_at_10 value: 35.61 - type: map_at_100 value: 37.002 - type: map_at_1000 value: 37.120999999999995 - type: map_at_3 value: 31.982 - type: map_at_5 value: 34.007 - type: mrr_at_1 value: 30.895 - type: mrr_at_10 value: 41.095 - type: mrr_at_100 value: 41.983 - type: mrr_at_1000 value: 42.031 - type: mrr_at_3 value: 38.114 - type: mrr_at_5 value: 39.798 - type: ndcg_at_1 value: 30.895 - type: ndcg_at_10 value: 42.138999999999996 - type: ndcg_at_100 value: 47.741 - type: ndcg_at_1000 value: 49.931 - type: ndcg_at_3 value: 36.179 - type: ndcg_at_5 value: 38.998 - type: precision_at_1 value: 30.895 - type: precision_at_10 value: 8.065 - type: precision_at_100 value: 1.274 - type: precision_at_1000 value: 0.165 - type: precision_at_3 value: 17.645 - type: precision_at_5 value: 12.955 - type: recall_at_1 value: 25.041999999999998 - type: recall_at_10 value: 56.169999999999995 - type: recall_at_100 value: 79.3 - type: recall_at_1000 value: 93.618 - type: recall_at_3 value: 39.359 - type: recall_at_5 value: 46.650000000000006 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackProgrammersRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 23.854 - type: map_at_10 value: 32.088 - type: map_at_100 value: 33.511 - type: map_at_1000 value: 33.629999999999995 - type: map_at_3 value: 29.079 - type: map_at_5 value: 30.663 - type: mrr_at_1 value: 29.110000000000003 - type: mrr_at_10 value: 36.902 - type: mrr_at_100 value: 37.927 - type: mrr_at_1000 value: 37.99 - type: mrr_at_3 value: 34.285 - type: mrr_at_5 value: 35.757 - type: ndcg_at_1 value: 29.110000000000003 - type: ndcg_at_10 value: 37.429 - type: ndcg_at_100 value: 43.59 - type: ndcg_at_1000 value: 46.207 - type: ndcg_at_3 value: 32.394 - type: ndcg_at_5 value: 34.562 - type: precision_at_1 value: 29.110000000000003 - type: precision_at_10 value: 6.895 - type: precision_at_100 value: 1.176 - type: precision_at_1000 value: 0.158 - type: precision_at_3 value: 15.107000000000001 - type: precision_at_5 value: 10.982 - type: recall_at_1 value: 23.854 - type: recall_at_10 value: 48.589 - type: recall_at_100 value: 74.78 - type: recall_at_1000 value: 92.836 - type: recall_at_3 value: 34.489 - type: recall_at_5 value: 40.182 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 21.159999999999997 - type: map_at_10 value: 29.421333333333337 - type: map_at_100 value: 30.61058333333333 - type: map_at_1000 value: 30.742416666666667 - type: map_at_3 value: 26.745833333333337 - type: map_at_5 value: 28.20291666666667 - type: mrr_at_1 value: 25.308249999999997 - type: mrr_at_10 value: 33.21275 - type: mrr_at_100 value: 34.09341666666666 - type: mrr_at_1000 value: 34.163000000000004 - type: mrr_at_3 value: 30.81675 - type: mrr_at_5 value: 32.16816666666667 - type: ndcg_at_1 value: 25.308249999999997 - type: ndcg_at_10 value: 34.46208333333333 - type: ndcg_at_100 value: 39.77183333333334 - type: ndcg_at_1000 value: 42.461916666666674 - type: ndcg_at_3 value: 29.797916666666662 - type: ndcg_at_5 value: 31.935166666666664 - type: precision_at_1 value: 25.308249999999997 - type: precision_at_10 value: 6.260916666666666 - type: precision_at_100 value: 1.0716666666666665 - type: precision_at_1000 value: 0.15025000000000002 - type: precision_at_3 value: 13.926916666666667 - type: precision_at_5 value: 10.043916666666664 - type: recall_at_1 value: 21.159999999999997 - type: recall_at_10 value: 45.61408333333334 - type: recall_at_100 value: 69.26583333333332 - type: recall_at_1000 value: 88.22541666666667 - type: recall_at_3 value: 32.67691666666666 - type: recall_at_5 value: 38.12716666666667 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackStatsRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 19.293 - type: map_at_10 value: 25.316 - type: map_at_100 value: 26.211000000000002 - type: map_at_1000 value: 26.316 - type: map_at_3 value: 23.200000000000003 - type: map_at_5 value: 24.538 - type: mrr_at_1 value: 21.471999999999998 - type: mrr_at_10 value: 27.583000000000002 - type: mrr_at_100 value: 28.371000000000002 - type: mrr_at_1000 value: 28.455000000000002 - type: mrr_at_3 value: 25.613000000000003 - type: mrr_at_5 value: 26.863 - type: ndcg_at_1 value: 21.471999999999998 - type: ndcg_at_10 value: 28.925 - type: ndcg_at_100 value: 33.489000000000004 - type: ndcg_at_1000 value: 36.313 - type: ndcg_at_3 value: 25.003999999999998 - type: ndcg_at_5 value: 27.232 - type: precision_at_1 value: 21.471999999999998 - type: precision_at_10 value: 4.693 - type: precision_at_100 value: 0.762 - type: precision_at_1000 value: 0.108 - type: precision_at_3 value: 10.838000000000001 - type: precision_at_5 value: 7.945 - type: recall_at_1 value: 19.293 - type: recall_at_10 value: 37.63 - type: recall_at_100 value: 58.818000000000005 - type: recall_at_1000 value: 80.026 - type: recall_at_3 value: 27.389000000000003 - type: recall_at_5 value: 32.71 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackTexRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 12.087 - type: map_at_10 value: 17.777 - type: map_at_100 value: 18.837 - type: map_at_1000 value: 18.973000000000003 - type: map_at_3 value: 15.956999999999999 - type: map_at_5 value: 16.902 - type: mrr_at_1 value: 14.763000000000002 - type: mrr_at_10 value: 20.8 - type: mrr_at_100 value: 21.757 - type: mrr_at_1000 value: 21.85 - type: mrr_at_3 value: 18.989 - type: mrr_at_5 value: 19.905 - type: ndcg_at_1 value: 14.763000000000002 - type: ndcg_at_10 value: 21.512999999999998 - type: ndcg_at_100 value: 26.822000000000003 - type: ndcg_at_1000 value: 30.270999999999997 - type: ndcg_at_3 value: 18.16 - type: ndcg_at_5 value: 19.573999999999998 - type: precision_at_1 value: 14.763000000000002 - type: precision_at_10 value: 4.043 - type: precision_at_100 value: 0.7979999999999999 - type: precision_at_1000 value: 0.128 - type: precision_at_3 value: 8.741 - type: precision_at_5 value: 6.325 - type: recall_at_1 value: 12.087 - type: recall_at_10 value: 29.805 - type: recall_at_100 value: 53.787 - type: recall_at_1000 value: 78.884 - type: recall_at_3 value: 20.497 - type: recall_at_5 value: 24.148 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackUnixRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 22.099 - type: map_at_10 value: 29.487999999999996 - type: map_at_100 value: 30.553 - type: map_at_1000 value: 30.669999999999998 - type: map_at_3 value: 27.250000000000004 - type: map_at_5 value: 28.416000000000004 - type: mrr_at_1 value: 26.026 - type: mrr_at_10 value: 33.238 - type: mrr_at_100 value: 34.114 - type: mrr_at_1000 value: 34.188 - type: mrr_at_3 value: 31.157 - type: mrr_at_5 value: 32.262 - type: ndcg_at_1 value: 26.026 - type: ndcg_at_10 value: 34.036 - type: ndcg_at_100 value: 39.443 - type: ndcg_at_1000 value: 42.181999999999995 - type: ndcg_at_3 value: 29.942 - type: ndcg_at_5 value: 31.682 - type: precision_at_1 value: 26.026 - type: precision_at_10 value: 5.7090000000000005 - type: precision_at_100 value: 0.9560000000000001 - type: precision_at_1000 value: 0.131 - type: precision_at_3 value: 13.495 - type: precision_at_5 value: 9.366 - type: recall_at_1 value: 22.099 - type: recall_at_10 value: 44.098 - type: recall_at_100 value: 68.726 - type: recall_at_1000 value: 87.992 - type: recall_at_3 value: 32.902 - type: recall_at_5 value: 37.389 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWebmastersRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 19.195 - type: map_at_10 value: 27.298000000000002 - type: map_at_100 value: 28.875 - type: map_at_1000 value: 29.152 - type: map_at_3 value: 24.595 - type: map_at_5 value: 25.926 - type: mrr_at_1 value: 23.913 - type: mrr_at_10 value: 31.696999999999996 - type: mrr_at_100 value: 32.728 - type: mrr_at_1000 value: 32.808 - type: mrr_at_3 value: 29.249000000000002 - type: mrr_at_5 value: 30.623 - type: ndcg_at_1 value: 23.913 - type: ndcg_at_10 value: 32.745999999999995 - type: ndcg_at_100 value: 38.663 - type: ndcg_at_1000 value: 41.984 - type: ndcg_at_3 value: 28.272000000000002 - type: ndcg_at_5 value: 30.184 - type: precision_at_1 value: 23.913 - type: precision_at_10 value: 6.601 - type: precision_at_100 value: 1.462 - type: precision_at_1000 value: 0.241 - type: precision_at_3 value: 13.439 - type: precision_at_5 value: 10.079 - type: recall_at_1 value: 19.195 - type: recall_at_10 value: 42.933 - type: recall_at_100 value: 69.762 - type: recall_at_1000 value: 91.57 - type: recall_at_3 value: 30.302 - type: recall_at_5 value: 35.17 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWordpressRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 13.816999999999998 - type: map_at_10 value: 19.314 - type: map_at_100 value: 20.328 - type: map_at_1000 value: 20.439 - type: map_at_3 value: 16.658 - type: map_at_5 value: 18.169 - type: mrr_at_1 value: 15.342 - type: mrr_at_10 value: 21.098 - type: mrr_at_100 value: 22.031 - type: mrr_at_1000 value: 22.126 - type: mrr_at_3 value: 18.453 - type: mrr_at_5 value: 19.923 - type: ndcg_at_1 value: 15.342 - type: ndcg_at_10 value: 23.558 - type: ndcg_at_100 value: 28.889 - type: ndcg_at_1000 value: 31.89 - type: ndcg_at_3 value: 18.186 - type: ndcg_at_5 value: 20.751 - type: precision_at_1 value: 15.342 - type: precision_at_10 value: 4.011 - type: precision_at_100 value: 0.749 - type: precision_at_1000 value: 0.109 - type: precision_at_3 value: 7.763000000000001 - type: precision_at_5 value: 6.026 - type: recall_at_1 value: 13.816999999999998 - type: recall_at_10 value: 35.459 - type: recall_at_100 value: 60.612 - type: recall_at_1000 value: 83.174 - type: recall_at_3 value: 20.601 - type: recall_at_5 value: 26.83 - task: type: Retrieval dataset: type: climate-fever name: MTEB ClimateFEVER config: default split: test revision: None metrics: - type: map_at_1 value: 8.770999999999999 - type: map_at_10 value: 14.948 - type: map_at_100 value: 16.668 - type: map_at_1000 value: 16.865 - type: map_at_3 value: 12.264 - type: map_at_5 value: 13.623 - type: mrr_at_1 value: 18.502 - type: mrr_at_10 value: 28.782000000000004 - type: mrr_at_100 value: 29.875 - type: mrr_at_1000 value: 29.929 - type: mrr_at_3 value: 25.147000000000002 - type: mrr_at_5 value: 27.322000000000003 - type: ndcg_at_1 value: 18.502 - type: ndcg_at_10 value: 21.815 - type: ndcg_at_100 value: 29.174 - type: ndcg_at_1000 value: 32.946999999999996 - type: ndcg_at_3 value: 16.833000000000002 - type: ndcg_at_5 value: 18.792 - type: precision_at_1 value: 18.502 - type: precision_at_10 value: 7.016 - type: precision_at_100 value: 1.486 - type: precision_at_1000 value: 0.219 - type: precision_at_3 value: 12.421 - type: precision_at_5 value: 10.15 - type: recall_at_1 value: 8.770999999999999 - type: recall_at_10 value: 27.542 - type: recall_at_100 value: 53.481 - type: recall_at_1000 value: 74.67399999999999 - type: recall_at_3 value: 15.986 - type: recall_at_5 value: 20.669 - task: type: Retrieval dataset: type: dbpedia-entity name: MTEB DBPedia config: default split: test revision: None metrics: - type: map_at_1 value: 6.0249999999999995 - type: map_at_10 value: 11.924 - type: map_at_100 value: 15.801000000000002 - type: map_at_1000 value: 16.878999999999998 - type: map_at_3 value: 9.031 - type: map_at_5 value: 10.181 - type: mrr_at_1 value: 48.0 - type: mrr_at_10 value: 56.928 - type: mrr_at_100 value: 57.619 - type: mrr_at_1000 value: 57.646 - type: mrr_at_3 value: 55.25 - type: mrr_at_5 value: 55.974999999999994 - type: ndcg_at_1 value: 36.875 - type: ndcg_at_10 value: 26.508 - type: ndcg_at_100 value: 29.692 - type: ndcg_at_1000 value: 36.658 - type: ndcg_at_3 value: 30.764000000000003 - type: ndcg_at_5 value: 28.049000000000003 - type: precision_at_1 value: 48.0 - type: precision_at_10 value: 21.175 - type: precision_at_100 value: 6.535 - type: precision_at_1000 value: 1.6230000000000002 - type: precision_at_3 value: 34.75 - type: precision_at_5 value: 27.700000000000003 - type: recall_at_1 value: 6.0249999999999995 - type: recall_at_10 value: 16.454 - type: recall_at_100 value: 35.026 - type: recall_at_1000 value: 58.031 - type: recall_at_3 value: 10.058 - type: recall_at_5 value: 12.145999999999999 - task: type: Classification dataset: type: mteb/emotion name: MTEB EmotionClassification config: default split: test revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 metrics: - type: accuracy value: 43.470000000000006 - type: f1 value: 39.27142511079909 - task: type: Retrieval dataset: type: fever name: MTEB FEVER config: default split: test revision: None metrics: - type: map_at_1 value: 37.468 - type: map_at_10 value: 49.652 - type: map_at_100 value: 50.314 - type: map_at_1000 value: 50.346999999999994 - type: map_at_3 value: 46.592 - type: map_at_5 value: 48.553000000000004 - type: mrr_at_1 value: 40.384 - type: mrr_at_10 value: 53.03099999999999 - type: mrr_at_100 value: 53.629000000000005 - type: mrr_at_1000 value: 53.65299999999999 - type: mrr_at_3 value: 49.967 - type: mrr_at_5 value: 51.951 - type: ndcg_at_1 value: 40.384 - type: ndcg_at_10 value: 56.318 - type: ndcg_at_100 value: 59.43000000000001 - type: ndcg_at_1000 value: 60.266 - type: ndcg_at_3 value: 50.341 - type: ndcg_at_5 value: 53.756 - type: precision_at_1 value: 40.384 - type: precision_at_10 value: 8.062999999999999 - type: precision_at_100 value: 0.972 - type: precision_at_1000 value: 0.106 - type: precision_at_3 value: 20.897 - type: precision_at_5 value: 14.374 - type: recall_at_1 value: 37.468 - type: recall_at_10 value: 73.68900000000001 - type: recall_at_100 value: 87.844 - type: recall_at_1000 value: 94.098 - type: recall_at_3 value: 57.768 - type: recall_at_5 value: 65.979 - task: type: Retrieval dataset: type: fiqa name: MTEB FiQA2018 config: default split: test revision: None metrics: - type: map_at_1 value: 14.071 - type: map_at_10 value: 23.455000000000002 - type: map_at_100 value: 25.358999999999998 - type: map_at_1000 value: 25.55 - type: map_at_3 value: 20.164 - type: map_at_5 value: 21.654999999999998 - type: mrr_at_1 value: 28.395 - type: mrr_at_10 value: 37.21 - type: mrr_at_100 value: 38.086999999999996 - type: mrr_at_1000 value: 38.145 - type: mrr_at_3 value: 34.336 - type: mrr_at_5 value: 35.795 - type: ndcg_at_1 value: 28.395 - type: ndcg_at_10 value: 30.595 - type: ndcg_at_100 value: 37.885000000000005 - type: ndcg_at_1000 value: 41.55 - type: ndcg_at_3 value: 26.858999999999998 - type: ndcg_at_5 value: 27.528999999999996 - type: precision_at_1 value: 28.395 - type: precision_at_10 value: 8.92 - type: precision_at_100 value: 1.6389999999999998 - type: precision_at_1000 value: 0.22999999999999998 - type: precision_at_3 value: 18.004 - type: precision_at_5 value: 13.302 - type: recall_at_1 value: 14.071 - type: recall_at_10 value: 37.635000000000005 - type: recall_at_100 value: 65.18599999999999 - type: recall_at_1000 value: 87.58399999999999 - type: recall_at_3 value: 24.490000000000002 - type: recall_at_5 value: 28.621999999999996 - task: type: Retrieval dataset: type: hotpotqa name: MTEB HotpotQA config: default split: test revision: None metrics: - type: map_at_1 value: 24.659 - type: map_at_10 value: 33.622 - type: map_at_100 value: 34.488 - type: map_at_1000 value: 34.58 - type: map_at_3 value: 31.317 - type: map_at_5 value: 32.689 - type: mrr_at_1 value: 49.318 - type: mrr_at_10 value: 57.028999999999996 - type: mrr_at_100 value: 57.567 - type: mrr_at_1000 value: 57.603 - type: mrr_at_3 value: 55.152 - type: mrr_at_5 value: 56.289 - type: ndcg_at_1 value: 49.318 - type: ndcg_at_10 value: 42.091 - type: ndcg_at_100 value: 45.812999999999995 - type: ndcg_at_1000 value: 47.902 - type: ndcg_at_3 value: 38.012 - type: ndcg_at_5 value: 40.160000000000004 - type: precision_at_1 value: 49.318 - type: precision_at_10 value: 8.921 - type: precision_at_100 value: 1.189 - type: precision_at_1000 value: 0.147 - type: precision_at_3 value: 23.655 - type: precision_at_5 value: 15.897 - type: recall_at_1 value: 24.659 - type: recall_at_10 value: 44.605 - type: recall_at_100 value: 59.453 - type: recall_at_1000 value: 73.40299999999999 - type: recall_at_3 value: 35.483 - type: recall_at_5 value: 39.743 - task: type: Classification dataset: type: mteb/imdb name: MTEB ImdbClassification config: default split: test revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 metrics: - type: accuracy value: 67.2992 - type: ap value: 61.82215741645874 - type: f1 value: 67.04790333380426 - task: type: Retrieval dataset: type: msmarco name: MTEB MSMARCO config: default split: dev revision: None metrics: - type: map_at_1 value: 13.635 - type: map_at_10 value: 22.412000000000003 - type: map_at_100 value: 23.622 - type: map_at_1000 value: 23.707 - type: map_at_3 value: 19.368 - type: map_at_5 value: 21.095 - type: mrr_at_1 value: 14.04 - type: mrr_at_10 value: 22.858 - type: mrr_at_100 value: 24.049 - type: mrr_at_1000 value: 24.127000000000002 - type: mrr_at_3 value: 19.852 - type: mrr_at_5 value: 21.552 - type: ndcg_at_1 value: 14.04 - type: ndcg_at_10 value: 27.676000000000002 - type: ndcg_at_100 value: 33.917 - type: ndcg_at_1000 value: 36.217 - type: ndcg_at_3 value: 21.432000000000002 - type: ndcg_at_5 value: 24.519 - type: precision_at_1 value: 14.04 - type: precision_at_10 value: 4.585999999999999 - type: precision_at_100 value: 0.776 - type: precision_at_1000 value: 0.097 - type: precision_at_3 value: 9.298 - type: precision_at_5 value: 7.135 - type: recall_at_1 value: 13.635 - type: recall_at_10 value: 44.015 - type: recall_at_100 value: 73.756 - type: recall_at_1000 value: 91.743 - type: recall_at_3 value: 26.941 - type: recall_at_5 value: 34.378 - task: type: Classification dataset: type: mteb/mtop_domain name: MTEB MTOPDomainClassification (en) config: en split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 91.81714546283631 - type: f1 value: 91.67516531750526 - task: type: Classification dataset: type: mteb/mtop_intent name: MTEB MTOPIntentClassification (en) config: en split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - type: accuracy value: 74.69904240766073 - type: f1 value: 57.9559746458099 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (en) config: en split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 71.76866173503699 - type: f1 value: 69.95643410077002 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (en) config: en split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 77.85137861466038 - type: f1 value: 77.66496420028315 - task: type: Clustering dataset: type: mteb/medrxiv-clustering-p2p name: MTEB MedrxivClusteringP2P config: default split: test revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 metrics: - type: v_measure value: 36.646200212660744 - task: type: Clustering dataset: type: mteb/medrxiv-clustering-s2s name: MTEB MedrxivClusteringS2S config: default split: test revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 metrics: - type: v_measure value: 32.57381797665868 - task: type: Reranking dataset: type: mteb/mind_small name: MTEB MindSmallReranking config: default split: test revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 metrics: - type: map value: 30.54815546178676 - type: mrr value: 31.40311212966208 - task: type: Retrieval dataset: type: nfcorpus name: MTEB NFCorpus config: default split: test revision: None metrics: - type: map_at_1 value: 3.005 - type: map_at_10 value: 8.125 - type: map_at_100 value: 11.439 - type: map_at_1000 value: 12.908 - type: map_at_3 value: 5.299 - type: map_at_5 value: 6.654 - type: mrr_at_1 value: 33.745999999999995 - type: mrr_at_10 value: 43.513000000000005 - type: mrr_at_100 value: 44.330999999999996 - type: mrr_at_1000 value: 44.388 - type: mrr_at_3 value: 41.28 - type: mrr_at_5 value: 42.766 - type: ndcg_at_1 value: 31.889 - type: ndcg_at_10 value: 26.432 - type: ndcg_at_100 value: 26.191 - type: ndcg_at_1000 value: 35.413 - type: ndcg_at_3 value: 29.625 - type: ndcg_at_5 value: 28.588 - type: precision_at_1 value: 33.745999999999995 - type: precision_at_10 value: 21.146 - type: precision_at_100 value: 7.736999999999999 - type: precision_at_1000 value: 2.08 - type: precision_at_3 value: 29.102 - type: precision_at_5 value: 26.316 - type: recall_at_1 value: 3.005 - type: recall_at_10 value: 12.29 - type: recall_at_100 value: 30.06 - type: recall_at_1000 value: 63.148 - type: recall_at_3 value: 6.587 - type: recall_at_5 value: 9.095 - task: type: Retrieval dataset: type: nq name: MTEB NQ config: default split: test revision: None metrics: - type: map_at_1 value: 19.839000000000002 - type: map_at_10 value: 31.424999999999997 - type: map_at_100 value: 32.641999999999996 - type: map_at_1000 value: 32.704 - type: map_at_3 value: 27.742 - type: map_at_5 value: 29.854999999999997 - type: mrr_at_1 value: 22.451 - type: mrr_at_10 value: 33.632 - type: mrr_at_100 value: 34.653 - type: mrr_at_1000 value: 34.699000000000005 - type: mrr_at_3 value: 30.427 - type: mrr_at_5 value: 32.263 - type: ndcg_at_1 value: 22.422 - type: ndcg_at_10 value: 37.929 - type: ndcg_at_100 value: 43.667 - type: ndcg_at_1000 value: 45.231 - type: ndcg_at_3 value: 30.814999999999998 - type: ndcg_at_5 value: 34.379 - type: precision_at_1 value: 22.422 - type: precision_at_10 value: 6.59 - type: precision_at_100 value: 0.9860000000000001 - type: precision_at_1000 value: 0.11399999999999999 - type: precision_at_3 value: 14.301 - type: precision_at_5 value: 10.626 - type: recall_at_1 value: 19.839000000000002 - type: recall_at_10 value: 55.769999999999996 - type: recall_at_100 value: 81.733 - type: recall_at_1000 value: 93.559 - type: recall_at_3 value: 37.078 - type: recall_at_5 value: 45.318999999999996 - task: type: Retrieval dataset: type: quora name: MTEB QuoraRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 67.534 - type: map_at_10 value: 81.449 - type: map_at_100 value: 82.15400000000001 - type: map_at_1000 value: 82.173 - type: map_at_3 value: 78.412 - type: map_at_5 value: 80.268 - type: mrr_at_1 value: 77.77 - type: mrr_at_10 value: 84.60499999999999 - type: mrr_at_100 value: 84.765 - type: mrr_at_1000 value: 84.76700000000001 - type: mrr_at_3 value: 83.493 - type: mrr_at_5 value: 84.221 - type: ndcg_at_1 value: 77.79 - type: ndcg_at_10 value: 85.555 - type: ndcg_at_100 value: 87.105 - type: ndcg_at_1000 value: 87.261 - type: ndcg_at_3 value: 82.401 - type: ndcg_at_5 value: 84.071 - type: precision_at_1 value: 77.79 - type: precision_at_10 value: 13.104 - type: precision_at_100 value: 1.5190000000000001 - type: precision_at_1000 value: 0.156 - type: precision_at_3 value: 36.157000000000004 - type: precision_at_5 value: 23.86 - type: recall_at_1 value: 67.534 - type: recall_at_10 value: 93.573 - type: recall_at_100 value: 99.10799999999999 - type: recall_at_1000 value: 99.911 - type: recall_at_3 value: 84.575 - type: recall_at_5 value: 89.251 - task: type: Clustering dataset: type: mteb/reddit-clustering name: MTEB RedditClustering config: default split: test revision: 24640382cdbf8abc73003fb0fa6d111a705499eb metrics: - type: v_measure value: 50.622402916164575 - task: type: Clustering dataset: type: mteb/reddit-clustering-p2p name: MTEB RedditClusteringP2P config: default split: test revision: 282350215ef01743dc01b456c7f5241fa8937f16 metrics: - type: v_measure value: 54.43689895218044 - task: type: Retrieval dataset: type: scidocs name: MTEB SCIDOCS config: default split: test revision: None metrics: - type: map_at_1 value: 3.723 - type: map_at_10 value: 9.524000000000001 - type: map_at_100 value: 11.407 - type: map_at_1000 value: 11.721 - type: map_at_3 value: 6.678000000000001 - type: map_at_5 value: 7.881 - type: mrr_at_1 value: 18.2 - type: mrr_at_10 value: 28.349999999999998 - type: mrr_at_100 value: 29.528 - type: mrr_at_1000 value: 29.601 - type: mrr_at_3 value: 25.15 - type: mrr_at_5 value: 26.765 - type: ndcg_at_1 value: 18.2 - type: ndcg_at_10 value: 16.603 - type: ndcg_at_100 value: 24.331 - type: ndcg_at_1000 value: 30.086000000000002 - type: ndcg_at_3 value: 15.151 - type: ndcg_at_5 value: 13.199 - type: precision_at_1 value: 18.2 - type: precision_at_10 value: 8.86 - type: precision_at_100 value: 2.012 - type: precision_at_1000 value: 0.33999999999999997 - type: precision_at_3 value: 14.2 - type: precision_at_5 value: 11.559999999999999 - type: recall_at_1 value: 3.723 - type: recall_at_10 value: 17.965 - type: recall_at_100 value: 40.803 - type: recall_at_1000 value: 69.053 - type: recall_at_3 value: 8.633000000000001 - type: recall_at_5 value: 11.722000000000001 - task: type: STS dataset: type: mteb/sickr-sts name: MTEB SICK-R config: default split: test revision: a6ea5a8cab320b040a23452cc28066d9beae2cee metrics: - 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type: precision_at_3 value: 21.0 - type: precision_at_5 value: 14.667 - type: recall_at_1 value: 44.917 - type: recall_at_10 value: 74.483 - type: recall_at_100 value: 89.1 - type: recall_at_1000 value: 98.0 - type: recall_at_3 value: 58.15 - type: recall_at_5 value: 66.033 - task: type: PairClassification dataset: type: mteb/sprintduplicatequestions-pairclassification name: MTEB SprintDuplicateQuestions config: default split: test revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 metrics: - type: cos_sim_accuracy value: 99.66534653465347 - type: cos_sim_ap value: 90.67883265196161 - type: cos_sim_f1 value: 82.81327389796928 - type: cos_sim_precision value: 82.04121687929342 - type: cos_sim_recall value: 83.6 - type: dot_accuracy value: 99.6009900990099 - type: dot_ap value: 85.37859415933599 - type: dot_f1 value: 79.68285431119922 - type: dot_precision value: 78.97838899803537 - type: dot_recall value: 80.4 - type: euclidean_accuracy value: 99.66435643564357 - type: euclidean_ap value: 90.28983244955695 - type: euclidean_f1 value: 82.47925817471938 - type: euclidean_precision value: 80.55290753098188 - type: euclidean_recall value: 84.5 - type: manhattan_accuracy value: 99.65247524752475 - type: manhattan_ap value: 89.75455076116366 - type: manhattan_f1 value: 81.63682864450128 - type: manhattan_precision value: 83.56020942408377 - type: manhattan_recall value: 79.80000000000001 - type: max_accuracy value: 99.66534653465347 - type: max_ap value: 90.67883265196161 - type: max_f1 value: 82.81327389796928 - task: type: Clustering dataset: type: mteb/stackexchange-clustering name: MTEB StackExchangeClustering config: default split: test revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 metrics: - type: v_measure value: 54.25773656414605 - task: type: Clustering dataset: type: mteb/stackexchange-clustering-p2p name: MTEB StackExchangeClusteringP2P config: default split: test revision: 815ca46b2622cec33ccafc3735d572c266efdb44 metrics: - type: v_measure value: 32.52034918177213 - task: type: Reranking dataset: type: mteb/stackoverflowdupquestions-reranking name: MTEB StackOverflowDupQuestions config: default split: test revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 metrics: - type: map value: 47.10460797458404 - type: mrr value: 47.67126358119005 - task: type: Retrieval dataset: type: trec-covid name: MTEB TRECCOVID config: default split: test revision: None metrics: - type: map_at_1 value: 0.159 - type: map_at_10 value: 0.9979999999999999 - type: map_at_100 value: 5.806 - type: map_at_1000 value: 16.575 - type: map_at_3 value: 0.391 - type: map_at_5 value: 0.596 - type: mrr_at_1 value: 56.00000000000001 - type: mrr_at_10 value: 68.7 - type: mrr_at_100 value: 68.892 - type: mrr_at_1000 value: 68.892 - type: mrr_at_3 value: 65.667 - type: mrr_at_5 value: 68.367 - type: ndcg_at_1 value: 51.0 - type: ndcg_at_10 value: 45.1 - type: ndcg_at_100 value: 36.834 - type: ndcg_at_1000 value: 39.329 - type: ndcg_at_3 value: 49.458 - type: ndcg_at_5 value: 48.177 - type: precision_at_1 value: 56.00000000000001 - type: precision_at_10 value: 47.8 - type: precision_at_100 value: 38.6 - type: precision_at_1000 value: 18.285999999999998 - type: precision_at_3 value: 54.0 - type: precision_at_5 value: 52.400000000000006 - type: recall_at_1 value: 0.159 - type: recall_at_10 value: 1.2510000000000001 - type: recall_at_100 value: 9.237 - type: recall_at_1000 value: 38.984 - type: recall_at_3 value: 0.44 - type: recall_at_5 value: 0.7080000000000001 - task: type: Retrieval dataset: type: webis-touche2020 name: MTEB Touche2020 config: default split: test revision: None metrics: - type: map_at_1 value: 1.6660000000000001 - type: map_at_10 value: 7.444000000000001 - type: map_at_100 value: 12.078 - type: map_at_1000 value: 13.716999999999999 - type: map_at_3 value: 4.06 - type: map_at_5 value: 5.172000000000001 - type: mrr_at_1 value: 20.408 - type: mrr_at_10 value: 33.547 - type: mrr_at_100 value: 35.281 - type: mrr_at_1000 value: 35.289 - type: mrr_at_3 value: 29.252 - type: mrr_at_5 value: 31.19 - type: ndcg_at_1 value: 18.367 - type: ndcg_at_10 value: 18.848000000000003 - type: ndcg_at_100 value: 29.938 - type: ndcg_at_1000 value: 42.792 - type: ndcg_at_3 value: 20.005 - type: ndcg_at_5 value: 18.617 - type: precision_at_1 value: 20.408 - type: precision_at_10 value: 17.143 - type: precision_at_100 value: 6.571000000000001 - type: precision_at_1000 value: 1.492 - type: precision_at_3 value: 21.088 - type: precision_at_5 value: 18.776 - type: recall_at_1 value: 1.6660000000000001 - type: recall_at_10 value: 12.736 - type: recall_at_100 value: 41.485 - type: recall_at_1000 value: 80.301 - type: recall_at_3 value: 5.137 - type: recall_at_5 value: 7.317 - task: type: Classification dataset: type: mteb/toxic_conversations_50k name: MTEB ToxicConversationsClassification config: default split: test revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c metrics: - type: accuracy value: 67.481 - type: ap value: 12.474830532963725 - type: f1 value: 51.720124230716834 - task: type: Classification dataset: type: mteb/tweet_sentiment_extraction name: MTEB TweetSentimentExtractionClassification config: default split: test revision: d604517c81ca91fe16a244d1248fc021f9ecee7a metrics: - type: accuracy value: 55.62252405206565 - type: f1 value: 55.87133173318741 - task: type: Clustering dataset: type: mteb/twentynewsgroups-clustering name: MTEB TwentyNewsgroupsClustering config: default split: test revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 metrics: - type: v_measure value: 45.695133575997474 - task: type: PairClassification dataset: type: mteb/twittersemeval2015-pairclassification name: MTEB TwitterSemEval2015 config: default split: test revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 metrics: - type: cos_sim_accuracy value: 84.16284198605233 - type: cos_sim_ap value: 67.77133994574282 - type: cos_sim_f1 value: 63.007767732076914 - type: cos_sim_precision value: 60.89096726556732 - type: cos_sim_recall value: 65.27704485488127 - type: dot_accuracy value: 80.60439887941826 - type: dot_ap value: 55.17278808505333 - type: dot_f1 value: 55.023250784038055 - type: dot_precision value: 46.619021440351844 - type: dot_recall value: 67.12401055408971 - type: euclidean_accuracy value: 84.75889610776659 - type: euclidean_ap value: 69.33925609880741 - type: euclidean_f1 value: 64.72887151929653 - type: euclidean_precision value: 60.254661209640744 - type: euclidean_recall value: 69.92084432717678 - type: manhattan_accuracy value: 84.84234368480658 - type: manhattan_ap value: 69.50780726475959 - type: manhattan_f1 value: 64.78766430738119 - type: manhattan_precision value: 62.17855409995148 - type: manhattan_recall value: 67.62532981530343 - type: max_accuracy value: 84.84234368480658 - type: max_ap value: 69.50780726475959 - type: max_f1 value: 64.78766430738119 - task: type: PairClassification dataset: type: mteb/twitterurlcorpus-pairclassification name: MTEB TwitterURLCorpus config: default split: test revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf metrics: - type: cos_sim_accuracy value: 88.46198626149726 - type: cos_sim_ap value: 84.64911720373662 - type: cos_sim_f1 value: 77.18601251827143 - type: cos_sim_precision value: 75.19900679179142 - type: cos_sim_recall value: 79.28087465352634 - type: dot_accuracy value: 86.79512554818179 - type: dot_ap value: 80.43213280609042 - type: dot_f1 value: 74.18943791589976 - type: dot_precision value: 68.65828092243187 - type: dot_recall value: 80.68986757006468 - type: euclidean_accuracy value: 88.2368921488726 - type: euclidean_ap value: 84.2791000321804 - type: euclidean_f1 value: 76.62216238453198 - type: euclidean_precision value: 74.49640026179914 - type: euclidean_recall value: 78.87280566676932 - type: manhattan_accuracy value: 88.29122521054062 - type: manhattan_ap value: 84.25495067571485 - type: manhattan_f1 value: 76.60077590984667 - type: manhattan_precision value: 73.63784897350287 - type: manhattan_recall value: 79.81213427779488 - type: max_accuracy value: 88.46198626149726 - type: max_ap value: 84.64911720373662 - type: max_f1 value: 77.18601251827143 --- # embedder-100p This is a ms-marco bi-encoder from sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. It is trained on more than 20GiB of german text. It used the knowledge distillation to be a bi-language embedding model (English and German). <!--- Describe your model here --> ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('embedder-100p') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('embedder-100p') model = AutoModel.from_pretrained('embedder-100p') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(encoded_input) # Perform pooling. In this case, mean pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results <!--- Describe how your model was evaluated --> The evaluation on MTEB ## Training The model was trained with the parameters: DataLoader: `torch.utils.data.dataloader.DataLoader` of length 231230 with parameters: ``` {'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` Loss**: `sentence_transformers.losses.MSELoss.MSELoss` Parameters of the fit()-Method: ``` { "epochs": 20, "evaluation_steps": 1000, "evaluator": "sentence_transformers.evaluation.SequentialEvaluator.SequentialEvaluator", "max_grad_norm": 1, "optimizer_class": "<class 'torch.optim.adamw.AdamW'>", "optimizer_params": { "eps": 1e-06, "lr": 7e-06 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 5000, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 384, 'do_lower_case': False}) with Transformer model: XLMRobertaModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) ) ``` ## By @[bayang](https://huggingface.co/bayang) <!--- Describe where people can find more information -->
MarieAngeA13/Sentiment-Analysis-BERT	MarieAngeA13	2023-07-01T18:26:06Z	4,702	9	transformers	[ "transformers", "pytorch", "bert", "text-classification", "sentiment", "sentiment-analysis", "en", "license:bsd-3-clause", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-07-01T17:42:40Z	--- license: bsd-3-clause language: - en tags: - sentiment - bert - sentiment-analysis - transformers pipeline_tag: text-classification --- > Authors : GRP209 # User Comment Sentiment Analysis This model aims to analyze user comments on products and extracting the expressed sentiments. User ratings on the internet do not always provide detailed qualitative information about their experience. Therefore, it is important to go beyond these ratings and extract more insightful information that can help a brand improve their product or service. # Objective The model utilizes the BERT architecture and is trained on a dataset of user comments with sentiment labels. The model is capable of analyzing comments and extracting sentiments such as positive, negative, or neutral. # Features Sentiment Classification: The model can classify user comments into positive, negative, or neutral sentiments, providing an overall indication of the expressed opinion. Improvement Suggestions: In cases where a comment expresses a negative or neutral sentiment, the model suggests an improved version of the text with a more positive sentiment. This can help businesses understand consumer reactions and identify areas for product or service improvement. # Usage To use this sentiment analysis system, follow these steps: - Install the required dependencies by running the command pip install -r requirements.txt. - Once the training is complete, the best-trained model will be saved in the best_model_state.bin file. - To make predictions on new comments, use the analyze_sentiment(comment_text) function, replacing comment_text with the actual comment text to analyze. - The model will return the sentiment expressed in the comment. - To suggest an improved version of a comment, use the suggest_improved_text(comment_text) function. - If the comment expresses a negative or neutral sentiment, the function will generate an improved version of the text with a more positive sentiment. Otherwise, the original text will be returned without modification.
mradermacher/Mixtral_AI_ARCHIVE-GGUF	mradermacher	2024-06-04T19:36:18Z	4,702	0	transformers	[ "transformers", "gguf", "mergekit", "merge", "en", "base_model:LeroyDyer/Mixtral_AI_ARCHIVE", "endpoints_compatible", "region:us" ]	null	2024-06-04T19:10:44Z	--- base_model: LeroyDyer/Mixtral_AI_ARCHIVE language: - en library_name: transformers quantized_by: mradermacher tags: - mergekit - merge --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/LeroyDyer/Mixtral_AI_ARCHIVE <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_ARCHIVE-GGUF/resolve/main/Mixtral_AI_ARCHIVE.Q2_K.gguf) \| Q2_K \| 2.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_ARCHIVE-GGUF/resolve/main/Mixtral_AI_ARCHIVE.IQ3_XS.gguf) \| IQ3_XS \| 3.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_ARCHIVE-GGUF/resolve/main/Mixtral_AI_ARCHIVE.Q3_K_S.gguf) \| Q3_K_S \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_ARCHIVE-GGUF/resolve/main/Mixtral_AI_ARCHIVE.IQ3_S.gguf) \| IQ3_S \| 3.3 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_ARCHIVE-GGUF/resolve/main/Mixtral_AI_ARCHIVE.IQ3_M.gguf) \| IQ3_M \| 3.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_ARCHIVE-GGUF/resolve/main/Mixtral_AI_ARCHIVE.Q3_K_M.gguf) \| Q3_K_M \| 3.6 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_ARCHIVE-GGUF/resolve/main/Mixtral_AI_ARCHIVE.Q3_K_L.gguf) \| Q3_K_L \| 3.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_ARCHIVE-GGUF/resolve/main/Mixtral_AI_ARCHIVE.IQ4_XS.gguf) \| IQ4_XS \| 4.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_ARCHIVE-GGUF/resolve/main/Mixtral_AI_ARCHIVE.Q4_K_S.gguf) \| Q4_K_S \| 4.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_ARCHIVE-GGUF/resolve/main/Mixtral_AI_ARCHIVE.Q4_K_M.gguf) \| Q4_K_M \| 4.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_ARCHIVE-GGUF/resolve/main/Mixtral_AI_ARCHIVE.Q5_K_S.gguf) \| Q5_K_S \| 5.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_ARCHIVE-GGUF/resolve/main/Mixtral_AI_ARCHIVE.Q5_K_M.gguf) \| Q5_K_M \| 5.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_ARCHIVE-GGUF/resolve/main/Mixtral_AI_ARCHIVE.Q6_K.gguf) \| Q6_K \| 6.0 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_ARCHIVE-GGUF/resolve/main/Mixtral_AI_ARCHIVE.Q8_0.gguf) \| Q8_0 \| 7.8 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_ARCHIVE-GGUF/resolve/main/Mixtral_AI_ARCHIVE.f16.gguf) \| f16 \| 14.6 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
thibaud/controlnet-sd21-depth-diffusers	thibaud	2023-08-14T07:45:00Z	4,698	5	diffusers	[ "diffusers", "art", "stable diffusion", "controlnet", "en", "license:other", "region:us" ]	null	2023-03-09T08:19:34Z	--- license: other language: - en tags: - art - diffusers - stable diffusion - controlnet --- Here's the first version of controlnet for stablediffusion 2.1 for diffusers Trained on a subset of laion/laion-art License: refers to the different preprocessor's ones. ### Depth: ![<depth> 0](https://huggingface.co/thibaud/controlnet-sd21/resolve/main/example_depth.png) ### Misuse, Malicious Use, and Out-of-Scope Use The model should not be used to intentionally create or disseminate images that create hostile or alienating environments for people. This includes generating images that people would foreseeably find disturbing, distressing, or offensive; or content that propagates historical or current stereotypes. Thanks - https://huggingface.co/lllyasviel/ControlNet for the implementation and the release of 1.5 models. - https://huggingface.co/thepowefuldeez for the conversion script to diffusers
sgugger/tiny-distilbert-classification	sgugger	2021-07-29T17:12:02Z	4,696	1	transformers	[ "transformers", "pytorch", "tf", "distilbert", "text-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-03-02T23:29:05Z	Entry not found
djovak/multi-qa-MiniLM-L6-cos-v1	djovak	2023-10-20T10:08:21Z	4,696	0	transformers	[ "transformers", "bert", "feature-extraction", "mteb", "model-index", "endpoints_compatible", "text-embeddings-inference", "region:us" ]	feature-extraction	2023-10-20T09:12:04Z	--- tags: - mteb model-index: - name: multi-qa-MiniLM-L6-cos-v1 results: - task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (en) config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 61.791044776119406 - type: ap value: 25.829130082463124 - type: f1 value: 56.00432262887535 - task: type: Classification dataset: type: mteb/amazon_polarity name: MTEB AmazonPolarityClassification config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 62.36077499999999 - type: ap value: 57.68938427410222 - type: f1 value: 62.247666843818436 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (en) config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 29.59 - type: f1 value: 29.241975951560622 - task: type: Retrieval dataset: type: arguana name: MTEB ArguAna config: default split: test revision: None metrics: - type: map_at_1 value: 25.249 - type: map_at_10 value: 40.196 - type: map_at_100 value: 41.336 - type: map_at_1000 value: 41.343 - type: map_at_3 value: 34.934 - type: map_at_5 value: 37.871 - type: mrr_at_1 value: 26.031 - type: mrr_at_10 value: 40.488 - type: mrr_at_100 value: 41.628 - type: mrr_at_1000 value: 41.634 - type: mrr_at_3 value: 35.171 - type: mrr_at_5 value: 38.126 - type: ndcg_at_1 value: 25.249 - type: ndcg_at_10 value: 49.11 - type: ndcg_at_100 value: 53.827999999999996 - type: ndcg_at_1000 value: 53.993 - type: ndcg_at_3 value: 38.175 - type: ndcg_at_5 value: 43.488 - type: precision_at_1 value: 25.249 - type: precision_at_10 value: 7.788 - type: precision_at_100 value: 0.9820000000000001 - type: precision_at_1000 value: 0.1 - type: precision_at_3 value: 15.861 - type: precision_at_5 value: 12.105 - type: recall_at_1 value: 25.249 - type: recall_at_10 value: 77.881 - type: recall_at_100 value: 98.222 - type: recall_at_1000 value: 99.502 - type: recall_at_3 value: 47.582 - type: recall_at_5 value: 60.526 - task: type: Clustering dataset: type: mteb/arxiv-clustering-p2p name: MTEB ArxivClusteringP2P config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics: - type: v_measure value: 37.75242616816114 - task: type: Clustering dataset: type: mteb/arxiv-clustering-s2s name: MTEB ArxivClusteringS2S config: default split: test revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 metrics: - type: v_measure value: 27.70031808300247 - task: type: Reranking dataset: type: mteb/askubuntudupquestions-reranking name: MTEB AskUbuntuDupQuestions config: default split: test revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 metrics: - type: map value: 63.09199068762668 - type: mrr value: 76.08055225783757 - task: type: STS dataset: type: mteb/biosses-sts name: MTEB BIOSSES config: default split: test revision: d3fb88f8f02e40887cd149695127462bbcf29b4a metrics: - type: cos_sim_pearson value: 80.83007234777145 - type: cos_sim_spearman value: 79.76446808992547 - type: euclidean_pearson value: 80.24418669808917 - type: euclidean_spearman value: 79.76446808992547 - type: manhattan_pearson value: 79.58896133042379 - type: manhattan_spearman value: 78.9614377441415 - task: type: Classification dataset: type: mteb/banking77 name: MTEB Banking77Classification config: default split: test revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 metrics: - type: accuracy value: 78.6038961038961 - type: f1 value: 77.95572823168757 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-p2p name: MTEB BiorxivClusteringP2P config: default split: test revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 metrics: - type: v_measure value: 30.240388191413935 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-s2s name: MTEB BiorxivClusteringS2S config: default split: test revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 metrics: - type: v_measure value: 22.670413424756212 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackAndroidRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 32.694 - type: map_at_10 value: 43.811 - type: map_at_100 value: 45.274 - type: map_at_1000 value: 45.393 - type: map_at_3 value: 40.043 - type: map_at_5 value: 41.983 - type: mrr_at_1 value: 39.628 - type: mrr_at_10 value: 49.748 - type: mrr_at_100 value: 50.356 - type: mrr_at_1000 value: 50.39900000000001 - type: mrr_at_3 value: 46.924 - type: mrr_at_5 value: 48.598 - type: ndcg_at_1 value: 39.628 - type: ndcg_at_10 value: 50.39 - type: ndcg_at_100 value: 55.489 - type: ndcg_at_1000 value: 57.291000000000004 - type: ndcg_at_3 value: 44.849 - type: ndcg_at_5 value: 47.195 - type: precision_at_1 value: 39.628 - type: precision_at_10 value: 9.714 - type: precision_at_100 value: 1.591 - type: precision_at_1000 value: 0.2 - type: precision_at_3 value: 21.507 - type: precision_at_5 value: 15.393 - type: recall_at_1 value: 32.694 - type: recall_at_10 value: 63.031000000000006 - type: recall_at_100 value: 84.49 - type: recall_at_1000 value: 96.148 - type: recall_at_3 value: 46.851 - type: recall_at_5 value: 53.64 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackEnglishRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 28.183000000000003 - type: map_at_10 value: 38.796 - type: map_at_100 value: 40.117000000000004 - type: map_at_1000 value: 40.251 - type: map_at_3 value: 35.713 - type: map_at_5 value: 37.446 - type: mrr_at_1 value: 35.605 - type: mrr_at_10 value: 44.824000000000005 - type: mrr_at_100 value: 45.544000000000004 - type: mrr_at_1000 value: 45.59 - type: mrr_at_3 value: 42.452 - type: mrr_at_5 value: 43.891999999999996 - type: ndcg_at_1 value: 35.605 - type: ndcg_at_10 value: 44.857 - type: ndcg_at_100 value: 49.68 - type: ndcg_at_1000 value: 51.841 - type: ndcg_at_3 value: 40.445 - type: ndcg_at_5 value: 42.535000000000004 - type: precision_at_1 value: 35.605 - type: precision_at_10 value: 8.624 - type: precision_at_100 value: 1.438 - type: precision_at_1000 value: 0.193 - type: precision_at_3 value: 19.808999999999997 - type: precision_at_5 value: 14.191 - type: recall_at_1 value: 28.183000000000003 - type: recall_at_10 value: 55.742000000000004 - type: recall_at_100 value: 76.416 - type: recall_at_1000 value: 90.20899999999999 - type: recall_at_3 value: 42.488 - type: recall_at_5 value: 48.431999999999995 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGamingRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 36.156 - type: map_at_10 value: 47.677 - type: map_at_100 value: 48.699999999999996 - type: map_at_1000 value: 48.756 - type: map_at_3 value: 44.467 - type: map_at_5 value: 46.132 - type: mrr_at_1 value: 41.567 - type: mrr_at_10 value: 51.06699999999999 - type: mrr_at_100 value: 51.800000000000004 - type: mrr_at_1000 value: 51.827999999999996 - type: mrr_at_3 value: 48.620999999999995 - type: mrr_at_5 value: 50.013 - type: ndcg_at_1 value: 41.567 - type: ndcg_at_10 value: 53.418 - type: ndcg_at_100 value: 57.743 - type: ndcg_at_1000 value: 58.940000000000005 - type: ndcg_at_3 value: 47.923 - type: ndcg_at_5 value: 50.352 - type: precision_at_1 value: 41.567 - type: precision_at_10 value: 8.74 - type: precision_at_100 value: 1.1809999999999998 - type: precision_at_1000 value: 0.133 - type: precision_at_3 value: 21.337999999999997 - type: precision_at_5 value: 14.646 - type: recall_at_1 value: 36.156 - type: recall_at_10 value: 67.084 - type: recall_at_100 value: 86.299 - type: recall_at_1000 value: 94.82000000000001 - type: recall_at_3 value: 52.209 - type: recall_at_5 value: 58.175 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGisRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 23.513 - type: map_at_10 value: 32.699 - type: map_at_100 value: 33.788000000000004 - type: map_at_1000 value: 33.878 - type: map_at_3 value: 30.044999999999998 - type: map_at_5 value: 31.506 - type: mrr_at_1 value: 25.311 - type: mrr_at_10 value: 34.457 - type: mrr_at_100 value: 35.443999999999996 - type: mrr_at_1000 value: 35.504999999999995 - type: mrr_at_3 value: 31.902 - type: mrr_at_5 value: 33.36 - type: ndcg_at_1 value: 25.311 - type: ndcg_at_10 value: 37.929 - type: ndcg_at_100 value: 43.1 - type: ndcg_at_1000 value: 45.275999999999996 - type: ndcg_at_3 value: 32.745999999999995 - type: ndcg_at_5 value: 35.235 - type: precision_at_1 value: 25.311 - type: precision_at_10 value: 6.034 - type: precision_at_100 value: 0.8959999999999999 - type: precision_at_1000 value: 0.11299999999999999 - type: precision_at_3 value: 14.237 - type: precision_at_5 value: 10.034 - type: recall_at_1 value: 23.513 - type: recall_at_10 value: 52.312999999999995 - type: recall_at_100 value: 75.762 - type: recall_at_1000 value: 91.85799999999999 - type: recall_at_3 value: 38.222 - type: recall_at_5 value: 44.316 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackMathematicaRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 16.333000000000002 - type: map_at_10 value: 24.605 - type: map_at_100 value: 25.924000000000003 - type: map_at_1000 value: 26.039 - type: map_at_3 value: 21.907 - type: map_at_5 value: 23.294999999999998 - type: mrr_at_1 value: 20.647 - type: mrr_at_10 value: 29.442 - type: mrr_at_100 value: 30.54 - type: mrr_at_1000 value: 30.601 - type: mrr_at_3 value: 26.802999999999997 - type: mrr_at_5 value: 28.147 - type: ndcg_at_1 value: 20.647 - type: ndcg_at_10 value: 30.171999999999997 - type: ndcg_at_100 value: 36.466 - type: ndcg_at_1000 value: 39.095 - type: ndcg_at_3 value: 25.134 - type: ndcg_at_5 value: 27.211999999999996 - type: precision_at_1 value: 20.647 - type: precision_at_10 value: 5.659 - type: precision_at_100 value: 1.012 - type: precision_at_1000 value: 0.13899999999999998 - type: precision_at_3 value: 12.148 - type: precision_at_5 value: 8.881 - type: recall_at_1 value: 16.333000000000002 - type: recall_at_10 value: 42.785000000000004 - type: recall_at_100 value: 70.282 - type: recall_at_1000 value: 88.539 - type: recall_at_3 value: 28.307 - type: recall_at_5 value: 33.751 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackPhysicsRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 26.821 - type: map_at_10 value: 37.188 - type: map_at_100 value: 38.516 - type: map_at_1000 value: 38.635000000000005 - type: map_at_3 value: 33.821 - type: map_at_5 value: 35.646 - type: mrr_at_1 value: 33.109 - type: mrr_at_10 value: 43.003 - type: mrr_at_100 value: 43.849 - type: mrr_at_1000 value: 43.889 - type: mrr_at_3 value: 40.263 - type: mrr_at_5 value: 41.957 - type: ndcg_at_1 value: 33.109 - type: ndcg_at_10 value: 43.556 - type: ndcg_at_100 value: 49.197 - type: ndcg_at_1000 value: 51.269 - type: ndcg_at_3 value: 38.01 - type: ndcg_at_5 value: 40.647 - type: precision_at_1 value: 33.109 - type: precision_at_10 value: 8.085 - type: precision_at_100 value: 1.286 - type: precision_at_1000 value: 0.166 - type: precision_at_3 value: 18.191 - type: precision_at_5 value: 13.050999999999998 - type: recall_at_1 value: 26.821 - type: recall_at_10 value: 56.818000000000005 - type: recall_at_100 value: 80.63 - type: recall_at_1000 value: 94.042 - type: recall_at_3 value: 41.266000000000005 - type: recall_at_5 value: 48.087999999999994 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackProgrammersRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 22.169 - type: map_at_10 value: 31.682 - type: map_at_100 value: 32.988 - type: map_at_1000 value: 33.097 - type: map_at_3 value: 28.708 - type: map_at_5 value: 30.319000000000003 - type: mrr_at_1 value: 27.854 - type: mrr_at_10 value: 36.814 - type: mrr_at_100 value: 37.741 - type: mrr_at_1000 value: 37.798 - type: mrr_at_3 value: 34.418 - type: mrr_at_5 value: 35.742000000000004 - type: ndcg_at_1 value: 27.854 - type: ndcg_at_10 value: 37.388 - type: ndcg_at_100 value: 43.342999999999996 - type: ndcg_at_1000 value: 45.829 - type: ndcg_at_3 value: 32.512 - type: ndcg_at_5 value: 34.613 - type: precision_at_1 value: 27.854 - type: precision_at_10 value: 7.031999999999999 - type: precision_at_100 value: 1.18 - type: precision_at_1000 value: 0.158 - type: precision_at_3 value: 15.753 - type: precision_at_5 value: 11.301 - type: recall_at_1 value: 22.169 - type: recall_at_10 value: 49.44 - type: recall_at_100 value: 75.644 - type: recall_at_1000 value: 92.919 - type: recall_at_3 value: 35.528999999999996 - type: recall_at_5 value: 41.271 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 24.20158333333334 - type: map_at_10 value: 33.509 - type: map_at_100 value: 34.76525 - type: map_at_1000 value: 34.885999999999996 - type: map_at_3 value: 30.594333333333335 - type: map_at_5 value: 32.160666666666664 - type: mrr_at_1 value: 28.803833333333333 - type: mrr_at_10 value: 37.61358333333333 - type: mrr_at_100 value: 38.5105 - type: mrr_at_1000 value: 38.56841666666667 - type: mrr_at_3 value: 35.090666666666664 - type: mrr_at_5 value: 36.49575 - type: ndcg_at_1 value: 28.803833333333333 - type: ndcg_at_10 value: 39.038333333333334 - type: ndcg_at_100 value: 44.49175 - type: ndcg_at_1000 value: 46.835499999999996 - type: ndcg_at_3 value: 34.011916666666664 - type: ndcg_at_5 value: 36.267 - type: precision_at_1 value: 28.803833333333333 - type: precision_at_10 value: 6.974583333333334 - type: precision_at_100 value: 1.1565 - type: precision_at_1000 value: 0.15533333333333332 - type: precision_at_3 value: 15.78025 - type: precision_at_5 value: 11.279583333333333 - type: recall_at_1 value: 24.20158333333334 - type: recall_at_10 value: 51.408 - type: recall_at_100 value: 75.36958333333334 - type: recall_at_1000 value: 91.5765 - type: recall_at_3 value: 37.334500000000006 - type: recall_at_5 value: 43.14666666666667 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackStatsRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 21.394 - type: map_at_10 value: 28.807 - type: map_at_100 value: 29.851 - type: map_at_1000 value: 29.959999999999997 - type: map_at_3 value: 26.694000000000003 - type: map_at_5 value: 27.805999999999997 - type: mrr_at_1 value: 23.773 - type: mrr_at_10 value: 30.895 - type: mrr_at_100 value: 31.894 - type: mrr_at_1000 value: 31.971 - type: mrr_at_3 value: 28.988000000000003 - type: mrr_at_5 value: 29.908 - type: ndcg_at_1 value: 23.773 - type: ndcg_at_10 value: 32.976 - type: ndcg_at_100 value: 38.109 - type: ndcg_at_1000 value: 40.797 - type: ndcg_at_3 value: 28.993999999999996 - type: ndcg_at_5 value: 30.659999999999997 - type: precision_at_1 value: 23.773 - type: precision_at_10 value: 5.2299999999999995 - type: precision_at_100 value: 0.857 - type: precision_at_1000 value: 0.117 - type: precision_at_3 value: 12.73 - type: precision_at_5 value: 8.741999999999999 - type: recall_at_1 value: 21.394 - type: recall_at_10 value: 43.75 - type: recall_at_100 value: 66.765 - type: recall_at_1000 value: 86.483 - type: recall_at_3 value: 32.542 - type: recall_at_5 value: 36.689 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackTexRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 16.266 - type: map_at_10 value: 23.639 - type: map_at_100 value: 24.814 - type: map_at_1000 value: 24.948 - type: map_at_3 value: 21.401999999999997 - type: map_at_5 value: 22.581 - type: mrr_at_1 value: 19.718 - type: mrr_at_10 value: 27.276 - type: mrr_at_100 value: 28.252 - type: mrr_at_1000 value: 28.33 - type: mrr_at_3 value: 25.086000000000002 - type: mrr_at_5 value: 26.304 - type: ndcg_at_1 value: 19.718 - type: ndcg_at_10 value: 28.254 - type: ndcg_at_100 value: 34.022999999999996 - type: ndcg_at_1000 value: 37.031 - type: ndcg_at_3 value: 24.206 - type: ndcg_at_5 value: 26.009 - type: precision_at_1 value: 19.718 - type: precision_at_10 value: 5.189 - type: precision_at_100 value: 0.9690000000000001 - type: precision_at_1000 value: 0.14200000000000002 - type: precision_at_3 value: 11.551 - type: precision_at_5 value: 8.362 - type: recall_at_1 value: 16.266 - type: recall_at_10 value: 38.550000000000004 - type: recall_at_100 value: 64.63499999999999 - type: recall_at_1000 value: 86.059 - type: recall_at_3 value: 27.156000000000002 - type: recall_at_5 value: 31.829 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackUnixRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 26.124000000000002 - type: map_at_10 value: 35.099000000000004 - type: map_at_100 value: 36.269 - type: map_at_1000 value: 36.388999999999996 - type: map_at_3 value: 32.017 - type: map_at_5 value: 33.614 - type: mrr_at_1 value: 31.25 - type: mrr_at_10 value: 39.269999999999996 - type: mrr_at_100 value: 40.134 - type: mrr_at_1000 value: 40.197 - type: mrr_at_3 value: 36.536 - type: mrr_at_5 value: 37.842 - type: ndcg_at_1 value: 31.25 - type: ndcg_at_10 value: 40.643 - type: ndcg_at_100 value: 45.967999999999996 - type: ndcg_at_1000 value: 48.455999999999996 - type: ndcg_at_3 value: 34.954 - type: ndcg_at_5 value: 37.273 - type: precision_at_1 value: 31.25 - type: precision_at_10 value: 6.894 - type: precision_at_100 value: 1.086 - type: precision_at_1000 value: 0.14200000000000002 - type: precision_at_3 value: 15.672 - type: precision_at_5 value: 11.082 - type: recall_at_1 value: 26.124000000000002 - type: recall_at_10 value: 53.730999999999995 - type: recall_at_100 value: 76.779 - type: recall_at_1000 value: 93.908 - type: recall_at_3 value: 37.869 - type: recall_at_5 value: 43.822 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWebmastersRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 21.776 - type: map_at_10 value: 31.384 - type: map_at_100 value: 33.108 - type: map_at_1000 value: 33.339 - type: map_at_3 value: 28.269 - type: map_at_5 value: 30.108 - type: mrr_at_1 value: 26.482 - type: mrr_at_10 value: 35.876000000000005 - type: mrr_at_100 value: 36.887 - type: mrr_at_1000 value: 36.949 - type: mrr_at_3 value: 32.971000000000004 - type: mrr_at_5 value: 34.601 - type: ndcg_at_1 value: 26.482 - type: ndcg_at_10 value: 37.403999999999996 - type: ndcg_at_100 value: 43.722 - type: ndcg_at_1000 value: 46.417 - type: ndcg_at_3 value: 32.149 - type: ndcg_at_5 value: 34.818 - type: precision_at_1 value: 26.482 - type: precision_at_10 value: 7.411 - type: precision_at_100 value: 1.532 - type: precision_at_1000 value: 0.24 - type: precision_at_3 value: 15.152 - type: precision_at_5 value: 11.501999999999999 - type: recall_at_1 value: 21.776 - type: recall_at_10 value: 49.333 - type: recall_at_100 value: 76.753 - type: recall_at_1000 value: 93.762 - type: recall_at_3 value: 35.329 - type: recall_at_5 value: 41.82 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWordpressRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 18.990000000000002 - type: map_at_10 value: 26.721 - type: map_at_100 value: 27.833999999999996 - type: map_at_1000 value: 27.947 - type: map_at_3 value: 24.046 - type: map_at_5 value: 25.491999999999997 - type: mrr_at_1 value: 20.702 - type: mrr_at_10 value: 28.691 - type: mrr_at_100 value: 29.685 - type: mrr_at_1000 value: 29.764000000000003 - type: mrr_at_3 value: 26.124000000000002 - type: mrr_at_5 value: 27.584999999999997 - type: ndcg_at_1 value: 20.702 - type: ndcg_at_10 value: 31.473000000000003 - type: ndcg_at_100 value: 37.061 - type: ndcg_at_1000 value: 39.784000000000006 - type: ndcg_at_3 value: 26.221 - type: ndcg_at_5 value: 28.655 - type: precision_at_1 value: 20.702 - type: precision_at_10 value: 5.083 - type: precision_at_100 value: 0.8500000000000001 - type: precision_at_1000 value: 0.121 - type: precision_at_3 value: 11.275 - type: precision_at_5 value: 8.17 - type: recall_at_1 value: 18.990000000000002 - type: recall_at_10 value: 44.318999999999996 - type: recall_at_100 value: 69.98 - type: recall_at_1000 value: 90.171 - type: recall_at_3 value: 30.246000000000002 - type: recall_at_5 value: 35.927 - task: type: Retrieval dataset: type: climate-fever name: MTEB ClimateFEVER config: default split: test revision: None metrics: - type: map_at_1 value: 9.584 - type: map_at_10 value: 16.148 - type: map_at_100 value: 17.727 - type: map_at_1000 value: 17.913999999999998 - type: map_at_3 value: 13.456000000000001 - type: map_at_5 value: 14.841999999999999 - type: mrr_at_1 value: 21.564 - type: mrr_at_10 value: 31.579 - type: mrr_at_100 value: 32.586999999999996 - type: mrr_at_1000 value: 32.638 - type: mrr_at_3 value: 28.294999999999998 - type: mrr_at_5 value: 30.064 - type: ndcg_at_1 value: 21.564 - type: ndcg_at_10 value: 23.294999999999998 - type: ndcg_at_100 value: 29.997 - type: ndcg_at_1000 value: 33.517 - type: ndcg_at_3 value: 18.759 - type: ndcg_at_5 value: 20.324 - type: precision_at_1 value: 21.564 - type: precision_at_10 value: 7.362 - type: precision_at_100 value: 1.451 - type: precision_at_1000 value: 0.21 - type: precision_at_3 value: 13.919999999999998 - type: precision_at_5 value: 10.879 - type: recall_at_1 value: 9.584 - type: recall_at_10 value: 28.508 - type: recall_at_100 value: 51.873999999999995 - type: recall_at_1000 value: 71.773 - type: recall_at_3 value: 17.329 - type: recall_at_5 value: 21.823 - task: type: Retrieval dataset: type: dbpedia-entity name: MTEB DBPedia config: default split: test revision: None metrics: - type: map_at_1 value: 7.034 - type: map_at_10 value: 14.664 - type: map_at_100 value: 19.652 - type: map_at_1000 value: 20.701 - type: map_at_3 value: 10.626 - type: map_at_5 value: 12.334 - type: mrr_at_1 value: 54.0 - type: mrr_at_10 value: 63.132 - type: mrr_at_100 value: 63.639 - type: mrr_at_1000 value: 63.663000000000004 - type: mrr_at_3 value: 61.083 - type: mrr_at_5 value: 62.483 - type: ndcg_at_1 value: 42.875 - type: ndcg_at_10 value: 32.04 - type: ndcg_at_100 value: 35.157 - type: ndcg_at_1000 value: 41.4 - type: ndcg_at_3 value: 35.652 - type: ndcg_at_5 value: 33.617000000000004 - type: precision_at_1 value: 54.0 - type: precision_at_10 value: 25.55 - type: precision_at_100 value: 7.5600000000000005 - type: precision_at_1000 value: 1.577 - type: precision_at_3 value: 38.833 - type: precision_at_5 value: 33.15 - type: recall_at_1 value: 7.034 - type: recall_at_10 value: 19.627 - type: recall_at_100 value: 40.528 - type: recall_at_1000 value: 60.789 - type: recall_at_3 value: 11.833 - type: recall_at_5 value: 14.804 - task: type: Classification dataset: type: mteb/emotion name: MTEB EmotionClassification config: default split: test revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 metrics: - type: accuracy value: 39.6 - type: f1 value: 35.3770765501984 - task: type: Retrieval dataset: type: fever name: MTEB FEVER config: default split: test revision: None metrics: - type: map_at_1 value: 35.098 - type: map_at_10 value: 46.437 - type: map_at_100 value: 47.156 - type: map_at_1000 value: 47.193000000000005 - type: map_at_3 value: 43.702000000000005 - type: map_at_5 value: 45.326 - type: mrr_at_1 value: 37.774 - type: mrr_at_10 value: 49.512 - type: mrr_at_100 value: 50.196 - type: mrr_at_1000 value: 50.224000000000004 - type: mrr_at_3 value: 46.747 - type: mrr_at_5 value: 48.415 - type: ndcg_at_1 value: 37.774 - type: ndcg_at_10 value: 52.629000000000005 - type: ndcg_at_100 value: 55.995 - type: ndcg_at_1000 value: 56.962999999999994 - type: ndcg_at_3 value: 47.188 - type: ndcg_at_5 value: 50.019000000000005 - type: precision_at_1 value: 37.774 - type: precision_at_10 value: 7.541 - type: precision_at_100 value: 0.931 - type: precision_at_1000 value: 0.10300000000000001 - type: precision_at_3 value: 19.572 - type: precision_at_5 value: 13.288 - type: recall_at_1 value: 35.098 - type: recall_at_10 value: 68.818 - type: recall_at_100 value: 84.004 - type: recall_at_1000 value: 91.36800000000001 - type: recall_at_3 value: 54.176 - type: recall_at_5 value: 60.968999999999994 - task: type: Retrieval dataset: type: fiqa name: MTEB FiQA2018 config: default split: test revision: None metrics: - type: map_at_1 value: 17.982 - type: map_at_10 value: 28.994999999999997 - type: map_at_100 value: 30.868000000000002 - type: map_at_1000 value: 31.045 - type: map_at_3 value: 25.081999999999997 - type: map_at_5 value: 27.303 - type: mrr_at_1 value: 35.031 - type: mrr_at_10 value: 43.537 - type: mrr_at_100 value: 44.422 - type: mrr_at_1000 value: 44.471 - type: mrr_at_3 value: 41.024 - type: mrr_at_5 value: 42.42 - type: ndcg_at_1 value: 35.031 - type: ndcg_at_10 value: 36.346000000000004 - type: ndcg_at_100 value: 43.275000000000006 - type: ndcg_at_1000 value: 46.577 - type: ndcg_at_3 value: 32.42 - type: ndcg_at_5 value: 33.841 - type: precision_at_1 value: 35.031 - type: precision_at_10 value: 10.231 - type: precision_at_100 value: 1.728 - type: precision_at_1000 value: 0.231 - type: precision_at_3 value: 21.553 - type: precision_at_5 value: 16.204 - type: recall_at_1 value: 17.982 - type: recall_at_10 value: 43.169000000000004 - type: recall_at_100 value: 68.812 - type: recall_at_1000 value: 89.008 - type: recall_at_3 value: 29.309 - type: recall_at_5 value: 35.514 - task: type: Retrieval dataset: type: hotpotqa name: MTEB HotpotQA config: default split: test revision: None metrics: - type: map_at_1 value: 27.387 - type: map_at_10 value: 36.931000000000004 - type: map_at_100 value: 37.734 - type: map_at_1000 value: 37.818000000000005 - type: map_at_3 value: 34.691 - type: map_at_5 value: 36.016999999999996 - type: mrr_at_1 value: 54.774 - type: mrr_at_10 value: 62.133 - type: mrr_at_100 value: 62.587 - type: mrr_at_1000 value: 62.61600000000001 - type: mrr_at_3 value: 60.49099999999999 - type: mrr_at_5 value: 61.480999999999995 - type: ndcg_at_1 value: 54.774 - type: ndcg_at_10 value: 45.657 - type: ndcg_at_100 value: 48.954 - type: ndcg_at_1000 value: 50.78 - type: ndcg_at_3 value: 41.808 - type: ndcg_at_5 value: 43.816 - type: precision_at_1 value: 54.774 - type: precision_at_10 value: 9.479 - type: precision_at_100 value: 1.208 - type: precision_at_1000 value: 0.145 - type: precision_at_3 value: 25.856 - type: precision_at_5 value: 17.102 - type: recall_at_1 value: 27.387 - type: recall_at_10 value: 47.394 - type: recall_at_100 value: 60.397999999999996 - type: recall_at_1000 value: 72.54599999999999 - type: recall_at_3 value: 38.785 - type: recall_at_5 value: 42.754999999999995 - task: type: Classification dataset: type: mteb/imdb name: MTEB ImdbClassification config: default split: test revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 metrics: - type: accuracy value: 61.217999999999996 - type: ap value: 56.84286974948407 - type: f1 value: 60.99211195455131 - task: type: Retrieval dataset: type: msmarco name: MTEB MSMARCO config: default split: dev revision: None metrics: - type: map_at_1 value: 19.224 - type: map_at_10 value: 30.448999999999998 - type: map_at_100 value: 31.663999999999998 - type: map_at_1000 value: 31.721 - type: map_at_3 value: 26.922 - type: map_at_5 value: 28.906 - type: mrr_at_1 value: 19.756 - type: mrr_at_10 value: 30.994 - type: mrr_at_100 value: 32.161 - type: mrr_at_1000 value: 32.213 - type: mrr_at_3 value: 27.502 - type: mrr_at_5 value: 29.48 - type: ndcg_at_1 value: 19.742 - type: ndcg_at_10 value: 36.833 - type: ndcg_at_100 value: 42.785000000000004 - type: ndcg_at_1000 value: 44.291000000000004 - type: ndcg_at_3 value: 29.580000000000002 - type: ndcg_at_5 value: 33.139 - type: precision_at_1 value: 19.742 - type: precision_at_10 value: 5.894 - type: precision_at_100 value: 0.889 - type: precision_at_1000 value: 0.10200000000000001 - type: precision_at_3 value: 12.665000000000001 - type: precision_at_5 value: 9.393 - type: recall_at_1 value: 19.224 - type: recall_at_10 value: 56.538999999999994 - type: recall_at_100 value: 84.237 - type: recall_at_1000 value: 95.965 - type: recall_at_3 value: 36.71 - type: recall_at_5 value: 45.283 - task: type: Classification dataset: type: mteb/mtop_domain name: MTEB MTOPDomainClassification (en) config: en split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 89.97264021887824 - type: f1 value: 89.53607318488027 - task: type: Classification dataset: type: mteb/mtop_intent name: MTEB MTOPIntentClassification (en) config: en split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - type: accuracy value: 59.566803465572285 - type: f1 value: 40.94003955225124 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (en) config: en split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 66.7787491593813 - type: f1 value: 64.51190971513093 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (en) config: en split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 73.7794216543376 - type: f1 value: 72.71852261076475 - task: type: Clustering dataset: type: mteb/medrxiv-clustering-p2p name: MTEB MedrxivClusteringP2P config: default split: test revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 metrics: - type: v_measure value: 28.40883054472429 - task: type: Clustering dataset: type: mteb/medrxiv-clustering-s2s name: MTEB MedrxivClusteringS2S config: default split: test revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 metrics: - type: v_measure value: 26.144338339113617 - task: type: Reranking dataset: type: mteb/mind_small name: MTEB MindSmallReranking config: default split: test revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 metrics: - type: map value: 30.894071459751267 - type: mrr value: 31.965886150526256 - task: type: Retrieval dataset: type: nfcorpus name: MTEB NFCorpus config: default split: test revision: None metrics: - type: map_at_1 value: 5.024 - type: map_at_10 value: 10.533 - type: map_at_100 value: 12.97 - type: map_at_1000 value: 14.163 - type: map_at_3 value: 7.971 - type: map_at_5 value: 9.15 - type: mrr_at_1 value: 40.867 - type: mrr_at_10 value: 48.837 - type: mrr_at_100 value: 49.464999999999996 - type: mrr_at_1000 value: 49.509 - type: mrr_at_3 value: 46.800999999999995 - type: mrr_at_5 value: 47.745 - type: ndcg_at_1 value: 38.854 - type: ndcg_at_10 value: 29.674 - type: ndcg_at_100 value: 26.66 - type: ndcg_at_1000 value: 35.088 - type: ndcg_at_3 value: 34.838 - type: ndcg_at_5 value: 32.423 - type: precision_at_1 value: 40.248 - type: precision_at_10 value: 21.826999999999998 - type: precision_at_100 value: 6.78 - type: precision_at_1000 value: 1.889 - type: precision_at_3 value: 32.405 - type: precision_at_5 value: 27.74 - type: recall_at_1 value: 5.024 - type: recall_at_10 value: 13.996 - type: recall_at_100 value: 26.636 - type: recall_at_1000 value: 57.816 - type: recall_at_3 value: 9.063 - type: recall_at_5 value: 10.883 - task: type: Retrieval dataset: type: nq name: MTEB NQ config: default split: test revision: None metrics: - type: map_at_1 value: 23.088 - type: map_at_10 value: 36.915 - type: map_at_100 value: 38.141999999999996 - type: map_at_1000 value: 38.191 - type: map_at_3 value: 32.458999999999996 - type: map_at_5 value: 35.004999999999995 - type: mrr_at_1 value: 26.101000000000003 - type: mrr_at_10 value: 39.1 - type: mrr_at_100 value: 40.071 - type: mrr_at_1000 value: 40.106 - type: mrr_at_3 value: 35.236000000000004 - type: mrr_at_5 value: 37.43 - type: ndcg_at_1 value: 26.072 - type: ndcg_at_10 value: 44.482 - type: ndcg_at_100 value: 49.771 - type: ndcg_at_1000 value: 50.903 - type: ndcg_at_3 value: 35.922 - type: ndcg_at_5 value: 40.178000000000004 - type: precision_at_1 value: 26.072 - type: precision_at_10 value: 7.795000000000001 - type: precision_at_100 value: 1.072 - type: precision_at_1000 value: 0.11800000000000001 - type: precision_at_3 value: 16.725 - type: precision_at_5 value: 12.468 - type: recall_at_1 value: 23.088 - type: recall_at_10 value: 65.534 - type: recall_at_100 value: 88.68 - type: recall_at_1000 value: 97.101 - type: recall_at_3 value: 43.161 - type: recall_at_5 value: 52.959999999999994 - task: type: Retrieval dataset: type: quora name: MTEB QuoraRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 69.612 - type: map_at_10 value: 83.292 - type: map_at_100 value: 83.96000000000001 - type: map_at_1000 value: 83.978 - type: map_at_3 value: 80.26299999999999 - type: map_at_5 value: 82.11500000000001 - type: mrr_at_1 value: 80.21000000000001 - type: mrr_at_10 value: 86.457 - type: mrr_at_100 value: 86.58500000000001 - type: mrr_at_1000 value: 86.587 - type: mrr_at_3 value: 85.452 - type: mrr_at_5 value: 86.101 - type: ndcg_at_1 value: 80.21000000000001 - type: ndcg_at_10 value: 87.208 - type: ndcg_at_100 value: 88.549 - type: ndcg_at_1000 value: 88.683 - type: ndcg_at_3 value: 84.20400000000001 - type: ndcg_at_5 value: 85.768 - type: precision_at_1 value: 80.21000000000001 - type: precision_at_10 value: 13.29 - type: precision_at_100 value: 1.5230000000000001 - type: precision_at_1000 value: 0.156 - type: precision_at_3 value: 36.767 - type: precision_at_5 value: 24.2 - type: recall_at_1 value: 69.612 - type: recall_at_10 value: 94.651 - type: recall_at_100 value: 99.297 - type: recall_at_1000 value: 99.95100000000001 - type: recall_at_3 value: 86.003 - type: recall_at_5 value: 90.45100000000001 - task: type: Clustering dataset: type: mteb/reddit-clustering name: MTEB RedditClustering config: default split: test revision: 24640382cdbf8abc73003fb0fa6d111a705499eb metrics: - type: v_measure value: 46.28945925252077 - task: type: Clustering dataset: type: mteb/reddit-clustering-p2p name: MTEB RedditClusteringP2P config: default split: test revision: 282350215ef01743dc01b456c7f5241fa8937f16 metrics: - type: v_measure value: 50.954446620859684 - task: type: Retrieval dataset: type: scidocs name: MTEB SCIDOCS config: default split: test revision: None metrics: - type: map_at_1 value: 3.888 - type: map_at_10 value: 9.21 - type: map_at_100 value: 10.629 - type: map_at_1000 value: 10.859 - type: map_at_3 value: 6.743 - type: map_at_5 value: 7.982 - type: mrr_at_1 value: 19.1 - type: mrr_at_10 value: 28.294000000000004 - type: mrr_at_100 value: 29.326999999999998 - type: mrr_at_1000 value: 29.414 - type: mrr_at_3 value: 25.367 - type: mrr_at_5 value: 27.002 - type: ndcg_at_1 value: 19.1 - type: ndcg_at_10 value: 15.78 - type: ndcg_at_100 value: 21.807000000000002 - type: ndcg_at_1000 value: 26.593 - type: ndcg_at_3 value: 15.204999999999998 - type: ndcg_at_5 value: 13.217 - type: precision_at_1 value: 19.1 - type: precision_at_10 value: 7.9799999999999995 - type: precision_at_100 value: 1.667 - type: precision_at_1000 value: 0.28300000000000003 - type: precision_at_3 value: 13.933000000000002 - type: precision_at_5 value: 11.379999999999999 - type: recall_at_1 value: 3.888 - type: recall_at_10 value: 16.17 - type: recall_at_100 value: 33.848 - type: recall_at_1000 value: 57.345 - type: recall_at_3 value: 8.468 - type: recall_at_5 value: 11.540000000000001 - task: type: STS dataset: type: mteb/sickr-sts name: MTEB SICK-R config: default split: test revision: a6ea5a8cab320b040a23452cc28066d9beae2cee metrics: - type: cos_sim_pearson value: 79.05803116288386 - type: cos_sim_spearman value: 70.0403855402571 - type: euclidean_pearson value: 75.59006280166072 - type: euclidean_spearman value: 70.04038926247613 - type: manhattan_pearson value: 75.48136278078455 - type: manhattan_spearman value: 69.9608897701754 - task: type: STS dataset: type: mteb/sts12-sts name: MTEB STS12 config: default split: test revision: a0d554a64d88156834ff5ae9920b964011b16384 metrics: - type: cos_sim_pearson value: 68.56836430603597 - type: cos_sim_spearman value: 64.38407759822387 - type: euclidean_pearson value: 65.93619045541732 - type: euclidean_spearman value: 64.38184049884836 - type: manhattan_pearson value: 65.97148637646873 - type: manhattan_spearman value: 64.48011982438929 - task: type: STS dataset: type: mteb/sts13-sts name: MTEB STS13 config: default split: test revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca metrics: - type: cos_sim_pearson value: 75.990362280318 - type: cos_sim_spearman value: 76.40621890996734 - type: euclidean_pearson value: 76.01739766577184 - type: euclidean_spearman value: 76.4062736496846 - type: manhattan_pearson value: 76.04738378838042 - type: manhattan_spearman value: 76.44991409719592 - task: type: STS dataset: type: mteb/sts14-sts name: MTEB STS14 config: default split: test revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 metrics: - type: cos_sim_pearson value: 74.8516957692617 - type: cos_sim_spearman value: 69.325199098278 - type: euclidean_pearson value: 73.37922793254768 - type: euclidean_spearman value: 69.32520119670215 - type: manhattan_pearson value: 73.3795212376615 - type: manhattan_spearman value: 69.35306787926315 - task: type: STS dataset: type: mteb/sts15-sts name: MTEB STS15 config: default split: test revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 metrics: - type: cos_sim_pearson value: 78.644002190612 - type: cos_sim_spearman value: 80.18337978181648 - type: euclidean_pearson value: 79.7628642371887 - type: euclidean_spearman value: 80.18337906907526 - type: manhattan_pearson value: 79.68810722704522 - type: manhattan_spearman value: 80.10664518173466 - task: type: STS dataset: type: mteb/sts16-sts name: MTEB STS16 config: default split: test revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 metrics: - type: cos_sim_pearson value: 77.8303940874723 - type: cos_sim_spearman value: 79.56812599677549 - type: euclidean_pearson value: 79.38928950396344 - type: euclidean_spearman value: 79.56812556750812 - type: manhattan_pearson value: 79.41057583507681 - type: manhattan_spearman value: 79.57604428731142 - task: type: STS dataset: type: mteb/sts17-crosslingual-sts name: MTEB STS17 (en-en) config: en-en split: test revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d metrics: - type: cos_sim_pearson value: 78.90792116013353 - type: cos_sim_spearman value: 81.18059230233499 - type: euclidean_pearson value: 80.2622631297375 - type: euclidean_spearman value: 81.18059230233499 - type: manhattan_pearson value: 80.23946026135997 - type: manhattan_spearman value: 81.11947325071426 - task: type: STS dataset: type: mteb/sts22-crosslingual-sts name: MTEB STS22 (en) config: en split: test revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 metrics: - type: cos_sim_pearson value: 64.46850619973324 - type: cos_sim_spearman value: 65.50839374141563 - type: euclidean_pearson value: 66.60130812260707 - type: euclidean_spearman value: 65.50839374141563 - type: manhattan_pearson value: 66.58871918195092 - type: manhattan_spearman value: 65.7347325297592 - task: type: STS dataset: type: mteb/stsbenchmark-sts name: MTEB STSBenchmark config: default split: test revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 metrics: - type: cos_sim_pearson value: 75.71536124107834 - type: cos_sim_spearman value: 75.98365906208434 - type: euclidean_pearson value: 76.64573753881218 - type: euclidean_spearman value: 75.98365906208434 - type: manhattan_pearson value: 76.63637189172626 - type: manhattan_spearman value: 75.9660207821009 - task: type: Reranking dataset: type: mteb/scidocs-reranking name: MTEB SciDocsRR config: default split: test revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab metrics: - type: map value: 74.27669440147513 - type: mrr value: 91.7729356699945 - task: type: Retrieval dataset: type: scifact name: MTEB SciFact config: default split: test revision: None metrics: - type: map_at_1 value: 41.028 - type: map_at_10 value: 49.919000000000004 - type: map_at_100 value: 50.91 - type: map_at_1000 value: 50.955 - type: map_at_3 value: 47.785 - type: map_at_5 value: 49.084 - type: mrr_at_1 value: 43.667 - type: mrr_at_10 value: 51.342 - type: mrr_at_100 value: 52.197 - type: mrr_at_1000 value: 52.236000000000004 - type: mrr_at_3 value: 49.667 - type: mrr_at_5 value: 50.766999999999996 - type: ndcg_at_1 value: 43.667 - type: ndcg_at_10 value: 54.029 - type: ndcg_at_100 value: 58.909 - type: ndcg_at_1000 value: 60.131 - type: ndcg_at_3 value: 50.444 - type: ndcg_at_5 value: 52.354 - type: precision_at_1 value: 43.667 - type: precision_at_10 value: 7.432999999999999 - type: precision_at_100 value: 1.0 - type: precision_at_1000 value: 0.11100000000000002 - type: precision_at_3 value: 20.444000000000003 - type: precision_at_5 value: 13.533000000000001 - type: recall_at_1 value: 41.028 - type: recall_at_10 value: 65.011 - type: recall_at_100 value: 88.033 - type: recall_at_1000 value: 97.667 - type: recall_at_3 value: 55.394 - type: recall_at_5 value: 60.183 - task: type: PairClassification dataset: type: mteb/sprintduplicatequestions-pairclassification name: MTEB SprintDuplicateQuestions config: default split: test revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 metrics: - type: cos_sim_accuracy value: 99.76534653465346 - type: cos_sim_ap value: 93.83756773536699 - type: cos_sim_f1 value: 87.91097622660598 - type: cos_sim_precision value: 88.94575230296827 - type: cos_sim_recall value: 86.9 - type: dot_accuracy value: 99.76534653465346 - type: dot_ap value: 93.83756773536699 - type: dot_f1 value: 87.91097622660598 - type: dot_precision value: 88.94575230296827 - type: dot_recall value: 86.9 - type: euclidean_accuracy value: 99.76534653465346 - type: euclidean_ap value: 93.837567735367 - type: euclidean_f1 value: 87.91097622660598 - type: euclidean_precision value: 88.94575230296827 - type: euclidean_recall value: 86.9 - type: manhattan_accuracy value: 99.76633663366337 - type: manhattan_ap value: 93.84480825492724 - type: manhattan_f1 value: 87.97145769622833 - type: manhattan_precision value: 89.70893970893971 - type: manhattan_recall value: 86.3 - type: max_accuracy value: 99.76633663366337 - type: max_ap value: 93.84480825492724 - type: max_f1 value: 87.97145769622833 - task: type: Clustering dataset: type: mteb/stackexchange-clustering name: MTEB StackExchangeClustering config: default split: test revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 metrics: - type: v_measure value: 48.078155553339585 - task: type: Clustering dataset: type: mteb/stackexchange-clustering-p2p name: MTEB StackExchangeClusteringP2P config: default split: test revision: 815ca46b2622cec33ccafc3735d572c266efdb44 metrics: - type: v_measure value: 33.34857297824906 - task: type: Reranking dataset: type: mteb/stackoverflowdupquestions-reranking name: MTEB StackOverflowDupQuestions config: default split: test revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 metrics: - type: map value: 50.06219491505384 - type: mrr value: 50.77479097699686 - task: type: Summarization dataset: type: mteb/summeval name: MTEB SummEval config: default split: test revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c metrics: - type: cos_sim_pearson value: 30.48401937651373 - type: cos_sim_spearman value: 31.048654273022606 - type: dot_pearson value: 30.484020082707847 - type: dot_spearman value: 31.048654273022606 - task: type: Retrieval dataset: type: trec-covid name: MTEB TRECCOVID config: default split: test revision: None metrics: - type: map_at_1 value: 0.183 - type: map_at_10 value: 1.32 - type: map_at_100 value: 7.01 - type: map_at_1000 value: 16.957 - type: map_at_3 value: 0.481 - type: map_at_5 value: 0.737 - type: mrr_at_1 value: 66.0 - type: mrr_at_10 value: 78.7 - type: mrr_at_100 value: 78.7 - type: mrr_at_1000 value: 78.7 - type: mrr_at_3 value: 76.0 - type: mrr_at_5 value: 78.7 - type: ndcg_at_1 value: 56.99999999999999 - type: ndcg_at_10 value: 55.846 - type: ndcg_at_100 value: 43.138 - type: ndcg_at_1000 value: 39.4 - type: ndcg_at_3 value: 57.306999999999995 - type: ndcg_at_5 value: 57.294 - type: precision_at_1 value: 66.0 - type: precision_at_10 value: 60.0 - type: precision_at_100 value: 44.6 - type: precision_at_1000 value: 17.8 - type: precision_at_3 value: 62.0 - type: precision_at_5 value: 62.0 - type: recall_at_1 value: 0.183 - type: recall_at_10 value: 1.583 - type: recall_at_100 value: 10.412 - type: recall_at_1000 value: 37.358999999999995 - type: recall_at_3 value: 0.516 - type: recall_at_5 value: 0.845 - task: type: Retrieval dataset: type: webis-touche2020 name: MTEB Touche2020 config: default split: test revision: None metrics: - type: map_at_1 value: 1.7420000000000002 - type: map_at_10 value: 6.4879999999999995 - type: map_at_100 value: 11.654 - type: map_at_1000 value: 13.23 - type: map_at_3 value: 3.148 - type: map_at_5 value: 4.825 - type: mrr_at_1 value: 18.367 - type: mrr_at_10 value: 30.258000000000003 - type: mrr_at_100 value: 31.570999999999998 - type: mrr_at_1000 value: 31.594 - type: mrr_at_3 value: 26.19 - type: mrr_at_5 value: 28.027 - type: ndcg_at_1 value: 15.306000000000001 - type: ndcg_at_10 value: 15.608 - type: ndcg_at_100 value: 28.808 - type: ndcg_at_1000 value: 41.603 - type: ndcg_at_3 value: 13.357 - type: ndcg_at_5 value: 15.306000000000001 - type: precision_at_1 value: 18.367 - type: precision_at_10 value: 15.101999999999999 - type: precision_at_100 value: 6.49 - type: precision_at_1000 value: 1.488 - type: precision_at_3 value: 14.966 - type: precision_at_5 value: 17.143 - type: recall_at_1 value: 1.7420000000000002 - type: recall_at_10 value: 12.267 - type: recall_at_100 value: 41.105999999999995 - type: recall_at_1000 value: 80.569 - type: recall_at_3 value: 4.009 - type: recall_at_5 value: 7.417999999999999 - task: type: Classification dataset: type: mteb/toxic_conversations_50k name: MTEB ToxicConversationsClassification config: default split: test revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c metrics: - type: accuracy value: 65.1178 - type: ap value: 11.974961582206614 - type: f1 value: 50.24491996814835 - task: type: Classification dataset: type: mteb/tweet_sentiment_extraction name: MTEB TweetSentimentExtractionClassification config: default split: test revision: d604517c81ca91fe16a244d1248fc021f9ecee7a metrics: - type: accuracy value: 51.63271080928127 - type: f1 value: 51.81589904316042 - task: type: Clustering dataset: type: mteb/twentynewsgroups-clustering name: MTEB TwentyNewsgroupsClustering config: default split: test revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 metrics: - type: v_measure value: 40.791709673552276 - task: type: PairClassification dataset: type: mteb/twittersemeval2015-pairclassification name: MTEB TwitterSemEval2015 config: default split: test revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 metrics: - type: cos_sim_accuracy value: 83.05418131966383 - type: cos_sim_ap value: 64.72353098186304 - type: cos_sim_f1 value: 61.313330054107226 - type: cos_sim_precision value: 57.415937356057114 - type: cos_sim_recall value: 65.77836411609499 - type: dot_accuracy value: 83.05418131966383 - type: dot_ap value: 64.72352701424393 - type: dot_f1 value: 61.313330054107226 - type: dot_precision value: 57.415937356057114 - type: dot_recall value: 65.77836411609499 - type: euclidean_accuracy value: 83.05418131966383 - type: euclidean_ap value: 64.72353124585976 - type: euclidean_f1 value: 61.313330054107226 - type: euclidean_precision value: 57.415937356057114 - type: euclidean_recall value: 65.77836411609499 - type: manhattan_accuracy value: 82.98861536627525 - type: manhattan_ap value: 64.53981837182303 - type: manhattan_f1 value: 60.94911377930246 - type: manhattan_precision value: 53.784056508577194 - type: manhattan_recall value: 70.31662269129288 - type: max_accuracy value: 83.05418131966383 - type: max_ap value: 64.72353124585976 - type: max_f1 value: 61.313330054107226 - task: type: PairClassification dataset: type: mteb/twitterurlcorpus-pairclassification name: MTEB TwitterURLCorpus config: default split: test revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf metrics: - type: cos_sim_accuracy value: 88.06225016493966 - type: cos_sim_ap value: 84.00829172423475 - type: cos_sim_f1 value: 76.1288446157202 - type: cos_sim_precision value: 72.11737153877945 - type: cos_sim_recall value: 80.61287342161995 - type: dot_accuracy value: 88.06225016493966 - type: dot_ap value: 84.00827913374181 - type: dot_f1 value: 76.1288446157202 - type: dot_precision value: 72.11737153877945 - type: dot_recall value: 80.61287342161995 - type: euclidean_accuracy value: 88.06225016493966 - type: euclidean_ap value: 84.00827099295034 - type: euclidean_f1 value: 76.1288446157202 - type: euclidean_precision value: 72.11737153877945 - type: euclidean_recall value: 80.61287342161995 - type: manhattan_accuracy value: 88.05642876547523 - type: manhattan_ap value: 83.9157542691417 - type: manhattan_f1 value: 76.09045667447307 - type: manhattan_precision value: 72.50348675034869 - type: manhattan_recall value: 80.05081613797351 - type: max_accuracy value: 88.06225016493966 - type: max_ap value: 84.00829172423475 - type: max_f1 value: 76.1288446157202 --- MTEB evaluation results on English language for 'multi-qa-MiniLM-L6-cos-v1' sbert model Model and licence can be found [here](https://huggingface.co/sentence-transformers/multi-qa-MiniLM-L6-cos-v1)

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