diff --git "a/README.md" "b/README.md" deleted file mode 100644--- "a/README.md" +++ /dev/null @@ -1,11351 +0,0 @@ ---- -tags: -- mteb -- sentence-transformers -- transformers -- Qwen2 -- sentence-similarity -license: apache-2.0 -model-index: -- name: gte-qwen2-7B-instruct - results: - - task: - type: Classification - dataset: - type: mteb/amazon_counterfactual - name: MTEB AmazonCounterfactualClassification (en) - config: en - split: test - revision: e8379541af4e31359cca9fbcf4b00f2671dba205 - metrics: - - type: accuracy - value: 91.31343283582089 - - type: ap - value: 67.64251402604096 - - type: f1 - value: 87.53372530755692 - - task: - type: Classification - dataset: - type: mteb/amazon_polarity - name: MTEB AmazonPolarityClassification - config: default - split: test - revision: e2d317d38cd51312af73b3d32a06d1a08b442046 - metrics: - - type: accuracy - value: 97.497825 - - type: ap - value: 96.30329547047529 - - type: f1 - value: 97.49769793778039 - - task: - type: Classification - dataset: - type: mteb/amazon_reviews_multi - name: MTEB AmazonReviewsClassification (en) - config: en - split: test - revision: 1399c76144fd37290681b995c656ef9b2e06e26d - metrics: - - type: accuracy - value: 62.564 - - type: f1 - value: 60.975777935041066 - - task: - type: Retrieval - dataset: - type: mteb/arguana - name: MTEB ArguAna - config: default - split: test - revision: c22ab2a51041ffd869aaddef7af8d8215647e41a - metrics: - - type: map_at_1 - value: 36.486000000000004 - - type: map_at_10 - value: 54.842 - - type: map_at_100 - value: 55.206999999999994 - - type: map_at_1000 - value: 55.206999999999994 - - type: map_at_3 - value: 49.893 - - type: map_at_5 - value: 53.105000000000004 - - type: mrr_at_1 - value: 37.34 - - type: mrr_at_10 - value: 55.143 - - type: mrr_at_100 - value: 55.509 - - type: mrr_at_1000 - value: 55.509 - - type: mrr_at_3 - value: 50.212999999999994 - - type: mrr_at_5 - value: 53.432 - - type: ndcg_at_1 - value: 36.486000000000004 - - type: ndcg_at_10 - value: 64.273 - - type: ndcg_at_100 - value: 65.66199999999999 - - type: ndcg_at_1000 - value: 65.66199999999999 - - type: ndcg_at_3 - value: 54.352999999999994 - - type: ndcg_at_5 - value: 60.131 - - type: precision_at_1 - value: 36.486000000000004 - - type: precision_at_10 - value: 9.395000000000001 - - type: precision_at_100 - value: 0.996 - - type: precision_at_1000 - value: 0.1 - - type: precision_at_3 - value: 22.428 - - type: precision_at_5 - value: 16.259 - - type: recall_at_1 - value: 36.486000000000004 - - type: recall_at_10 - value: 93.95400000000001 - - type: recall_at_100 - value: 99.644 - - type: recall_at_1000 - value: 99.644 - - type: recall_at_3 - value: 67.283 - - type: recall_at_5 - value: 81.294 - - task: - type: Clustering - dataset: - type: mteb/arxiv-clustering-p2p - name: MTEB ArxivClusteringP2P - config: default - split: test - revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d - metrics: - - type: v_measure - value: 56.461169803700564 - - task: - type: Clustering - dataset: - type: mteb/arxiv-clustering-s2s - name: MTEB ArxivClusteringS2S - config: default - split: test - revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 - metrics: - - type: v_measure - value: 51.73600434466286 - - task: - type: Reranking - dataset: - type: mteb/askubuntudupquestions-reranking - name: MTEB AskUbuntuDupQuestions - config: default - split: test - revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 - metrics: - - type: map - value: 67.57827065898053 - - type: mrr - value: 79.08136569493911 - - task: - type: STS - dataset: - type: mteb/biosses-sts - name: MTEB BIOSSES - config: default - split: test - revision: d3fb88f8f02e40887cd149695127462bbcf29b4a - metrics: - - type: cos_sim_pearson - value: 83.53324575999243 - - type: cos_sim_spearman - value: 81.37173362822374 - - type: euclidean_pearson - value: 82.19243335103444 - - type: euclidean_spearman - value: 81.33679307304334 - - type: manhattan_pearson - value: 82.38752665975699 - - type: manhattan_spearman - value: 81.31510583189689 - - task: - type: Classification - dataset: - type: mteb/banking77 - name: MTEB Banking77Classification - config: default - split: test - revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 - metrics: - - type: accuracy - value: 87.56818181818181 - - type: f1 - value: 87.25826722019875 - - task: - type: Clustering - dataset: - type: mteb/biorxiv-clustering-p2p - name: MTEB BiorxivClusteringP2P - config: default - split: test - revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 - metrics: - - type: v_measure - value: 50.09239610327673 - - task: - type: Clustering - dataset: - type: mteb/biorxiv-clustering-s2s - name: MTEB BiorxivClusteringS2S - config: default - split: test - revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 - metrics: - - type: v_measure - value: 46.64733054606282 - - task: - type: Retrieval - dataset: - type: BeIR/cqadupstack - name: MTEB CQADupstackAndroidRetrieval - config: default - split: test - revision: f46a197baaae43b4f621051089b82a364682dfeb - metrics: - - type: map_at_1 - value: 33.997 - - type: map_at_10 - value: 48.176 - - type: map_at_100 - value: 49.82 - - type: map_at_1000 - value: 49.924 - - type: map_at_3 - value: 43.626 - - type: map_at_5 - value: 46.275 - - type: mrr_at_1 - value: 42.059999999999995 - - type: mrr_at_10 - value: 53.726 - - type: mrr_at_100 - value: 54.398 - - type: mrr_at_1000 - value: 54.416 - - type: mrr_at_3 - value: 50.714999999999996 - - type: mrr_at_5 - value: 52.639 - - type: ndcg_at_1 - value: 42.059999999999995 - - type: ndcg_at_10 - value: 55.574999999999996 - - type: ndcg_at_100 - value: 60.744 - - type: ndcg_at_1000 - value: 61.85699999999999 - - type: ndcg_at_3 - value: 49.363 - - type: ndcg_at_5 - value: 52.44 - - type: precision_at_1 - value: 42.059999999999995 - - type: precision_at_10 - value: 11.101999999999999 - - type: precision_at_100 - value: 1.73 - - type: precision_at_1000 - value: 0.218 - - type: precision_at_3 - value: 24.464 - - type: precision_at_5 - value: 18.026 - - type: recall_at_1 - value: 33.997 - - type: recall_at_10 - value: 70.35900000000001 - - type: recall_at_100 - value: 91.642 - - type: recall_at_1000 - value: 97.977 - - type: recall_at_3 - value: 52.76 - - type: recall_at_5 - value: 61.148 - - task: - type: Retrieval - dataset: - type: BeIR/cqadupstack - name: MTEB CQADupstackEnglishRetrieval - config: default - split: test - revision: ad9991cb51e31e31e430383c75ffb2885547b5f0 - metrics: - - type: map_at_1 - value: 35.884 - - type: map_at_10 - value: 48.14 - - type: map_at_100 - value: 49.5 - - type: map_at_1000 - value: 49.63 - - type: map_at_3 - value: 44.646 - - type: map_at_5 - value: 46.617999999999995 - - type: mrr_at_1 - value: 44.458999999999996 - - type: mrr_at_10 - value: 53.751000000000005 - - type: mrr_at_100 - value: 54.37800000000001 - - type: mrr_at_1000 - value: 54.415 - - type: mrr_at_3 - value: 51.815 - - type: mrr_at_5 - value: 52.882 - - type: ndcg_at_1 - value: 44.458999999999996 - - type: ndcg_at_10 - value: 54.157 - - type: ndcg_at_100 - value: 58.362 - - type: ndcg_at_1000 - value: 60.178 - - type: ndcg_at_3 - value: 49.661 - - type: ndcg_at_5 - value: 51.74999999999999 - - type: precision_at_1 - value: 44.458999999999996 - - type: precision_at_10 - value: 10.248 - - type: precision_at_100 - value: 1.5890000000000002 - - type: precision_at_1000 - value: 0.207 - - type: precision_at_3 - value: 23.928 - - type: precision_at_5 - value: 16.878999999999998 - - type: recall_at_1 - value: 35.884 - - type: recall_at_10 - value: 64.798 - - type: recall_at_100 - value: 82.345 - - type: recall_at_1000 - value: 93.267 - - type: recall_at_3 - value: 51.847 - - type: recall_at_5 - value: 57.601 - - task: - type: Retrieval - dataset: - type: BeIR/cqadupstack - name: MTEB CQADupstackGamingRetrieval - config: default - split: test - revision: 4885aa143210c98657558c04aaf3dc47cfb54340 - metrics: - - type: map_at_1 - value: 39.383 - - type: map_at_10 - value: 53.714 - - type: map_at_100 - value: 54.838 - - type: map_at_1000 - value: 54.87800000000001 - - type: map_at_3 - value: 50.114999999999995 - - type: map_at_5 - value: 52.153000000000006 - - type: mrr_at_1 - value: 45.016 - - type: mrr_at_10 - value: 56.732000000000006 - - type: mrr_at_100 - value: 57.411 - - type: mrr_at_1000 - value: 57.431 - - type: mrr_at_3 - value: 54.044000000000004 - - type: mrr_at_5 - value: 55.639 - - type: ndcg_at_1 - value: 45.016 - - type: ndcg_at_10 - value: 60.228 - - type: ndcg_at_100 - value: 64.277 - - type: ndcg_at_1000 - value: 65.07 - - type: ndcg_at_3 - value: 54.124 - - type: ndcg_at_5 - value: 57.147000000000006 - - type: precision_at_1 - value: 45.016 - - type: precision_at_10 - value: 9.937 - - type: precision_at_100 - value: 1.288 - - type: precision_at_1000 - value: 0.13899999999999998 - - type: precision_at_3 - value: 24.471999999999998 - - type: precision_at_5 - value: 16.991 - - type: recall_at_1 - value: 39.383 - - type: recall_at_10 - value: 76.175 - - type: recall_at_100 - value: 93.02 - - type: recall_at_1000 - value: 98.60900000000001 - - type: recall_at_3 - value: 60.265 - - type: recall_at_5 - value: 67.46600000000001 - - task: - type: Retrieval - dataset: - type: BeIR/cqadupstack - name: MTEB CQADupstackGisRetrieval - config: default - split: test - revision: 5003b3064772da1887988e05400cf3806fe491f2 - metrics: - - type: map_at_1 - value: 27.426000000000002 - - type: map_at_10 - value: 37.397000000000006 - - type: map_at_100 - value: 38.61 - - type: map_at_1000 - value: 38.678000000000004 - - type: map_at_3 - value: 34.150999999999996 - - type: map_at_5 - value: 36.137 - - type: mrr_at_1 - value: 29.944 - - type: mrr_at_10 - value: 39.654 - - type: mrr_at_100 - value: 40.638000000000005 - - type: mrr_at_1000 - value: 40.691 - - type: mrr_at_3 - value: 36.817 - - type: mrr_at_5 - value: 38.524 - - type: ndcg_at_1 - value: 29.944 - - type: ndcg_at_10 - value: 43.094 - - type: ndcg_at_100 - value: 48.789 - - type: ndcg_at_1000 - value: 50.339999999999996 - - type: ndcg_at_3 - value: 36.984 - - type: ndcg_at_5 - value: 40.248 - - type: precision_at_1 - value: 29.944 - - type: precision_at_10 - value: 6.78 - - type: precision_at_100 - value: 1.024 - - type: precision_at_1000 - value: 0.11800000000000001 - - type: precision_at_3 - value: 15.895000000000001 - - type: precision_at_5 - value: 11.39 - - type: recall_at_1 - value: 27.426000000000002 - - type: recall_at_10 - value: 58.464000000000006 - - type: recall_at_100 - value: 84.193 - - type: recall_at_1000 - value: 95.52000000000001 - - type: recall_at_3 - value: 42.172 - - type: recall_at_5 - value: 50.101 - - task: - type: Retrieval - dataset: - type: BeIR/cqadupstack - name: MTEB CQADupstackMathematicaRetrieval - config: default - split: test - revision: 90fceea13679c63fe563ded68f3b6f06e50061de - metrics: - - type: map_at_1 - value: 19.721 - - type: map_at_10 - value: 31.604 - - type: map_at_100 - value: 32.972 - - type: map_at_1000 - value: 33.077 - - type: map_at_3 - value: 27.218999999999998 - - type: map_at_5 - value: 29.53 - - type: mrr_at_1 - value: 25.0 - - type: mrr_at_10 - value: 35.843 - - type: mrr_at_100 - value: 36.785000000000004 - - type: mrr_at_1000 - value: 36.842000000000006 - - type: mrr_at_3 - value: 32.193 - - type: mrr_at_5 - value: 34.264 - - type: ndcg_at_1 - value: 25.0 - - type: ndcg_at_10 - value: 38.606 - - type: ndcg_at_100 - value: 44.272 - - type: ndcg_at_1000 - value: 46.527 - - type: ndcg_at_3 - value: 30.985000000000003 - - type: ndcg_at_5 - value: 34.43 - - type: precision_at_1 - value: 25.0 - - type: precision_at_10 - value: 7.811 - - type: precision_at_100 - value: 1.203 - - type: precision_at_1000 - value: 0.15 - - type: precision_at_3 - value: 15.423 - - type: precision_at_5 - value: 11.791 - - type: recall_at_1 - value: 19.721 - - type: recall_at_10 - value: 55.625 - - type: recall_at_100 - value: 79.34400000000001 - - type: recall_at_1000 - value: 95.208 - - type: recall_at_3 - value: 35.19 - - type: recall_at_5 - value: 43.626 - - task: - type: Retrieval - dataset: - type: BeIR/cqadupstack - name: MTEB CQADupstackPhysicsRetrieval - config: default - split: test - revision: 79531abbd1fb92d06c6d6315a0cbbbf5bb247ea4 - metrics: - - type: map_at_1 - value: 33.784 - - type: map_at_10 - value: 47.522 - - type: map_at_100 - value: 48.949999999999996 - - type: map_at_1000 - value: 49.038 - - type: map_at_3 - value: 43.284 - - type: map_at_5 - value: 45.629 - - type: mrr_at_1 - value: 41.482 - - type: mrr_at_10 - value: 52.830999999999996 - - type: mrr_at_100 - value: 53.559999999999995 - - type: mrr_at_1000 - value: 53.588 - - type: mrr_at_3 - value: 50.016000000000005 - - type: mrr_at_5 - value: 51.614000000000004 - - type: ndcg_at_1 - value: 41.482 - - type: ndcg_at_10 - value: 54.569 - - type: ndcg_at_100 - value: 59.675999999999995 - - type: ndcg_at_1000 - value: 60.989000000000004 - - type: ndcg_at_3 - value: 48.187000000000005 - - type: ndcg_at_5 - value: 51.183 - - type: precision_at_1 - value: 41.482 - - type: precision_at_10 - value: 10.221 - - type: precision_at_100 - value: 1.486 - - type: precision_at_1000 - value: 0.17500000000000002 - - type: precision_at_3 - value: 23.548 - - type: precision_at_5 - value: 16.805 - - type: recall_at_1 - value: 33.784 - - type: recall_at_10 - value: 69.798 - - type: recall_at_100 - value: 90.098 - - type: recall_at_1000 - value: 98.176 - - type: recall_at_3 - value: 52.127 - - type: recall_at_5 - value: 59.861 - - task: - type: Retrieval - dataset: - type: BeIR/cqadupstack - name: MTEB CQADupstackProgrammersRetrieval - config: default - split: test - revision: 6184bc1440d2dbc7612be22b50686b8826d22b32 - metrics: - - type: map_at_1 - value: 28.038999999999998 - - type: map_at_10 - value: 41.904 - - type: map_at_100 - value: 43.36 - - type: map_at_1000 - value: 43.453 - - type: map_at_3 - value: 37.785999999999994 - - type: map_at_5 - value: 40.105000000000004 - - type: mrr_at_1 - value: 35.046 - - type: mrr_at_10 - value: 46.926 - - type: mrr_at_100 - value: 47.815000000000005 - - type: mrr_at_1000 - value: 47.849000000000004 - - type: mrr_at_3 - value: 44.273 - - type: mrr_at_5 - value: 45.774 - - type: ndcg_at_1 - value: 35.046 - - type: ndcg_at_10 - value: 48.937000000000005 - - type: ndcg_at_100 - value: 54.544000000000004 - - type: ndcg_at_1000 - value: 56.069 - - type: ndcg_at_3 - value: 42.858000000000004 - - type: ndcg_at_5 - value: 45.644 - - type: precision_at_1 - value: 35.046 - - type: precision_at_10 - value: 9.452 - - type: precision_at_100 - value: 1.429 - - type: precision_at_1000 - value: 0.173 - - type: precision_at_3 - value: 21.346999999999998 - - type: precision_at_5 - value: 15.342 - - type: recall_at_1 - value: 28.038999999999998 - - type: recall_at_10 - value: 64.59700000000001 - - type: recall_at_100 - value: 87.735 - - type: recall_at_1000 - value: 97.41300000000001 - - type: recall_at_3 - value: 47.368 - - type: recall_at_5 - value: 54.93900000000001 - - task: - type: Retrieval - dataset: - type: BeIR/cqadupstack - name: MTEB CQADupstackRetrieval - config: default - split: test - revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4 - metrics: - - type: map_at_1 - value: 28.17291666666667 - - type: map_at_10 - value: 40.025749999999995 - - type: map_at_100 - value: 41.39208333333333 - - type: map_at_1000 - value: 41.499249999999996 - - type: map_at_3 - value: 36.347 - - type: map_at_5 - value: 38.41391666666667 - - type: mrr_at_1 - value: 33.65925 - - type: mrr_at_10 - value: 44.085499999999996 - - type: mrr_at_100 - value: 44.94116666666667 - - type: mrr_at_1000 - value: 44.9855 - - type: mrr_at_3 - value: 41.2815 - - type: mrr_at_5 - value: 42.91491666666666 - - type: ndcg_at_1 - value: 33.65925 - - type: ndcg_at_10 - value: 46.430833333333325 - - type: ndcg_at_100 - value: 51.761 - - type: ndcg_at_1000 - value: 53.50899999999999 - - type: ndcg_at_3 - value: 40.45133333333333 - - type: ndcg_at_5 - value: 43.31483333333334 - - type: precision_at_1 - value: 33.65925 - - type: precision_at_10 - value: 8.4995 - - type: precision_at_100 - value: 1.3210000000000004 - - type: precision_at_1000 - value: 0.16591666666666666 - - type: precision_at_3 - value: 19.165083333333335 - - type: precision_at_5 - value: 13.81816666666667 - - type: recall_at_1 - value: 28.17291666666667 - - type: recall_at_10 - value: 61.12624999999999 - - type: recall_at_100 - value: 83.97266666666667 - - type: recall_at_1000 - value: 95.66550000000001 - - type: recall_at_3 - value: 44.661249999999995 - - type: recall_at_5 - value: 51.983333333333334 - - task: - type: Retrieval - dataset: - type: BeIR/cqadupstack - name: MTEB CQADupstackStatsRetrieval - config: default - split: test - revision: 65ac3a16b8e91f9cee4c9828cc7c335575432a2a - metrics: - - type: map_at_1 - value: 24.681 - - type: map_at_10 - value: 34.892 - - type: map_at_100 - value: 35.996 - - type: map_at_1000 - value: 36.083 - - type: map_at_3 - value: 31.491999999999997 - - type: map_at_5 - value: 33.632 - - type: mrr_at_1 - value: 28.528 - - type: mrr_at_10 - value: 37.694 - - type: mrr_at_100 - value: 38.613 - - type: mrr_at_1000 - value: 38.668 - - type: mrr_at_3 - value: 34.714 - - type: mrr_at_5 - value: 36.616 - - type: ndcg_at_1 - value: 28.528 - - type: ndcg_at_10 - value: 40.703 - - type: ndcg_at_100 - value: 45.993 - - type: ndcg_at_1000 - value: 47.847 - - type: ndcg_at_3 - value: 34.622 - - type: ndcg_at_5 - value: 38.035999999999994 - - type: precision_at_1 - value: 28.528 - - type: precision_at_10 - value: 6.902 - - type: precision_at_100 - value: 1.0370000000000001 - - type: precision_at_1000 - value: 0.126 - - type: precision_at_3 - value: 15.798000000000002 - - type: precision_at_5 - value: 11.655999999999999 - - type: recall_at_1 - value: 24.681 - - type: recall_at_10 - value: 55.81 - - type: recall_at_100 - value: 79.785 - - type: recall_at_1000 - value: 92.959 - - type: recall_at_3 - value: 39.074 - - type: recall_at_5 - value: 47.568 - - task: - type: Retrieval - dataset: - type: BeIR/cqadupstack - name: MTEB CQADupstackTexRetrieval - config: default - split: test - revision: 46989137a86843e03a6195de44b09deda022eec7 - metrics: - - type: map_at_1 - value: 18.627 - - type: map_at_10 - value: 27.872000000000003 - - type: map_at_100 - value: 29.237999999999996 - - type: map_at_1000 - value: 29.363 - - type: map_at_3 - value: 24.751 - - type: map_at_5 - value: 26.521 - - type: mrr_at_1 - value: 23.021 - - type: mrr_at_10 - value: 31.924000000000003 - - type: mrr_at_100 - value: 32.922000000000004 - - type: mrr_at_1000 - value: 32.988 - - type: mrr_at_3 - value: 29.192 - - type: mrr_at_5 - value: 30.798 - - type: ndcg_at_1 - value: 23.021 - - type: ndcg_at_10 - value: 33.535 - - type: ndcg_at_100 - value: 39.732 - - type: ndcg_at_1000 - value: 42.201 - - type: ndcg_at_3 - value: 28.153 - - type: ndcg_at_5 - value: 30.746000000000002 - - type: precision_at_1 - value: 23.021 - - type: precision_at_10 - value: 6.459 - - type: precision_at_100 - value: 1.1320000000000001 - - type: precision_at_1000 - value: 0.153 - - type: precision_at_3 - value: 13.719000000000001 - - type: precision_at_5 - value: 10.193000000000001 - - type: recall_at_1 - value: 18.627 - - type: recall_at_10 - value: 46.463 - - type: recall_at_100 - value: 74.226 - - type: recall_at_1000 - value: 91.28500000000001 - - type: recall_at_3 - value: 31.357000000000003 - - type: recall_at_5 - value: 38.067 - - task: - type: Retrieval - dataset: - type: BeIR/cqadupstack - name: MTEB CQADupstackUnixRetrieval - config: default - split: test - revision: 6c6430d3a6d36f8d2a829195bc5dc94d7e063e53 - metrics: - - type: map_at_1 - value: 31.457 - - type: map_at_10 - value: 42.888 - - type: map_at_100 - value: 44.24 - - type: map_at_1000 - value: 44.327 - - type: map_at_3 - value: 39.588 - - type: map_at_5 - value: 41.423 - - type: mrr_at_1 - value: 37.126999999999995 - - type: mrr_at_10 - value: 47.083000000000006 - - type: mrr_at_100 - value: 47.997 - - type: mrr_at_1000 - value: 48.044 - - type: mrr_at_3 - value: 44.574000000000005 - - type: mrr_at_5 - value: 46.202 - - type: ndcg_at_1 - value: 37.126999999999995 - - type: ndcg_at_10 - value: 48.833 - - type: ndcg_at_100 - value: 54.327000000000005 - - type: ndcg_at_1000 - value: 56.011 - - type: ndcg_at_3 - value: 43.541999999999994 - - type: ndcg_at_5 - value: 46.127 - - type: precision_at_1 - value: 37.126999999999995 - - type: precision_at_10 - value: 8.376999999999999 - - type: precision_at_100 - value: 1.2309999999999999 - - type: precision_at_1000 - value: 0.146 - - type: precision_at_3 - value: 20.211000000000002 - - type: precision_at_5 - value: 14.16 - - type: recall_at_1 - value: 31.457 - - type: recall_at_10 - value: 62.369 - - type: recall_at_100 - value: 85.444 - - type: recall_at_1000 - value: 96.65599999999999 - - type: recall_at_3 - value: 47.961 - - type: recall_at_5 - value: 54.676 - - task: - type: Retrieval - dataset: - type: BeIR/cqadupstack - name: MTEB CQADupstackWebmastersRetrieval - config: default - split: test - revision: 160c094312a0e1facb97e55eeddb698c0abe3571 - metrics: - - type: map_at_1 - value: 27.139999999999997 - - type: map_at_10 - value: 38.801 - - type: map_at_100 - value: 40.549 - - type: map_at_1000 - value: 40.802 - - type: map_at_3 - value: 35.05 - - type: map_at_5 - value: 36.884 - - type: mrr_at_1 - value: 33.004 - - type: mrr_at_10 - value: 43.864 - - type: mrr_at_100 - value: 44.667 - - type: mrr_at_1000 - value: 44.717 - - type: mrr_at_3 - value: 40.777 - - type: mrr_at_5 - value: 42.319 - - type: ndcg_at_1 - value: 33.004 - - type: ndcg_at_10 - value: 46.022 - - type: ndcg_at_100 - value: 51.542 - - type: ndcg_at_1000 - value: 53.742000000000004 - - type: ndcg_at_3 - value: 39.795 - - type: ndcg_at_5 - value: 42.272 - - type: precision_at_1 - value: 33.004 - - type: precision_at_10 - value: 9.012 - - type: precision_at_100 - value: 1.7770000000000001 - - type: precision_at_1000 - value: 0.26 - - type: precision_at_3 - value: 19.038 - - type: precision_at_5 - value: 13.675999999999998 - - type: recall_at_1 - value: 27.139999999999997 - - type: recall_at_10 - value: 60.961 - - type: recall_at_100 - value: 84.451 - - type: recall_at_1000 - value: 98.113 - - type: recall_at_3 - value: 43.001 - - type: recall_at_5 - value: 49.896 - - task: - type: Retrieval - dataset: - type: BeIR/cqadupstack - name: MTEB CQADupstackWordpressRetrieval - config: default - split: test - revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4 - metrics: - - type: map_at_1 - value: 17.936 - - type: map_at_10 - value: 27.399 - - type: map_at_100 - value: 28.632 - - type: map_at_1000 - value: 28.738000000000003 - - type: map_at_3 - value: 24.456 - - type: map_at_5 - value: 26.06 - - type: mrr_at_1 - value: 19.224 - - type: mrr_at_10 - value: 28.998 - - type: mrr_at_100 - value: 30.11 - - type: mrr_at_1000 - value: 30.177 - - type: mrr_at_3 - value: 26.247999999999998 - - type: mrr_at_5 - value: 27.708 - - type: ndcg_at_1 - value: 19.224 - - type: ndcg_at_10 - value: 32.911 - - type: ndcg_at_100 - value: 38.873999999999995 - - type: ndcg_at_1000 - value: 41.277 - - type: ndcg_at_3 - value: 27.142 - - type: ndcg_at_5 - value: 29.755 - - type: precision_at_1 - value: 19.224 - - type: precision_at_10 - value: 5.6930000000000005 - - type: precision_at_100 - value: 0.9259999999999999 - - type: precision_at_1000 - value: 0.126 - - type: precision_at_3 - value: 12.138 - - type: precision_at_5 - value: 8.909 - - type: recall_at_1 - value: 17.936 - - type: recall_at_10 - value: 48.096 - - type: recall_at_100 - value: 75.389 - - type: recall_at_1000 - value: 92.803 - - type: recall_at_3 - value: 32.812999999999995 - - type: recall_at_5 - value: 38.851 - - task: - type: Retrieval - dataset: - type: mteb/climate-fever - name: MTEB ClimateFEVER - config: default - split: test - revision: 47f2ac6acb640fc46020b02a5b59fdda04d39380 - metrics: - - type: map_at_1 - value: 22.076999999999998 - - type: map_at_10 - value: 35.44 - - type: map_at_100 - value: 37.651 - - type: map_at_1000 - value: 37.824999999999996 - - type: map_at_3 - value: 30.764999999999997 - - type: map_at_5 - value: 33.26 - - type: mrr_at_1 - value: 50.163000000000004 - - type: mrr_at_10 - value: 61.207 - - type: mrr_at_100 - value: 61.675000000000004 - - type: mrr_at_1000 - value: 61.692 - - type: mrr_at_3 - value: 58.60999999999999 - - type: mrr_at_5 - value: 60.307 - - type: ndcg_at_1 - value: 50.163000000000004 - - type: ndcg_at_10 - value: 45.882 - - type: ndcg_at_100 - value: 53.239999999999995 - - type: ndcg_at_1000 - value: 55.852000000000004 - - type: ndcg_at_3 - value: 40.514 - - type: ndcg_at_5 - value: 42.038 - - type: precision_at_1 - value: 50.163000000000004 - - type: precision_at_10 - value: 13.466000000000001 - - type: precision_at_100 - value: 2.164 - - type: precision_at_1000 - value: 0.266 - - type: precision_at_3 - value: 29.707 - - type: precision_at_5 - value: 21.694 - - type: recall_at_1 - value: 22.076999999999998 - - type: recall_at_10 - value: 50.193 - - type: recall_at_100 - value: 74.993 - - type: recall_at_1000 - value: 89.131 - - type: recall_at_3 - value: 35.472 - - type: recall_at_5 - value: 41.814 - - task: - type: Retrieval - dataset: - type: mteb/dbpedia - name: MTEB DBPedia - config: default - split: test - revision: c0f706b76e590d620bd6618b3ca8efdd34e2d659 - metrics: - - type: map_at_1 - value: 9.953 - - type: map_at_10 - value: 24.515 - - type: map_at_100 - value: 36.173 - - type: map_at_1000 - value: 38.351 - - type: map_at_3 - value: 16.592000000000002 - - type: map_at_5 - value: 20.036 - - type: mrr_at_1 - value: 74.25 - - type: mrr_at_10 - value: 81.813 - - type: mrr_at_100 - value: 82.006 - - type: mrr_at_1000 - value: 82.011 - - type: mrr_at_3 - value: 80.875 - - type: mrr_at_5 - value: 81.362 - - type: ndcg_at_1 - value: 62.5 - - type: ndcg_at_10 - value: 52.42 - - type: ndcg_at_100 - value: 56.808 - - type: ndcg_at_1000 - value: 63.532999999999994 - - type: ndcg_at_3 - value: 56.654 - - type: ndcg_at_5 - value: 54.18300000000001 - - type: precision_at_1 - value: 74.25 - - type: precision_at_10 - value: 42.699999999999996 - - type: precision_at_100 - value: 13.675 - - type: precision_at_1000 - value: 2.664 - - type: precision_at_3 - value: 60.5 - - type: precision_at_5 - value: 52.800000000000004 - - type: recall_at_1 - value: 9.953 - - type: recall_at_10 - value: 30.253999999999998 - - type: recall_at_100 - value: 62.516000000000005 - - type: recall_at_1000 - value: 84.163 - - type: recall_at_3 - value: 18.13 - - type: recall_at_5 - value: 22.771 - - task: - type: Classification - dataset: - type: mteb/emotion - name: MTEB EmotionClassification - config: default - split: test - revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 - metrics: - - type: accuracy - value: 79.455 - - type: f1 - value: 74.16798697647569 - - task: - type: Retrieval - dataset: - type: mteb/fever - name: MTEB FEVER - config: default - split: test - revision: bea83ef9e8fb933d90a2f1d5515737465d613e12 - metrics: - - type: map_at_1 - value: 87.531 - - type: map_at_10 - value: 93.16799999999999 - - type: map_at_100 - value: 93.341 - - type: map_at_1000 - value: 93.349 - - type: map_at_3 - value: 92.444 - - type: map_at_5 - value: 92.865 - - type: mrr_at_1 - value: 94.014 - - type: mrr_at_10 - value: 96.761 - - type: mrr_at_100 - value: 96.762 - - type: mrr_at_1000 - value: 96.762 - - type: mrr_at_3 - value: 96.672 - - type: mrr_at_5 - value: 96.736 - - type: ndcg_at_1 - value: 94.014 - - type: ndcg_at_10 - value: 95.112 - - type: ndcg_at_100 - value: 95.578 - - type: ndcg_at_1000 - value: 95.68900000000001 - - type: ndcg_at_3 - value: 94.392 - - type: ndcg_at_5 - value: 94.72500000000001 - - type: precision_at_1 - value: 94.014 - - type: precision_at_10 - value: 11.065 - - type: precision_at_100 - value: 1.157 - - type: precision_at_1000 - value: 0.11800000000000001 - - type: precision_at_3 - value: 35.259 - - type: precision_at_5 - value: 21.599 - - type: recall_at_1 - value: 87.531 - - type: recall_at_10 - value: 97.356 - - type: recall_at_100 - value: 98.965 - - type: recall_at_1000 - value: 99.607 - - type: recall_at_3 - value: 95.312 - - type: recall_at_5 - value: 96.295 - - task: - type: Retrieval - dataset: - type: mteb/fiqa - name: MTEB FiQA2018 - config: default - split: test - revision: 27a168819829fe9bcd655c2df245fb19452e8e06 - metrics: - - type: map_at_1 - value: 32.055 - - type: map_at_10 - value: 53.114 - - type: map_at_100 - value: 55.235 - - type: map_at_1000 - value: 55.345 - - type: map_at_3 - value: 45.854 - - type: map_at_5 - value: 50.025 - - type: mrr_at_1 - value: 60.34 - - type: mrr_at_10 - value: 68.804 - - type: mrr_at_100 - value: 69.309 - - type: mrr_at_1000 - value: 69.32199999999999 - - type: mrr_at_3 - value: 66.40899999999999 - - type: mrr_at_5 - value: 67.976 - - type: ndcg_at_1 - value: 60.34 - - type: ndcg_at_10 - value: 62.031000000000006 - - type: ndcg_at_100 - value: 68.00500000000001 - - type: ndcg_at_1000 - value: 69.286 - - type: ndcg_at_3 - value: 56.355999999999995 - - type: ndcg_at_5 - value: 58.687 - - type: precision_at_1 - value: 60.34 - - type: precision_at_10 - value: 17.176 - - type: precision_at_100 - value: 2.36 - - type: precision_at_1000 - value: 0.259 - - type: precision_at_3 - value: 37.14 - - type: precision_at_5 - value: 27.809 - - type: recall_at_1 - value: 32.055 - - type: recall_at_10 - value: 70.91 - - type: recall_at_100 - value: 91.83 - - type: recall_at_1000 - value: 98.871 - - type: recall_at_3 - value: 51.202999999999996 - - type: recall_at_5 - value: 60.563 - - task: - type: Retrieval - dataset: - type: mteb/hotpotqa - name: MTEB HotpotQA - config: default - split: test - revision: ab518f4d6fcca38d87c25209f94beba119d02014 - metrics: - - type: map_at_1 - value: 43.68 - - type: map_at_10 - value: 64.389 - - type: map_at_100 - value: 65.24 - - type: map_at_1000 - value: 65.303 - - type: map_at_3 - value: 61.309000000000005 - - type: map_at_5 - value: 63.275999999999996 - - type: mrr_at_1 - value: 87.36 - - type: mrr_at_10 - value: 91.12 - - type: mrr_at_100 - value: 91.227 - - type: mrr_at_1000 - value: 91.229 - - type: mrr_at_3 - value: 90.57600000000001 - - type: mrr_at_5 - value: 90.912 - - type: ndcg_at_1 - value: 87.36 - - type: ndcg_at_10 - value: 73.076 - - type: ndcg_at_100 - value: 75.895 - - type: ndcg_at_1000 - value: 77.049 - - type: ndcg_at_3 - value: 68.929 - - type: ndcg_at_5 - value: 71.28 - - type: precision_at_1 - value: 87.36 - - type: precision_at_10 - value: 14.741000000000001 - - type: precision_at_100 - value: 1.694 - - type: precision_at_1000 - value: 0.185 - - type: precision_at_3 - value: 43.043 - - type: precision_at_5 - value: 27.681 - - type: recall_at_1 - value: 43.68 - - type: recall_at_10 - value: 73.707 - - type: recall_at_100 - value: 84.7 - - type: recall_at_1000 - value: 92.309 - - type: recall_at_3 - value: 64.564 - - type: recall_at_5 - value: 69.203 - - task: - type: Classification - dataset: - type: mteb/imdb - name: MTEB ImdbClassification - config: default - split: test - revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 - metrics: - - type: accuracy - value: 96.75399999999999 - - type: ap - value: 95.29389839242187 - - type: f1 - value: 96.75348377433475 - - task: - type: Retrieval - dataset: - type: mteb/msmarco - name: MTEB MSMARCO - config: default - split: dev - revision: c5a29a104738b98a9e76336939199e264163d4a0 - metrics: - - type: map_at_1 - value: 25.176 - - type: map_at_10 - value: 38.598 - - type: map_at_100 - value: 39.707 - - type: map_at_1000 - value: 39.744 - - type: map_at_3 - value: 34.566 - - type: map_at_5 - value: 36.863 - - type: mrr_at_1 - value: 25.874000000000002 - - type: mrr_at_10 - value: 39.214 - - type: mrr_at_100 - value: 40.251 - - type: mrr_at_1000 - value: 40.281 - - type: mrr_at_3 - value: 35.291 - - type: mrr_at_5 - value: 37.545 - - type: ndcg_at_1 - value: 25.874000000000002 - - type: ndcg_at_10 - value: 45.98 - - type: ndcg_at_100 - value: 51.197 - - type: ndcg_at_1000 - value: 52.073 - - type: ndcg_at_3 - value: 37.785999999999994 - - type: ndcg_at_5 - value: 41.870000000000005 - - type: precision_at_1 - value: 25.874000000000002 - - type: precision_at_10 - value: 7.181 - - type: precision_at_100 - value: 0.979 - - type: precision_at_1000 - value: 0.106 - - type: precision_at_3 - value: 16.051000000000002 - - type: precision_at_5 - value: 11.713 - - type: recall_at_1 - value: 25.176 - - type: recall_at_10 - value: 68.67699999999999 - - type: recall_at_100 - value: 92.55 - - type: recall_at_1000 - value: 99.164 - - type: recall_at_3 - value: 46.372 - - type: recall_at_5 - value: 56.16 - - task: - type: Classification - dataset: - type: mteb/mtop_domain - name: MTEB MTOPDomainClassification (en) - config: en - split: test - revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf - metrics: - - type: accuracy - value: 99.03784769721841 - - type: f1 - value: 98.97791641821495 - - task: - type: Classification - dataset: - type: mteb/mtop_intent - name: MTEB MTOPIntentClassification (en) - config: en - split: test - revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba - metrics: - - type: accuracy - value: 91.88326493388054 - - type: f1 - value: 73.74809928034335 - - task: - type: Classification - dataset: - type: mteb/amazon_massive_intent - name: MTEB MassiveIntentClassification (en) - config: en - split: test - revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 - metrics: - - type: accuracy - value: 85.41358439811701 - - type: f1 - value: 83.503679460639 - - task: - type: Classification - dataset: - type: mteb/amazon_massive_scenario - name: MTEB MassiveScenarioClassification (en) - config: en - split: test - revision: 7d571f92784cd94a019292a1f45445077d0ef634 - metrics: - - type: accuracy - value: 89.77135171486215 - - type: f1 - value: 88.89843747468366 - - task: - type: Clustering - dataset: - type: mteb/medrxiv-clustering-p2p - name: MTEB MedrxivClusteringP2P - config: default - split: test - revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 - metrics: - - type: v_measure - value: 46.22695362087359 - - task: - type: Clustering - dataset: - type: mteb/medrxiv-clustering-s2s - name: MTEB MedrxivClusteringS2S - config: default - split: test - revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 - metrics: - - type: v_measure - value: 44.132372165849425 - - task: - type: Reranking - dataset: - type: mteb/mind_small - name: MTEB MindSmallReranking - config: default - split: test - revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 - metrics: - - type: map - value: 33.35680810650402 - - type: mrr - value: 34.72625715637218 - - task: - type: Retrieval - dataset: - type: mteb/nfcorpus - name: MTEB NFCorpus - config: default - split: test - revision: ec0fa4fe99da2ff19ca1214b7966684033a58814 - metrics: - - type: map_at_1 - value: 7.165000000000001 - - type: map_at_10 - value: 15.424 - - type: map_at_100 - value: 20.28 - - type: map_at_1000 - value: 22.065 - - type: map_at_3 - value: 11.236 - - type: map_at_5 - value: 13.025999999999998 - - type: mrr_at_1 - value: 51.702999999999996 - - type: mrr_at_10 - value: 59.965 - - type: mrr_at_100 - value: 60.667 - - type: mrr_at_1000 - value: 60.702999999999996 - - type: mrr_at_3 - value: 58.772000000000006 - - type: mrr_at_5 - value: 59.267 - - type: ndcg_at_1 - value: 49.536 - - type: ndcg_at_10 - value: 40.6 - - type: ndcg_at_100 - value: 37.848 - - type: ndcg_at_1000 - value: 46.657 - - type: ndcg_at_3 - value: 46.117999999999995 - - type: ndcg_at_5 - value: 43.619 - - type: precision_at_1 - value: 51.393 - - type: precision_at_10 - value: 30.31 - - type: precision_at_100 - value: 9.972 - - type: precision_at_1000 - value: 2.329 - - type: precision_at_3 - value: 43.137 - - type: precision_at_5 - value: 37.585 - - type: recall_at_1 - value: 7.165000000000001 - - type: recall_at_10 - value: 19.689999999999998 - - type: recall_at_100 - value: 39.237 - - type: recall_at_1000 - value: 71.417 - - type: recall_at_3 - value: 12.247 - - type: recall_at_5 - value: 14.902999999999999 - - task: - type: Retrieval - dataset: - type: mteb/nq - name: MTEB NQ - config: default - split: test - revision: b774495ed302d8c44a3a7ea25c90dbce03968f31 - metrics: - - type: map_at_1 - value: 42.653999999999996 - - type: map_at_10 - value: 59.611999999999995 - - type: map_at_100 - value: 60.32300000000001 - - type: map_at_1000 - value: 60.336 - - type: map_at_3 - value: 55.584999999999994 - - type: map_at_5 - value: 58.19 - 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- task: - type: Retrieval - dataset: - type: mteb/quora - name: MTEB QuoraRetrieval - config: default - split: test - revision: None - metrics: - - type: map_at_1 - value: 72.538 - - type: map_at_10 - value: 86.702 - - type: map_at_100 - value: 87.31 - - type: map_at_1000 - value: 87.323 - - type: map_at_3 - value: 83.87 - - type: map_at_5 - value: 85.682 - - type: mrr_at_1 - value: 83.31 - - type: mrr_at_10 - value: 89.225 - - type: mrr_at_100 - value: 89.30399999999999 - - type: mrr_at_1000 - value: 89.30399999999999 - - type: mrr_at_3 - value: 88.44300000000001 - - type: mrr_at_5 - value: 89.005 - - type: ndcg_at_1 - value: 83.32000000000001 - - type: ndcg_at_10 - value: 90.095 - - type: ndcg_at_100 - value: 91.12 - - type: ndcg_at_1000 - value: 91.179 - - type: ndcg_at_3 - value: 87.606 - - type: ndcg_at_5 - value: 89.031 - - type: precision_at_1 - value: 83.32000000000001 - - type: precision_at_10 - value: 13.641 - - type: precision_at_100 - value: 1.541 - - type: precision_at_1000 - value: 0.157 - - type: precision_at_3 - value: 38.377 - - type: precision_at_5 - value: 25.162000000000003 - - type: recall_at_1 - value: 72.538 - - type: recall_at_10 - value: 96.47200000000001 - - type: recall_at_100 - value: 99.785 - - type: recall_at_1000 - value: 99.99900000000001 - - type: recall_at_3 - value: 89.278 - - type: recall_at_5 - value: 93.367 - - task: - type: Clustering - dataset: - type: mteb/reddit-clustering - name: MTEB RedditClustering - config: default - split: test - revision: 24640382cdbf8abc73003fb0fa6d111a705499eb - metrics: - - type: v_measure - value: 73.55219145406065 - - task: - type: Clustering - dataset: - type: mteb/reddit-clustering-p2p - name: MTEB RedditClusteringP2P - config: default - split: test - revision: 282350215ef01743dc01b456c7f5241fa8937f16 - metrics: - - type: v_measure - value: 74.13437105242755 - - task: - type: Retrieval - dataset: - type: mteb/scidocs - name: MTEB SCIDOCS - config: default - split: test - revision: None - metrics: - 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value: 6.873 - - type: recall_at_10 - value: 30.568 - - type: recall_at_100 - value: 62.062 - - type: recall_at_1000 - value: 85.37700000000001 - - type: recall_at_3 - value: 15.312999999999999 - - type: recall_at_5 - value: 21.575 - - task: - type: STS - dataset: - type: mteb/sickr-sts - name: MTEB SICK-R - config: default - split: test - revision: a6ea5a8cab320b040a23452cc28066d9beae2cee - metrics: - - type: cos_sim_pearson - value: 82.37009118256057 - - type: cos_sim_spearman - value: 79.27986395671529 - - type: euclidean_pearson - value: 79.18037715442115 - - type: euclidean_spearman - value: 79.28004791561621 - - type: manhattan_pearson - value: 79.34062972800541 - - type: manhattan_spearman - value: 79.43106695543402 - - task: - type: STS - dataset: - type: mteb/sts12-sts - name: MTEB STS12 - config: default - split: test - revision: a0d554a64d88156834ff5ae9920b964011b16384 - metrics: - - type: cos_sim_pearson - value: 87.48474767383833 - - type: cos_sim_spearman - value: 79.54505388752513 - 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value: 84.2601737035379 - - type: euclidean_spearman - value: 83.87431082479074 - - type: manhattan_pearson - value: 84.3621547772745 - - type: manhattan_spearman - value: 84.12094375000423 - - task: - type: STS - dataset: - type: mteb/sts15-sts - name: MTEB STS15 - config: default - split: test - revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 - metrics: - - type: cos_sim_pearson - value: 88.12749863201587 - - type: cos_sim_spearman - value: 88.54287568368565 - - type: euclidean_pearson - value: 87.90429700607999 - - type: euclidean_spearman - value: 88.5437689576261 - - type: manhattan_pearson - value: 88.19276653356833 - - type: manhattan_spearman - value: 88.99995393814679 - - task: - type: STS - dataset: - type: mteb/sts16-sts - name: MTEB STS16 - config: default - split: test - revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 - metrics: - - type: cos_sim_pearson - value: 85.68398747560902 - - type: cos_sim_spearman - value: 86.48815303460574 - - type: euclidean_pearson - value: 85.52356631237954 - - type: euclidean_spearman - value: 86.486391949551 - - type: manhattan_pearson - value: 85.67267981761788 - - type: manhattan_spearman - value: 86.7073696332485 - - 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: 88.9057107443124 - - type: cos_sim_spearman - value: 88.7312168757697 - - type: euclidean_pearson - value: 88.72810439714794 - - type: euclidean_spearman - value: 88.71976185854771 - - type: manhattan_pearson - value: 88.50433745949111 - - type: manhattan_spearman - value: 88.51726175544195 - - task: - type: STS - dataset: - type: mteb/sts22-crosslingual-sts - name: MTEB STS22 (en) - config: en - split: test - revision: eea2b4fe26a775864c896887d910b76a8098ad3f - metrics: - - type: cos_sim_pearson - value: 67.59391795109886 - - type: cos_sim_spearman - value: 66.87613008631367 - - type: euclidean_pearson - value: 69.23198488262217 - - type: euclidean_spearman - value: 66.85427723013692 - - type: manhattan_pearson - value: 69.50730124841084 - - type: manhattan_spearman - value: 67.10404669820792 - - task: - type: STS - dataset: - type: mteb/stsbenchmark-sts - name: MTEB STSBenchmark - config: default - split: test - revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 - metrics: - - type: cos_sim_pearson - value: 87.0820605344619 - - type: cos_sim_spearman - value: 86.8518089863434 - - type: euclidean_pearson - value: 86.31087134689284 - - type: euclidean_spearman - value: 86.8518520517941 - - type: manhattan_pearson - value: 86.47203796160612 - - type: manhattan_spearman - value: 87.1080149734421 - - task: - type: Reranking - dataset: - type: mteb/scidocs-reranking - name: MTEB SciDocsRR - config: default - split: test - revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab - metrics: - - type: map - value: 89.09255369305481 - - type: mrr - value: 97.10323445617563 - - task: - type: Retrieval - 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- type: precision_at_1000 - value: 0.11299999999999999 - - type: precision_at_3 - value: 29.889 - - type: precision_at_5 - value: 19.533 - - type: recall_at_1 - value: 61.260999999999996 - - type: recall_at_10 - value: 93.167 - - type: recall_at_100 - value: 99.0 - - type: recall_at_1000 - value: 100.0 - - type: recall_at_3 - value: 81.667 - - type: recall_at_5 - value: 87.394 - - task: - type: PairClassification - dataset: - type: mteb/sprintduplicatequestions-pairclassification - name: MTEB SprintDuplicateQuestions - config: default - split: test - revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 - metrics: - - type: cos_sim_accuracy - value: 99.71980198019801 - - type: cos_sim_ap - value: 92.81616007802704 - - type: cos_sim_f1 - value: 85.17548454688318 - - type: cos_sim_precision - value: 89.43894389438944 - - type: cos_sim_recall - value: 81.3 - - type: dot_accuracy - value: 99.71980198019801 - - type: dot_ap - value: 92.81398760591358 - - type: dot_f1 - value: 85.17548454688318 - - type: dot_precision - value: 89.43894389438944 - - type: dot_recall - value: 81.3 - - type: euclidean_accuracy - value: 99.71980198019801 - - type: euclidean_ap - value: 92.81560637245072 - - type: euclidean_f1 - value: 85.17548454688318 - - type: euclidean_precision - value: 89.43894389438944 - - type: euclidean_recall - value: 81.3 - - type: manhattan_accuracy - value: 99.73069306930694 - - type: manhattan_ap - value: 93.14005487480794 - - type: manhattan_f1 - value: 85.56263269639068 - - type: manhattan_precision - value: 91.17647058823529 - - type: manhattan_recall - value: 80.60000000000001 - - type: max_accuracy - value: 99.73069306930694 - - type: max_ap - value: 93.14005487480794 - - type: max_f1 - value: 85.56263269639068 - - task: - type: Clustering - dataset: - type: mteb/stackexchange-clustering - name: MTEB StackExchangeClustering - config: default - split: test - revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 - metrics: - - type: v_measure - value: 79.86443362395185 - - task: - type: Clustering - dataset: - type: mteb/stackexchange-clustering-p2p - name: MTEB StackExchangeClusteringP2P - config: default - split: test - revision: 815ca46b2622cec33ccafc3735d572c266efdb44 - metrics: - - type: v_measure - value: 49.40897096662564 - - task: - type: Reranking - dataset: - type: mteb/stackoverflowdupquestions-reranking - name: MTEB StackOverflowDupQuestions - config: default - split: test - revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 - metrics: - - type: map - value: 55.66040806627947 - - type: mrr - value: 56.58670475766064 - - task: - type: Summarization - dataset: - type: mteb/summeval - name: MTEB SummEval - config: default - split: test - revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c - metrics: - - type: cos_sim_pearson - value: 31.51015090598575 - - type: cos_sim_spearman - value: 31.35016454939226 - - type: dot_pearson - value: 31.5150068731 - - type: dot_spearman - value: 31.34790869023487 - - task: - type: Retrieval - dataset: - type: mteb/trec-covid - name: MTEB TRECCOVID - config: default - split: test - revision: None - metrics: - - type: map_at_1 - value: 0.254 - - type: map_at_10 - value: 2.064 - - type: map_at_100 - value: 12.909 - - type: map_at_1000 - value: 31.761 - - type: map_at_3 - value: 0.738 - - type: map_at_5 - value: 1.155 - - type: mrr_at_1 - value: 96.0 - - type: mrr_at_10 - value: 98.0 - - type: mrr_at_100 - value: 98.0 - - type: mrr_at_1000 - value: 98.0 - - type: mrr_at_3 - value: 98.0 - - type: mrr_at_5 - value: 98.0 - - type: ndcg_at_1 - value: 93.0 - - type: ndcg_at_10 - value: 82.258 - - type: ndcg_at_100 - value: 64.34 - - type: ndcg_at_1000 - value: 57.912 - - type: ndcg_at_3 - value: 90.827 - - type: ndcg_at_5 - value: 86.79 - - type: precision_at_1 - value: 96.0 - - type: precision_at_10 - value: 84.8 - - type: precision_at_100 - value: 66.0 - - type: precision_at_1000 - value: 25.356 - - type: precision_at_3 - value: 94.667 - - type: precision_at_5 - value: 90.4 - - type: recall_at_1 - value: 0.254 - - type: recall_at_10 - 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name: MTEB TweetSentimentExtractionClassification - config: default - split: test - revision: d604517c81ca91fe16a244d1248fc021f9ecee7a - metrics: - - type: accuracy - value: 72.58347481607245 - - type: f1 - value: 72.74877295564937 - - task: - type: Clustering - dataset: - type: mteb/twentynewsgroups-clustering - name: MTEB TwentyNewsgroupsClustering - config: default - split: test - revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 - metrics: - - type: v_measure - value: 53.90586138221305 - - task: - type: PairClassification - dataset: - type: mteb/twittersemeval2015-pairclassification - name: MTEB TwitterSemEval2015 - config: default - split: test - revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 - metrics: - - type: cos_sim_accuracy - value: 87.35769207844072 - - type: cos_sim_ap - value: 77.9645072410354 - - type: cos_sim_f1 - value: 71.32352941176471 - - type: cos_sim_precision - value: 66.5903890160183 - - type: cos_sim_recall - value: 76.78100263852242 - - type: dot_accuracy - value: 87.37557370209214 - - type: dot_ap - value: 77.96250046429908 - - type: dot_f1 - value: 71.28932757557064 - - type: dot_precision - value: 66.95249130938586 - - type: dot_recall - value: 76.22691292875989 - - type: euclidean_accuracy - value: 87.35173153722357 - - type: euclidean_ap - value: 77.96520460741593 - - type: euclidean_f1 - value: 71.32470733210104 - - type: euclidean_precision - value: 66.91329479768785 - - type: euclidean_recall - value: 76.35883905013192 - - type: manhattan_accuracy - value: 87.25636287774931 - - type: manhattan_ap - value: 77.77752485611796 - - type: manhattan_f1 - value: 71.18148599269183 - - type: manhattan_precision - value: 66.10859728506787 - - type: manhattan_recall - value: 77.0976253298153 - - type: max_accuracy - value: 87.37557370209214 - - type: max_ap - value: 77.96520460741593 - - type: max_f1 - value: 71.32470733210104 - - task: - type: PairClassification - dataset: - type: mteb/twitterurlcorpus-pairclassification - name: MTEB TwitterURLCorpus - 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value: 63.0 - - type: mrr_at_10 - value: 73.471 - - type: mrr_at_100 - value: 73.87 - - type: mrr_at_1000 - value: 73.87 - - type: mrr_at_3 - value: 70.5 - - type: mrr_at_5 - value: 73.05 - - type: ndcg_at_1 - value: 63.0 - - type: ndcg_at_10 - value: 78.255 - - type: ndcg_at_100 - value: 79.88 - - type: ndcg_at_1000 - value: 79.88 - - type: ndcg_at_3 - value: 72.702 - - type: ndcg_at_5 - value: 77.264 - - type: precision_at_1 - value: 63.0 - - type: precision_at_10 - value: 9.3 - - type: precision_at_100 - value: 1.0 - - type: precision_at_1000 - value: 0.1 - - type: precision_at_3 - value: 26.333000000000002 - - type: precision_at_5 - value: 18.0 - - type: recall_at_1 - value: 63.0 - - type: recall_at_10 - value: 93.0 - - type: recall_at_100 - value: 100.0 - - type: recall_at_1000 - value: 100.0 - - type: recall_at_3 - value: 79.0 - - type: recall_at_5 - value: 90.0 - task: - type: Retrieval - - dataset: - config: fr - name: MTEB XPQARetrieval (fr) - revision: c99d599f0a6ab9b85b065da6f9d94f9cf731679f - split: test - type: jinaai/xpqa - metrics: - - type: map_at_1 - value: 40.338 - - type: map_at_10 - value: 61.927 - - type: map_at_100 - value: 63.361999999999995 - - type: map_at_1000 - value: 63.405 - - type: map_at_3 - value: 55.479 - - type: map_at_5 - value: 59.732 - - type: mrr_at_1 - value: 63.551 - - type: mrr_at_10 - value: 71.006 - - type: mrr_at_100 - value: 71.501 - - type: mrr_at_1000 - value: 71.509 - - type: mrr_at_3 - value: 69.07 - - type: mrr_at_5 - value: 70.165 - - type: ndcg_at_1 - value: 63.551 - - type: ndcg_at_10 - value: 68.297 - - type: ndcg_at_100 - value: 73.13199999999999 - - type: ndcg_at_1000 - value: 73.751 - - type: ndcg_at_3 - value: 62.999 - - type: ndcg_at_5 - value: 64.89 - - type: precision_at_1 - value: 63.551 - - type: precision_at_10 - value: 15.661 - - type: precision_at_100 - value: 1.9789999999999999 - - type: precision_at_1000 - value: 0.207 - - type: precision_at_3 - value: 38.273 - - type: precision_at_5 - value: 27.61 - - type: recall_at_1 - value: 40.338 - - type: recall_at_10 - value: 77.267 - - type: recall_at_100 - value: 95.892 - - type: recall_at_1000 - value: 99.75500000000001 - - type: recall_at_3 - value: 60.36 - - type: recall_at_5 - value: 68.825 - task: - type: Retrieval - - dataset: - config: default - name: MTEB 8TagsClustering - revision: None - split: test - type: PL-MTEB/8tags-clustering - metrics: - - type: v_measure - value: 51.36126303874126 - task: - type: Clustering - - dataset: - config: default - name: MTEB AllegroReviews - revision: None - split: test - type: PL-MTEB/allegro-reviews - metrics: - - type: accuracy - value: 67.13717693836979 - - type: f1 - value: 57.27609848003782 - task: - type: Classification - - dataset: - config: default - name: MTEB ArguAna-PL - revision: 63fc86750af76253e8c760fc9e534bbf24d260a2 - split: test - type: clarin-knext/arguana-pl - metrics: - - type: map_at_1 - value: 35.276999999999994 - - type: map_at_10 - value: 51.086 - - type: map_at_100 - value: 51.788000000000004 - - type: map_at_1000 - value: 51.791 - - type: map_at_3 - value: 46.147 - - type: map_at_5 - value: 49.078 - - type: mrr_at_1 - value: 35.917 - - type: mrr_at_10 - value: 51.315999999999995 - - type: mrr_at_100 - value: 52.018 - - type: mrr_at_1000 - value: 52.022 - - type: mrr_at_3 - value: 46.349000000000004 - - type: mrr_at_5 - value: 49.297000000000004 - - type: ndcg_at_1 - value: 35.276999999999994 - - type: ndcg_at_10 - value: 59.870999999999995 - - type: ndcg_at_100 - value: 62.590999999999994 - - type: ndcg_at_1000 - value: 62.661 - - type: ndcg_at_3 - value: 49.745 - - type: ndcg_at_5 - value: 55.067 - - type: precision_at_1 - value: 35.276999999999994 - - type: precision_at_10 - value: 8.791 - - type: precision_at_100 - value: 0.991 - - type: precision_at_1000 - value: 0.1 - - type: precision_at_3 - value: 20.057 - - type: precision_at_5 - value: 14.637 - - type: recall_at_1 - value: 35.276999999999994 - - type: recall_at_10 - value: 87.909 - - type: recall_at_100 - value: 99.14699999999999 - - type: recall_at_1000 - value: 99.644 - - type: recall_at_3 - value: 60.171 - - type: recall_at_5 - value: 73.18599999999999 - task: - type: Retrieval - - dataset: - config: default - name: MTEB CBD - revision: None - split: test - type: PL-MTEB/cbd - metrics: - - type: accuracy - value: 78.03000000000002 - - type: ap - value: 29.12548553897622 - - type: f1 - value: 66.54857118886073 - task: - type: Classification - - dataset: - config: default - name: MTEB CDSC-E - revision: None - split: test - type: PL-MTEB/cdsce-pairclassification - metrics: - - type: cos_sim_accuracy - value: 89.0 - - type: cos_sim_ap - value: 76.75437826834582 - - type: cos_sim_f1 - value: 66.4850136239782 - - type: cos_sim_precision - value: 68.92655367231639 - - type: cos_sim_recall - value: 64.21052631578948 - - type: dot_accuracy - value: 89.0 - - type: dot_ap - value: 76.75437826834582 - - type: dot_f1 - value: 66.4850136239782 - - type: dot_precision - value: 68.92655367231639 - - type: dot_recall - value: 64.21052631578948 - - type: euclidean_accuracy - value: 89.0 - - type: euclidean_ap - value: 76.75437826834582 - - type: euclidean_f1 - value: 66.4850136239782 - - type: euclidean_precision - value: 68.92655367231639 - - type: euclidean_recall - value: 64.21052631578948 - - type: manhattan_accuracy - value: 89.0 - - type: manhattan_ap - value: 76.66074220647083 - - type: manhattan_f1 - value: 66.47058823529412 - - type: manhattan_precision - value: 75.33333333333333 - - type: manhattan_recall - value: 59.473684210526315 - - type: max_accuracy - value: 89.0 - - type: max_ap - value: 76.75437826834582 - - type: max_f1 - value: 66.4850136239782 - task: - type: PairClassification - - dataset: - config: default - name: MTEB CDSC-R - revision: None - split: test - type: PL-MTEB/cdscr-sts - metrics: - - type: cos_sim_pearson - value: 93.12903172428328 - - type: cos_sim_spearman - value: 92.66381487060741 - - type: euclidean_pearson - value: 90.37278396708922 - - type: euclidean_spearman - value: 92.66381487060741 - - type: manhattan_pearson - value: 90.32503296540962 - - type: manhattan_spearman - value: 92.6902938354313 - task: - type: STS - - dataset: - config: default - name: MTEB DBPedia-PL - revision: 76afe41d9af165cc40999fcaa92312b8b012064a - split: test - type: clarin-knext/dbpedia-pl - metrics: - - type: map_at_1 - value: 8.83 - - type: map_at_10 - value: 18.326 - - type: map_at_100 - value: 26.496 - - type: map_at_1000 - value: 28.455000000000002 - - type: map_at_3 - value: 12.933 - - type: map_at_5 - value: 15.168000000000001 - - type: mrr_at_1 - value: 66.0 - - type: mrr_at_10 - value: 72.76700000000001 - - type: mrr_at_100 - value: 73.203 - - type: mrr_at_1000 - value: 73.219 - - type: mrr_at_3 - value: 71.458 - - type: mrr_at_5 - value: 72.246 - - type: ndcg_at_1 - value: 55.375 - - type: ndcg_at_10 - value: 41.3 - - type: ndcg_at_100 - value: 45.891 - - type: ndcg_at_1000 - value: 52.905 - - type: ndcg_at_3 - value: 46.472 - - type: ndcg_at_5 - value: 43.734 - - type: precision_at_1 - value: 66.0 - - type: precision_at_10 - value: 33.074999999999996 - - type: precision_at_100 - value: 11.094999999999999 - - type: precision_at_1000 - value: 2.374 - - type: precision_at_3 - value: 48.583 - - type: precision_at_5 - value: 42.0 - - type: recall_at_1 - value: 8.83 - - type: recall_at_10 - value: 22.587 - - type: recall_at_100 - value: 50.61600000000001 - - type: recall_at_1000 - value: 73.559 - - type: recall_at_3 - value: 13.688 - - type: recall_at_5 - value: 16.855 - task: - type: Retrieval - - dataset: - config: default - name: MTEB FiQA-PL - revision: 2e535829717f8bf9dc829b7f911cc5bbd4e6608e - split: test - type: clarin-knext/fiqa-pl - metrics: - - type: map_at_1 - value: 20.587 - - type: map_at_10 - value: 33.095 - - type: map_at_100 - value: 35.24 - - type: map_at_1000 - value: 35.429 - - type: map_at_3 - value: 28.626 - - type: map_at_5 - value: 31.136999999999997 - - type: mrr_at_1 - value: 40.586 - - type: mrr_at_10 - value: 49.033 - - type: mrr_at_100 - value: 49.952999999999996 - - type: mrr_at_1000 - value: 49.992 - - type: mrr_at_3 - value: 46.553 - - type: mrr_at_5 - value: 48.035 - - type: ndcg_at_1 - value: 40.586 - - type: ndcg_at_10 - value: 41.046 - - type: ndcg_at_100 - value: 48.586 - - type: ndcg_at_1000 - value: 51.634 - - type: ndcg_at_3 - value: 36.773 - - type: ndcg_at_5 - value: 38.389 - - type: precision_at_1 - value: 40.586 - - type: precision_at_10 - value: 11.466 - - type: precision_at_100 - value: 1.909 - - type: precision_at_1000 - value: 0.245 - - type: precision_at_3 - value: 24.434 - - type: precision_at_5 - value: 18.426000000000002 - - type: recall_at_1 - value: 20.587 - - type: recall_at_10 - value: 47.986000000000004 - - type: recall_at_100 - value: 75.761 - - type: recall_at_1000 - value: 94.065 - - type: recall_at_3 - value: 33.339 - - type: recall_at_5 - value: 39.765 - task: - type: Retrieval - - dataset: - config: default - name: MTEB HotpotQA-PL - revision: a0bd479ac97b4ccb5bd6ce320c415d0bb4beb907 - split: test - type: clarin-knext/hotpotqa-pl - metrics: - - type: map_at_1 - value: 40.878 - - type: map_at_10 - value: 58.775999999999996 - - type: map_at_100 - value: 59.632 - - type: map_at_1000 - value: 59.707 - - type: map_at_3 - value: 56.074 - - type: map_at_5 - value: 57.629 - - type: mrr_at_1 - value: 81.756 - - type: mrr_at_10 - value: 86.117 - - type: mrr_at_100 - value: 86.299 - - type: mrr_at_1000 - value: 86.30600000000001 - - type: mrr_at_3 - value: 85.345 - - type: mrr_at_5 - value: 85.832 - - type: ndcg_at_1 - value: 81.756 - - type: ndcg_at_10 - value: 67.608 - - type: ndcg_at_100 - value: 70.575 - - type: ndcg_at_1000 - value: 71.99600000000001 - - type: ndcg_at_3 - value: 63.723 - - type: ndcg_at_5 - value: 65.70700000000001 - - type: precision_at_1 - value: 81.756 - - type: precision_at_10 - value: 13.619 - - type: precision_at_100 - value: 1.5939999999999999 - - type: precision_at_1000 - value: 0.178 - - type: precision_at_3 - value: 39.604 - - type: precision_at_5 - value: 25.332 - - type: recall_at_1 - value: 40.878 - - type: recall_at_10 - value: 68.096 - - type: recall_at_100 - value: 79.696 - - type: recall_at_1000 - value: 89.082 - - type: recall_at_3 - value: 59.406000000000006 - - type: recall_at_5 - value: 63.329 - task: - type: Retrieval - - dataset: - config: default - name: MTEB MSMARCO-PL - revision: 8634c07806d5cce3a6138e260e59b81760a0a640 - split: test - type: clarin-knext/msmarco-pl - metrics: - - type: map_at_1 - value: 2.1839999999999997 - - type: map_at_10 - value: 11.346 - - type: map_at_100 - value: 30.325000000000003 - - type: map_at_1000 - value: 37.806 - - type: map_at_3 - value: 4.842 - - type: map_at_5 - value: 6.891 - - type: mrr_at_1 - value: 86.047 - - type: mrr_at_10 - value: 89.14699999999999 - - type: mrr_at_100 - value: 89.46600000000001 - - type: mrr_at_1000 - value: 89.46600000000001 - - type: mrr_at_3 - value: 89.14699999999999 - - type: mrr_at_5 - value: 89.14699999999999 - - type: ndcg_at_1 - value: 67.829 - - type: ndcg_at_10 - value: 62.222 - - type: ndcg_at_100 - value: 55.337 - - type: ndcg_at_1000 - value: 64.076 - - type: ndcg_at_3 - value: 68.12700000000001 - - type: ndcg_at_5 - value: 64.987 - - type: precision_at_1 - value: 86.047 - - type: precision_at_10 - value: 69.535 - - type: precision_at_100 - value: 32.93 - - type: precision_at_1000 - value: 6.6049999999999995 - - type: precision_at_3 - value: 79.845 - - type: precision_at_5 - value: 75.349 - - type: recall_at_1 - value: 2.1839999999999997 - - type: recall_at_10 - value: 12.866 - - type: recall_at_100 - value: 43.505 - - type: recall_at_1000 - value: 72.366 - - type: recall_at_3 - value: 4.947 - - type: recall_at_5 - value: 7.192 - task: - type: Retrieval - - dataset: - config: pl - name: MTEB MassiveIntentClassification (pl) - revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 - split: test - type: mteb/amazon_massive_intent - metrics: - - type: accuracy - value: 80.75319435104238 - - type: f1 - value: 77.58961444860606 - task: - type: Classification - - dataset: - config: pl - name: MTEB MassiveScenarioClassification (pl) - revision: 7d571f92784cd94a019292a1f45445077d0ef634 - split: test - type: mteb/amazon_massive_scenario - metrics: - - type: accuracy - value: 85.54472091459313 - - type: f1 - value: 84.29498563572106 - task: - type: Classification - - dataset: - config: default - name: MTEB NFCorpus-PL - revision: 9a6f9567fda928260afed2de480d79c98bf0bec0 - split: test - type: clarin-knext/nfcorpus-pl - metrics: - - type: map_at_1 - value: 4.367 - - type: map_at_10 - value: 10.38 - - type: map_at_100 - value: 13.516 - - type: map_at_1000 - value: 14.982000000000001 - - type: map_at_3 - value: 7.367 - - type: map_at_5 - value: 8.59 - - type: mrr_at_1 - value: 41.486000000000004 - - type: mrr_at_10 - value: 48.886 - - type: mrr_at_100 - value: 49.657000000000004 - - type: mrr_at_1000 - value: 49.713 - - type: mrr_at_3 - value: 46.904 - - type: mrr_at_5 - value: 48.065000000000005 - - type: ndcg_at_1 - value: 40.402 - - type: ndcg_at_10 - value: 30.885 - - type: ndcg_at_100 - value: 28.393 - - type: ndcg_at_1000 - value: 37.428 - - type: ndcg_at_3 - value: 35.394999999999996 - - type: ndcg_at_5 - value: 33.391999999999996 - - type: precision_at_1 - value: 41.486000000000004 - - type: precision_at_10 - value: 23.437 - - type: precision_at_100 - value: 7.638 - - type: precision_at_1000 - value: 2.0389999999999997 - - type: precision_at_3 - value: 32.817 - - type: precision_at_5 - value: 28.915999999999997 - - type: recall_at_1 - value: 4.367 - - type: recall_at_10 - value: 14.655000000000001 - - type: recall_at_100 - value: 29.665999999999997 - - type: recall_at_1000 - value: 62.073 - - type: recall_at_3 - value: 8.51 - - type: recall_at_5 - value: 10.689 - task: - type: Retrieval - - dataset: - config: default - name: MTEB NQ-PL - revision: f171245712cf85dd4700b06bef18001578d0ca8d - split: test - type: clarin-knext/nq-pl - metrics: - - type: map_at_1 - value: 28.616000000000003 - - type: map_at_10 - value: 41.626000000000005 - - type: map_at_100 - value: 42.689 - - type: map_at_1000 - value: 42.733 - - type: map_at_3 - value: 37.729 - - type: map_at_5 - value: 39.879999999999995 - - type: mrr_at_1 - value: 32.068000000000005 - - type: mrr_at_10 - value: 44.029 - - type: mrr_at_100 - value: 44.87 - - type: mrr_at_1000 - value: 44.901 - - type: mrr_at_3 - value: 40.687 - - type: mrr_at_5 - value: 42.625 - - type: ndcg_at_1 - value: 32.068000000000005 - - type: ndcg_at_10 - value: 48.449999999999996 - - type: ndcg_at_100 - value: 53.13 - - type: ndcg_at_1000 - value: 54.186 - - type: ndcg_at_3 - value: 40.983999999999995 - - type: ndcg_at_5 - value: 44.628 - - type: precision_at_1 - value: 32.068000000000005 - - type: precision_at_10 - value: 7.9750000000000005 - - type: precision_at_100 - value: 1.061 - - type: precision_at_1000 - value: 0.116 - - type: precision_at_3 - value: 18.404999999999998 - - type: precision_at_5 - value: 13.111 - - type: recall_at_1 - value: 28.616000000000003 - - type: recall_at_10 - value: 66.956 - - type: recall_at_100 - value: 87.657 - - type: recall_at_1000 - value: 95.548 - - type: recall_at_3 - value: 47.453 - - type: recall_at_5 - value: 55.87800000000001 - task: - type: Retrieval - - dataset: - config: default - name: MTEB PAC - revision: None - split: test - type: laugustyniak/abusive-clauses-pl - metrics: - - type: accuracy - value: 69.04141326382856 - - type: ap - value: 77.47589122111044 - - type: f1 - value: 66.6332277374775 - task: - type: Classification - - dataset: - config: default - name: MTEB PPC - revision: None - split: test - type: PL-MTEB/ppc-pairclassification - metrics: - - type: cos_sim_accuracy - value: 86.4 - - type: cos_sim_ap - value: 94.1044939667201 - - type: cos_sim_f1 - value: 88.78048780487805 - - type: cos_sim_precision - value: 87.22044728434504 - - type: cos_sim_recall - value: 90.39735099337747 - - type: dot_accuracy - value: 86.4 - - type: dot_ap - value: 94.1044939667201 - - type: dot_f1 - value: 88.78048780487805 - - type: dot_precision - value: 87.22044728434504 - - type: dot_recall - value: 90.39735099337747 - - type: euclidean_accuracy - value: 86.4 - - type: euclidean_ap - value: 94.1044939667201 - - type: euclidean_f1 - value: 88.78048780487805 - - type: euclidean_precision - value: 87.22044728434504 - - type: euclidean_recall - value: 90.39735099337747 - - type: manhattan_accuracy - value: 86.4 - - type: manhattan_ap - value: 94.11438365697387 - - type: manhattan_f1 - value: 88.77968877968877 - - type: manhattan_precision - value: 87.84440842787681 - - type: manhattan_recall - value: 89.73509933774835 - - type: max_accuracy - value: 86.4 - - type: max_ap - value: 94.11438365697387 - - type: max_f1 - value: 88.78048780487805 - task: - type: PairClassification - - dataset: - config: default - name: MTEB PSC - revision: None - split: test - type: PL-MTEB/psc-pairclassification - metrics: - - type: cos_sim_accuracy - value: 97.86641929499072 - - type: cos_sim_ap - value: 99.36904211868182 - - type: cos_sim_f1 - value: 96.56203288490283 - - type: cos_sim_precision - value: 94.72140762463343 - - type: cos_sim_recall - value: 98.47560975609755 - - type: dot_accuracy - value: 97.86641929499072 - - type: dot_ap - value: 99.36904211868183 - - type: dot_f1 - value: 96.56203288490283 - - type: dot_precision - value: 94.72140762463343 - - type: dot_recall - value: 98.47560975609755 - - type: euclidean_accuracy - value: 97.86641929499072 - - type: euclidean_ap - value: 99.36904211868183 - - type: euclidean_f1 - value: 96.56203288490283 - - type: euclidean_precision - value: 94.72140762463343 - - type: euclidean_recall - value: 98.47560975609755 - - type: manhattan_accuracy - value: 98.14471243042672 - - type: manhattan_ap - value: 99.43359540492416 - - type: manhattan_f1 - value: 96.98795180722892 - - type: manhattan_precision - value: 95.83333333333334 - - type: manhattan_recall - value: 98.17073170731707 - - type: max_accuracy - value: 98.14471243042672 - - type: max_ap - value: 99.43359540492416 - - type: max_f1 - value: 96.98795180722892 - task: - type: PairClassification - - dataset: - config: default - name: MTEB PolEmo2.0-IN - revision: None - split: test - type: PL-MTEB/polemo2_in - metrics: - - type: accuracy - value: 89.39058171745152 - - type: f1 - value: 86.8552093529568 - task: - type: Classification - - dataset: - config: default - name: MTEB PolEmo2.0-OUT - revision: None - split: test - type: PL-MTEB/polemo2_out - metrics: - - type: accuracy - value: 74.97975708502024 - - type: f1 - value: 58.73081628832407 - task: - type: Classification - - dataset: - config: default - name: MTEB Quora-PL - revision: 0be27e93455051e531182b85e85e425aba12e9d4 - split: test - type: clarin-knext/quora-pl - metrics: - - type: map_at_1 - value: 64.917 - - type: map_at_10 - value: 78.74600000000001 - - type: map_at_100 - value: 79.501 - - type: map_at_1000 - value: 79.524 - - type: map_at_3 - value: 75.549 - - type: map_at_5 - value: 77.495 - - type: mrr_at_1 - value: 74.9 - - type: mrr_at_10 - value: 82.112 - - type: mrr_at_100 - value: 82.314 - - type: mrr_at_1000 - value: 82.317 - - type: mrr_at_3 - value: 80.745 - - type: mrr_at_5 - value: 81.607 - - type: ndcg_at_1 - value: 74.83999999999999 - - type: ndcg_at_10 - value: 83.214 - - type: ndcg_at_100 - value: 84.997 - - type: ndcg_at_1000 - value: 85.207 - - type: ndcg_at_3 - value: 79.547 - - type: ndcg_at_5 - value: 81.46600000000001 - - type: precision_at_1 - value: 74.83999999999999 - - type: precision_at_10 - value: 12.822 - - type: precision_at_100 - value: 1.506 - - type: precision_at_1000 - value: 0.156 - - type: precision_at_3 - value: 34.903 - - type: precision_at_5 - value: 23.16 - - type: recall_at_1 - value: 64.917 - - type: recall_at_10 - value: 92.27199999999999 - - type: recall_at_100 - value: 98.715 - - type: recall_at_1000 - value: 99.854 - - type: recall_at_3 - value: 82.04599999999999 - - type: recall_at_5 - value: 87.2 - task: - type: Retrieval - - dataset: - config: default - name: MTEB SCIDOCS-PL - revision: 45452b03f05560207ef19149545f168e596c9337 - split: test - type: clarin-knext/scidocs-pl - metrics: - - type: map_at_1 - value: 3.51 - - type: map_at_10 - value: 9.046999999999999 - - type: map_at_100 - value: 10.823 - - type: map_at_1000 - value: 11.144 - - type: map_at_3 - value: 6.257 - - type: map_at_5 - value: 7.648000000000001 - - type: mrr_at_1 - value: 17.299999999999997 - - type: mrr_at_10 - value: 27.419 - - type: mrr_at_100 - value: 28.618 - - type: mrr_at_1000 - value: 28.685 - - type: mrr_at_3 - value: 23.817 - - type: mrr_at_5 - value: 25.927 - - type: ndcg_at_1 - value: 17.299999999999997 - - type: ndcg_at_10 - value: 16.084 - - type: ndcg_at_100 - value: 23.729 - - type: ndcg_at_1000 - value: 29.476999999999997 - - type: ndcg_at_3 - value: 14.327000000000002 - - type: ndcg_at_5 - value: 13.017999999999999 - - type: precision_at_1 - value: 17.299999999999997 - - type: precision_at_10 - value: 8.63 - - type: precision_at_100 - value: 1.981 - - type: precision_at_1000 - value: 0.336 - - type: precision_at_3 - value: 13.4 - - type: precision_at_5 - value: 11.700000000000001 - - type: recall_at_1 - value: 3.51 - - type: recall_at_10 - value: 17.518 - - type: recall_at_100 - value: 40.275 - - type: recall_at_1000 - value: 68.203 - - type: recall_at_3 - value: 8.155 - - type: recall_at_5 - value: 11.875 - task: - type: Retrieval - - dataset: - config: default - name: MTEB SICK-E-PL - revision: None - split: test - type: PL-MTEB/sicke-pl-pairclassification - metrics: - - type: cos_sim_accuracy - value: 86.30248675091724 - - type: cos_sim_ap - value: 83.6756734006714 - - type: cos_sim_f1 - value: 74.97367497367497 - - type: cos_sim_precision - value: 73.91003460207612 - - type: cos_sim_recall - value: 76.06837606837607 - - type: dot_accuracy - value: 86.30248675091724 - - type: dot_ap - value: 83.6756734006714 - - type: dot_f1 - value: 74.97367497367497 - - type: dot_precision - value: 73.91003460207612 - - type: dot_recall - value: 76.06837606837607 - - type: euclidean_accuracy - value: 86.30248675091724 - - type: euclidean_ap - value: 83.67566984333091 - - type: euclidean_f1 - value: 74.97367497367497 - - type: euclidean_precision - value: 73.91003460207612 - - type: euclidean_recall - value: 76.06837606837607 - - type: manhattan_accuracy - value: 86.28210354667753 - - type: manhattan_ap - value: 83.64216119130171 - - type: manhattan_f1 - value: 74.92152075340078 - - type: manhattan_precision - value: 73.4107997265892 - - type: manhattan_recall - value: 76.49572649572649 - - type: max_accuracy - value: 86.30248675091724 - - type: max_ap - value: 83.6756734006714 - - type: max_f1 - value: 74.97367497367497 - task: - type: PairClassification - - dataset: - config: default - name: MTEB SICK-R-PL - revision: None - split: test - type: PL-MTEB/sickr-pl-sts - metrics: - - type: cos_sim_pearson - value: 82.23295940859121 - - type: cos_sim_spearman - value: 78.89329160768719 - - type: euclidean_pearson - value: 79.56019107076818 - - type: euclidean_spearman - value: 78.89330209904084 - - type: manhattan_pearson - value: 79.76098513973719 - - type: manhattan_spearman - value: 79.05490162570123 - task: - type: STS - - dataset: - config: pl - name: MTEB STS22 (pl) - revision: eea2b4fe26a775864c896887d910b76a8098ad3f - split: test - type: mteb/sts22-crosslingual-sts - metrics: - - type: cos_sim_pearson - value: 37.732606308062486 - - type: cos_sim_spearman - value: 41.01645667030284 - - type: euclidean_pearson - value: 26.61722556367085 - - type: euclidean_spearman - value: 41.01645667030284 - - type: manhattan_pearson - value: 26.60917378970807 - - type: manhattan_spearman - value: 41.51335727617614 - task: - type: STS - - dataset: - config: default - name: MTEB SciFact-PL - revision: 47932a35f045ef8ed01ba82bf9ff67f6e109207e - split: test - type: clarin-knext/scifact-pl - metrics: - - type: map_at_1 - value: 54.31700000000001 - - type: map_at_10 - value: 65.564 - - type: map_at_100 - value: 66.062 - - type: map_at_1000 - value: 66.08699999999999 - - type: map_at_3 - value: 62.592999999999996 - - type: map_at_5 - value: 63.888 - - type: mrr_at_1 - value: 56.99999999999999 - - type: mrr_at_10 - value: 66.412 - - type: mrr_at_100 - value: 66.85900000000001 - - type: mrr_at_1000 - value: 66.88 - - type: mrr_at_3 - value: 64.22200000000001 - - type: mrr_at_5 - value: 65.206 - - type: ndcg_at_1 - value: 56.99999999999999 - - type: ndcg_at_10 - value: 70.577 - - type: ndcg_at_100 - value: 72.879 - - type: ndcg_at_1000 - value: 73.45 - - type: ndcg_at_3 - value: 65.5 - - type: ndcg_at_5 - value: 67.278 - - type: precision_at_1 - value: 56.99999999999999 - - type: precision_at_10 - value: 9.667 - - type: precision_at_100 - value: 1.083 - - type: precision_at_1000 - value: 0.11299999999999999 - - type: precision_at_3 - value: 26.0 - - type: precision_at_5 - value: 16.933 - - type: recall_at_1 - value: 54.31700000000001 - - type: recall_at_10 - value: 85.056 - - type: recall_at_100 - value: 95.667 - - type: recall_at_1000 - value: 100.0 - - type: recall_at_3 - value: 71.0 - - type: recall_at_5 - value: 75.672 - task: - type: Retrieval - - dataset: - config: default - name: MTEB TRECCOVID-PL - revision: 81bcb408f33366c2a20ac54adafad1ae7e877fdd - split: test - type: clarin-knext/trec-covid-pl - metrics: - - type: map_at_1 - value: 0.245 - - type: map_at_10 - value: 2.051 - - type: map_at_100 - value: 12.009 - - type: map_at_1000 - value: 27.448 - - type: map_at_3 - value: 0.721 - - type: map_at_5 - value: 1.13 - - type: mrr_at_1 - value: 88.0 - - type: mrr_at_10 - value: 93.0 - - type: mrr_at_100 - value: 93.0 - - type: mrr_at_1000 - value: 93.0 - - type: mrr_at_3 - value: 93.0 - - type: mrr_at_5 - value: 93.0 - - type: ndcg_at_1 - value: 85.0 - - type: ndcg_at_10 - value: 80.303 - - type: ndcg_at_100 - value: 61.23499999999999 - - type: ndcg_at_1000 - value: 52.978 - - type: ndcg_at_3 - value: 84.419 - - type: ndcg_at_5 - value: 82.976 - - type: precision_at_1 - value: 88.0 - - type: precision_at_10 - value: 83.39999999999999 - - type: precision_at_100 - value: 61.96 - - type: precision_at_1000 - value: 22.648 - - type: precision_at_3 - value: 89.333 - - type: precision_at_5 - value: 87.2 - - type: recall_at_1 - value: 0.245 - - type: recall_at_10 - value: 2.193 - - type: recall_at_100 - value: 14.938 - - type: recall_at_1000 - value: 48.563 - - type: recall_at_3 - value: 0.738 - - type: recall_at_5 - value: 1.173 - task: - type: Retrieval ---- - -## gte-Qwen2-7B-instruct - -**gte-Qwen2-7B-instruct** is the latest model in the gte (General Text Embedding) model family that ranks **No.1** in both English and Chinese evaluations on the Massive Text Embedding Benchmark [MTEB benchmark](https://huggingface.co/spaces/mteb/leaderboard) (as of June 16, 2024). - -Recently, the [**Qwen team**](https://huggingface.co/Qwen) released the Qwen2 series models, and we have trained the **gte-Qwen2-7B-instruct** model based on the [Qwen2-7B](https://huggingface.co/Qwen/Qwen2-7B) LLM model. Compared to the [gte-Qwen1.5-7B-instruct](https://huggingface.co/Alibaba-NLP/gte-Qwen1.5-7B-instruct) model, the **gte-Qwen2-7B-instruct** model uses the same training data and training strategies during the finetuning stage, with the only difference being the upgraded base model to Qwen2-7B. Considering the improvements in the Qwen2 series models compared to the Qwen1.5 series, we can also expect consistent performance enhancements in the embedding models. - -The model incorporates several key advancements: - -- Integration of bidirectional attention mechanisms, enriching its contextual understanding. -- Instruction tuning, applied solely on the query side for streamlined efficiency -- Comprehensive training across a vast, multilingual text corpus spanning diverse domains and scenarios. This training leverages both weakly supervised and supervised data, ensuring the model's applicability across numerous languages and a wide array of downstream tasks. - - -## Model Information -- Model Size: 7B -- Embedding Dimension: 3584 -- Max Input Tokens: 32k - -## Requirements -``` -transformers>=4.39.2 -flash_attn>=2.5.6 -``` -## Usage - -### Sentence Transformers - -```python -from sentence_transformers import SentenceTransformer - -model = SentenceTransformer("Alibaba-NLP/gte-Qwen2-7B-instruct", trust_remote_code=True) -# In case you want to reduce the maximum length: -model.max_seq_length = 8192 - -queries = [ - "how much protein should a female eat", - "summit define", -] -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.", -] - -query_embeddings = model.encode(queries, prompt_name="query") -document_embeddings = model.encode(documents) - -scores = (query_embeddings @ document_embeddings.T) * 100 -print(scores.tolist()) -``` - -Observe the [config_sentence_transformers.json](config_sentence_transformers.json) to see all pre-built prompt names. Otherwise, you can use `model.encode(queries, prompt="Instruct: ...\nQuery: "` to use a custom prompt of your choice. - -### Transformers - -```python -import torch -import torch.nn.functional as F - -from torch import Tensor -from transformers import AutoTokenizer, AutoModel - - -def last_token_pool(last_hidden_states: Tensor, - attention_mask: Tensor) -> Tensor: - left_padding = (attention_mask[:, -1].sum() == attention_mask.shape[0]) - if left_padding: - return last_hidden_states[:, -1] - else: - sequence_lengths = attention_mask.sum(dim=1) - 1 - batch_size = last_hidden_states.shape[0] - return last_hidden_states[torch.arange(batch_size, device=last_hidden_states.device), sequence_lengths] - - -def get_detailed_instruct(task_description: str, query: str) -> str: - return f'Instruct: {task_description}\nQuery: {query}' - - -# Each query must come with a one-sentence instruction that describes the task -task = 'Given a web search query, retrieve relevant passages that answer the query' -queries = [ - get_detailed_instruct(task, 'how much protein should a female eat'), - get_detailed_instruct(task, 'summit define') -] -# No need to add instruction for retrieval 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." -] -input_texts = queries + documents - -tokenizer = AutoTokenizer.from_pretrained('Alibaba-NLP/gte-Qwen2-7B-instruct', trust_remote_code=True) -model = AutoModel.from_pretrained('Alibaba-NLP/gte-Qwen2-7B-instruct', trust_remote_code=True) - -max_length = 8192 - -# Tokenize the input texts -batch_dict = tokenizer(input_texts, max_length=max_length, padding=True, truncation=True, return_tensors='pt') -outputs = model(**batch_dict) -embeddings = last_token_pool(outputs.last_hidden_state, batch_dict['attention_mask']) - -# normalize embeddings -embeddings = F.normalize(embeddings, p=2, dim=1) -scores = (embeddings[:2] @ embeddings[2:].T) * 100 -print(scores.tolist()) -``` - -## Evaluation - -### MTEB & C-MTEB - -You can use the [scripts/eval_mteb.py](https://huggingface.co/Alibaba-NLP/gte-Qwen2-7B-instruct/blob/main/scripts/eval_mteb.py) to reproduce the following result of **gte-Qwen2-7B-instruct** on MTEB(English)/C-MTEB(Chinese): - -| Model Name | MTEB(56) | C-MTEB(35) | -|:----:|:---------:|:----------:| -| [bge-base-en-1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) | 64.23 | - | -| [bge-large-en-1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) | 63.55 | - | -| [gte-large-en-v1.5](https://huggingface.co/Alibaba-NLP/gte-large-en-v1.5) | 65.39 | - | -| [gte-base-en-v1.5](https://huggingface.co/Alibaba-NLP/gte-large-en-v1.5) | 64.11 | - | -| [mxbai-embed-large-v1](https://huggingface.co/mixedbread-ai/mxbai-embed-large-v1) | 64.68 | - | -| [acge_text_embedding](https://huggingface.co/aspire/acge_text_embedding) | - | 69.07 | -| [stella-mrl-large-zh-v3.5-1792d](https://huggingface.co/infgrad/stella-mrl-large-zh-v3.5-1792d) | - | 68.55 | -| [gte-large-zh](https://huggingface.co/thenlper/gte-large-zh) | - | 66.72 | -| [multilingual-e5-base](https://huggingface.co/intfloat/multilingual-e5-base) | 59.45 | 56.21 | -| [multilingual-e5-large](https://huggingface.co/intfloat/multilingual-e5-large) | 61.50 | 58.81 | -| [e5-mistral-7b-instruct](https://huggingface.co/intfloat/e5-mistral-7b-instruct) | 66.63 | 60.81 | -| [gte-Qwen1.5-7B-instruct](https://--- -tags: -- mteb -- sentence-transformers -- transformers -- Qwen2 -- sentence-similarity -license: apache-2.0 -model-index: -- name: gte-qwen2-7B-instruct - results: - - task: - type: Classification - dataset: - type: mteb/amazon_counterfactual - name: MTEB AmazonCounterfactualClassification (en) - config: en - split: test - revision: e8379541af4e31359cca9fbcf4b00f2671dba205 - metrics: - - type: accuracy - value: 91.31343283582089 - - type: ap - value: 67.64251402604096 - - type: f1 - value: 87.53372530755692 - - task: - type: Classification - dataset: - type: mteb/amazon_polarity - name: MTEB AmazonPolarityClassification - config: default - split: test - revision: e2d317d38cd51312af73b3d32a06d1a08b442046 - metrics: - - type: accuracy - value: 97.497825 - - type: ap - value: 96.30329547047529 - - type: f1 - value: 97.49769793778039 - - task: - type: Classification - dataset: - type: mteb/amazon_reviews_multi - name: MTEB AmazonReviewsClassification (en) - config: en - split: test - revision: 1399c76144fd37290681b995c656ef9b2e06e26d - metrics: - - type: accuracy - value: 62.564 - - type: f1 - value: 60.975777935041066 - - task: - type: Retrieval - dataset: - type: mteb/arguana - name: MTEB ArguAna - config: default - split: test - revision: c22ab2a51041ffd869aaddef7af8d8215647e41a - metrics: - - type: map_at_1 - value: 36.486000000000004 - - type: map_at_10 - value: 54.842 - - type: map_at_100 - value: 55.206999999999994 - - type: map_at_1000 - value: 55.206999999999994 - - type: map_at_3 - value: 49.893 - - type: map_at_5 - value: 53.105000000000004 - - type: mrr_at_1 - value: 37.34 - - type: mrr_at_10 - value: 55.143 - - type: mrr_at_100 - value: 55.509 - - type: mrr_at_1000 - value: 55.509 - - type: mrr_at_3 - value: 50.212999999999994 - - type: mrr_at_5 - value: 53.432 - - type: ndcg_at_1 - value: 36.486000000000004 - - type: ndcg_at_10 - value: 64.273 - - type: ndcg_at_100 - value: 65.66199999999999 - - type: ndcg_at_1000 - value: 65.66199999999999 - - type: ndcg_at_3 - value: 54.352999999999994 - - type: ndcg_at_5 - value: 60.131 - - type: precision_at_1 - value: 36.486000000000004 - - type: precision_at_10 - value: 9.395000000000001 - - type: precision_at_100 - value: 0.996 - - type: precision_at_1000 - value: 0.1 - - type: precision_at_3 - value: 22.428 - - type: precision_at_5 - value: 16.259 - - type: recall_at_1 - value: 36.486000000000004 - - type: recall_at_10 - value: 93.95400000000001 - - type: recall_at_100 - value: 99.644 - - type: recall_at_1000 - value: 99.644 - - type: recall_at_3 - value: 67.283 - - type: recall_at_5 - value: 81.294 - - task: - type: Clustering - dataset: - type: mteb/arxiv-clustering-p2p - name: MTEB ArxivClusteringP2P - config: default - split: test - revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d - metrics: - - type: v_measure - value: 56.461169803700564 - - task: - type: Clustering - dataset: - type: mteb/arxiv-clustering-s2s - name: MTEB ArxivClusteringS2S - config: default - split: test - revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 - metrics: - - type: v_measure - value: 51.73600434466286 - - task: - type: Reranking - dataset: - type: mteb/askubuntudupquestions-reranking - name: MTEB AskUbuntuDupQuestions - config: default - split: test - revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 - metrics: - - type: map - value: 67.57827065898053 - - type: mrr - value: 79.08136569493911 - - task: - type: STS - dataset: - type: mteb/biosses-sts - name: MTEB BIOSSES - config: default - split: test - revision: d3fb88f8f02e40887cd149695127462bbcf29b4a - metrics: - - type: cos_sim_pearson - value: 83.53324575999243 - - type: cos_sim_spearman - value: 81.37173362822374 - - type: euclidean_pearson - value: 82.19243335103444 - - type: euclidean_spearman - value: 81.33679307304334 - - type: manhattan_pearson - value: 82.38752665975699 - - type: manhattan_spearman - value: 81.31510583189689 - - task: - type: Classification - dataset: - type: mteb/banking77 - name: MTEB Banking77Classification - config: default - split: test - revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 - metrics: - - type: accuracy - value: 87.56818181818181 - - type: f1 - value: 87.25826722019875 - - task: - type: Clustering - dataset: - type: mteb/biorxiv-clustering-p2p - name: MTEB BiorxivClusteringP2P - config: default - split: test - revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 - metrics: - - type: v_measure - value: 50.09239610327673 - - task: - type: Clustering - dataset: - type: mteb/biorxiv-clustering-s2s - name: MTEB BiorxivClusteringS2S - config: default - split: test - revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 - metrics: - - type: v_measure - value: 46.64733054606282 - - task: - type: Retrieval - dataset: - type: BeIR/cqadupstack - name: MTEB CQADupstackAndroidRetrieval - config: default - split: test - revision: f46a197baaae43b4f621051089b82a364682dfeb - metrics: - - type: map_at_1 - value: 33.997 - - type: map_at_10 - value: 48.176 - - type: map_at_100 - value: 49.82 - - type: map_at_1000 - value: 49.924 - - type: map_at_3 - value: 43.626 - - type: map_at_5 - value: 46.275 - - type: mrr_at_1 - value: 42.059999999999995 - - type: mrr_at_10 - value: 53.726 - - type: mrr_at_100 - value: 54.398 - - type: mrr_at_1000 - value: 54.416 - - type: mrr_at_3 - value: 50.714999999999996 - - type: mrr_at_5 - value: 52.639 - - type: ndcg_at_1 - value: 42.059999999999995 - - type: ndcg_at_10 - value: 55.574999999999996 - - type: ndcg_at_100 - value: 60.744 - - type: ndcg_at_1000 - value: 61.85699999999999 - - type: ndcg_at_3 - value: 49.363 - - type: ndcg_at_5 - value: 52.44 - - type: precision_at_1 - value: 42.059999999999995 - - type: precision_at_10 - value: 11.101999999999999 - 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- type: ndcg_at_1000 - value: 41.277 - - type: ndcg_at_3 - value: 27.142 - - type: ndcg_at_5 - value: 29.755 - - type: precision_at_1 - value: 19.224 - - type: precision_at_10 - value: 5.6930000000000005 - - type: precision_at_100 - value: 0.9259999999999999 - - type: precision_at_1000 - value: 0.126 - - type: precision_at_3 - value: 12.138 - - type: precision_at_5 - value: 8.909 - - type: recall_at_1 - value: 17.936 - - type: recall_at_10 - value: 48.096 - - type: recall_at_100 - value: 75.389 - - type: recall_at_1000 - value: 92.803 - - type: recall_at_3 - value: 32.812999999999995 - - type: recall_at_5 - value: 38.851 - - task: - type: Retrieval - dataset: - type: mteb/climate-fever - name: MTEB ClimateFEVER - config: default - split: test - revision: 47f2ac6acb640fc46020b02a5b59fdda04d39380 - metrics: - - type: map_at_1 - value: 22.076999999999998 - - type: map_at_10 - value: 35.44 - - type: map_at_100 - value: 37.651 - - type: map_at_1000 - value: 37.824999999999996 - - type: map_at_3 - value: 30.764999999999997 - - type: map_at_5 - value: 33.26 - - type: mrr_at_1 - value: 50.163000000000004 - - type: mrr_at_10 - value: 61.207 - - type: mrr_at_100 - value: 61.675000000000004 - - type: mrr_at_1000 - value: 61.692 - - type: mrr_at_3 - value: 58.60999999999999 - - type: mrr_at_5 - value: 60.307 - - type: ndcg_at_1 - value: 50.163000000000004 - - type: ndcg_at_10 - value: 45.882 - - type: ndcg_at_100 - value: 53.239999999999995 - - type: ndcg_at_1000 - value: 55.852000000000004 - - type: ndcg_at_3 - value: 40.514 - - type: ndcg_at_5 - value: 42.038 - - type: precision_at_1 - value: 50.163000000000004 - - type: precision_at_10 - value: 13.466000000000001 - - type: precision_at_100 - value: 2.164 - - type: precision_at_1000 - value: 0.266 - - type: precision_at_3 - value: 29.707 - - type: precision_at_5 - value: 21.694 - - type: recall_at_1 - value: 22.076999999999998 - - type: recall_at_10 - value: 50.193 - - type: recall_at_100 - value: 74.993 - - type: recall_at_1000 - value: 89.131 - - type: recall_at_3 - value: 35.472 - - type: recall_at_5 - value: 41.814 - - task: - type: Retrieval - dataset: - type: mteb/dbpedia - name: MTEB DBPedia - config: default - split: test - revision: c0f706b76e590d620bd6618b3ca8efdd34e2d659 - metrics: - - type: map_at_1 - value: 9.953 - - type: map_at_10 - value: 24.515 - - type: map_at_100 - value: 36.173 - - type: map_at_1000 - value: 38.351 - - type: map_at_3 - value: 16.592000000000002 - - type: map_at_5 - value: 20.036 - - type: mrr_at_1 - value: 74.25 - - type: mrr_at_10 - value: 81.813 - - type: mrr_at_100 - value: 82.006 - - type: mrr_at_1000 - value: 82.011 - - type: mrr_at_3 - value: 80.875 - - type: mrr_at_5 - value: 81.362 - - type: ndcg_at_1 - value: 62.5 - - type: ndcg_at_10 - value: 52.42 - - type: ndcg_at_100 - value: 56.808 - - type: ndcg_at_1000 - value: 63.532999999999994 - - type: ndcg_at_3 - value: 56.654 - - type: ndcg_at_5 - value: 54.18300000000001 - - type: precision_at_1 - value: 74.25 - - type: precision_at_10 - value: 42.699999999999996 - - type: precision_at_100 - value: 13.675 - - type: precision_at_1000 - value: 2.664 - - type: precision_at_3 - value: 60.5 - - type: precision_at_5 - value: 52.800000000000004 - - type: recall_at_1 - value: 9.953 - - type: recall_at_10 - value: 30.253999999999998 - - type: recall_at_100 - value: 62.516000000000005 - - type: recall_at_1000 - value: 84.163 - - type: recall_at_3 - value: 18.13 - - type: recall_at_5 - value: 22.771 - - task: - type: Classification - dataset: - type: mteb/emotion - name: MTEB EmotionClassification - config: default - split: test - revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 - metrics: - - type: accuracy - value: 79.455 - - type: f1 - value: 74.16798697647569 - - task: - type: Retrieval - dataset: - type: mteb/fever - name: MTEB FEVER - config: default - split: test - revision: bea83ef9e8fb933d90a2f1d5515737465d613e12 - metrics: - - type: map_at_1 - value: 87.531 - - type: map_at_10 - value: 93.16799999999999 - - type: map_at_100 - value: 93.341 - - type: map_at_1000 - value: 93.349 - - type: map_at_3 - value: 92.444 - - type: map_at_5 - value: 92.865 - - type: mrr_at_1 - value: 94.014 - - type: mrr_at_10 - value: 96.761 - - type: mrr_at_100 - value: 96.762 - - type: mrr_at_1000 - value: 96.762 - - type: mrr_at_3 - value: 96.672 - - type: mrr_at_5 - value: 96.736 - - type: ndcg_at_1 - value: 94.014 - - type: ndcg_at_10 - value: 95.112 - - type: ndcg_at_100 - value: 95.578 - - type: ndcg_at_1000 - value: 95.68900000000001 - - type: ndcg_at_3 - value: 94.392 - - type: ndcg_at_5 - value: 94.72500000000001 - - type: precision_at_1 - value: 94.014 - - type: precision_at_10 - value: 11.065 - - type: precision_at_100 - value: 1.157 - - type: precision_at_1000 - value: 0.11800000000000001 - - type: precision_at_3 - value: 35.259 - - type: precision_at_5 - value: 21.599 - - type: recall_at_1 - value: 87.531 - - type: recall_at_10 - value: 97.356 - - type: recall_at_100 - value: 98.965 - - type: recall_at_1000 - value: 99.607 - - type: recall_at_3 - value: 95.312 - - type: recall_at_5 - value: 96.295 - - task: - type: Retrieval - dataset: - type: mteb/fiqa - name: MTEB FiQA2018 - config: default - split: test - revision: 27a168819829fe9bcd655c2df245fb19452e8e06 - metrics: - - type: map_at_1 - value: 32.055 - - type: map_at_10 - value: 53.114 - - type: map_at_100 - value: 55.235 - - type: map_at_1000 - value: 55.345 - - type: map_at_3 - value: 45.854 - - type: map_at_5 - value: 50.025 - - type: mrr_at_1 - value: 60.34 - - type: mrr_at_10 - value: 68.804 - - type: mrr_at_100 - value: 69.309 - - type: mrr_at_1000 - value: 69.32199999999999 - - type: mrr_at_3 - value: 66.40899999999999 - - type: mrr_at_5 - value: 67.976 - - type: ndcg_at_1 - value: 60.34 - - type: ndcg_at_10 - value: 62.031000000000006 - - type: ndcg_at_100 - value: 68.00500000000001 - - type: ndcg_at_1000 - value: 69.286 - - type: ndcg_at_3 - value: 56.355999999999995 - - type: ndcg_at_5 - value: 58.687 - - type: precision_at_1 - value: 60.34 - - type: precision_at_10 - value: 17.176 - - type: precision_at_100 - value: 2.36 - - type: precision_at_1000 - value: 0.259 - - type: precision_at_3 - value: 37.14 - - type: precision_at_5 - value: 27.809 - - type: recall_at_1 - value: 32.055 - - type: recall_at_10 - value: 70.91 - - type: recall_at_100 - value: 91.83 - - type: recall_at_1000 - value: 98.871 - - type: recall_at_3 - value: 51.202999999999996 - - type: recall_at_5 - value: 60.563 - - task: - type: Retrieval - dataset: - type: mteb/hotpotqa - name: MTEB HotpotQA - config: default - split: test - revision: ab518f4d6fcca38d87c25209f94beba119d02014 - metrics: - - type: map_at_1 - value: 43.68 - - type: map_at_10 - value: 64.389 - - type: map_at_100 - value: 65.24 - - type: map_at_1000 - value: 65.303 - - type: map_at_3 - value: 61.309000000000005 - - type: map_at_5 - value: 63.275999999999996 - - type: mrr_at_1 - value: 87.36 - - type: mrr_at_10 - value: 91.12 - - type: mrr_at_100 - value: 91.227 - - type: mrr_at_1000 - value: 91.229 - - type: mrr_at_3 - value: 90.57600000000001 - - type: mrr_at_5 - value: 90.912 - - type: ndcg_at_1 - value: 87.36 - - type: ndcg_at_10 - value: 73.076 - - type: ndcg_at_100 - value: 75.895 - - type: ndcg_at_1000 - value: 77.049 - - type: ndcg_at_3 - value: 68.929 - - type: ndcg_at_5 - value: 71.28 - - type: precision_at_1 - value: 87.36 - - type: precision_at_10 - value: 14.741000000000001 - - type: precision_at_100 - value: 1.694 - - type: precision_at_1000 - value: 0.185 - - type: precision_at_3 - value: 43.043 - - type: precision_at_5 - value: 27.681 - - type: recall_at_1 - value: 43.68 - - type: recall_at_10 - value: 73.707 - - type: recall_at_100 - value: 84.7 - - type: recall_at_1000 - value: 92.309 - - type: recall_at_3 - value: 64.564 - - type: recall_at_5 - value: 69.203 - - task: - type: Classification - dataset: - type: mteb/imdb - name: MTEB ImdbClassification - config: default - split: test - revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 - metrics: - - type: accuracy - value: 96.75399999999999 - - type: ap - value: 95.29389839242187 - - type: f1 - value: 96.75348377433475 - - task: - type: Retrieval - dataset: - type: mteb/msmarco - name: MTEB MSMARCO - config: default - split: dev - revision: c5a29a104738b98a9e76336939199e264163d4a0 - metrics: - - type: map_at_1 - value: 25.176 - - type: map_at_10 - value: 38.598 - - type: map_at_100 - value: 39.707 - - type: map_at_1000 - value: 39.744 - - type: map_at_3 - value: 34.566 - - type: map_at_5 - value: 36.863 - - type: mrr_at_1 - value: 25.874000000000002 - - type: mrr_at_10 - value: 39.214 - - type: mrr_at_100 - value: 40.251 - - type: mrr_at_1000 - value: 40.281 - - type: mrr_at_3 - value: 35.291 - - type: mrr_at_5 - value: 37.545 - - type: ndcg_at_1 - value: 25.874000000000002 - - type: ndcg_at_10 - value: 45.98 - - type: ndcg_at_100 - value: 51.197 - - type: ndcg_at_1000 - value: 52.073 - - type: ndcg_at_3 - value: 37.785999999999994 - - type: ndcg_at_5 - value: 41.870000000000005 - - type: precision_at_1 - value: 25.874000000000002 - - type: precision_at_10 - value: 7.181 - - type: precision_at_100 - value: 0.979 - - type: precision_at_1000 - value: 0.106 - - type: precision_at_3 - value: 16.051000000000002 - - type: precision_at_5 - value: 11.713 - - type: recall_at_1 - value: 25.176 - - type: recall_at_10 - value: 68.67699999999999 - - type: recall_at_100 - value: 92.55 - - type: recall_at_1000 - value: 99.164 - - type: recall_at_3 - value: 46.372 - - type: recall_at_5 - value: 56.16 - - task: - type: Classification - dataset: - type: mteb/mtop_domain - name: MTEB MTOPDomainClassification (en) - config: en - split: test - revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf - metrics: - - type: accuracy - value: 99.03784769721841 - - type: f1 - value: 98.97791641821495 - - task: - type: Classification - dataset: - type: mteb/mtop_intent - name: MTEB MTOPIntentClassification (en) - config: en - split: test - revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba - metrics: - - type: accuracy - value: 91.88326493388054 - - type: f1 - value: 73.74809928034335 - - task: - type: Classification - dataset: - type: mteb/amazon_massive_intent - name: MTEB MassiveIntentClassification (en) - config: en - split: test - revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 - metrics: - - type: accuracy - value: 85.41358439811701 - - type: f1 - value: 83.503679460639 - - task: - type: Classification - dataset: - type: mteb/amazon_massive_scenario - name: MTEB MassiveScenarioClassification (en) - config: en - split: test - revision: 7d571f92784cd94a019292a1f45445077d0ef634 - metrics: - - type: accuracy - value: 89.77135171486215 - - type: f1 - value: 88.89843747468366 - - task: - type: Clustering - dataset: - type: mteb/medrxiv-clustering-p2p - name: MTEB MedrxivClusteringP2P - config: default - split: test - revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 - metrics: - - type: v_measure - value: 46.22695362087359 - - task: - type: Clustering - dataset: - type: mteb/medrxiv-clustering-s2s - name: MTEB MedrxivClusteringS2S - config: default - split: test - revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 - metrics: - - type: v_measure - value: 44.132372165849425 - - task: - type: Reranking - dataset: - type: mteb/mind_small - name: MTEB MindSmallReranking - config: default - split: test - revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 - metrics: - - type: map - value: 33.35680810650402 - - type: mrr - value: 34.72625715637218 - - task: - type: Retrieval - dataset: - type: mteb/nfcorpus - name: MTEB NFCorpus - config: default - split: test - revision: ec0fa4fe99da2ff19ca1214b7966684033a58814 - metrics: - - type: map_at_1 - value: 7.165000000000001 - - type: map_at_10 - value: 15.424 - - type: map_at_100 - value: 20.28 - - type: map_at_1000 - value: 22.065 - - type: map_at_3 - value: 11.236 - - type: map_at_5 - value: 13.025999999999998 - - type: mrr_at_1 - value: 51.702999999999996 - - type: mrr_at_10 - value: 59.965 - - type: mrr_at_100 - value: 60.667 - - type: mrr_at_1000 - value: 60.702999999999996 - - type: mrr_at_3 - value: 58.772000000000006 - - type: mrr_at_5 - value: 59.267 - - type: ndcg_at_1 - value: 49.536 - - type: ndcg_at_10 - value: 40.6 - - type: ndcg_at_100 - value: 37.848 - - type: ndcg_at_1000 - value: 46.657 - - type: ndcg_at_3 - value: 46.117999999999995 - - type: ndcg_at_5 - value: 43.619 - - type: precision_at_1 - value: 51.393 - - type: precision_at_10 - value: 30.31 - - type: precision_at_100 - value: 9.972 - - type: precision_at_1000 - value: 2.329 - - type: precision_at_3 - value: 43.137 - - type: precision_at_5 - value: 37.585 - - type: recall_at_1 - value: 7.165000000000001 - - type: recall_at_10 - value: 19.689999999999998 - - type: recall_at_100 - value: 39.237 - - type: recall_at_1000 - value: 71.417 - - type: recall_at_3 - value: 12.247 - - type: recall_at_5 - value: 14.902999999999999 - - task: - type: Retrieval - dataset: - type: mteb/nq - name: MTEB NQ - config: default - split: test - revision: b774495ed302d8c44a3a7ea25c90dbce03968f31 - metrics: - - type: map_at_1 - value: 42.653999999999996 - - type: map_at_10 - value: 59.611999999999995 - - type: map_at_100 - value: 60.32300000000001 - - type: map_at_1000 - value: 60.336 - - type: map_at_3 - value: 55.584999999999994 - - type: map_at_5 - value: 58.19 - - type: mrr_at_1 - value: 47.683 - - type: mrr_at_10 - value: 62.06700000000001 - - type: mrr_at_100 - value: 62.537 - - type: mrr_at_1000 - value: 62.544999999999995 - - type: mrr_at_3 - value: 59.178 - - type: mrr_at_5 - value: 61.034 - - type: ndcg_at_1 - value: 47.654 - - type: ndcg_at_10 - value: 67.001 - - type: ndcg_at_100 - value: 69.73899999999999 - - type: ndcg_at_1000 - value: 69.986 - - type: ndcg_at_3 - value: 59.95700000000001 - - type: ndcg_at_5 - value: 64.025 - - type: precision_at_1 - value: 47.654 - - type: precision_at_10 - value: 10.367999999999999 - - type: precision_at_100 - value: 1.192 - - type: precision_at_1000 - value: 0.121 - - type: precision_at_3 - value: 26.651000000000003 - - type: precision_at_5 - value: 18.459 - - type: recall_at_1 - value: 42.653999999999996 - - type: recall_at_10 - value: 86.619 - - type: recall_at_100 - value: 98.04899999999999 - - type: recall_at_1000 - value: 99.812 - - type: recall_at_3 - value: 68.987 - - type: recall_at_5 - value: 78.158 - - task: - type: Retrieval - dataset: - type: mteb/quora - name: MTEB QuoraRetrieval - config: default - split: test - revision: None - metrics: - - type: map_at_1 - value: 72.538 - - type: map_at_10 - value: 86.702 - - type: map_at_100 - value: 87.31 - - type: map_at_1000 - value: 87.323 - - type: map_at_3 - value: 83.87 - - type: map_at_5 - value: 85.682 - - type: mrr_at_1 - value: 83.31 - - type: mrr_at_10 - value: 89.225 - - type: mrr_at_100 - value: 89.30399999999999 - - type: mrr_at_1000 - value: 89.30399999999999 - - type: mrr_at_3 - value: 88.44300000000001 - - type: mrr_at_5 - value: 89.005 - - type: ndcg_at_1 - value: 83.32000000000001 - - type: ndcg_at_10 - value: 90.095 - - type: ndcg_at_100 - value: 91.12 - - type: ndcg_at_1000 - value: 91.179 - - type: ndcg_at_3 - value: 87.606 - - type: ndcg_at_5 - value: 89.031 - - type: precision_at_1 - value: 83.32000000000001 - - type: precision_at_10 - value: 13.641 - - type: precision_at_100 - value: 1.541 - - type: precision_at_1000 - value: 0.157 - - type: precision_at_3 - value: 38.377 - - type: precision_at_5 - value: 25.162000000000003 - - type: recall_at_1 - value: 72.538 - - type: recall_at_10 - value: 96.47200000000001 - - type: recall_at_100 - value: 99.785 - - type: recall_at_1000 - value: 99.99900000000001 - - type: recall_at_3 - value: 89.278 - - type: recall_at_5 - value: 93.367 - - task: - type: Clustering - dataset: - type: mteb/reddit-clustering - name: MTEB RedditClustering - config: default - split: test - revision: 24640382cdbf8abc73003fb0fa6d111a705499eb - metrics: - - type: v_measure - value: 73.55219145406065 - - task: - type: Clustering - dataset: - type: mteb/reddit-clustering-p2p - name: MTEB RedditClusteringP2P - config: default - split: test - revision: 282350215ef01743dc01b456c7f5241fa8937f16 - metrics: - - type: v_measure - value: 74.13437105242755 - - task: - type: Retrieval - dataset: - type: mteb/scidocs - name: MTEB SCIDOCS - config: default - split: test - revision: None - metrics: - - type: map_at_1 - value: 6.873 - - type: map_at_10 - value: 17.944 - - type: map_at_100 - value: 21.171 - - type: map_at_1000 - value: 21.528 - - type: map_at_3 - value: 12.415 - - type: map_at_5 - value: 15.187999999999999 - - type: mrr_at_1 - value: 33.800000000000004 - - type: mrr_at_10 - value: 46.455 - - type: mrr_at_100 - value: 47.378 - - type: mrr_at_1000 - value: 47.394999999999996 - - type: mrr_at_3 - value: 42.367 - - type: mrr_at_5 - value: 44.972 - - type: ndcg_at_1 - value: 33.800000000000004 - - type: ndcg_at_10 - value: 28.907 - - type: ndcg_at_100 - value: 39.695 - - type: ndcg_at_1000 - value: 44.582 - - type: ndcg_at_3 - value: 26.949 - - type: ndcg_at_5 - value: 23.988 - - type: precision_at_1 - value: 33.800000000000004 - 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type: Classification - dataset: - type: mteb/amazon_massive_intent - name: MTEB MassiveIntentClassification (zh-CN) - config: zh-CN - split: test - revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 - metrics: - - type: accuracy - value: 81.08607935440484 - - type: f1 - value: 78.24879986066307 - - 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: 86.05917955615332 - - type: f1 - value: 85.05279279434997 - - task: - type: Retrieval - dataset: - type: C-MTEB/MedicalRetrieval - name: MTEB MedicalRetrieval - config: default - split: dev - revision: 2039188fb5800a9803ba5048df7b76e6fb151fc6 - metrics: - - type: map_at_1 - value: 56.2 - - type: map_at_10 - value: 62.57899999999999 - - type: map_at_100 - value: 63.154999999999994 - - type: map_at_1000 - value: 63.193 - - type: map_at_3 - value: 61.217 - 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dataset: - type: mteb/sts22-crosslingual-sts - name: MTEB STS22 (zh) - config: zh - split: test - revision: eea2b4fe26a775864c896887d910b76a8098ad3f - metrics: - - type: cos_sim_pearson - value: 63.64868296271406 - - type: cos_sim_spearman - value: 66.12800618164744 - - type: euclidean_pearson - value: 63.21405767340238 - - type: euclidean_spearman - value: 66.12786567790748 - - type: manhattan_pearson - value: 64.04300276525848 - - type: manhattan_spearman - value: 66.5066857145652 - - task: - type: STS - dataset: - type: C-MTEB/STSB - name: MTEB STSB - config: default - split: test - revision: 0cde68302b3541bb8b3c340dc0644b0b745b3dc0 - metrics: - - type: cos_sim_pearson - value: 81.2302623912794 - - type: cos_sim_spearman - value: 81.16833673266562 - - type: euclidean_pearson - value: 79.47647843876024 - - type: euclidean_spearman - value: 81.16944349524972 - - type: manhattan_pearson - value: 79.84947238492208 - - type: manhattan_spearman - value: 81.64626599410026 - - task: - type: Reranking - 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- dataset: - config: default - name: MTEB ArguAna-PL - revision: 63fc86750af76253e8c760fc9e534bbf24d260a2 - split: test - type: clarin-knext/arguana-pl - metrics: - - type: map_at_1 - value: 35.276999999999994 - - type: map_at_10 - value: 51.086 - - type: map_at_100 - value: 51.788000000000004 - - type: map_at_1000 - value: 51.791 - - type: map_at_3 - value: 46.147 - - type: map_at_5 - value: 49.078 - - type: mrr_at_1 - value: 35.917 - - type: mrr_at_10 - value: 51.315999999999995 - - type: mrr_at_100 - value: 52.018 - - type: mrr_at_1000 - value: 52.022 - - type: mrr_at_3 - value: 46.349000000000004 - - type: mrr_at_5 - value: 49.297000000000004 - - type: ndcg_at_1 - value: 35.276999999999994 - - type: ndcg_at_10 - value: 59.870999999999995 - - type: ndcg_at_100 - value: 62.590999999999994 - - type: ndcg_at_1000 - value: 62.661 - - type: ndcg_at_3 - value: 49.745 - - type: ndcg_at_5 - value: 55.067 - - type: precision_at_1 - value: 35.276999999999994 - - type: precision_at_10 - value: 8.791 - - type: precision_at_100 - value: 0.991 - - type: precision_at_1000 - value: 0.1 - - type: precision_at_3 - value: 20.057 - - type: precision_at_5 - value: 14.637 - - type: recall_at_1 - value: 35.276999999999994 - - type: recall_at_10 - value: 87.909 - - type: recall_at_100 - value: 99.14699999999999 - - type: recall_at_1000 - value: 99.644 - - type: recall_at_3 - value: 60.171 - - type: recall_at_5 - value: 73.18599999999999 - task: - type: Retrieval - - dataset: - config: default - name: MTEB CBD - revision: None - split: test - type: PL-MTEB/cbd - metrics: - - type: accuracy - value: 78.03000000000002 - - type: ap - value: 29.12548553897622 - - type: f1 - value: 66.54857118886073 - task: - type: Classification - - dataset: - config: default - name: MTEB CDSC-E - revision: None - split: test - type: PL-MTEB/cdsce-pairclassification - metrics: - - type: cos_sim_accuracy - value: 89.0 - - type: cos_sim_ap - value: 76.75437826834582 - - type: cos_sim_f1 - value: 66.4850136239782 - - type: cos_sim_precision - value: 68.92655367231639 - - type: cos_sim_recall - value: 64.21052631578948 - - type: dot_accuracy - value: 89.0 - - type: dot_ap - value: 76.75437826834582 - - type: dot_f1 - value: 66.4850136239782 - - type: dot_precision - value: 68.92655367231639 - - type: dot_recall - value: 64.21052631578948 - - type: euclidean_accuracy - value: 89.0 - - type: euclidean_ap - value: 76.75437826834582 - - type: euclidean_f1 - value: 66.4850136239782 - - type: euclidean_precision - value: 68.92655367231639 - - type: euclidean_recall - value: 64.21052631578948 - - type: manhattan_accuracy - value: 89.0 - - type: manhattan_ap - value: 76.66074220647083 - - type: manhattan_f1 - value: 66.47058823529412 - - type: manhattan_precision - value: 75.33333333333333 - - type: manhattan_recall - value: 59.473684210526315 - - type: max_accuracy - value: 89.0 - - type: max_ap - value: 76.75437826834582 - - type: max_f1 - value: 66.4850136239782 - task: - type: PairClassification - - dataset: - config: default - name: MTEB CDSC-R - revision: None - split: test - type: PL-MTEB/cdscr-sts - metrics: - - type: cos_sim_pearson - value: 93.12903172428328 - - type: cos_sim_spearman - value: 92.66381487060741 - - type: euclidean_pearson - value: 90.37278396708922 - - type: euclidean_spearman - value: 92.66381487060741 - - type: manhattan_pearson - value: 90.32503296540962 - - type: manhattan_spearman - value: 92.6902938354313 - task: - type: STS - - dataset: - config: default - name: MTEB DBPedia-PL - revision: 76afe41d9af165cc40999fcaa92312b8b012064a - split: test - type: clarin-knext/dbpedia-pl - metrics: - - type: map_at_1 - value: 8.83 - - type: map_at_10 - value: 18.326 - - type: map_at_100 - value: 26.496 - - type: map_at_1000 - value: 28.455000000000002 - - type: map_at_3 - value: 12.933 - - type: map_at_5 - value: 15.168000000000001 - - type: mrr_at_1 - value: 66.0 - - type: mrr_at_10 - value: 72.76700000000001 - - type: mrr_at_100 - value: 73.203 - - type: mrr_at_1000 - value: 73.219 - - type: mrr_at_3 - value: 71.458 - - type: mrr_at_5 - value: 72.246 - - type: ndcg_at_1 - value: 55.375 - - type: ndcg_at_10 - value: 41.3 - - type: ndcg_at_100 - value: 45.891 - - type: ndcg_at_1000 - value: 52.905 - - type: ndcg_at_3 - value: 46.472 - - type: ndcg_at_5 - value: 43.734 - - type: precision_at_1 - value: 66.0 - - type: precision_at_10 - value: 33.074999999999996 - - type: precision_at_100 - value: 11.094999999999999 - - type: precision_at_1000 - value: 2.374 - - type: precision_at_3 - value: 48.583 - - type: precision_at_5 - value: 42.0 - - type: recall_at_1 - value: 8.83 - - type: recall_at_10 - value: 22.587 - - type: recall_at_100 - value: 50.61600000000001 - - type: recall_at_1000 - value: 73.559 - - type: recall_at_3 - value: 13.688 - - type: recall_at_5 - value: 16.855 - task: - type: Retrieval - - dataset: - config: default - name: MTEB FiQA-PL - revision: 2e535829717f8bf9dc829b7f911cc5bbd4e6608e - split: test - type: clarin-knext/fiqa-pl - metrics: - - type: map_at_1 - value: 20.587 - - type: map_at_10 - value: 33.095 - - type: map_at_100 - value: 35.24 - - type: map_at_1000 - value: 35.429 - - type: map_at_3 - value: 28.626 - - type: map_at_5 - value: 31.136999999999997 - - type: mrr_at_1 - value: 40.586 - - type: mrr_at_10 - value: 49.033 - - type: mrr_at_100 - value: 49.952999999999996 - - type: mrr_at_1000 - value: 49.992 - - type: mrr_at_3 - value: 46.553 - - type: mrr_at_5 - value: 48.035 - - type: ndcg_at_1 - value: 40.586 - - type: ndcg_at_10 - value: 41.046 - - type: ndcg_at_100 - value: 48.586 - - type: ndcg_at_1000 - value: 51.634 - - type: ndcg_at_3 - value: 36.773 - - type: ndcg_at_5 - value: 38.389 - - type: precision_at_1 - value: 40.586 - - type: precision_at_10 - value: 11.466 - - type: precision_at_100 - value: 1.909 - - type: precision_at_1000 - value: 0.245 - - type: precision_at_3 - value: 24.434 - - type: precision_at_5 - value: 18.426000000000002 - - type: recall_at_1 - value: 20.587 - - type: recall_at_10 - value: 47.986000000000004 - - type: recall_at_100 - value: 75.761 - - type: recall_at_1000 - value: 94.065 - - type: recall_at_3 - value: 33.339 - - type: recall_at_5 - value: 39.765 - task: - type: Retrieval - - dataset: - config: default - name: MTEB HotpotQA-PL - revision: a0bd479ac97b4ccb5bd6ce320c415d0bb4beb907 - split: test - type: clarin-knext/hotpotqa-pl - metrics: - - type: map_at_1 - value: 40.878 - - type: map_at_10 - value: 58.775999999999996 - - type: map_at_100 - value: 59.632 - - type: map_at_1000 - value: 59.707 - - type: map_at_3 - value: 56.074 - - type: map_at_5 - value: 57.629 - - type: mrr_at_1 - value: 81.756 - - type: mrr_at_10 - value: 86.117 - - type: mrr_at_100 - value: 86.299 - - type: mrr_at_1000 - value: 86.30600000000001 - - type: mrr_at_3 - value: 85.345 - - type: mrr_at_5 - value: 85.832 - - type: ndcg_at_1 - value: 81.756 - - type: ndcg_at_10 - value: 67.608 - - type: ndcg_at_100 - value: 70.575 - - type: ndcg_at_1000 - value: 71.99600000000001 - - type: ndcg_at_3 - value: 63.723 - - type: ndcg_at_5 - value: 65.70700000000001 - - type: precision_at_1 - value: 81.756 - - type: precision_at_10 - value: 13.619 - - type: precision_at_100 - value: 1.5939999999999999 - - type: precision_at_1000 - value: 0.178 - - type: precision_at_3 - value: 39.604 - - type: precision_at_5 - value: 25.332 - - type: recall_at_1 - value: 40.878 - - type: recall_at_10 - value: 68.096 - - type: recall_at_100 - value: 79.696 - - type: recall_at_1000 - value: 89.082 - - type: recall_at_3 - value: 59.406000000000006 - - type: recall_at_5 - value: 63.329 - task: - type: Retrieval - - dataset: - config: default - name: MTEB MSMARCO-PL - revision: 8634c07806d5cce3a6138e260e59b81760a0a640 - split: test - type: clarin-knext/msmarco-pl - metrics: - - type: map_at_1 - value: 2.1839999999999997 - - type: map_at_10 - value: 11.346 - - type: map_at_100 - value: 30.325000000000003 - - type: map_at_1000 - value: 37.806 - - type: map_at_3 - value: 4.842 - - type: map_at_5 - value: 6.891 - - type: mrr_at_1 - value: 86.047 - - type: mrr_at_10 - value: 89.14699999999999 - - type: mrr_at_100 - value: 89.46600000000001 - - type: mrr_at_1000 - value: 89.46600000000001 - - type: mrr_at_3 - value: 89.14699999999999 - - type: mrr_at_5 - value: 89.14699999999999 - - type: ndcg_at_1 - value: 67.829 - - type: ndcg_at_10 - value: 62.222 - - type: ndcg_at_100 - value: 55.337 - - type: ndcg_at_1000 - value: 64.076 - - type: ndcg_at_3 - value: 68.12700000000001 - - type: ndcg_at_5 - value: 64.987 - - type: precision_at_1 - value: 86.047 - - type: precision_at_10 - value: 69.535 - - type: precision_at_100 - value: 32.93 - - type: precision_at_1000 - value: 6.6049999999999995 - - type: precision_at_3 - value: 79.845 - - type: precision_at_5 - value: 75.349 - - type: recall_at_1 - value: 2.1839999999999997 - - type: recall_at_10 - value: 12.866 - - type: recall_at_100 - value: 43.505 - - type: recall_at_1000 - value: 72.366 - - type: recall_at_3 - value: 4.947 - - type: recall_at_5 - value: 7.192 - task: - type: Retrieval - - dataset: - config: pl - name: MTEB MassiveIntentClassification (pl) - revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 - split: test - type: mteb/amazon_massive_intent - metrics: - - type: accuracy - value: 80.75319435104238 - - type: f1 - value: 77.58961444860606 - task: - type: Classification - - dataset: - config: pl - name: MTEB MassiveScenarioClassification (pl) - revision: 7d571f92784cd94a019292a1f45445077d0ef634 - split: test - type: mteb/amazon_massive_scenario - metrics: - - type: accuracy - value: 85.54472091459313 - - type: f1 - value: 84.29498563572106 - task: - type: Classification - - dataset: - config: default - name: MTEB NFCorpus-PL - revision: 9a6f9567fda928260afed2de480d79c98bf0bec0 - split: test - type: clarin-knext/nfcorpus-pl - metrics: - - type: map_at_1 - value: 4.367 - - type: map_at_10 - value: 10.38 - - type: map_at_100 - value: 13.516 - - type: map_at_1000 - value: 14.982000000000001 - - type: map_at_3 - value: 7.367 - - type: map_at_5 - value: 8.59 - - type: mrr_at_1 - value: 41.486000000000004 - - type: mrr_at_10 - value: 48.886 - - type: mrr_at_100 - value: 49.657000000000004 - - type: mrr_at_1000 - value: 49.713 - - type: mrr_at_3 - value: 46.904 - - type: mrr_at_5 - value: 48.065000000000005 - - type: ndcg_at_1 - value: 40.402 - - type: ndcg_at_10 - value: 30.885 - - type: ndcg_at_100 - value: 28.393 - - type: ndcg_at_1000 - value: 37.428 - - type: ndcg_at_3 - value: 35.394999999999996 - - type: ndcg_at_5 - value: 33.391999999999996 - - type: precision_at_1 - value: 41.486000000000004 - - type: precision_at_10 - value: 23.437 - - type: precision_at_100 - value: 7.638 - - type: precision_at_1000 - value: 2.0389999999999997 - - type: precision_at_3 - value: 32.817 - - type: precision_at_5 - value: 28.915999999999997 - - type: recall_at_1 - value: 4.367 - - type: recall_at_10 - value: 14.655000000000001 - - type: recall_at_100 - value: 29.665999999999997 - - type: recall_at_1000 - value: 62.073 - - type: recall_at_3 - value: 8.51 - - type: recall_at_5 - value: 10.689 - task: - type: Retrieval - - dataset: - config: default - name: MTEB NQ-PL - revision: f171245712cf85dd4700b06bef18001578d0ca8d - split: test - type: clarin-knext/nq-pl - metrics: - - type: map_at_1 - value: 28.616000000000003 - - type: map_at_10 - value: 41.626000000000005 - - type: map_at_100 - value: 42.689 - - type: map_at_1000 - value: 42.733 - - type: map_at_3 - value: 37.729 - - type: map_at_5 - value: 39.879999999999995 - - type: mrr_at_1 - value: 32.068000000000005 - - type: mrr_at_10 - value: 44.029 - - type: mrr_at_100 - value: 44.87 - - type: mrr_at_1000 - value: 44.901 - - type: mrr_at_3 - value: 40.687 - - type: mrr_at_5 - value: 42.625 - - type: ndcg_at_1 - value: 32.068000000000005 - - type: ndcg_at_10 - value: 48.449999999999996 - - type: ndcg_at_100 - value: 53.13 - - type: ndcg_at_1000 - value: 54.186 - - type: ndcg_at_3 - value: 40.983999999999995 - - type: ndcg_at_5 - value: 44.628 - - type: precision_at_1 - value: 32.068000000000005 - - type: precision_at_10 - value: 7.9750000000000005 - - type: precision_at_100 - value: 1.061 - - type: precision_at_1000 - value: 0.116 - - type: precision_at_3 - value: 18.404999999999998 - - type: precision_at_5 - value: 13.111 - - type: recall_at_1 - value: 28.616000000000003 - - type: recall_at_10 - value: 66.956 - - type: recall_at_100 - value: 87.657 - - type: recall_at_1000 - value: 95.548 - - type: recall_at_3 - value: 47.453 - - type: recall_at_5 - value: 55.87800000000001 - task: - type: Retrieval - - dataset: - config: default - name: MTEB PAC - revision: None - split: test - type: laugustyniak/abusive-clauses-pl - metrics: - - type: accuracy - value: 69.04141326382856 - - type: ap - value: 77.47589122111044 - - type: f1 - value: 66.6332277374775 - task: - type: Classification - - dataset: - config: default - name: MTEB PPC - revision: None - split: test - type: PL-MTEB/ppc-pairclassification - metrics: - - type: cos_sim_accuracy - value: 86.4 - - type: cos_sim_ap - value: 94.1044939667201 - - type: cos_sim_f1 - value: 88.78048780487805 - - type: cos_sim_precision - value: 87.22044728434504 - - type: cos_sim_recall - value: 90.39735099337747 - - type: dot_accuracy - value: 86.4 - - type: dot_ap - value: 94.1044939667201 - - type: dot_f1 - value: 88.78048780487805 - - type: dot_precision - value: 87.22044728434504 - - type: dot_recall - value: 90.39735099337747 - - type: euclidean_accuracy - value: 86.4 - - type: euclidean_ap - value: 94.1044939667201 - - type: euclidean_f1 - value: 88.78048780487805 - - type: euclidean_precision - value: 87.22044728434504 - - type: euclidean_recall - value: 90.39735099337747 - - type: manhattan_accuracy - value: 86.4 - - type: manhattan_ap - value: 94.11438365697387 - - type: manhattan_f1 - value: 88.77968877968877 - - type: manhattan_precision - value: 87.84440842787681 - - type: manhattan_recall - value: 89.73509933774835 - - type: max_accuracy - value: 86.4 - - type: max_ap - value: 94.11438365697387 - - type: max_f1 - value: 88.78048780487805 - task: - type: PairClassification - - dataset: - config: default - name: MTEB PSC - revision: None - split: test - type: PL-MTEB/psc-pairclassification - metrics: - - type: cos_sim_accuracy - value: 97.86641929499072 - - type: cos_sim_ap - value: 99.36904211868182 - - type: cos_sim_f1 - value: 96.56203288490283 - - type: cos_sim_precision - value: 94.72140762463343 - - type: cos_sim_recall - value: 98.47560975609755 - - type: dot_accuracy - value: 97.86641929499072 - - type: dot_ap - value: 99.36904211868183 - - type: dot_f1 - value: 96.56203288490283 - - type: dot_precision - value: 94.72140762463343 - - type: dot_recall - value: 98.47560975609755 - - type: euclidean_accuracy - value: 97.86641929499072 - - type: euclidean_ap - value: 99.36904211868183 - - type: euclidean_f1 - value: 96.56203288490283 - - type: euclidean_precision - value: 94.72140762463343 - - type: euclidean_recall - value: 98.47560975609755 - - type: manhattan_accuracy - value: 98.14471243042672 - - type: manhattan_ap - value: 99.43359540492416 - - type: manhattan_f1 - value: 96.98795180722892 - - type: manhattan_precision - value: 95.83333333333334 - - type: manhattan_recall - value: 98.17073170731707 - - type: max_accuracy - value: 98.14471243042672 - - type: max_ap - value: 99.43359540492416 - - type: max_f1 - value: 96.98795180722892 - task: - type: PairClassification - - dataset: - config: default - name: MTEB PolEmo2.0-IN - revision: None - split: test - type: PL-MTEB/polemo2_in - metrics: - - type: accuracy - value: 89.39058171745152 - - type: f1 - value: 86.8552093529568 - task: - type: Classification - - dataset: - config: default - name: MTEB PolEmo2.0-OUT - revision: None - split: test - type: PL-MTEB/polemo2_out - metrics: - - type: accuracy - value: 74.97975708502024 - - type: f1 - value: 58.73081628832407 - task: - type: Classification - - dataset: - config: default - name: MTEB Quora-PL - revision: 0be27e93455051e531182b85e85e425aba12e9d4 - split: test - type: clarin-knext/quora-pl - metrics: - - type: map_at_1 - value: 64.917 - - type: map_at_10 - value: 78.74600000000001 - - type: map_at_100 - value: 79.501 - - type: map_at_1000 - value: 79.524 - - type: map_at_3 - value: 75.549 - - type: map_at_5 - value: 77.495 - - type: mrr_at_1 - value: 74.9 - - type: mrr_at_10 - value: 82.112 - - type: mrr_at_100 - value: 82.314 - - type: mrr_at_1000 - value: 82.317 - - type: mrr_at_3 - value: 80.745 - - type: mrr_at_5 - value: 81.607 - - type: ndcg_at_1 - value: 74.83999999999999 - - type: ndcg_at_10 - value: 83.214 - - type: ndcg_at_100 - value: 84.997 - - type: ndcg_at_1000 - value: 85.207 - - type: ndcg_at_3 - value: 79.547 - - type: ndcg_at_5 - value: 81.46600000000001 - - type: precision_at_1 - value: 74.83999999999999 - - type: precision_at_10 - value: 12.822 - - type: precision_at_100 - value: 1.506 - - type: precision_at_1000 - value: 0.156 - - type: precision_at_3 - value: 34.903 - - type: precision_at_5 - value: 23.16 - - type: recall_at_1 - value: 64.917 - - type: recall_at_10 - value: 92.27199999999999 - - type: recall_at_100 - value: 98.715 - - type: recall_at_1000 - value: 99.854 - - type: recall_at_3 - value: 82.04599999999999 - - type: recall_at_5 - value: 87.2 - task: - type: Retrieval - - dataset: - config: default - name: MTEB SCIDOCS-PL - revision: 45452b03f05560207ef19149545f168e596c9337 - split: test - type: clarin-knext/scidocs-pl - metrics: - - type: map_at_1 - value: 3.51 - - type: map_at_10 - value: 9.046999999999999 - - type: map_at_100 - value: 10.823 - - type: map_at_1000 - value: 11.144 - - type: map_at_3 - value: 6.257 - - type: map_at_5 - value: 7.648000000000001 - - type: mrr_at_1 - value: 17.299999999999997 - - type: mrr_at_10 - value: 27.419 - - type: mrr_at_100 - value: 28.618 - - type: mrr_at_1000 - value: 28.685 - - type: mrr_at_3 - value: 23.817 - - type: mrr_at_5 - value: 25.927 - - type: ndcg_at_1 - value: 17.299999999999997 - - type: ndcg_at_10 - value: 16.084 - - type: ndcg_at_100 - value: 23.729 - - type: ndcg_at_1000 - value: 29.476999999999997 - - type: ndcg_at_3 - value: 14.327000000000002 - - type: ndcg_at_5 - value: 13.017999999999999 - - type: precision_at_1 - value: 17.299999999999997 - - type: precision_at_10 - value: 8.63 - - type: precision_at_100 - value: 1.981 - - type: precision_at_1000 - value: 0.336 - - type: precision_at_3 - value: 13.4 - - type: precision_at_5 - value: 11.700000000000001 - - type: recall_at_1 - value: 3.51 - - type: recall_at_10 - value: 17.518 - - type: recall_at_100 - value: 40.275 - - type: recall_at_1000 - value: 68.203 - - type: recall_at_3 - value: 8.155 - - type: recall_at_5 - value: 11.875 - task: - type: Retrieval - - dataset: - config: default - name: MTEB SICK-E-PL - revision: None - split: test - type: PL-MTEB/sicke-pl-pairclassification - metrics: - - type: cos_sim_accuracy - value: 86.30248675091724 - - type: cos_sim_ap - value: 83.6756734006714 - - type: cos_sim_f1 - value: 74.97367497367497 - - type: cos_sim_precision - value: 73.91003460207612 - - type: cos_sim_recall - value: 76.06837606837607 - - type: dot_accuracy - value: 86.30248675091724 - - type: dot_ap - value: 83.6756734006714 - - type: dot_f1 - value: 74.97367497367497 - - type: dot_precision - value: 73.91003460207612 - - type: dot_recall - value: 76.06837606837607 - - type: euclidean_accuracy - value: 86.30248675091724 - - type: euclidean_ap - value: 83.67566984333091 - - type: euclidean_f1 - value: 74.97367497367497 - - type: euclidean_precision - value: 73.91003460207612 - - type: euclidean_recall - value: 76.06837606837607 - - type: manhattan_accuracy - value: 86.28210354667753 - - type: manhattan_ap - value: 83.64216119130171 - - type: manhattan_f1 - value: 74.92152075340078 - - type: manhattan_precision - value: 73.4107997265892 - - type: manhattan_recall - value: 76.49572649572649 - - type: max_accuracy - value: 86.30248675091724 - - type: max_ap - value: 83.6756734006714 - - type: max_f1 - value: 74.97367497367497 - task: - type: PairClassification - - dataset: - config: default - name: MTEB SICK-R-PL - revision: None - split: test - type: PL-MTEB/sickr-pl-sts - metrics: - - type: cos_sim_pearson - value: 82.23295940859121 - - type: cos_sim_spearman - value: 78.89329160768719 - - type: euclidean_pearson - value: 79.56019107076818 - - type: euclidean_spearman - value: 78.89330209904084 - - type: manhattan_pearson - value: 79.76098513973719 - - type: manhattan_spearman - value: 79.05490162570123 - task: - type: STS - - dataset: - config: pl - name: MTEB STS22 (pl) - revision: eea2b4fe26a775864c896887d910b76a8098ad3f - split: test - type: mteb/sts22-crosslingual-sts - metrics: - - type: cos_sim_pearson - value: 37.732606308062486 - - type: cos_sim_spearman - value: 41.01645667030284 - - type: euclidean_pearson - value: 26.61722556367085 - - type: euclidean_spearman - value: 41.01645667030284 - - type: manhattan_pearson - value: 26.60917378970807 - - type: manhattan_spearman - value: 41.51335727617614 - task: - type: STS - - dataset: - config: default - name: MTEB SciFact-PL - revision: 47932a35f045ef8ed01ba82bf9ff67f6e109207e - split: test - type: clarin-knext/scifact-pl - metrics: - - type: map_at_1 - value: 54.31700000000001 - - type: map_at_10 - value: 65.564 - - type: map_at_100 - value: 66.062 - - type: map_at_1000 - value: 66.08699999999999 - - type: map_at_3 - value: 62.592999999999996 - - type: map_at_5 - value: 63.888 - - type: mrr_at_1 - value: 56.99999999999999 - - type: mrr_at_10 - value: 66.412 - - type: mrr_at_100 - value: 66.85900000000001 - - type: mrr_at_1000 - value: 66.88 - - type: mrr_at_3 - value: 64.22200000000001 - - type: mrr_at_5 - value: 65.206 - - type: ndcg_at_1 - value: 56.99999999999999 - - type: ndcg_at_10 - value: 70.577 - - type: ndcg_at_100 - value: 72.879 - - type: ndcg_at_1000 - value: 73.45 - - type: ndcg_at_3 - value: 65.5 - - type: ndcg_at_5 - value: 67.278 - - type: precision_at_1 - value: 56.99999999999999 - - type: precision_at_10 - value: 9.667 - - type: precision_at_100 - value: 1.083 - - type: precision_at_1000 - value: 0.11299999999999999 - - type: precision_at_3 - value: 26.0 - - type: precision_at_5 - value: 16.933 - - type: recall_at_1 - value: 54.31700000000001 - - type: recall_at_10 - value: 85.056 - - type: recall_at_100 - value: 95.667 - - type: recall_at_1000 - value: 100.0 - - type: recall_at_3 - value: 71.0 - - type: recall_at_5 - value: 75.672 - task: - type: Retrieval - - dataset: - config: default - name: MTEB TRECCOVID-PL - revision: 81bcb408f33366c2a20ac54adafad1ae7e877fdd - split: test - type: clarin-knext/trec-covid-pl - metrics: - - type: map_at_1 - value: 0.245 - - type: map_at_10 - value: 2.051 - - type: map_at_100 - value: 12.009 - - type: map_at_1000 - value: 27.448 - - type: map_at_3 - value: 0.721 - - type: map_at_5 - value: 1.13 - - type: mrr_at_1 - value: 88.0 - - type: mrr_at_10 - value: 93.0 - - type: mrr_at_100 - value: 93.0 - - type: mrr_at_1000 - value: 93.0 - - type: mrr_at_3 - value: 93.0 - - type: mrr_at_5 - value: 93.0 - - type: ndcg_at_1 - value: 85.0 - - type: ndcg_at_10 - value: 80.303 - - type: ndcg_at_100 - value: 61.23499999999999 - - type: ndcg_at_1000 - value: 52.978 - - type: ndcg_at_3 - value: 84.419 - - type: ndcg_at_5 - value: 82.976 - - type: precision_at_1 - value: 88.0 - - type: precision_at_10 - value: 83.39999999999999 - - type: precision_at_100 - value: 61.96 - - type: precision_at_1000 - value: 22.648 - - type: precision_at_3 - value: 89.333 - - type: precision_at_5 - value: 87.2 - - type: recall_at_1 - value: 0.245 - - type: recall_at_10 - value: 2.193 - - type: recall_at_100 - value: 14.938 - - type: recall_at_1000 - value: 48.563 - - type: recall_at_3 - value: 0.738 - - type: recall_at_5 - value: 1.173 - task: - type: Retrieval ---- - -## gte-Qwen2-7B-instruct - -**gte-Qwen2-7B-instruct** is the latest model in the gte (General Text Embedding) model family that ranks **No.1** in both English and Chinese evaluations on the Massive Text Embedding Benchmark [MTEB benchmark](https://huggingface.co/spaces/mteb/leaderboard) (as of June 16, 2024). - -Recently, the [**Qwen team**](https://huggingface.co/Qwen) released the Qwen2 series models, and we have trained the **gte-Qwen2-7B-instruct** model based on the [Qwen2-7B](https://huggingface.co/Qwen/Qwen2-7B) LLM model. Compared to the [gte-Qwen1.5-7B-instruct](https://huggingface.co/Alibaba-NLP/gte-Qwen1.5-7B-instruct) model, the **gte-Qwen2-7B-instruct** model uses the same training data and training strategies during the finetuning stage, with the only difference being the upgraded base model to Qwen2-7B. Considering the improvements in the Qwen2 series models compared to the Qwen1.5 series, we can also expect consistent performance enhancements in the embedding models. - -The model incorporates several key advancements: - -- Integration of bidirectional attention mechanisms, enriching its contextual understanding. -- Instruction tuning, applied solely on the query side for streamlined efficiency -- Comprehensive training across a vast, multilingual text corpus spanning diverse domains and scenarios. This training leverages both weakly supervised and supervised data, ensuring the model's applicability across numerous languages and a wide array of downstream tasks. - - -## Model Information -- Model Size: 7B -- Embedding Dimension: 3584 -- Max Input Tokens: 32k - -## Requirements -``` -transformers>=4.39.2 -flash_attn>=2.5.6 -``` -## Usage - -### Sentence Transformers - -```python -from sentence_transformers import SentenceTransformer - -model = SentenceTransformer("Alibaba-NLP/gte-Qwen2-7B-instruct", trust_remote_code=True) -# In case you want to reduce the maximum length: -model.max_seq_length = 8192 - -queries = [ - "how much protein should a female eat", - "summit define", -] -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.", -] - -query_embeddings = model.encode(queries, prompt_name="query") -document_embeddings = model.encode(documents) - -scores = (query_embeddings @ document_embeddings.T) * 100 -print(scores.tolist()) -``` - -Observe the [config_sentence_transformers.json](config_sentence_transformers.json) to see all pre-built prompt names. Otherwise, you can use `model.encode(queries, prompt="Instruct: ...\nQuery: "` to use a custom prompt of your choice. - -### Transformers - -```python -import torch -import torch.nn.functional as F - -from torch import Tensor -from transformers import AutoTokenizer, AutoModel - - -def last_token_pool(last_hidden_states: Tensor, - attention_mask: Tensor) -> Tensor: - left_padding = (attention_mask[:, -1].sum() == attention_mask.shape[0]) - if left_padding: - return last_hidden_states[:, -1] - else: - sequence_lengths = attention_mask.sum(dim=1) - 1 - batch_size = last_hidden_states.shape[0] - return last_hidden_states[torch.arange(batch_size, device=last_hidden_states.device), sequence_lengths] - - -def get_detailed_instruct(task_description: str, query: str) -> str: - return f'Instruct: {task_description}\nQuery: {query}' - - -# Each query must come with a one-sentence instruction that describes the task -task = 'Given a web search query, retrieve relevant passages that answer the query' -queries = [ - get_detailed_instruct(task, 'how much protein should a female eat'), - get_detailed_instruct(task, 'summit define') -] -# No need to add instruction for retrieval 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." -] -input_texts = queries + documents - -tokenizer = AutoTokenizer.from_pretrained('Alibaba-NLP/gte-Qwen2-7B-instruct', trust_remote_code=True) -model = AutoModel.from_pretrained('Alibaba-NLP/gte-Qwen2-7B-instruct', trust_remote_code=True) - -max_length = 8192 - -# Tokenize the input texts -batch_dict = tokenizer(input_texts, max_length=max_length, padding=True, truncation=True, return_tensors='pt') -outputs = model(**batch_dict) -embeddings = last_token_pool(outputs.last_hidden_state, batch_dict['attention_mask']) - -# normalize embeddings -embeddings = F.normalize(embeddings, p=2, dim=1) -scores = (embeddings[:2] @ embeddings[2:].T) * 100 -print(scores.tolist()) -``` - -## Evaluation - -### MTEB & C-MTEB - -You can use the [scripts/eval_mteb.py](https://huggingface.co/Alibaba-NLP/gte-Qwen2-7B-instruct/blob/main/scripts/eval_mteb.py) to reproduce the following result of **gte-Qwen2-7B-instruct** on MTEB(English)/C-MTEB(Chinese): - -| Model Name | MTEB(56) | C-MTEB(35) | -|:----:|:---------:|:----------:| -| [bge-base-en-1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) | 64.23 | - | -| [bge-large-en-1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) | 63.55 | - | -| [gte-large-en-v1.5](https://huggingface.co/Alibaba-NLP/gte-large-en-v1.5) | 65.39 | - | -| [gte-base-en-v1.5](https://huggingface.co/Alibaba-NLP/gte-large-en-v1.5) | 64.11 | - | -| [mxbai-embed-large-v1](https://huggingface.co/mixedbread-ai/mxbai-embed-large-v1) | 64.68 | - | -| [acge_text_embedding](https://huggingface.co/aspire/acge_text_embedding) | - | 69.07 | -| [stella-mrl-large-zh-v3.5-1792d](https://huggingface.co/infgrad/stella-mrl-large-zh-v3.5-1792d) | - | 68.55 | -| [gte-large-zh](https://huggingface.co/thenlper/gte-large-zh) | - | 66.72 | -| [multilingual-e5-base](https://huggingface.co/intfloat/multilingual-e5-base) | 59.45 | 56.21 | -| [multilingual-e5-large](https://huggingface.co/intfloat/multilingual-e5-large) | 61.50 | 58.81 | -| [e5-mistral-7b-instruct](https://huggingface.co/intfloat/e5-mistral-7b-instruct) | 66.63 | 60.81 | -| [gte-Qwen1.5-7B-instruct](https://huggingface.co/Alibaba-NLP/gte-Qwen1.5-7B-instruct) | 67.34 | 69.52 | -| [NV-Embed-v1](https://huggingface.co/nvidia/NV-Embed-v1) | 69.32 | - | -| [**gte-Qwen2-7B-instruct**](https://huggingface.co/Alibaba-NLP/gte-Qwen2-7B-instruct) | **70.24** | **72.05** | - -### GTE Models - -The gte series models have consistently released two types of models: encoder-only models (based on the BERT architecture) and decode-only models (based on the LLM architecture). - -| Models | Language | Max Sequence Length | Dimension | Model Size (Memory Usage, fp32) | -|:-------------------------------------------------------------------------------------:|:--------:|:-----: |:---------:|:-------------------------------:| -| [GTE-large-zh](https://huggingface.co/thenlper/gte-large-zh) | Chinese | 512 | 1024 | 1.25GB | -| [GTE-base-zh](https://huggingface.co/thenlper/gte-base-zh) | Chinese | 512 | 512 | 0.41GB | -| [GTE-small-zh](https://huggingface.co/thenlper/gte-small-zh) | Chinese | 512 | 512 | 0.12GB | -| [GTE-large](https://huggingface.co/thenlper/gte-large) | English | 512 | 1024 | 1.25GB | -| [GTE-base](https://huggingface.co/thenlper/gte-base) | English | 512 | 512 | 0.21GB | -| [GTE-small](https://huggingface.co/thenlper/gte-small) | English | 512 | 384 | 0.10GB | -| [GTE-large-en-v1.5](https://huggingface.co/Alibaba-NLP/gte-large-en-v1.5) | English | 8192 | 1024 | 1.74GB | -| [GTE-base-en-v1.5](https://huggingface.co/Alibaba-NLP/gte-base-en-v1.5) | English | 8192 | 768 | 0.51GB | -| [GTE-Qwen1.5-7B-instruct](https://huggingface.co/Alibaba-NLP/gte-Qwen1.5-7B-instruct) | Multilingual | 32000 | 4096 | 26.45GB | -| [GTE-Qwen2-7B-instruct](https://huggingface.co/Alibaba-NLP/gte-Qwen2-7B-instruct) | Multilingual | 32000 | 3584 | 26.45GB | - -## Citation - -If you find our paper or models helpful, please consider cite: - -``` -@article{li2023towards, - title={Towards general text embeddings with multi-stage contrastive learning}, - author={Li, Zehan and Zhang, Xin and Zhang, Yanzhao and Long, Dingkun and Xie, Pengjun and Zhang, Meishan}, - journal={arXiv preprint arXiv:2308.03281}, - year={2023} -} -```huggingface.co/Alibaba-NLP/gte-Qwen1.5-7B-instruct) | 67.34 | 69.52 | -| [NV-Embed-v1](https://huggingface.co/nvidia/NV-Embed-v1) | 69.32 | - | -| [**gte-Qwen2-7B-instruct**](https://huggingface.co/Alibaba-NLP/gte-Qwen2-7B-instruct) | **70.24** | **72.05** | - -### GTE Models - -The gte series models have consistently released two types of models: encoder-only models (based on the BERT architecture) and decode-only models (based on the LLM architecture). - -| Models | Language | Max Sequence Length | Dimension | Model Size (Memory Usage, fp32) | -|:-------------------------------------------------------------------------------------:|:--------:|:-----: |:---------:|:-------------------------------:| -| [GTE-large-zh](https://huggingface.co/thenlper/gte-large-zh) | Chinese | 512 | 1024 | 1.25GB | -| [GTE-base-zh](https://huggingface.co/thenlper/gte-base-zh) | Chinese | 512 | 512 | 0.41GB | -| [GTE-small-zh](https://huggingface.co/thenlper/gte-small-zh) | Chinese | 512 | 512 | 0.12GB | -| [GTE-large](https://huggingface.co/thenlper/gte-large) | English | 512 | 1024 | 1.25GB | -| [GTE-base](https://huggingface.co/thenlper/gte-base) | English | 512 | 512 | 0.21GB | -| [GTE-small](https://huggingface.co/thenlper/gte-small) | English | 512 | 384 | 0.10GB | -| [GTE-large-en-v1.5](https://huggingface.co/Alibaba-NLP/gte-large-en-v1.5) | English | 8192 | 1024 | 1.74GB | -| [GTE-base-en-v1.5](https://huggingface.co/Alibaba-NLP/gte-base-en-v1.5) | English | 8192 | 768 | 0.51GB | -| [GTE-Qwen1.5-7B-instruct](https://huggingface.co/Alibaba-NLP/gte-Qwen1.5-7B-instruct) | Multilingual | 32000 | 4096 | 26.45GB | -| [GTE-Qwen2-7B-instruct](https://huggingface.co/Alibaba-NLP/gte-Qwen2-7B-instruct) | Multilingual | 32000 | 3584 | 26.45GB | - -## Citation - -If you find our paper or models helpful, please consider cite: - -``` -@article{li2023towards, - title={Towards general text embeddings with multi-stage contrastive learning}, - author={Li, Zehan and Zhang, Xin and Zhang, Yanzhao and Long, Dingkun and Xie, Pengjun and Zhang, Meishan}, - journal={arXiv preprint arXiv:2308.03281}, - year={2023} -} -``` \ No newline at end of file