--- base_model: BAAI/bge-m3 library_name: sentence-transformers metrics: - cosine_accuracy@1 - cosine_accuracy@3 - cosine_accuracy@5 - cosine_accuracy@10 - cosine_precision@1 - cosine_precision@3 - cosine_precision@5 - cosine_precision@10 - cosine_recall@1 - cosine_recall@3 - cosine_recall@5 - cosine_recall@10 - cosine_ndcg@10 - cosine_mrr@10 - cosine_map@100 pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:5175 - loss:MatryoshkaLoss - loss:MultipleNegativesRankingLoss widget: - source_sentence: Caldrà executar l'obra comunicada prèviament d'acord amb les condicions específiques que es contenen en el model normalitzat CT02. sentences: - Quin és el propòsit de la instal·lació d'un circ sense animals a la via pública? - Quin és el destinatari de les dades bloquejades? - Quin és el format de presentació de la comunicació prèvia? - source_sentence: Armes utilitzables en activitats lúdico-esportives d’airsoft i paintball... sentences: - Quin és el paper de l'AFA en la venda de llibres? - Quin és el benefici de tenir dades personals correctes? - Quin és el tipus d'activitats que es poden practicar amb les armes de 4a categoria? - source_sentence: En les activitats sotmeses al règim d’autorització ambiental o llicència municipal d’activitat (Annex I o Annex II de la Llei 20/2009) cal demanar aquest certificat previ a la presentació de la sol·licitud d’autorització ambiental o llicència municipal. sentences: - Quin és el benefici de tenir el certificat de compatibilitat urbanística en les activitats sotmeses a llicència municipal d’activitat? - Com puc controlar la recepció de propaganda electoral per correu? - Quin és el benefici de la cessió d'un compostador domèstic per a l'entorn? - source_sentence: La persona interessada posa en coneixement de l’Administració, les actuacions urbanístiques que pretén dur a terme consistents en l'apuntalament o reforç provisional d'estructures existents fins a la intervenció definitiva. sentences: - Qui pot participar en el Consell d'Adolescents? - Quin és el resultat de la presentació de la comunicació prèvia? - Quin és el paper de la persona interessada en relació amb la presentació de la comunicació prèvia? - source_sentence: La persona consumidora presenti la reclamació davant de l'entitat acreditada en un termini superior a un any des de la data en què va presentar la reclamació a l'empresa. sentences: - Quin és el tràmit per inscriure'm al Padró d'Habitants sense tenir constància de la meva anterior residència? - Quin és el resultat de la modificació substancial de la llicència d'obres en relació a les autoritzacions administratives? - Quin és el paper de l'entitat acreditada en la tramitació d'una reclamació? model-index: - name: SentenceTransformer based on BAAI/bge-m3 results: - task: type: information-retrieval name: Information Retrieval dataset: name: dim 1024 type: dim_1024 metrics: - type: cosine_accuracy@1 value: 0.057391304347826085 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.1791304347826087 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.2539130434782609 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.42434782608695654 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.057391304347826085 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.05971014492753622 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.05078260869565218 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.042434782608695654 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.057391304347826085 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.1791304347826087 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.2539130434782609 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.42434782608695654 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.21132731792814036 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.1471621808143548 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.16876601661954835 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 768 type: dim_768 metrics: - type: cosine_accuracy@1 value: 0.059130434782608696 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.16695652173913045 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.2417391304347826 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.41739130434782606 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.059130434782608696 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.05565217391304348 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.04834782608695652 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.04173913043478261 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.059130434782608696 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.16695652173913045 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.2417391304347826 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.41739130434782606 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.2073596053307957 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.14417184265010352 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.16633232312496227 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 512 type: dim_512 metrics: - type: cosine_accuracy@1 value: 0.06434782608695652 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.1617391304347826 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.2417391304347826 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.4052173913043478 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.06434782608695652 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.05391304347826086 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.04834782608695652 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.04052173913043479 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.06434782608695652 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.1617391304347826 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.2417391304347826 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.4052173913043478 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.20633605278226078 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.1464064872325742 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.16999201443118514 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 256 type: dim_256 metrics: - type: cosine_accuracy@1 value: 0.05565217391304348 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.1565217391304348 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.22782608695652173 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.391304347826087 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.05565217391304348 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.05217391304347826 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.045565217391304355 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.0391304347826087 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.05565217391304348 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.1565217391304348 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.22782608695652173 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.391304347826087 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.19646870519287135 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.13765838509316777 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.16205285151749863 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 128 type: dim_128 metrics: - type: cosine_accuracy@1 value: 0.06782608695652174 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.17043478260869566 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.2573913043478261 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.41739130434782606 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.06782608695652174 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.05681159420289853 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.05147826086956522 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.04173913043478261 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.06782608695652174 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.17043478260869566 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.2573913043478261 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.41739130434782606 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.2141738525949419 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.15279848171152532 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.17543729180964374 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 64 type: dim_64 metrics: - type: cosine_accuracy@1 value: 0.05217391304347826 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.14608695652173914 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.23304347826086957 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.40347826086956523 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.05217391304347826 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.04869565217391304 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.04660869565217392 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.04034782608695652 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.05217391304347826 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.14608695652173914 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.23304347826086957 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.40347826086956523 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.19611597970227643 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.133929606625259 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.15637789403585464 name: Cosine Map@100 --- # SentenceTransformer based on BAAI/bge-m3 This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3) on the json dataset. It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. ## Model Details ### Model Description - **Model Type:** Sentence Transformer - **Base model:** [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3) - **Maximum Sequence Length:** 8192 tokens - **Output Dimensionality:** 1024 tokens - **Similarity Function:** Cosine Similarity - **Training Dataset:** - json ### Model Sources - **Documentation:** [Sentence Transformers Documentation](https://sbert.net) - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 8192, 'do_lower_case': False}) with Transformer model: XLMRobertaModel (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ) ``` ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SentenceTransformer # Download from the 🤗 Hub model = SentenceTransformer("adriansanz/sqv-v4") # Run inference sentences = [ "La persona consumidora presenti la reclamació davant de l'entitat acreditada en un termini superior a un any des de la data en què va presentar la reclamació a l'empresa.", "Quin és el paper de l'entitat acreditada en la tramitació d'una reclamació?", "Quin és el resultat de la modificació substancial de la llicència d'obres en relació a les autoritzacions administratives?", ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 1024] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` ## Evaluation ### Metrics #### Information Retrieval * Dataset: `dim_1024` * Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) | Metric | Value | |:--------------------|:-----------| | cosine_accuracy@1 | 0.0574 | | cosine_accuracy@3 | 0.1791 | | cosine_accuracy@5 | 0.2539 | | cosine_accuracy@10 | 0.4243 | | cosine_precision@1 | 0.0574 | | cosine_precision@3 | 0.0597 | | cosine_precision@5 | 0.0508 | | cosine_precision@10 | 0.0424 | | cosine_recall@1 | 0.0574 | | cosine_recall@3 | 0.1791 | | cosine_recall@5 | 0.2539 | | cosine_recall@10 | 0.4243 | | cosine_ndcg@10 | 0.2113 | | cosine_mrr@10 | 0.1472 | | **cosine_map@100** | **0.1688** | #### Information Retrieval * Dataset: `dim_768` * Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) | Metric | Value | |:--------------------|:-----------| | cosine_accuracy@1 | 0.0591 | | cosine_accuracy@3 | 0.167 | | cosine_accuracy@5 | 0.2417 | | cosine_accuracy@10 | 0.4174 | | cosine_precision@1 | 0.0591 | | cosine_precision@3 | 0.0557 | | cosine_precision@5 | 0.0483 | | cosine_precision@10 | 0.0417 | | cosine_recall@1 | 0.0591 | | cosine_recall@3 | 0.167 | | cosine_recall@5 | 0.2417 | | cosine_recall@10 | 0.4174 | | cosine_ndcg@10 | 0.2074 | | cosine_mrr@10 | 0.1442 | | **cosine_map@100** | **0.1663** | #### Information Retrieval * Dataset: `dim_512` * Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) | Metric | Value | |:--------------------|:---------| | cosine_accuracy@1 | 0.0643 | | cosine_accuracy@3 | 0.1617 | | cosine_accuracy@5 | 0.2417 | | cosine_accuracy@10 | 0.4052 | | cosine_precision@1 | 0.0643 | | cosine_precision@3 | 0.0539 | | cosine_precision@5 | 0.0483 | | cosine_precision@10 | 0.0405 | | cosine_recall@1 | 0.0643 | | cosine_recall@3 | 0.1617 | | cosine_recall@5 | 0.2417 | | cosine_recall@10 | 0.4052 | | cosine_ndcg@10 | 0.2063 | | cosine_mrr@10 | 0.1464 | | **cosine_map@100** | **0.17** | #### Information Retrieval * Dataset: `dim_256` * Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) | Metric | Value | |:--------------------|:-----------| | cosine_accuracy@1 | 0.0557 | | cosine_accuracy@3 | 0.1565 | | cosine_accuracy@5 | 0.2278 | | cosine_accuracy@10 | 0.3913 | | cosine_precision@1 | 0.0557 | | cosine_precision@3 | 0.0522 | | cosine_precision@5 | 0.0456 | | cosine_precision@10 | 0.0391 | | cosine_recall@1 | 0.0557 | | cosine_recall@3 | 0.1565 | | cosine_recall@5 | 0.2278 | | cosine_recall@10 | 0.3913 | | cosine_ndcg@10 | 0.1965 | | cosine_mrr@10 | 0.1377 | | **cosine_map@100** | **0.1621** | #### Information Retrieval * Dataset: `dim_128` * Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) | Metric | Value | |:--------------------|:-----------| | cosine_accuracy@1 | 0.0678 | | cosine_accuracy@3 | 0.1704 | | cosine_accuracy@5 | 0.2574 | | cosine_accuracy@10 | 0.4174 | | cosine_precision@1 | 0.0678 | | cosine_precision@3 | 0.0568 | | cosine_precision@5 | 0.0515 | | cosine_precision@10 | 0.0417 | | cosine_recall@1 | 0.0678 | | cosine_recall@3 | 0.1704 | | cosine_recall@5 | 0.2574 | | cosine_recall@10 | 0.4174 | | cosine_ndcg@10 | 0.2142 | | cosine_mrr@10 | 0.1528 | | **cosine_map@100** | **0.1754** | #### Information Retrieval * Dataset: `dim_64` * Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) | Metric | Value | |:--------------------|:-----------| | cosine_accuracy@1 | 0.0522 | | cosine_accuracy@3 | 0.1461 | | cosine_accuracy@5 | 0.233 | | cosine_accuracy@10 | 0.4035 | | cosine_precision@1 | 0.0522 | | cosine_precision@3 | 0.0487 | | cosine_precision@5 | 0.0466 | | cosine_precision@10 | 0.0403 | | cosine_recall@1 | 0.0522 | | cosine_recall@3 | 0.1461 | | cosine_recall@5 | 0.233 | | cosine_recall@10 | 0.4035 | | cosine_ndcg@10 | 0.1961 | | cosine_mrr@10 | 0.1339 | | **cosine_map@100** | **0.1564** | ## Training Details ### Training Dataset #### json * Dataset: json * Size: 5,175 training samples * Columns: positive and anchor * Approximate statistics based on the first 1000 samples: | | positive | anchor | |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | positive | anchor | |:----------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------| | Aquest tràmit us permet consultar informació de les anotacions d'entrada i sortida que hi consten al registre de l'Ajuntament de Sant Quirze del Vallès. | Quin és el format de les dades de sortida del tràmit? | | Tràmit a través del qual la persona interessada posa en coneixement de l’Ajuntament la voluntat de: ... Renunciar a una llicència prèviament atorgada. | Quin és el resultat de la renúncia a una llicència urbanística prèviament atorgada? | | D’acord amb el plànol d'ubicació de parades: Mercat de diumenges a Les Fonts | Quin és el plànol d'ubicació de parades del mercat de diumenges a Les Fonts? | * Loss: [MatryoshkaLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "loss": "MultipleNegativesRankingLoss", "matryoshka_dims": [ 1024, 768, 512, 256, 128, 64 ], "matryoshka_weights": [ 1, 1, 1, 1, 1, 1 ], "n_dims_per_step": -1 } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: epoch - `per_device_train_batch_size`: 16 - `per_device_eval_batch_size`: 16 - `gradient_accumulation_steps`: 16 - `learning_rate`: 2e-05 - `num_train_epochs`: 5 - `lr_scheduler_type`: cosine - `warmup_ratio`: 0.2 - `bf16`: True - `tf32`: True - `load_best_model_at_end`: True - `optim`: adamw_torch_fused - `batch_sampler`: no_duplicates #### All Hyperparameters
Click to expand - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: epoch - `prediction_loss_only`: True - `per_device_train_batch_size`: 16 - `per_device_eval_batch_size`: 16 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 16 - `eval_accumulation_steps`: None - `torch_empty_cache_steps`: None - `learning_rate`: 2e-05 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 - `num_train_epochs`: 5 - `max_steps`: -1 - `lr_scheduler_type`: cosine - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.2 - `warmup_steps`: 0 - `log_level`: passive - `log_level_replica`: warning - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `save_safetensors`: True - `save_on_each_node`: False - `save_only_model`: False - `restore_callback_states_from_checkpoint`: False - `no_cuda`: False - `use_cpu`: False - `use_mps_device`: False - `seed`: 42 - `data_seed`: None - `jit_mode_eval`: False - `use_ipex`: False - `bf16`: True - `fp16`: False - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: True - `local_rank`: 0 - `ddp_backend`: None - `tpu_num_cores`: None - `tpu_metrics_debug`: False - `debug`: [] - `dataloader_drop_last`: False - `dataloader_num_workers`: 0 - `dataloader_prefetch_factor`: None - `past_index`: -1 - `disable_tqdm`: False - `remove_unused_columns`: True - `label_names`: None - `load_best_model_at_end`: True - `ignore_data_skip`: False - `fsdp`: [] - `fsdp_min_num_params`: 0 - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `fsdp_transformer_layer_cls_to_wrap`: None - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch_fused - `optim_args`: None - `adafactor`: False - `group_by_length`: False - `length_column_name`: length - `ddp_find_unused_parameters`: None - `ddp_bucket_cap_mb`: None - `ddp_broadcast_buffers`: False - `dataloader_pin_memory`: True - `dataloader_persistent_workers`: False - `skip_memory_metrics`: True - `use_legacy_prediction_loop`: False - `push_to_hub`: False - `resume_from_checkpoint`: None - `hub_model_id`: None - `hub_strategy`: every_save - `hub_private_repo`: False - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `eval_do_concat_batches`: True - `fp16_backend`: auto - `push_to_hub_model_id`: None - `push_to_hub_organization`: None - `mp_parameters`: - `auto_find_batch_size`: False - `full_determinism`: False - `torchdynamo`: None - `ray_scope`: last - `ddp_timeout`: 1800 - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `dispatch_batches`: None - `split_batches`: None - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: False - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_eval_metrics`: False - `eval_on_start`: False - `eval_use_gather_object`: False - `batch_sampler`: no_duplicates - `multi_dataset_batch_sampler`: proportional
### Training Logs | Epoch | Step | Training Loss | dim_1024_cosine_map@100 | dim_128_cosine_map@100 | dim_256_cosine_map@100 | dim_512_cosine_map@100 | dim_64_cosine_map@100 | dim_768_cosine_map@100 | |:----------:|:-------:|:-------------:|:-----------------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:| | 0.4938 | 10 | 3.936 | - | - | - | - | - | - | | 0.9877 | 20 | 2.7857 | 0.1550 | 0.1522 | 0.1557 | 0.1507 | 0.1344 | 0.1503 | | 1.4815 | 30 | 1.4901 | - | - | - | - | - | - | | 1.9753 | 40 | 1.3464 | 0.1580 | 0.1654 | 0.1695 | 0.1580 | 0.1510 | 0.1624 | | 2.4691 | 50 | 0.7755 | - | - | - | - | - | - | | 2.9630 | 60 | 0.8553 | 0.1608 | 0.1705 | 0.1647 | 0.1661 | 0.1564 | 0.1689 | | 3.4568 | 70 | 0.5817 | - | - | - | - | - | - | | 3.9506 | 80 | 0.6587 | - | - | - | - | - | - | | 4.0 | 81 | - | 0.1672 | 0.1657 | 0.1620 | 0.1689 | 0.1556 | 0.1669 | | 4.4444 | 90 | 0.4847 | - | - | - | - | - | - | | **4.9383** | **100** | **0.6024** | **0.1688** | **0.1754** | **0.1621** | **0.17** | **0.1564** | **0.1663** | * The bold row denotes the saved checkpoint. ### Framework Versions - Python: 3.10.12 - Sentence Transformers: 3.1.1 - Transformers: 4.44.2 - PyTorch: 2.4.1+cu121 - Accelerate: 0.35.0.dev0 - Datasets: 3.0.1 - Tokenizers: 0.19.1 ## Citation ### BibTeX #### Sentence Transformers ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/1908.10084", } ``` #### MatryoshkaLoss ```bibtex @misc{kusupati2024matryoshka, title={Matryoshka Representation Learning}, author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi}, year={2024}, eprint={2205.13147}, archivePrefix={arXiv}, primaryClass={cs.LG} } ``` #### MultipleNegativesRankingLoss ```bibtex @misc{henderson2017efficient, title={Efficient Natural Language Response Suggestion for Smart Reply}, author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil}, year={2017}, eprint={1705.00652}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```