--- 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:5520 - loss:MatryoshkaLoss - loss:MultipleNegativesRankingLoss widget: - source_sentence: Pagar un rebut o una liquidació pendent de pagament sentences: - Què és el tràmit per pagar un rebut o liquidació? - Quin és el tràmit que permet la inscripció d'una entitat o associació? - Quin és el límit de temps per a la instal·lació de tanques provisionals? - source_sentence: Mitjançant decret de data 11/10/2022 núm. 202204494 s'inicia el procés de concurrència competitiva per accedir a les parades vacants del mercat de les Fonts. sentences: - Quin és el mercat on es va iniciar el procés de concurrència competitiva per accedir a les parades vacants? - Puc sol·licitar un certificat històric d'empadronament per a una persona que ja no viu al municipi? - Necessito obtenir un duplicat del títol de dret funerari perquè he perdut l'original - source_sentence: Comunicar les dades per realitzar la notificació electrònica de tots els procediments en què l’obligat legal sigui titular o part implicada, i hagi de ser notificat o notificada. sentences: - Quin és el paper de l'Ajuntament en la inspecció de les condicions específiques? - Quin és el tràmit relacionat amb la targeta ciutadana de serveis? - Qui és el titular o part implicada en els procediments? - source_sentence: Aquest tràmit permet sol·licitar l'informe municipal sobre la integració social de persones estrangeres. sentences: - Puc canviar la concessió del meu dret funerari per una raó específica? - Quin és el procediment per a obtenir l'informe d'inserció social? - Quin és el propòsit de la formació en higiene alimentària - source_sentence: Permet tramitar la baixa de les activitats esportives municipals. sentences: - Quin és el procés per a donar de baixa una activitat esportiva? - On es pot recollir els dorsals el dia de la cursa? - Quin és el benefici fiscal que es pot obtenir? 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: 0.1 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.22608695652173913 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.30434782608695654 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.4956521739130435 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.1 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.0753623188405797 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.060869565217391314 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.04956521739130433 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.1 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.22608695652173913 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.30434782608695654 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.4956521739130435 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.2644535096144644 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.19486714975845426 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.21422014718167715 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 768 type: dim_768 metrics: - type: cosine_accuracy@1 value: 0.1 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.21304347826086956 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.3 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.49130434782608695 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.1 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.07101449275362319 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.06000000000000001 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.04913043478260868 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.1 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.21304347826086956 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.3 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.49130434782608695 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.2611989525147102 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.19224465148378198 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.21168860407432996 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 512 type: dim_512 metrics: - type: cosine_accuracy@1 value: 0.09565217391304348 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.25217391304347825 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.3217391304347826 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.5043478260869565 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.09565217391304348 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.08405797101449275 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.06434782608695652 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.05043478260869564 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.09565217391304348 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.25217391304347825 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.3217391304347826 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.5043478260869565 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.2736727362077943 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.20330400276052454 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.2225493022129085 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 256 type: dim_256 metrics: - type: cosine_accuracy@1 value: 0.09130434782608696 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.24347826086956523 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.32608695652173914 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.4782608695652174 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.09130434782608696 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.08115942028985507 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.06521739130434782 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.04782608695652173 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.09130434782608696 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.24347826086956523 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.32608695652173914 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.4782608695652174 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.25842339032219125 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.19112146307798494 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.21262325852877148 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 128 type: dim_128 metrics: - type: cosine_accuracy@1 value: 0.09565217391304348 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.2217391304347826 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.32608695652173914 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.5130434782608696 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.09565217391304348 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.07391304347826087 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.06521739130434782 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.05130434782608694 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.09565217391304348 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.2217391304347826 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.32608695652173914 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.5130434782608696 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.2703816814799584 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.1968685300207041 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.21575875323163748 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 64 type: dim_64 metrics: - type: cosine_accuracy@1 value: 0.10434782608695652 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.23478260869565218 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.3217391304347826 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.49130434782608695 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.10434782608695652 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.0782608695652174 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.06434782608695652 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.049130434782608694 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.10434782608695652 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.23478260869565218 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.3217391304347826 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.49130434782608695 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.268671836286108 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.20097135955831624 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.22058427749634182 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-v5-5ep") # Run inference sentences = [ 'Permet tramitar la baixa de les activitats esportives municipals.', 'Quin és el procés per a donar de baixa una activitat esportiva?', 'Quin és el benefici fiscal que es pot obtenir?', ] 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 | 0.1 | | cosine_accuracy@3 | 0.2261 | | cosine_accuracy@5 | 0.3043 | | cosine_accuracy@10 | 0.4957 | | cosine_precision@1 | 0.1 | | cosine_precision@3 | 0.0754 | | cosine_precision@5 | 0.0609 | | cosine_precision@10 | 0.0496 | | cosine_recall@1 | 0.1 | | cosine_recall@3 | 0.2261 | | cosine_recall@5 | 0.3043 | | cosine_recall@10 | 0.4957 | | cosine_ndcg@10 | 0.2645 | | cosine_mrr@10 | 0.1949 | | **cosine_map@100** | **0.2142** | #### 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 | 0.1 | | cosine_accuracy@3 | 0.213 | | cosine_accuracy@5 | 0.3 | | cosine_accuracy@10 | 0.4913 | | cosine_precision@1 | 0.1 | | cosine_precision@3 | 0.071 | | cosine_precision@5 | 0.06 | | cosine_precision@10 | 0.0491 | | cosine_recall@1 | 0.1 | | cosine_recall@3 | 0.213 | | cosine_recall@5 | 0.3 | | cosine_recall@10 | 0.4913 | | cosine_ndcg@10 | 0.2612 | | cosine_mrr@10 | 0.1922 | | **cosine_map@100** | **0.2117** | #### 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 | 0.0957 | | cosine_accuracy@3 | 0.2522 | | cosine_accuracy@5 | 0.3217 | | cosine_accuracy@10 | 0.5043 | | cosine_precision@1 | 0.0957 | | cosine_precision@3 | 0.0841 | | cosine_precision@5 | 0.0643 | | cosine_precision@10 | 0.0504 | | cosine_recall@1 | 0.0957 | | cosine_recall@3 | 0.2522 | | cosine_recall@5 | 0.3217 | | cosine_recall@10 | 0.5043 | | cosine_ndcg@10 | 0.2737 | | cosine_mrr@10 | 0.2033 | | **cosine_map@100** | **0.2225** | #### 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 | 0.0913 | | cosine_accuracy@3 | 0.2435 | | cosine_accuracy@5 | 0.3261 | | cosine_accuracy@10 | 0.4783 | | cosine_precision@1 | 0.0913 | | cosine_precision@3 | 0.0812 | | cosine_precision@5 | 0.0652 | | cosine_precision@10 | 0.0478 | | cosine_recall@1 | 0.0913 | | cosine_recall@3 | 0.2435 | | cosine_recall@5 | 0.3261 | | cosine_recall@10 | 0.4783 | | cosine_ndcg@10 | 0.2584 | | cosine_mrr@10 | 0.1911 | | **cosine_map@100** | **0.2126** | #### 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 | 0.0957 | | cosine_accuracy@3 | 0.2217 | | cosine_accuracy@5 | 0.3261 | | cosine_accuracy@10 | 0.513 | | cosine_precision@1 | 0.0957 | | cosine_precision@3 | 0.0739 | | cosine_precision@5 | 0.0652 | | cosine_precision@10 | 0.0513 | | cosine_recall@1 | 0.0957 | | cosine_recall@3 | 0.2217 | | cosine_recall@5 | 0.3261 | | cosine_recall@10 | 0.513 | | cosine_ndcg@10 | 0.2704 | | cosine_mrr@10 | 0.1969 | | **cosine_map@100** | **0.2158** | #### 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 | 0.1043 | | cosine_accuracy@3 | 0.2348 | | cosine_accuracy@5 | 0.3217 | | cosine_accuracy@10 | 0.4913 | | cosine_precision@1 | 0.1043 | | cosine_precision@3 | 0.0783 | | cosine_precision@5 | 0.0643 | | cosine_precision@10 | 0.0491 | | cosine_recall@1 | 0.1043 | | cosine_recall@3 | 0.2348 | | cosine_recall@5 | 0.3217 | | cosine_recall@10 | 0.4913 | | cosine_ndcg@10 | 0.2687 | | cosine_mrr@10 | 0.201 | | **cosine_map@100** | **0.2206** | ## Training Details ### Training Dataset #### json * Dataset: json * Size: 5,520 training samples * Columns: positive and anchor * Approximate statistics based on the first 1000 samples: | | positive | anchor | |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | positive | anchor | |:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------| | L’Ajuntament vol crear un banc de recursos on recollir tots els oferiments de la població i que servirà per atendre les necessitats de les famílies refugiades acollides al poble. | Quin és el paper de l’Ajuntament en la integració de les persones refugiades acollides? | | Aquest tipus d'actuació requereix la intervenció d'una persona tècnica competent que subscrigui el projecte o la documentació tècnica corresponent i que assumeixi la direcció facultativa de l'execució de les obres. | Quin és el requisit per a la intervenció d'una persona tècnica competent en les obres d'intervenció parcial interior en edificis amb elements catalogats? | | Aquest títol, adreçat a persones empadronades a Sant Quirze del Vallès, es concedirà segons el nivell d’ingressos, la condició d’edat o de discapacitat, en base als criteris específics que recull l’ordenança reguladora del sistema de tarifació social del transport públic municipal en autobús a Sant Quirze del Vallès. | Quin és el benefici de la TBUS GRATUÏTA per a les persones majors? | * 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 | |:----------:|:-------:|:-------------:|:-----------------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:| | 0.4638 | 10 | 4.122 | - | - | - | - | - | - | | 0.9275 | 20 | 2.7131 | - | - | - | - | - | - | | 0.9739 | 21 | - | 0.2085 | 0.1973 | 0.1884 | 0.2087 | 0.1886 | 0.2177 | | 1.3913 | 30 | 1.6964 | - | - | - | - | - | - | | 1.8551 | 40 | 1.2311 | - | - | - | - | - | - | | 1.9942 | 43 | - | 0.2148 | 0.2135 | 0.2170 | 0.2351 | 0.2091 | 0.2386 | | 2.3188 | 50 | 0.9216 | - | - | - | - | - | - | | 2.7826 | 60 | 0.737 | - | - | - | - | - | - | | 2.9681 | 64 | - | 0.2145 | 0.2058 | 0.2072 | 0.2277 | 0.2127 | 0.2085 | | 3.2464 | 70 | 0.6678 | - | - | - | - | - | - | | 3.7101 | 80 | 0.555 | - | - | - | - | - | - | | 3.9884 | 86 | - | 0.2028 | 0.2154 | 0.2117 | 0.2331 | 0.2113 | 0.2028 | | 4.1739 | 90 | 0.5542 | - | - | - | - | - | - | | 4.6377 | 100 | 0.5058 | - | - | - | - | - | - | | **4.8696** | **105** | **-** | **0.2142** | **0.2158** | **0.2126** | **0.2225** | **0.2206** | **0.2117** | * 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} } ```