--- base_model: BAAI/bge-m3 datasets: [] language: [] 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:4173 - loss:MatryoshkaLoss - loss:MultipleNegativesRankingLoss widget: - source_sentence: L'Espai d'escalada és una instal·lació municipal en forma de túnel a una sala interior, amb una llargada de 10m, una amplada de 4,6m i una alçada de 4m. sentences: - Quin és el registre on es comprova la inscripció dels estrangers amb ciutadania de l'Espai Econòmic Europeu? - On es pot trobar les bases generals de les convocatòries de selecció de personal de l'Ajuntament? - Quina és la llargada de l'Espai d'Escalada? - source_sentence: Les activitats s’organitzen per setmanes. sentences: - Quin és el format en què es desenvolupen les activitats de l'Estiu Jove? - Quin és el paper del subjecte passiu en la gestió de pagaments? - Quin és el benefici de les subvencions? - source_sentence: Les Estades Esportives cerquen que els infants aprenguin a relacionar-se i a compartir mitjançant l'esport, experiències i vivències amb d'altres infants amb qui no estan en contacte durant la resta de l'any. sentences: - Quin és el propòsit de l'ajut per a la creació de noves empreses? - Quin és el propòsit de la llicència de parcel·lació? - Quin és el benefici principal de les Estades Esportives? - source_sentence: Declaració tributària mitjançant la qual es sol·licita la baixa d'una activitat de la Taxa pel servei municipal complementari de recollida, tractament i eliminació de residus comercials. sentences: - Quin és el format de la Declaració de baixa? - Quin és el resultat de justificar una sol·licitud de canvi a les estades esportives? - Quin és el període de celebració de la Fira d'Art de Sitges? - source_sentence: Les entitats inscrites en el Registre resten obligades a comunicar a l’Ajuntament qualsevol modificació en les seves dades registrals, podent sol·licitar la seva cancel·lació o comunicant la seva dissolució. sentences: - Quin és el procediment per cancel·lar la inscripció d'una entitat al Registre municipal d'entitats? - Quin és el propòsit de la quota del servei de les Llars d'Infants Municipals? - Quin és el paper de les entitats de protecció dels animals en la gestió de les colònies urbanes felines? 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: 0.08620689655172414 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.21551724137931033 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.3275862068965517 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.5107758620689655 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.08620689655172414 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.07183908045977011 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.06551724137931034 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.05107758620689654 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.08620689655172414 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.21551724137931033 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.3275862068965517 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.5107758620689655 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.26401643418499254 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.1896731321839082 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.2150866107809785 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 768 type: dim_768 metrics: - type: cosine_accuracy@1 value: 0.08405172413793104 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.20905172413793102 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.31896551724137934 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.5 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.08405172413793104 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.069683908045977 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.06379310344827585 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.04999999999999999 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.08405172413793104 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.20905172413793102 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.31896551724137934 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.5 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.2594763687925116 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.18673713738368922 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.21319033477988852 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 512 type: dim_512 metrics: - type: cosine_accuracy@1 value: 0.08620689655172414 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.21120689655172414 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.32112068965517243 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.5129310344827587 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.08620689655172414 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.07040229885057471 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.06422413793103447 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.051293103448275854 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.08620689655172414 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.21120689655172414 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.32112068965517243 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.5129310344827587 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.2646539120704089 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.1899279898741108 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.21554766038692458 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 256 type: dim_256 metrics: - type: cosine_accuracy@1 value: 0.08189655172413793 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.20474137931034483 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.30603448275862066 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.5043103448275862 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.08189655172413793 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.0682471264367816 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.061206896551724135 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.05043103448275862 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.08189655172413793 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.20474137931034483 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.30603448275862066 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.5043103448275862 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.25554093803691474 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.1807856116584566 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.20657861277416045 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 128 type: dim_128 metrics: - type: cosine_accuracy@1 value: 0.08405172413793104 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.20043103448275862 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.3146551724137931 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.49137931034482757 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.08405172413793104 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.0668103448275862 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.06293103448275862 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.04913793103448275 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.08405172413793104 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.20043103448275862 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.3146551724137931 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.49137931034482757 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.2516576518560222 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.1794651409414343 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.20584710715396837 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 64 type: dim_64 metrics: - type: cosine_accuracy@1 value: 0.07974137931034483 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.2025862068965517 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.3017241379310345 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.4956896551724138 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.07974137931034483 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.06752873563218391 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.0603448275862069 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.04956896551724138 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.07974137931034483 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.2025862068965517 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.3017241379310345 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.4956896551724138 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.2527082338557514 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.17959085933223878 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.2058214047481906 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). 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 ### 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/sitgrsBAAIbge-m3-290824") # Run inference sentences = [ 'Les entitats inscrites en el Registre resten obligades a comunicar a l’Ajuntament qualsevol modificació en les seves dades registrals, podent sol·licitar la seva cancel·lació o comunicant la seva dissolució.', "Quin és el procediment per cancel·lar la inscripció d'una entitat al Registre municipal d'entitats?", 'Quin és el paper de les entitats de protecció dels animals en la gestió de les colònies urbanes felines?', ] 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 | 0.0862 | | cosine_accuracy@3 | 0.2155 | | cosine_accuracy@5 | 0.3276 | | cosine_accuracy@10 | 0.5108 | | cosine_precision@1 | 0.0862 | | cosine_precision@3 | 0.0718 | | cosine_precision@5 | 0.0655 | | cosine_precision@10 | 0.0511 | | cosine_recall@1 | 0.0862 | | cosine_recall@3 | 0.2155 | | cosine_recall@5 | 0.3276 | | cosine_recall@10 | 0.5108 | | cosine_ndcg@10 | 0.264 | | cosine_mrr@10 | 0.1897 | | **cosine_map@100** | **0.2151** | #### 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 | 0.0841 | | cosine_accuracy@3 | 0.2091 | | cosine_accuracy@5 | 0.319 | | cosine_accuracy@10 | 0.5 | | cosine_precision@1 | 0.0841 | | cosine_precision@3 | 0.0697 | | cosine_precision@5 | 0.0638 | | cosine_precision@10 | 0.05 | | cosine_recall@1 | 0.0841 | | cosine_recall@3 | 0.2091 | | cosine_recall@5 | 0.319 | | cosine_recall@10 | 0.5 | | cosine_ndcg@10 | 0.2595 | | cosine_mrr@10 | 0.1867 | | **cosine_map@100** | **0.2132** | #### 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 | 0.0862 | | cosine_accuracy@3 | 0.2112 | | cosine_accuracy@5 | 0.3211 | | cosine_accuracy@10 | 0.5129 | | cosine_precision@1 | 0.0862 | | cosine_precision@3 | 0.0704 | | cosine_precision@5 | 0.0642 | | cosine_precision@10 | 0.0513 | | cosine_recall@1 | 0.0862 | | cosine_recall@3 | 0.2112 | | cosine_recall@5 | 0.3211 | | cosine_recall@10 | 0.5129 | | cosine_ndcg@10 | 0.2647 | | cosine_mrr@10 | 0.1899 | | **cosine_map@100** | **0.2155** | #### 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 | 0.0819 | | cosine_accuracy@3 | 0.2047 | | cosine_accuracy@5 | 0.306 | | cosine_accuracy@10 | 0.5043 | | cosine_precision@1 | 0.0819 | | cosine_precision@3 | 0.0682 | | cosine_precision@5 | 0.0612 | | cosine_precision@10 | 0.0504 | | cosine_recall@1 | 0.0819 | | cosine_recall@3 | 0.2047 | | cosine_recall@5 | 0.306 | | cosine_recall@10 | 0.5043 | | cosine_ndcg@10 | 0.2555 | | cosine_mrr@10 | 0.1808 | | **cosine_map@100** | **0.2066** | #### 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 | 0.0841 | | cosine_accuracy@3 | 0.2004 | | cosine_accuracy@5 | 0.3147 | | cosine_accuracy@10 | 0.4914 | | cosine_precision@1 | 0.0841 | | cosine_precision@3 | 0.0668 | | cosine_precision@5 | 0.0629 | | cosine_precision@10 | 0.0491 | | cosine_recall@1 | 0.0841 | | cosine_recall@3 | 0.2004 | | cosine_recall@5 | 0.3147 | | cosine_recall@10 | 0.4914 | | cosine_ndcg@10 | 0.2517 | | cosine_mrr@10 | 0.1795 | | **cosine_map@100** | **0.2058** | #### 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 | 0.0797 | | cosine_accuracy@3 | 0.2026 | | cosine_accuracy@5 | 0.3017 | | cosine_accuracy@10 | 0.4957 | | cosine_precision@1 | 0.0797 | | cosine_precision@3 | 0.0675 | | cosine_precision@5 | 0.0603 | | cosine_precision@10 | 0.0496 | | cosine_recall@1 | 0.0797 | | cosine_recall@3 | 0.2026 | | cosine_recall@5 | 0.3017 | | cosine_recall@10 | 0.4957 | | cosine_ndcg@10 | 0.2527 | | cosine_mrr@10 | 0.1796 | | **cosine_map@100** | **0.2058** | ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 4,173 training samples * Columns: positive and anchor * Approximate statistics based on the first 1000 samples: | | positive | anchor | |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | positive | anchor | |:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------| | Els ajuts per a la realització d'activitats en el lleure esportiu estan destinats a les entitats sense ànim de lucre que desenvolupen activitats esportives i de lleure. | Quins són els sectors que es beneficien dels ajuts? | | En el certificat s'indiquen les dades de planejament vigent, classificació del sòl, qualificació urbanística, condicions de l’edificació i usos admesos referides a una finca o solar concreta. | Quin és el contingut de les condicions de l'edificació en el certificat d'aprofitament urbanístic? | | Aportació de documentació. Ajuts per compensar la disminució d'ingressos de les empreses o establiments del sector de l'hosteleria i restauració afectats per les mesures adoptades per la situació de crisis provocada pel SARS-CoV2 | Quin és el paper dels ajuts en la situació de crisis? | * 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 - `num_train_epochs`: 10 - `lr_scheduler_type`: cosine - `warmup_ratio`: 0.2 - `bf16`: True - `tf32`: False - `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 - `learning_rate`: 5e-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`: 10 - `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`: False - `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 - `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 | |:----------:|:-------:|:-------------:|:-----------------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:| | 0.6130 | 10 | 3.0594 | - | - | - | - | - | - | | 0.9808 | 16 | - | 0.2047 | 0.1922 | 0.2020 | 0.2016 | 0.1774 | 0.2115 | | 1.2261 | 20 | 1.525 | - | - | - | - | - | - | | 1.8391 | 30 | 0.7434 | - | - | - | - | - | - | | 1.9617 | 32 | - | 0.2186 | 0.2003 | 0.2102 | 0.2092 | 0.1870 | 0.2101 | | 2.4521 | 40 | 0.4451 | - | - | - | - | - | - | | 2.9425 | 48 | - | 0.2083 | 0.2054 | 0.2091 | 0.2118 | 0.2009 | 0.2140 | | 3.0651 | 50 | 0.2518 | - | - | - | - | - | - | | 3.6782 | 60 | 0.1801 | - | - | - | - | - | - | | 3.9847 | 65 | - | 0.2135 | 0.2071 | 0.2037 | 0.2115 | 0.2030 | 0.2191 | | 4.2912 | 70 | 0.1483 | - | - | - | - | - | - | | 4.9042 | 80 | 0.0893 | - | - | - | - | - | - | | 4.9655 | 81 | - | 0.2066 | 0.2053 | 0.2057 | 0.2137 | 0.1982 | 0.2176 | | 5.5172 | 90 | 0.0748 | - | - | - | - | - | - | | 5.9464 | 97 | - | 0.2171 | 0.2113 | 0.2086 | 0.2178 | 0.2120 | 0.2193 | | 6.1303 | 100 | 0.064 | - | - | - | - | - | - | | 6.7433 | 110 | 0.0458 | - | - | - | - | - | - | | 6.9885 | 114 | - | 0.2294 | 0.2132 | 0.2151 | 0.2227 | 0.2054 | 0.2138 | | 7.3563 | 120 | 0.0436 | - | - | - | - | - | - | | 7.9693 | 130 | 0.0241 | 0.2133 | 0.2083 | 0.2096 | 0.2138 | 0.2080 | 0.2124 | | 8.5824 | 140 | 0.021 | - | - | - | - | - | - | | **8.9502** | **146** | **-** | **0.216** | **0.2074** | **0.2081** | **0.2162** | **0.2094** | **0.2177** | | 9.1954 | 150 | 0.0237 | - | - | - | - | - | - | | 9.8084 | 160 | 0.0145 | 0.2151 | 0.2058 | 0.2066 | 0.2155 | 0.2058 | 0.2132 | * The bold row denotes the saved checkpoint. ### Framework Versions - Python: 3.10.12 - Sentence Transformers: 3.0.1 - Transformers: 4.42.4 - PyTorch: 2.4.0+cu121 - Accelerate: 0.34.0.dev0 - Datasets: 2.21.0 - 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} } ```