--- 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:828 - loss:MatryoshkaLoss - loss:MultipleNegativesRankingLoss widget: - source_sentence: Comunicació prèvia per l'execució de cales, pous i sondejos, en terreny privat, previs a l'actuació definitiva. sentences: - Quin és el requisit per a l'execució de les obres en terreny privat? - Quin és el propòsit del tràmit de rectificació de dades personals? - Quin és el requisit per a la crema en zones de conservació? - source_sentence: En el mateix tràmit també es pot actualitzar el canvi de domicili o dades personals, si escau. sentences: - Quins tributs puc domiciliar amb aquest tràmit? - Quin és el compromís del titular de l'activitat en la Declaració responsable? - Quin és el tràmit que permet actualitzar les dades personals? - source_sentence: El reconeixement administratiu del dret comunicat es produeix salvat el dret de propietat, sens perjudici del de tercers ni de les competències d’altres organismes i administracions. sentences: - Quin és el tràmit que permet una major transparència en la gestió dels animals domèstics? - Quin és el requisit per considerar una tala de masses arbòries? - Quin és el reconeixement administratiu del dret comunicat? - source_sentence: El seu objecte és que -prèviament a la seva execució material- l'Ajuntament comprovi l'adequació de l’actuació a la normativa i planejament, així com a les ordenances municipals. sentences: - Quin és el resultat de rectificar les meves dades personals? - Quin és el paper de les llicències urbanístiques en la instal·lació de construccions auxiliars o mòduls prefabricats? - Quin és l'objectiu de l'Ajuntament en aquest tràmit? - source_sentence: 'Permet sol·licitar l’autorització per a l’ús comú especial de la via pública per a reserves temporals d’estacionament i espai públic per: càrrega/descàrrega de materials diversos davant d''una obra;' sentences: - Quin és el propòsit de les actuacions de manteniment d'elements de façana i cobertes? - Quin és el tràmit per canviar el domicili del permís de conducció i del permís de circulació? - Quins són els materials que es poden càrregar/descarregar en l'ocupació i reserves temporals amb càrrega/descàrrega de materials? 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: 0.18478260869565216 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.5108695652173914 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.6304347826086957 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.7065217391304348 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.18478260869565216 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.17028985507246377 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.1260869565217391 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.07065217391304346 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.18478260869565216 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.5108695652173914 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.6304347826086957 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.7065217391304348 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.44954688371582935 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.3659981021394064 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.37514635687986436 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 768 type: dim_768 metrics: - type: cosine_accuracy@1 value: 0.20652173913043478 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.5217391304347826 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.6195652173913043 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.7065217391304348 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.20652173913043478 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.17391304347826086 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.12391304347826085 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.07065217391304346 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.20652173913043478 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.5217391304347826 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.6195652173913043 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.7065217391304348 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.45516703581266765 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.37413733609385785 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.3836171669286929 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 512 type: dim_512 metrics: - type: cosine_accuracy@1 value: 0.1956521739130435 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.5 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.5869565217391305 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.6630434782608695 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.1956521739130435 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.16666666666666669 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.11739130434782606 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.06630434782608695 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.1956521739130435 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.5 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.5869565217391305 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.6630434782608695 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.43246256156462615 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.357651828847481 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.36914470440220704 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 256 type: dim_256 metrics: - type: cosine_accuracy@1 value: 0.18478260869565216 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.5108695652173914 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.5978260869565217 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.6847826086956522 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.18478260869565216 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.17028985507246377 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.11956521739130431 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.06847826086956521 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.18478260869565216 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.5108695652173914 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.5978260869565217 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.6847826086956522 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.43256404920188013 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.3512983091787439 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.3600643856606516 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 128 type: dim_128 metrics: - type: cosine_accuracy@1 value: 0.14130434782608695 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.391304347826087 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.5434782608695652 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.6521739130434783 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.14130434782608695 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.13043478260869565 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.10869565217391303 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.06521739130434781 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.14130434782608695 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.391304347826087 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.5434782608695652 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.6521739130434783 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.3875392345536741 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.3032738095238095 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.31305191069743293 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 64 type: dim_64 metrics: - type: cosine_accuracy@1 value: 0.13043478260869565 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.32608695652173914 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.42391304347826086 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.5760869565217391 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.13043478260869565 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.10869565217391304 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.08478260869565218 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.0576086956521739 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.13043478260869565 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.32608695652173914 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.42391304347826086 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.5760869565217391 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.330379527375251 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.25482660455486533 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.2660220568888923 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-v3-10ep") # Run inference sentences = [ "Permet sol·licitar l’autorització per a l’ús comú especial de la via pública per a reserves temporals d’estacionament i espai públic per: càrrega/descàrrega de materials diversos davant d'una obra;", "Quins són els materials que es poden càrregar/descarregar en l'ocupació i reserves temporals amb càrrega/descàrrega de materials?", 'Quin és el tràmit per canviar el domicili del permís de conducció i del permís de circulació?', ] 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 | 0.1848 | | cosine_accuracy@3 | 0.5109 | | cosine_accuracy@5 | 0.6304 | | cosine_accuracy@10 | 0.7065 | | cosine_precision@1 | 0.1848 | | cosine_precision@3 | 0.1703 | | cosine_precision@5 | 0.1261 | | cosine_precision@10 | 0.0707 | | cosine_recall@1 | 0.1848 | | cosine_recall@3 | 0.5109 | | cosine_recall@5 | 0.6304 | | cosine_recall@10 | 0.7065 | | cosine_ndcg@10 | 0.4495 | | cosine_mrr@10 | 0.366 | | **cosine_map@100** | **0.3751** | #### 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 | 0.2065 | | cosine_accuracy@3 | 0.5217 | | cosine_accuracy@5 | 0.6196 | | cosine_accuracy@10 | 0.7065 | | cosine_precision@1 | 0.2065 | | cosine_precision@3 | 0.1739 | | cosine_precision@5 | 0.1239 | | cosine_precision@10 | 0.0707 | | cosine_recall@1 | 0.2065 | | cosine_recall@3 | 0.5217 | | cosine_recall@5 | 0.6196 | | cosine_recall@10 | 0.7065 | | cosine_ndcg@10 | 0.4552 | | cosine_mrr@10 | 0.3741 | | **cosine_map@100** | **0.3836** | #### 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 | 0.1957 | | cosine_accuracy@3 | 0.5 | | cosine_accuracy@5 | 0.587 | | cosine_accuracy@10 | 0.663 | | cosine_precision@1 | 0.1957 | | cosine_precision@3 | 0.1667 | | cosine_precision@5 | 0.1174 | | cosine_precision@10 | 0.0663 | | cosine_recall@1 | 0.1957 | | cosine_recall@3 | 0.5 | | cosine_recall@5 | 0.587 | | cosine_recall@10 | 0.663 | | cosine_ndcg@10 | 0.4325 | | cosine_mrr@10 | 0.3577 | | **cosine_map@100** | **0.3691** | #### 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 | 0.1848 | | cosine_accuracy@3 | 0.5109 | | cosine_accuracy@5 | 0.5978 | | cosine_accuracy@10 | 0.6848 | | cosine_precision@1 | 0.1848 | | cosine_precision@3 | 0.1703 | | cosine_precision@5 | 0.1196 | | cosine_precision@10 | 0.0685 | | cosine_recall@1 | 0.1848 | | cosine_recall@3 | 0.5109 | | cosine_recall@5 | 0.5978 | | cosine_recall@10 | 0.6848 | | cosine_ndcg@10 | 0.4326 | | cosine_mrr@10 | 0.3513 | | **cosine_map@100** | **0.3601** | #### 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 | 0.1413 | | cosine_accuracy@3 | 0.3913 | | cosine_accuracy@5 | 0.5435 | | cosine_accuracy@10 | 0.6522 | | cosine_precision@1 | 0.1413 | | cosine_precision@3 | 0.1304 | | cosine_precision@5 | 0.1087 | | cosine_precision@10 | 0.0652 | | cosine_recall@1 | 0.1413 | | cosine_recall@3 | 0.3913 | | cosine_recall@5 | 0.5435 | | cosine_recall@10 | 0.6522 | | cosine_ndcg@10 | 0.3875 | | cosine_mrr@10 | 0.3033 | | **cosine_map@100** | **0.3131** | #### 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 | 0.1304 | | cosine_accuracy@3 | 0.3261 | | cosine_accuracy@5 | 0.4239 | | cosine_accuracy@10 | 0.5761 | | cosine_precision@1 | 0.1304 | | cosine_precision@3 | 0.1087 | | cosine_precision@5 | 0.0848 | | cosine_precision@10 | 0.0576 | | cosine_recall@1 | 0.1304 | | cosine_recall@3 | 0.3261 | | cosine_recall@5 | 0.4239 | | cosine_recall@10 | 0.5761 | | cosine_ndcg@10 | 0.3304 | | cosine_mrr@10 | 0.2548 | | **cosine_map@100** | **0.266** | ## Training Details ### Training Dataset #### json * Dataset: json * Size: 828 training samples * Columns: positive and anchor * Approximate statistics based on the first 828 samples: | | positive | anchor | |:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | positive | anchor | |:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------| | Consultar l'estat tributari d'un contribuent. Us permet consultar l'estat dels rebuts i liquidacions que estan a nom del contribuent titular d'un certificat electrònic, així com els elements que configuren el càlcul per determinar el deute tributari de cadascun d'ells. | Com puc consultar l'estat tributari d'un contribuent? | | L'informe facultatiu servirà per tramitar una autorització de residència temporal per arrelament social. | Quin és el tràmit relacionat amb la residència a l'Ajuntament? | | Aquesta targeta, és el document que dona dret a persones físiques o jurídiques titulars de vehicles adaptats destinats al transport col·lectiu de persones amb discapacitat... | Quin és el benefici de tenir la targeta d'aparcament de transport col·lectiu per a les persones amb discapacitat? | * 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`: 10 - `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`: 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`: 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 | |:----------:|:-----:|:-------------:|:-----------------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:| | **0.9231** | **3** | **-** | **0.3751** | **0.3131** | **0.3601** | **0.3691** | **0.266** | **0.3836** | | 1.8462 | 6 | - | 0.3751 | 0.3131 | 0.3601 | 0.3691 | 0.2660 | 0.3836 | | 2.7692 | 9 | - | 0.3751 | 0.3131 | 0.3601 | 0.3691 | 0.2660 | 0.3836 | | 3.0769 | 10 | 0.6783 | - | - | - | - | - | - | | 4.0 | 13 | - | 0.3751 | 0.3131 | 0.3601 | 0.3691 | 0.2660 | 0.3836 | | 4.9231 | 16 | - | 0.3751 | 0.3131 | 0.3601 | 0.3691 | 0.2660 | 0.3836 | | 5.8462 | 19 | - | 0.3751 | 0.3131 | 0.3601 | 0.3691 | 0.2660 | 0.3836 | | 6.1538 | 20 | 0.2906 | - | - | - | - | - | - | | 6.7692 | 22 | - | 0.3751 | 0.3131 | 0.3601 | 0.3691 | 0.2660 | 0.3836 | | 8.0 | 26 | - | 0.3751 | 0.3131 | 0.3601 | 0.3691 | 0.2660 | 0.3836 | | 8.9231 | 29 | - | 0.3751 | 0.3131 | 0.3601 | 0.3691 | 0.2660 | 0.3836 | | 9.2308 | 30 | 0.1565 | 0.3751 | 0.3131 | 0.3601 | 0.3691 | 0.2660 | 0.3836 | * 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} } ```