--- 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:9593 - loss:MatryoshkaLoss - loss:MultipleNegativesRankingLoss widget: - source_sentence: Aquestes parades estaran ocupades per empreses del sector, entitats socials i culturals i centres escolars amb seu a Sitges, o empreses del sector amb activitat a Sitges, que prèviament han fet la sol·licitud, se'ls ha autoritzat i, si escau, han abonat la taxa corresponent. sentences: - Quin és el paper de les petites empreses i persones autònomes en aquests ajuts? - Quin és el tràmit que permet sol·licitar una nova placa de gual? - Quin és el requisit per a l'ocupació de les parades de la Fira de Sant Jordi? - source_sentence: L'Ajuntament de Sitges atorga subvencions pels projectes educatius que realitzen les escoles de Sitges que tinguin com a finalitat augmentar la qualitat educativa dels infants d'infantil i primària al llarg de l’exercici pel qual es sol·licita la subvenció. sentences: - Quin és el paper de la targeta 'smart Sitges' en la gestió de residus? - Quin és el requisit per rebre ajuts econòmics per la meva empresa en dificultats econòmiques? - Quin és el resultat esperat de les subvencions per a les escoles? - source_sentence: ocupades per empreses del sector i entitats culturals, amb activitat editorial acreditada sentences: - Quin és el percentatge de bonificació per als carrers i locals afectats indirectament? - Quin és el propòsit de presentar documents en un procés de selecció de personal de l'Ajuntament de Sitges? - Quin és el lloc on es troben les empreses del sector que participen en la Fira de la Vila del Llibre de Sitges? - source_sentence: Aquest tràmit permet a les persones interessades la presentació d'al·legacions i/o la interposició de recursos contra actes administratius dictats per l'Ajuntament de Sitges. sentences: - Quin és el tràmit per presentar una al·legació contra una decisió de l'Ajuntament de Sitges? - Quin és el benefici de la llicència per a obres a la via pública - Com puc promoure l'esport a la ciutat? - source_sentence: 'Per valorar l’interès de la proposta es tindrà en compte: Tipus d’activitat Antecedents Dates de celebració Accions de promoció dutes a terme des de l’organització Nivell de molèstia previst i interferència en la vida quotidiana.' sentences: - Quin és el benefici de la realització d'exposicions al Centre Cultural Miramar? - Quin és el paper de les accions de promoció en les subvencions per a projectes i activitats de l'àmbit turístic? - Quins són els productes que es venen al Mercat setmanal dels dijous? 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: 0.05909943714821764 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.1275797373358349 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.17354596622889307 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.2861163227016886 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.05909943714821764 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.04252657911194496 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.03470919324577861 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.028611632270168854 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.05909943714821764 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.1275797373358349 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.17354596622889307 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.2861163227016886 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.1537318058278305 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.11394435510289168 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.1397865116884934 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 768 type: dim_768 metrics: - type: cosine_accuracy@1 value: 0.05909943714821764 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.12570356472795496 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.1801125703564728 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.2945590994371482 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.05909943714821764 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.04190118824265165 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.036022514071294566 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.02945590994371482 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.05909943714821764 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.12570356472795496 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.1801125703564728 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.2945590994371482 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.15635010592942117 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.1149472140325799 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.14049204491324296 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 512 type: dim_512 metrics: - type: cosine_accuracy@1 value: 0.05909943714821764 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.12570356472795496 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.17073170731707318 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.29831144465290804 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.05909943714821764 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.04190118824265165 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.03414634146341463 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.029831144465290803 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.05909943714821764 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.12570356472795496 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.17073170731707318 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.29831144465290804 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.1571277123670345 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.1149557759313857 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.1397328880376811 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 256 type: dim_256 metrics: - type: cosine_accuracy@1 value: 0.051594746716697934 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.12101313320825516 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.16791744840525327 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.28893058161350843 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.051594746716697934 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.040337711069418386 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.03358348968105066 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.028893058161350845 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.051594746716697934 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.12101313320825516 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.16791744840525327 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.28893058161350843 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.14978486884903933 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.1081955984395009 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.13375931969408872 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 128 type: dim_128 metrics: - type: cosine_accuracy@1 value: 0.051594746716697934 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.11726078799249531 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.17166979362101314 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.28893058161350843 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.051594746716697934 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.039086929330831764 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.034333958724202626 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.028893058161350845 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.051594746716697934 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.11726078799249531 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.17166979362101314 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.28893058161350843 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.14877654954358344 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.1068536138658091 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.13283061923015374 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 64 type: dim_64 metrics: - type: cosine_accuracy@1 value: 0.05065666041275797 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.1125703564727955 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.16416510318949343 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.28236397748592873 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.05065666041275797 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.0375234521575985 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.03283302063789869 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.02823639774859287 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.05065666041275797 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.1125703564727955 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.16416510318949343 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.28236397748592873 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.14493487779487546 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.10395931981297837 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.1306497575595095 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-m3-300824") # Run inference sentences = [ 'Per valorar l’interès de la proposta es tindrà en compte: Tipus d’activitat Antecedents Dates de celebració Accions de promoció dutes a terme des de l’organització Nivell de molèstia previst i interferència en la vida quotidiana.', "Quin és el paper de les accions de promoció en les subvencions per a projectes i activitats de l'àmbit turístic?", "Quin és el benefici de la realització d'exposicions al Centre Cultural Miramar?", ] 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 | 0.0591 | | cosine_accuracy@3 | 0.1276 | | cosine_accuracy@5 | 0.1735 | | cosine_accuracy@10 | 0.2861 | | cosine_precision@1 | 0.0591 | | cosine_precision@3 | 0.0425 | | cosine_precision@5 | 0.0347 | | cosine_precision@10 | 0.0286 | | cosine_recall@1 | 0.0591 | | cosine_recall@3 | 0.1276 | | cosine_recall@5 | 0.1735 | | cosine_recall@10 | 0.2861 | | cosine_ndcg@10 | 0.1537 | | cosine_mrr@10 | 0.1139 | | **cosine_map@100** | **0.1398** | #### 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 | 0.1257 | | cosine_accuracy@5 | 0.1801 | | cosine_accuracy@10 | 0.2946 | | cosine_precision@1 | 0.0591 | | cosine_precision@3 | 0.0419 | | cosine_precision@5 | 0.036 | | cosine_precision@10 | 0.0295 | | cosine_recall@1 | 0.0591 | | cosine_recall@3 | 0.1257 | | cosine_recall@5 | 0.1801 | | cosine_recall@10 | 0.2946 | | cosine_ndcg@10 | 0.1564 | | cosine_mrr@10 | 0.1149 | | **cosine_map@100** | **0.1405** | #### 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 | 0.0591 | | cosine_accuracy@3 | 0.1257 | | cosine_accuracy@5 | 0.1707 | | cosine_accuracy@10 | 0.2983 | | cosine_precision@1 | 0.0591 | | cosine_precision@3 | 0.0419 | | cosine_precision@5 | 0.0341 | | cosine_precision@10 | 0.0298 | | cosine_recall@1 | 0.0591 | | cosine_recall@3 | 0.1257 | | cosine_recall@5 | 0.1707 | | cosine_recall@10 | 0.2983 | | cosine_ndcg@10 | 0.1571 | | cosine_mrr@10 | 0.115 | | **cosine_map@100** | **0.1397** | #### 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 | 0.0516 | | cosine_accuracy@3 | 0.121 | | cosine_accuracy@5 | 0.1679 | | cosine_accuracy@10 | 0.2889 | | cosine_precision@1 | 0.0516 | | cosine_precision@3 | 0.0403 | | cosine_precision@5 | 0.0336 | | cosine_precision@10 | 0.0289 | | cosine_recall@1 | 0.0516 | | cosine_recall@3 | 0.121 | | cosine_recall@5 | 0.1679 | | cosine_recall@10 | 0.2889 | | cosine_ndcg@10 | 0.1498 | | cosine_mrr@10 | 0.1082 | | **cosine_map@100** | **0.1338** | #### 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 | 0.0516 | | cosine_accuracy@3 | 0.1173 | | cosine_accuracy@5 | 0.1717 | | cosine_accuracy@10 | 0.2889 | | cosine_precision@1 | 0.0516 | | cosine_precision@3 | 0.0391 | | cosine_precision@5 | 0.0343 | | cosine_precision@10 | 0.0289 | | cosine_recall@1 | 0.0516 | | cosine_recall@3 | 0.1173 | | cosine_recall@5 | 0.1717 | | cosine_recall@10 | 0.2889 | | cosine_ndcg@10 | 0.1488 | | cosine_mrr@10 | 0.1069 | | **cosine_map@100** | **0.1328** | #### 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 | 0.0507 | | cosine_accuracy@3 | 0.1126 | | cosine_accuracy@5 | 0.1642 | | cosine_accuracy@10 | 0.2824 | | cosine_precision@1 | 0.0507 | | cosine_precision@3 | 0.0375 | | cosine_precision@5 | 0.0328 | | cosine_precision@10 | 0.0282 | | cosine_recall@1 | 0.0507 | | cosine_recall@3 | 0.1126 | | cosine_recall@5 | 0.1642 | | cosine_recall@10 | 0.2824 | | cosine_ndcg@10 | 0.1449 | | cosine_mrr@10 | 0.104 | | **cosine_map@100** | **0.1306** | ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 9,593 training samples * Columns: positive and anchor * Approximate statistics based on the first 1000 samples: | | positive | anchor | |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | positive | anchor | |:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------| | Mitjançant aquest tràmit la persona interessada posa en coneixement de l'Ajuntament l’inici o modificació substancial d’una activitat econòmica, i hi adjunta el certificat tècnic acreditatiu del compliment dels requisits necessaris que estableix la normativa vigent per a l‘exercici de l’activitat. | Quin és el resultat esperat després de presentar el certificat tècnic en el tràmit de comunicació d'inici d'activitat? | | L'Ajuntament de Sitges ofereix a aquelles famílies que acompleixin els requisits establerts, ajuts per al pagament de la quota del servei i de la quota del menjador dels infants matriculats a les Llars d'Infants Municipals ( 0-3 anys). | Quins són els requisits per a beneficiar-se dels ajuts de l'Ajuntament de Sitges? | | Les entitats o associacions culturals han de presentar la sol·licitud de subvenció dins del termini establert per l'Ajuntament de Sitges. | Quin és el termini per a presentar una sol·licitud de subvenció per a un projecte cultural? | * 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 | |:----------:|:-------:|:-------------:|:-----------------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:| | 0.2667 | 10 | 3.5318 | - | - | - | - | - | - | | 0.5333 | 20 | 2.3744 | - | - | - | - | - | - | | 0.8 | 30 | 1.6587 | - | - | - | - | - | - | | 0.9867 | 37 | - | 0.1350 | 0.1317 | 0.1349 | 0.1341 | 0.1207 | 0.1322 | | 1.0667 | 40 | 1.1513 | - | - | - | - | - | - | | 1.3333 | 50 | 1.0055 | - | - | - | - | - | - | | 1.6 | 60 | 0.7369 | - | - | - | - | - | - | | 1.8667 | 70 | 0.4855 | - | - | - | - | - | - | | 2.0 | 75 | - | 0.1366 | 0.1370 | 0.1376 | 0.1345 | 0.1290 | 0.1355 | | 2.1333 | 80 | 0.4362 | - | - | - | - | - | - | | 2.4 | 90 | 0.3943 | - | - | - | - | - | - | | 2.6667 | 100 | 0.3495 | - | - | - | - | - | - | | 2.9333 | 110 | 0.2138 | - | - | - | - | - | - | | **2.9867** | **112** | **-** | **0.1364** | **0.1342** | **0.1374** | **0.1361** | **0.1256** | **0.1367** | | 3.2 | 120 | 0.2176 | - | - | - | - | - | - | | 3.4667 | 130 | 0.2513 | - | - | - | - | - | - | | 3.7333 | 140 | 0.2163 | - | - | - | - | - | - | | 4.0 | 150 | 0.15 | 0.1401 | 0.1308 | 0.1332 | 0.1396 | 0.1279 | 0.1396 | | 4.2667 | 160 | 0.1613 | - | - | - | - | - | - | | 4.5333 | 170 | 0.1955 | - | - | - | - | - | - | | 4.8 | 180 | 0.1514 | - | - | - | - | - | - | | 4.9333 | 185 | - | 0.1398 | 0.1328 | 0.1338 | 0.1397 | 0.1306 | 0.1405 | * The bold row denotes the saved checkpoint. ### Framework Versions - Python: 3.10.12 - Sentence Transformers: 3.0.1 - Transformers: 4.44.2 - 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} } ```