--- base_model: cross-encoder/ms-marco-MiniLM-L-4-v2 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: 'Aquelles persones (físiques o jurídiques) que es disposin a exercir una de les següents activitats: ... Han de comunicar-ho a l''Ajuntament prèviament a la data prevista de la seva obertura.' sentences: - Quin és el benefici que es pretén obtenir amb aquests ajuts econòmics per a les empreses d'hostaleria i restauració? - Quin és el benefici del sistema de teleassistència per a les persones que viuen amb altres persones amb discapacitat? - Quin és el propòsit de la comunicació prèvia d'una activitat recreativa o un espectacle públic? - source_sentence: Les persones titulars d’activitats que generin residus comercials o industrials assimilables als municipals, vindran obligats a acreditar davant l’Ajuntament que tenen contractat un gestor autoritzat per la recollida, tractament i eliminació dels residus que produeixi l’activitat corresponent. sentences: - Quin és el paper de l'Ajuntament en l'acreditació de recollida de residus? - Quin és el benefici de les activitats d'animació socio-cultural? - Quin és el benefici de l'ajut per a la creació de noves empreses? - source_sentence: Modificació de sol·licitud de permís d'ocupació de la via pública per filmacions, rodatges o sessions fotogràfiques. sentences: - Quin és el grau de discapacitat mínim per a rebre l'ajut de 300€ anuals? - Quin és el requisit per a la constitució o modificació del règim de propietat horitzontal? - Quin és el tipus de permís que es modifica? - source_sentence: El beneficiari és l'encarregat de complir les condicions de la subvenció i de presentar els informes de seguiment del projecte. sentences: - Quin és el paper del beneficiari en el procés de subvencions? - Quin és el càlcul dels interessos de demora en el fraccionament i l'ajornament? - Quin és el període de temps en què es poden efectuar les despeses mèdiques per a rebre l'ajuda? - source_sentence: Aquest tràmit permet sol·licitar la llicència per a realitzar obres d'excavació a la via pública per a la instal·lació o reparació d'infraestructures de serveis i subministraments. sentences: - Quin és el paper de la via pública en aquest tràmit? - Quin és el requisit principal per obtenir el certificat? - Quin és l'objectiu de presentar una denúncia per presumpta infracció urbanística? model-index: - name: SentenceTransformer based on cross-encoder/ms-marco-MiniLM-L-4-v2 results: - task: type: information-retrieval name: Information Retrieval dataset: name: dim 768 type: dim_768 metrics: - type: cosine_accuracy@1 value: 0.03879310344827586 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.08836206896551724 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.12284482758620689 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.1875 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.03879310344827586 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.029454022988505746 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.024568965517241383 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.01875 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.03879310344827586 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.08836206896551724 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.12284482758620689 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.1875 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.10242929123982092 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.0765787493158183 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.09057915519577017 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 512 type: dim_512 metrics: - type: cosine_accuracy@1 value: 0.03879310344827586 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.08836206896551724 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.12284482758620689 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.1875 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.03879310344827586 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.029454022988505746 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.024568965517241383 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.01875 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.03879310344827586 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.08836206896551724 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.12284482758620689 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.1875 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.10242929123982092 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.0765787493158183 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.09057915519577017 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 256 type: dim_256 metrics: - type: cosine_accuracy@1 value: 0.03879310344827586 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.08405172413793104 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.12931034482758622 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.1853448275862069 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.03879310344827586 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.028017241379310345 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.02586206896551724 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.01853448275862069 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.03879310344827586 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.08405172413793104 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.12931034482758622 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.1853448275862069 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.10210354316803219 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.07668821839080461 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.08986051867751395 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 128 type: dim_128 metrics: - type: cosine_accuracy@1 value: 0.034482758620689655 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.09482758620689655 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.1271551724137931 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.1853448275862069 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.034482758620689655 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.03160919540229885 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.025431034482758623 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.018534482758620692 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.034482758620689655 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.09482758620689655 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.1271551724137931 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.1853448275862069 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.10100148719550092 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.07532755199781063 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.08992740582596234 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 64 type: dim_64 metrics: - type: cosine_accuracy@1 value: 0.034482758620689655 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.08405172413793104 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.10344827586206896 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.17025862068965517 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.034482758620689655 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.028017241379310345 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.020689655172413796 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.017025862068965517 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.034482758620689655 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.08405172413793104 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.10344827586206896 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.17025862068965517 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.09332320019708457 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.0700302750410509 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.08369264546406543 name: Cosine Map@100 --- # SentenceTransformer based on cross-encoder/ms-marco-MiniLM-L-4-v2 This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [cross-encoder/ms-marco-MiniLM-L-4-v2](https://huggingface.co/cross-encoder/ms-marco-MiniLM-L-4-v2). It maps sentences & paragraphs to a 384-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:** [cross-encoder/ms-marco-MiniLM-L-4-v2](https://huggingface.co/cross-encoder/ms-marco-MiniLM-L-4-v2) - **Maximum Sequence Length:** 512 tokens - **Output Dimensionality:** 384 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': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) ) ``` ## 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/sitges10242608-4ep-rerankv2") # Run inference sentences = [ "Aquest tràmit permet sol·licitar la llicència per a realitzar obres d'excavació a la via pública per a la instal·lació o reparació d'infraestructures de serveis i subministraments.", 'Quin és el paper de la via pública en aquest tràmit?', "Quin és l'objectiu de presentar una denúncia per presumpta infracció urbanística?", ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 384] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` ## Evaluation ### Metrics #### 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 | 0.0388 | | cosine_accuracy@3 | 0.0884 | | cosine_accuracy@5 | 0.1228 | | cosine_accuracy@10 | 0.1875 | | cosine_precision@1 | 0.0388 | | cosine_precision@3 | 0.0295 | | cosine_precision@5 | 0.0246 | | cosine_precision@10 | 0.0187 | | cosine_recall@1 | 0.0388 | | cosine_recall@3 | 0.0884 | | cosine_recall@5 | 0.1228 | | cosine_recall@10 | 0.1875 | | cosine_ndcg@10 | 0.1024 | | cosine_mrr@10 | 0.0766 | | **cosine_map@100** | **0.0906** | #### 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 | 0.0388 | | cosine_accuracy@3 | 0.0884 | | cosine_accuracy@5 | 0.1228 | | cosine_accuracy@10 | 0.1875 | | cosine_precision@1 | 0.0388 | | cosine_precision@3 | 0.0295 | | cosine_precision@5 | 0.0246 | | cosine_precision@10 | 0.0187 | | cosine_recall@1 | 0.0388 | | cosine_recall@3 | 0.0884 | | cosine_recall@5 | 0.1228 | | cosine_recall@10 | 0.1875 | | cosine_ndcg@10 | 0.1024 | | cosine_mrr@10 | 0.0766 | | **cosine_map@100** | **0.0906** | #### 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 | 0.0388 | | cosine_accuracy@3 | 0.0841 | | cosine_accuracy@5 | 0.1293 | | cosine_accuracy@10 | 0.1853 | | cosine_precision@1 | 0.0388 | | cosine_precision@3 | 0.028 | | cosine_precision@5 | 0.0259 | | cosine_precision@10 | 0.0185 | | cosine_recall@1 | 0.0388 | | cosine_recall@3 | 0.0841 | | cosine_recall@5 | 0.1293 | | cosine_recall@10 | 0.1853 | | cosine_ndcg@10 | 0.1021 | | cosine_mrr@10 | 0.0767 | | **cosine_map@100** | **0.0899** | #### 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 | 0.0345 | | cosine_accuracy@3 | 0.0948 | | cosine_accuracy@5 | 0.1272 | | cosine_accuracy@10 | 0.1853 | | cosine_precision@1 | 0.0345 | | cosine_precision@3 | 0.0316 | | cosine_precision@5 | 0.0254 | | cosine_precision@10 | 0.0185 | | cosine_recall@1 | 0.0345 | | cosine_recall@3 | 0.0948 | | cosine_recall@5 | 0.1272 | | cosine_recall@10 | 0.1853 | | cosine_ndcg@10 | 0.101 | | cosine_mrr@10 | 0.0753 | | **cosine_map@100** | **0.0899** | #### 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 | 0.0345 | | cosine_accuracy@3 | 0.0841 | | cosine_accuracy@5 | 0.1034 | | cosine_accuracy@10 | 0.1703 | | cosine_precision@1 | 0.0345 | | cosine_precision@3 | 0.028 | | cosine_precision@5 | 0.0207 | | cosine_precision@10 | 0.017 | | cosine_recall@1 | 0.0345 | | cosine_recall@3 | 0.0841 | | cosine_recall@5 | 0.1034 | | cosine_recall@10 | 0.1703 | | cosine_ndcg@10 | 0.0933 | | cosine_mrr@10 | 0.07 | | **cosine_map@100** | **0.0837** | ## 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 | |:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------| | Havent-se d'acreditar la matriculació i inscripció en el respectiu centre públic o concertat, així com el cost de les llars d'infants, de l'educació especialitzada per les discapacitats físiques, psíquiques i sensorials en centres públics, concertats o privats. | Quin és el requisit per acreditar la llar d'infants? | | El volant històric de convivència és el document que informa de la residencia en el municipi de Sitges, així com altres fets relatius a l'empadronament d'una persona, i detalla tots els domicilis, la data inicial i final en els que ha estat empadronada en cadascun d'ells, i les persones amb les què constava inscrites, segons les dades que consten al Padró Municipal d'Habitants fins a la data d'expedició. | Quin és el propòsit del volant històric de convivència? | | Instal·lació de tanques sense obra. | Quins són els exemples d'instal·lacions que es poden comunicar amb aquest tràmit? | * Loss: [MatryoshkaLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "loss": "MultipleNegativesRankingLoss", "matryoshka_dims": [ 384, 256, 128, 64 ], "matryoshka_weights": [ 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`: 5 - `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`: 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`: 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_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 | 11.3695 | - | - | - | - | - | | 0.9808 | 16 | - | 0.0214 | 0.0243 | 0.0234 | 0.0199 | 0.0234 | | 1.2261 | 20 | 10.653 | - | - | - | - | - | | 1.8391 | 30 | 9.0745 | - | - | - | - | - | | 1.9617 | 32 | - | 0.0495 | 0.0517 | 0.0589 | 0.0481 | 0.0589 | | 2.4521 | 40 | 7.3468 | - | - | - | - | - | | 2.9425 | 48 | - | 0.0764 | 0.0734 | 0.0811 | 0.0709 | 0.0811 | | 3.0651 | 50 | 5.887 | - | - | - | - | - | | 3.6782 | 60 | 5.3568 | - | - | - | - | - | | **3.9847** | **65** | **-** | **0.0922** | **0.0857** | **0.0896** | **0.0808** | **0.0896** | | 4.2912 | 70 | 4.8338 | - | - | - | - | - | | 4.9042 | 80 | 4.9251 | 0.0899 | 0.0899 | 0.0906 | 0.0837 | 0.0906 | * 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} } ```