--- base_model: BAAI/bge-base-en-v1.5 datasets: [] language: - en library_name: sentence-transformers license: apache-2.0 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: Les respectives convocatòries determinaran les parades disponibles així com les seves característiques i descripció. sentences: - Quin és l'objectiu secundari dels ajuts per a la creació de noves empreses? - Qui és responsable de la resolució de la situació en un domini particular? - Quin és el paper de les convocatòries? - source_sentence: Cal revisar la informació i els terminis de la convocatòria específica de cada procés que trobareu a la Seu electrònica de l'Ajuntament de Sitges. sentences: - Quin és el paper de la Seu electrònica de l'Ajuntament de Sitges en un procés de selecció de personal? - Quin és l'objectiu principal de sol·licitar el certificat d'antiguitat i legalitat d'una finca? - Quin és el propòsit dels ajuts a la contractació laboral en relació amb l'ocupació? - source_sentence: Per a poder rebre les subvencions pel suport educatiu a les escoles públiques de Sitges, els beneficiaris han de presentar un projecte d'acció que compleixi els requisits establerts en la convocatòria corresponent. sentences: - Quin és el propòsit de la sol·licitud d'ajuts? - Quin és el paper de l'Ajuntament de Sitges en la Fira de la Vila del Llibre de Sitges? - Quin és el requisit per a poder rebre les subvencions pel suport educatiu a les escoles públiques de Sitges? - source_sentence: Posteriorment a l’obtenció de l’informe favorable, caldrà realitzar l’acte de comprovació en matèria d’incendis i procedir a efectuar la comunicació prèvia corresponent. sentences: - Quin és el paper de la comunicació prèvia en la prevenció d'incendis? - Quins són els requisits per a presentar una sol·licitud de subvenció? - Quins animals es consideren animals de companyia? - source_sentence: El termini per a la presentació de les sol·licituds de modificació del projecte o activitat subvencionat és de 15 dies naturals abans de la finalització del projecte o activitat. sentences: - Quin és el termini per a la presentació de les sol·licituds de modificació del projecte o activitat subvencionat? - Quins són els tres tipus de llicència d'obra? - Quin és el registre on es troben les dades d'inscripció? model-index: - name: BGE base Financial Matryoshka results: - task: type: information-retrieval name: Information Retrieval dataset: name: dim 768 type: dim_768 metrics: - type: cosine_accuracy@1 value: 0.0625 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.11637931034482758 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.1810344827586207 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.35560344827586204 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.0625 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.03879310344827586 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.036206896551724134 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.03556034482758621 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.0625 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.11637931034482758 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.1810344827586207 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.35560344827586204 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.17546345429803745 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.12245227832512329 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.1487513151351338 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 512 type: dim_512 metrics: - type: cosine_accuracy@1 value: 0.0625 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.10991379310344827 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.17025862068965517 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.35560344827586204 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.0625 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.036637931034482756 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.03405172413793103 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.03556034482758621 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.0625 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.10991379310344827 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.17025862068965517 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.35560344827586204 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.17281692680622274 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.11932898877942 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.145505253139907 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 256 type: dim_256 metrics: - type: cosine_accuracy@1 value: 0.05603448275862069 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.12284482758620689 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.1724137931034483 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.34051724137931033 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.05603448275862069 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.040948275862068964 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.034482758620689655 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.03405172413793103 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.05603448275862069 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.12284482758620689 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.1724137931034483 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.34051724137931033 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.16797293983212122 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.11677955665024647 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.14311504496457605 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 128 type: dim_128 metrics: - type: cosine_accuracy@1 value: 0.05172413793103448 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.11422413793103449 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.18318965517241378 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.31896551724137934 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.05172413793103448 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.038074712643678156 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.036637931034482756 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.03189655172413793 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.05172413793103448 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.11422413793103449 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.18318965517241378 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.31896551724137934 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.15889833336121337 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.11119406814449927 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.1376499182467716 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 64 type: dim_64 metrics: - type: cosine_accuracy@1 value: 0.04525862068965517 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.10560344827586207 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.16594827586206898 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.30603448275862066 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.04525862068965517 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.035201149425287355 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.0331896551724138 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.030603448275862068 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.04525862068965517 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.10560344827586207 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.16594827586206898 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.30603448275862066 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.14903489989981042 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.10241516146688567 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.12594670041141745 name: Cosine Map@100 --- # BGE base Financial Matryoshka This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-base-en-v1.5](https://huggingface.co./BAAI/bge-base-en-v1.5). It maps sentences & paragraphs to a 768-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-base-en-v1.5](https://huggingface.co./BAAI/bge-base-en-v1.5) - **Maximum Sequence Length:** 512 tokens - **Output Dimensionality:** 768 tokens - **Similarity Function:** Cosine Similarity - **Language:** en - **License:** apache-2.0 ### 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': True}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, '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/sitges2608") # Run inference sentences = [ 'El termini per a la presentació de les sol·licituds de modificació del projecte o activitat subvencionat és de 15 dies naturals abans de la finalització del projecte o activitat.', 'Quin és el termini per a la presentació de les sol·licituds de modificació del projecte o activitat subvencionat?', "Quin és el registre on es troben les dades d'inscripció?", ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 768] # 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 | 0.0625 | | cosine_accuracy@3 | 0.1164 | | cosine_accuracy@5 | 0.181 | | cosine_accuracy@10 | 0.3556 | | cosine_precision@1 | 0.0625 | | cosine_precision@3 | 0.0388 | | cosine_precision@5 | 0.0362 | | cosine_precision@10 | 0.0356 | | cosine_recall@1 | 0.0625 | | cosine_recall@3 | 0.1164 | | cosine_recall@5 | 0.181 | | cosine_recall@10 | 0.3556 | | cosine_ndcg@10 | 0.1755 | | cosine_mrr@10 | 0.1225 | | **cosine_map@100** | **0.1488** | #### 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 | 0.0625 | | cosine_accuracy@3 | 0.1099 | | cosine_accuracy@5 | 0.1703 | | cosine_accuracy@10 | 0.3556 | | cosine_precision@1 | 0.0625 | | cosine_precision@3 | 0.0366 | | cosine_precision@5 | 0.0341 | | cosine_precision@10 | 0.0356 | | cosine_recall@1 | 0.0625 | | cosine_recall@3 | 0.1099 | | cosine_recall@5 | 0.1703 | | cosine_recall@10 | 0.3556 | | cosine_ndcg@10 | 0.1728 | | cosine_mrr@10 | 0.1193 | | **cosine_map@100** | **0.1455** | #### 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 | 0.056 | | cosine_accuracy@3 | 0.1228 | | cosine_accuracy@5 | 0.1724 | | cosine_accuracy@10 | 0.3405 | | cosine_precision@1 | 0.056 | | cosine_precision@3 | 0.0409 | | cosine_precision@5 | 0.0345 | | cosine_precision@10 | 0.0341 | | cosine_recall@1 | 0.056 | | cosine_recall@3 | 0.1228 | | cosine_recall@5 | 0.1724 | | cosine_recall@10 | 0.3405 | | cosine_ndcg@10 | 0.168 | | cosine_mrr@10 | 0.1168 | | **cosine_map@100** | **0.1431** | #### 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 | 0.0517 | | cosine_accuracy@3 | 0.1142 | | cosine_accuracy@5 | 0.1832 | | cosine_accuracy@10 | 0.319 | | cosine_precision@1 | 0.0517 | | cosine_precision@3 | 0.0381 | | cosine_precision@5 | 0.0366 | | cosine_precision@10 | 0.0319 | | cosine_recall@1 | 0.0517 | | cosine_recall@3 | 0.1142 | | cosine_recall@5 | 0.1832 | | cosine_recall@10 | 0.319 | | cosine_ndcg@10 | 0.1589 | | cosine_mrr@10 | 0.1112 | | **cosine_map@100** | **0.1376** | #### 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 | 0.0453 | | cosine_accuracy@3 | 0.1056 | | cosine_accuracy@5 | 0.1659 | | cosine_accuracy@10 | 0.306 | | cosine_precision@1 | 0.0453 | | cosine_precision@3 | 0.0352 | | cosine_precision@5 | 0.0332 | | cosine_precision@10 | 0.0306 | | cosine_recall@1 | 0.0453 | | cosine_recall@3 | 0.1056 | | cosine_recall@5 | 0.1659 | | cosine_recall@10 | 0.306 | | cosine_ndcg@10 | 0.149 | | cosine_mrr@10 | 0.1024 | | **cosine_map@100** | **0.1259** | ## 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 | |:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------| | La persona titular d'una llicència de vehicle lleuger per al servei públic (auto-taxi), en produïr-se un canvi de vehicle, ha de notificar a l'Ajuntament les dades del nou vehicle. | Quin és el propòsit de la notificació de les dades del nou vehicle? | | S'entén per garantia l'ingrés a la Tresoreria de l'Ajuntament d'una quantitat econòmica que garanteix el compliment d'una obligació adquirida amb aquest (garanties de concursos o licitacions, fraccionaments de tributs en via executiva, reposició de paviments per obres, etc.). | Què s'entén per garantia a l'Ajuntament de Sitges? | | L'ús d'espais del Centre Cultural Miramar per a la realització d'exposicions. | Quin és el centre cultural on es poden realitzar les exposicions d'art? | * Loss: [MatryoshkaLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "loss": "MultipleNegativesRankingLoss", "matryoshka_dims": [ 768, 512, 256, 128, 64 ], "matryoshka_weights": [ 1, 1, 1, 1, 1 ], "n_dims_per_step": -1 } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: epoch - `per_device_train_batch_size`: 32 - `per_device_eval_batch_size`: 16 - `gradient_accumulation_steps`: 16 - `learning_rate`: 2e-05 - `num_train_epochs`: 4 - `lr_scheduler_type`: cosine - `warmup_ratio`: 0.1 - `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`: 32 - `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`: 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`: 4 - `max_steps`: -1 - `lr_scheduler_type`: cosine - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.1 - `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 | |:----------:|:------:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:| | 0.9771 | 8 | - | 0.1210 | 0.1384 | 0.1341 | 0.1002 | 0.1376 | | 1.2137 | 10 | 7.5469 | - | - | - | - | - | | **1.9466** | **16** | **-** | **0.136** | **0.1404** | **0.1443** | **0.1249** | **0.1414** | | 2.4275 | 20 | 4.0024 | - | - | - | - | - | | 2.9160 | 24 | - | 0.1388 | 0.1460 | 0.1446 | 0.1278 | 0.1436 | | 3.6412 | 30 | 3.2149 | - | - | - | - | - | | 3.8855 | 32 | - | 0.1376 | 0.1431 | 0.1455 | 0.1259 | 0.1488 | * The bold row denotes the saved checkpoint. ### Framework Versions - Python: 3.10.12 - Sentence Transformers: 3.0.1 - Transformers: 4.42.4 - PyTorch: 2.3.1+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} } ```