--- language: - en license: apache-2.0 library_name: sentence-transformers tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:161 - loss:MatryoshkaLoss - loss:MultipleNegativesRankingLoss base_model: BAAI/bge-base-en-v1.5 datasets: [] 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 widget: - source_sentence: 'As per Part II of the PDPA, Personal Data Protection Commission (PDPC) is the regulatory body to enforce the provisions of PDPA. The PDPC is empowered with broad discretion to issue remedial directions, initiate investigation inquiries, and impose fines and penalties on the organisations in case of any non-compliance of PDPA. 1 If organisations misuse the personal data or hide information concerning its collection, use, or disclosure, PDPA states penalties not exceeding **S$50,000 (approx. $36,000)**. 2 Penalty for hindering a PDPC investigation can lead to a fine of not more than **S$100,000 (approx. $72,000)**. The PDPA states that companies are also liable for their employees’ actions, whether they are aware of them or not. 3 New amendments to PDPA have enforced increased financial penalties for breaches of the PDPA up to **10%** of annual gross turnover in Singapore, or **S$ 1 million** , whichever is higher. 4 Non-compliance with specific provisions under the PDPA may also constitute an offense, for which a fine or a term of **imprisonment** may be imposed. 5 An individual can bring a private civil action against an organisation for having suffered **loss or damage** directly due to a contravention of the provisions of the PDPA.' sentences: - What is the right to notice under the CCPA? - What are the risks of non-compliance with the PDPA? - What is the definition of personal data under the PDP Law? - source_sentence: The DPA requires all data controllers to take appropriate technical and organisational measures that are necessary to protect data from unauthorised destruction, negligent loss, unauthorised alteration or access and any other unauthorised processing of the data. sentences: - Which regulatory authority enforces GDPR in France? - What are the security requirements under the DPA? - How do PIPEDA and GDPR differ? - source_sentence: if the data controller or the data processor holds a valid registration certificate authorizing him or her to store personal data outside Rwanda sentences: - What is the difference between GDPR and a Data Protection Act? - What is the voluntary certification by the CPPA? - Where is personal data storage outside of Rwanda permitted? - source_sentence: The PDP law will regulate sensitive personal data as well as other personal data that may endanger or harm the privacy of the data subject. sentences: - What is the material scope of the PDP Law? - What is the definition of personal information under the DPA in the Philippines? - What does Securiti offer to help with data privacy compliance? - source_sentence: Thailand's PDPA applies to any legal entity collecting, using, or disclosing a natural (and alive) person's personal data. sentences: - Who does the Thailand's PDPA apply to? - What penalties could an organization face for infringing Kenya's Data Protection Act? - What is the CPRA? pipeline_tag: sentence-similarity model-index: - name: SentenceTransformer based on BAAI/bge-base-en-v1.5 results: - task: type: information-retrieval name: Information Retrieval dataset: name: dim 768 type: dim_768 metrics: - type: cosine_accuracy@1 value: 0.5555555555555556 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8333333333333334 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8888888888888888 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 1.0 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.5555555555555556 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.27777777777777773 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.17777777777777778 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.10000000000000002 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.5555555555555556 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.8333333333333334 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8888888888888888 name: Cosine Recall@5 - type: cosine_recall@10 value: 1.0 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.7730002998303461 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7011463844797178 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7011463844797178 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 512 type: dim_512 metrics: - type: cosine_accuracy@1 value: 0.5555555555555556 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8333333333333334 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8888888888888888 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 1.0 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.5555555555555556 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.27777777777777773 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.17777777777777778 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.10000000000000002 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.5555555555555556 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.8333333333333334 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8888888888888888 name: Cosine Recall@5 - type: cosine_recall@10 value: 1.0 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.7730002998303461 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7011463844797178 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7011463844797178 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 256 type: dim_256 metrics: - type: cosine_accuracy@1 value: 0.5555555555555556 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8888888888888888 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.9444444444444444 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 1.0 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.5555555555555556 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.2962962962962962 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.1888888888888889 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.10000000000000002 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.5555555555555556 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.8888888888888888 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.9444444444444444 name: Cosine Recall@5 - type: cosine_recall@10 value: 1.0 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.7903353721281168 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7217592592592593 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7217592592592593 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 128 type: dim_128 metrics: - type: cosine_accuracy@1 value: 0.5555555555555556 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8333333333333334 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8888888888888888 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9444444444444444 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.5555555555555556 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.27777777777777773 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.1777777777777778 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09444444444444446 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.5555555555555556 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.8333333333333334 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8888888888888888 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.9444444444444444 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.7641903093346225 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7052469135802469 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7080246913580247 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 64 type: dim_64 metrics: - type: cosine_accuracy@1 value: 0.4444444444444444 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.6666666666666666 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8333333333333334 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 1.0 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.4444444444444444 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.2222222222222222 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.16666666666666669 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.10000000000000002 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.4444444444444444 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.6666666666666666 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8333333333333334 name: Cosine Recall@5 - type: cosine_recall@10 value: 1.0 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.6976955584560773 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.6044753086419753 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.6044753086419754 name: Cosine Map@100 --- # SentenceTransformer based on BAAI/bge-base-en-v1.5 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("MugheesAwan11/bge-base-securiti-dataset-1-v3") # Run inference sentences = [ "Thailand's PDPA applies to any legal entity collecting, using, or disclosing a natural (and alive) person's personal data.", "Who does the Thailand's PDPA apply to?", "What penalties could an organization face for infringing Kenya's Data Protection Act?", ] 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 | 0.5556 | | cosine_accuracy@3 | 0.8333 | | cosine_accuracy@5 | 0.8889 | | cosine_accuracy@10 | 1.0 | | cosine_precision@1 | 0.5556 | | cosine_precision@3 | 0.2778 | | cosine_precision@5 | 0.1778 | | cosine_precision@10 | 0.1 | | cosine_recall@1 | 0.5556 | | cosine_recall@3 | 0.8333 | | cosine_recall@5 | 0.8889 | | cosine_recall@10 | 1.0 | | cosine_ndcg@10 | 0.773 | | cosine_mrr@10 | 0.7011 | | **cosine_map@100** | **0.7011** | #### 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 | 0.5556 | | cosine_accuracy@3 | 0.8333 | | cosine_accuracy@5 | 0.8889 | | cosine_accuracy@10 | 1.0 | | cosine_precision@1 | 0.5556 | | cosine_precision@3 | 0.2778 | | cosine_precision@5 | 0.1778 | | cosine_precision@10 | 0.1 | | cosine_recall@1 | 0.5556 | | cosine_recall@3 | 0.8333 | | cosine_recall@5 | 0.8889 | | cosine_recall@10 | 1.0 | | cosine_ndcg@10 | 0.773 | | cosine_mrr@10 | 0.7011 | | **cosine_map@100** | **0.7011** | #### 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 | 0.5556 | | cosine_accuracy@3 | 0.8889 | | cosine_accuracy@5 | 0.9444 | | cosine_accuracy@10 | 1.0 | | cosine_precision@1 | 0.5556 | | cosine_precision@3 | 0.2963 | | cosine_precision@5 | 0.1889 | | cosine_precision@10 | 0.1 | | cosine_recall@1 | 0.5556 | | cosine_recall@3 | 0.8889 | | cosine_recall@5 | 0.9444 | | cosine_recall@10 | 1.0 | | cosine_ndcg@10 | 0.7903 | | cosine_mrr@10 | 0.7218 | | **cosine_map@100** | **0.7218** | #### 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 | 0.5556 | | cosine_accuracy@3 | 0.8333 | | cosine_accuracy@5 | 0.8889 | | cosine_accuracy@10 | 0.9444 | | cosine_precision@1 | 0.5556 | | cosine_precision@3 | 0.2778 | | cosine_precision@5 | 0.1778 | | cosine_precision@10 | 0.0944 | | cosine_recall@1 | 0.5556 | | cosine_recall@3 | 0.8333 | | cosine_recall@5 | 0.8889 | | cosine_recall@10 | 0.9444 | | cosine_ndcg@10 | 0.7642 | | cosine_mrr@10 | 0.7052 | | **cosine_map@100** | **0.708** | #### 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 | 0.4444 | | cosine_accuracy@3 | 0.6667 | | cosine_accuracy@5 | 0.8333 | | cosine_accuracy@10 | 1.0 | | cosine_precision@1 | 0.4444 | | cosine_precision@3 | 0.2222 | | cosine_precision@5 | 0.1667 | | cosine_precision@10 | 0.1 | | cosine_recall@1 | 0.4444 | | cosine_recall@3 | 0.6667 | | cosine_recall@5 | 0.8333 | | cosine_recall@10 | 1.0 | | cosine_ndcg@10 | 0.6977 | | cosine_mrr@10 | 0.6045 | | **cosine_map@100** | **0.6045** | ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 161 training samples * Columns: positive and anchor * Approximate statistics based on the first 1000 samples: | | positive | anchor | |:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | positive | anchor | |:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------| | The DPA may impose administrative fines of up to €10 million, or up to 2%
of
worldwide turnover. The DPA may also impose heavier fines up to €20 million,
or up to 4% of worldwide turnover. | What is the penalty for non-compliance with the GDPR in Italy? | | As per the DPA, the data handler must seek consent in writing from the data subject to collect any sensitive personal data. | What are the consent requirements under the DPA? | | China's cybersecurity laws include the Cybersecurity Law, which governs
various aspects of cybersecurity, data protection, and the obligations of
organizations to ensure the security of networks and data within China's
territory. | What are the cybersecurity laws in China? | * 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`: 2 - `learning_rate`: 2e-05 - `lr_scheduler_type`: cosine - `warmup_ratio`: 0.1 - `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`: 32 - `per_device_eval_batch_size`: 16 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 2 - `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`: 3 - `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`: 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 - `batch_sampler`: no_duplicates - `multi_dataset_batch_sampler`: proportional
### Training Logs | Epoch | Step | 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 | |:-------:|:-----:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:| | 1.0 | 3 | 0.6510 | 0.6691 | 0.6534 | 0.5641 | 0.6515 | | **2.0** | **6** | **0.6605** | **0.679** | **0.6627** | **0.5768** | **0.6515** | | 1.0 | 3 | 0.6702 | 0.6914 | 0.6747 | 0.6014 | 0.7043 | | **2.0** | **6** | **0.7078** | **0.694** | **0.7011** | **0.6052** | **0.7025** | | 3.0 | 9 | 0.7080 | 0.7218 | 0.7011 | 0.6045 | 0.7011 | * The bold row denotes the saved checkpoint. ### Framework Versions - Python: 3.10.14 - Sentence Transformers: 3.0.1 - Transformers: 4.41.2 - PyTorch: 2.1.2+cu121 - Accelerate: 0.31.0 - Datasets: 2.19.1 - 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} } ```