--- base_model: BAAI/bge-base-en-v1.5 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:6300 - loss:MatryoshkaLoss - loss:MultipleNegativesRankingLoss widget: - source_sentence: Net carrying amount | 10,953 | Less short-term portion | (1,250) | Total long-term portion | $ | 9,703 sentences: - How much did restructuring costs amount to in the financial statement? - How much long-term debt remains after accounting for the short-term portion as of January 29, 2023? - What are the company's environmental sustainability strategies? - source_sentence: 'Total gross margin for 2023: $169,148 million, for 2022: $170,782 million, and for 2021: $152,836 million.' sentences: - How did the total gross margin for Apple Inc. change from 2022 to 2023? - What was the change in noninterest expense for Bank of America from 2022 to 2023? - How much did FS net revenue increase by in fiscal 2023 compared to fiscal 2022? - source_sentence: 'Goldman Sachs manages its activities in three business segments: Global Banking & Markets, Asset & Wealth Management, and Platform Solutions.' sentences: - What are the three business segments of Goldman Sachs as mentioned in their 2023 Form 10-K? - How are financial statement indexes presented in a document? - What was the total foreign currency transaction loss recorded for the year ended December 31, 2023? - source_sentence: NIKE, Inc. was incorporated in 1967 under the laws of the State of Oregon. sentences: - What was the global gender equity status at Meta in July 2023? - When was NIKE, Inc. incorporated and under the laws of which state? - How is product warranty liability estimated by the company? - source_sentence: In 2023, total assets associated with derivatives designated as hedging instruments amounted to $1,527 million, while total liabilities amounted to $5,962 million. sentences: - How are delivery sales categorized in financial statements? - What was the balance of deferred net loss on derivatives included in accumulated other comprehensive income as of December 31, 2023? - What was the total value of assets and liabilities associated with derivatives designated as hedging instruments in 2023? 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: 0.7314285714285714 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8414285714285714 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.88 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9171428571428571 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.7314285714285714 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.28047619047619043 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.17599999999999996 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09171428571428569 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.7314285714285714 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.8414285714285714 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.88 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.9171428571428571 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.8242643635674787 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7945634920634922 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7974204140430639 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 512 type: dim_512 metrics: - type: cosine_accuracy@1 value: 0.73 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8442857142857143 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8757142857142857 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9114285714285715 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.73 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.2814285714285714 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.1751428571428571 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09114285714285712 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.73 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.8442857142857143 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8757142857142857 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.9114285714285715 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.8208470282419681 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7917534013605444 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7950732633962434 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 256 type: dim_256 metrics: - type: cosine_accuracy@1 value: 0.7242857142857143 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8285714285714286 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8671428571428571 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9057142857142857 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.7242857142857143 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.27619047619047615 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.1734285714285714 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09057142857142855 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.7242857142857143 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.8285714285714286 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8671428571428571 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.9057142857142857 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.8144984416133947 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7853690476190478 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7887014688628511 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 128 type: dim_128 metrics: - type: cosine_accuracy@1 value: 0.7042857142857143 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8128571428571428 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8557142857142858 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9057142857142857 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.7042857142857143 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.2709523809523809 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.17114285714285712 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09057142857142855 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.7042857142857143 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.8128571428571428 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8557142857142858 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.9057142857142857 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.8018796849794548 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.769049886621315 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7722252928484385 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 64 type: dim_64 metrics: - type: cosine_accuracy@1 value: 0.6657142857142857 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.78 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.82 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.8685714285714285 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.6657142857142857 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.26 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.16399999999999998 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.08685714285714284 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.6657142857142857 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.78 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.82 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.8685714285714285 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.7667584555431229 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7344319727891154 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7397691471615258 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) on the json dataset. 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 - **Training Dataset:** - json - **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("gavinqiangli/bge-base-financial-matryoshka") # Run inference sentences = [ 'In 2023, total assets associated with derivatives designated as hedging instruments amounted to $1,527 million, while total liabilities amounted to $5,962 million.', 'What was the total value of assets and liabilities associated with derivatives designated as hedging instruments in 2023?', 'What was the balance of deferred net loss on derivatives included in accumulated other comprehensive income as of December 31, 2023?', ] 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 | 0.7314 | | cosine_accuracy@3 | 0.8414 | | cosine_accuracy@5 | 0.88 | | cosine_accuracy@10 | 0.9171 | | cosine_precision@1 | 0.7314 | | cosine_precision@3 | 0.2805 | | cosine_precision@5 | 0.176 | | cosine_precision@10 | 0.0917 | | cosine_recall@1 | 0.7314 | | cosine_recall@3 | 0.8414 | | cosine_recall@5 | 0.88 | | cosine_recall@10 | 0.9171 | | cosine_ndcg@10 | 0.8243 | | cosine_mrr@10 | 0.7946 | | **cosine_map@100** | **0.7974** | #### 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 | 0.73 | | cosine_accuracy@3 | 0.8443 | | cosine_accuracy@5 | 0.8757 | | cosine_accuracy@10 | 0.9114 | | cosine_precision@1 | 0.73 | | cosine_precision@3 | 0.2814 | | cosine_precision@5 | 0.1751 | | cosine_precision@10 | 0.0911 | | cosine_recall@1 | 0.73 | | cosine_recall@3 | 0.8443 | | cosine_recall@5 | 0.8757 | | cosine_recall@10 | 0.9114 | | cosine_ndcg@10 | 0.8208 | | cosine_mrr@10 | 0.7918 | | **cosine_map@100** | **0.7951** | #### 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 | 0.7243 | | cosine_accuracy@3 | 0.8286 | | cosine_accuracy@5 | 0.8671 | | cosine_accuracy@10 | 0.9057 | | cosine_precision@1 | 0.7243 | | cosine_precision@3 | 0.2762 | | cosine_precision@5 | 0.1734 | | cosine_precision@10 | 0.0906 | | cosine_recall@1 | 0.7243 | | cosine_recall@3 | 0.8286 | | cosine_recall@5 | 0.8671 | | cosine_recall@10 | 0.9057 | | cosine_ndcg@10 | 0.8145 | | cosine_mrr@10 | 0.7854 | | **cosine_map@100** | **0.7887** | #### 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 | 0.7043 | | cosine_accuracy@3 | 0.8129 | | cosine_accuracy@5 | 0.8557 | | cosine_accuracy@10 | 0.9057 | | cosine_precision@1 | 0.7043 | | cosine_precision@3 | 0.271 | | cosine_precision@5 | 0.1711 | | cosine_precision@10 | 0.0906 | | cosine_recall@1 | 0.7043 | | cosine_recall@3 | 0.8129 | | cosine_recall@5 | 0.8557 | | cosine_recall@10 | 0.9057 | | cosine_ndcg@10 | 0.8019 | | cosine_mrr@10 | 0.769 | | **cosine_map@100** | **0.7722** | #### 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 | 0.6657 | | cosine_accuracy@3 | 0.78 | | cosine_accuracy@5 | 0.82 | | cosine_accuracy@10 | 0.8686 | | cosine_precision@1 | 0.6657 | | cosine_precision@3 | 0.26 | | cosine_precision@5 | 0.164 | | cosine_precision@10 | 0.0869 | | cosine_recall@1 | 0.6657 | | cosine_recall@3 | 0.78 | | cosine_recall@5 | 0.82 | | cosine_recall@10 | 0.8686 | | cosine_ndcg@10 | 0.7668 | | cosine_mrr@10 | 0.7344 | | **cosine_map@100** | **0.7398** | ## Training Details ### Training Dataset #### json * Dataset: json * Size: 6,300 training samples * Columns: positive and anchor * Approximate statistics based on the first 1000 samples: | | positive | anchor | |:--------|:------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | positive | anchor | |:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------| | The Nominating and Corporate Governance Committee of our Board of Directors is responsible for reviewing and discussing with management our practices related to ESG. | What is the role of the Nominating and Corporate Governance Committee at NVIDIA? | | Deferred tax assets and deferred tax liabilities included in the Consolidated Balance Sheets as follows: As of October 31, 2023: Deferred tax assets were $3,155 million and Deferred tax liabilities were $44 million. As of October 31, 2022: Deferred tax assets were $2,167 million and Deferred tax liabilities were $121 million. The total net deferred tax assets were $3,111 million in 2023 and $2,046 million in 2022. | What was the change in HP's net deferred tax assets from 2022 to 2023? | | Sales and marketing expense increased $247 million, or 16%, in 2023, compared to 2022, primarily due to a $177 million increase in marketing activities associated with our marketing campaigns and launches and our search engine marketing and advertising spend. | What was the major reason for the increase in Sales and Marketing expenses in 2023? | * 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 - `fp16`: 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`: 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`: False - `fp16`: True - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: None - `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 | Training Loss | dim_768_cosine_map@100 | dim_512_cosine_map@100 | dim_256_cosine_map@100 | dim_128_cosine_map@100 | dim_64_cosine_map@100 | |:----------:|:------:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:| | 0.8122 | 10 | 1.5963 | - | - | - | - | - | | 0.9746 | 12 | - | 0.7791 | 0.7824 | 0.7662 | 0.7483 | 0.7086 | | 1.6244 | 20 | 0.6846 | - | - | - | - | - | | 1.9492 | 24 | - | 0.7924 | 0.7903 | 0.7859 | 0.7664 | 0.7327 | | 2.4365 | 30 | 0.4956 | - | - | - | - | - | | 2.9239 | 36 | - | 0.7962 | 0.7939 | 0.7886 | 0.7716 | 0.7378 | | 3.2487 | 40 | 0.3998 | - | - | - | - | - | | **3.8985** | **48** | **-** | **0.7974** | **0.7951** | **0.7887** | **0.7722** | **0.7398** | * The bold row denotes the saved checkpoint. ### Framework Versions - Python: 3.10.12 - Sentence Transformers: 3.2.1 - Transformers: 4.41.2 - PyTorch: 2.1.2+cu121 - Accelerate: 0.34.2 - 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} } ```