--- language: - en license: apache-2.0 library_name: sentence-transformers tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:6300 - 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: The company hedges foreign currency exchange-based cash flow variability of certain fees using forward contracts designated as hedging instruments. It also holds short-term forward contracts to offset exposure to fluctuations in certain of its foreign currency denominated cash balances and intercompany financing arrangements, without designating these forward contracts as hedging instruments. sentences: - What was the total stockholders' equity at Amazon.com, Inc. as of December 31, 2021? - How does the company manage fluctuations in foreign currency exchange rates? - What are some of the potential consequences for Meta Platforms, Inc. from inquiries or investigations as noted in the provided text? - source_sentence: The Financial Statement Schedule is located on page S-1 of IBM’s 2023 Form 10-K. sentences: - How is Hewlett Packard addressing competition in the enterprise IT infrastructure market? - Where in IBM’s 2023 Form 10-K can the Financial Statement Schedule be found? - What was Intuit's Net Income in fiscal year 2023? - source_sentence: Sales of DARZALEX in 2023 showed a 22.2% increase over the previous year. sentences: - How much did DARZALEX sales increase in 2023 compared to the previous year? - What strategic focus does Etsy have for its marketplace? - Since when has Mr. Goodarzi been the President and CEO of Intuit? - source_sentence: Chubb Limited further advanced their goal of greater product, customer, and geographical diversification with incremental purchases that led to a controlling majority interest in Huatai Insurance Group Co. Ltd, owning about 76.5 percent as of July 1, 2023. sentences: - What are the primary sources of revenue for Salesforce, Inc. as described in their consolidated financial statements? - What acquisitions did Hershey complete to expand its snacking portfolio, and when did these occur? - What percentage of the Huatai Insurance Group Co. Ltd does Chubb Limited own as of July 1, 2023? - source_sentence: The consolidated balance sheets of Visa Inc. as of September 30, 2023, list the total current assets at $33,532 million. sentences: - What was the total of Visa Inc.'s current assets as of September 30, 2023? - What was Garmin Ltd.'s net income for the fiscal year ended December 30, 2023? - By what percentage did online sales grow in fiscal 2022 compared to fiscal 2021? pipeline_tag: sentence-similarity 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: 0.6885714285714286 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: 0.9128571428571428 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.6885714285714286 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: 0.09128571428571426 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.6885714285714286 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: 0.9128571428571428 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.8022848173323525 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7666422902494329 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7696751281834099 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 512 type: dim_512 metrics: - type: cosine_accuracy@1 value: 0.6928571428571428 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8228571428571428 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8642857142857143 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.91 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.6928571428571428 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.27428571428571424 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.17285714285714285 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09099999999999998 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.6928571428571428 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.8228571428571428 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8642857142857143 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.91 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.8016907244180009 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7668412698412699 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.770110214157224 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 256 type: dim_256 metrics: - type: cosine_accuracy@1 value: 0.6871428571428572 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8185714285714286 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8628571428571429 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9014285714285715 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.6871428571428572 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.27285714285714285 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.17257142857142854 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09014285714285712 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.6871428571428572 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.8185714285714286 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8628571428571429 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.9014285714285715 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.7962767797304091 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7623021541950112 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7656765331908582 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 128 type: dim_128 metrics: - type: cosine_accuracy@1 value: 0.6742857142857143 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8057142857142857 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8528571428571429 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.8942857142857142 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.6742857142857143 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.26857142857142857 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.17057142857142854 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.08942857142857143 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.6742857142857143 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.8057142857142857 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8528571428571429 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.8942857142857142 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.7861958176742697 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7513151927437639 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7548627394954026 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 64 type: dim_64 metrics: - type: cosine_accuracy@1 value: 0.6428571428571429 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.7971428571428572 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8185714285714286 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.8685714285714285 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.6428571428571429 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.26571428571428574 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.1637142857142857 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.08685714285714284 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.6428571428571429 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.7971428571428572 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8185714285714286 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.8685714285714285 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.7590638034734002 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7236972789115643 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7282650681776726 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("WaheedLone/bge-base-financial-matryoshka") # Run inference sentences = [ 'The consolidated balance sheets of Visa Inc. as of September 30, 2023, list the total current assets at $33,532 million.', "What was the total of Visa Inc.'s current assets as of September 30, 2023?", "What was Garmin Ltd.'s net income for the fiscal year ended December 30, 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 | 0.6886 | | cosine_accuracy@3 | 0.8286 | | cosine_accuracy@5 | 0.8671 | | cosine_accuracy@10 | 0.9129 | | cosine_precision@1 | 0.6886 | | cosine_precision@3 | 0.2762 | | cosine_precision@5 | 0.1734 | | cosine_precision@10 | 0.0913 | | cosine_recall@1 | 0.6886 | | cosine_recall@3 | 0.8286 | | cosine_recall@5 | 0.8671 | | cosine_recall@10 | 0.9129 | | cosine_ndcg@10 | 0.8023 | | cosine_mrr@10 | 0.7666 | | **cosine_map@100** | **0.7697** | #### 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 | 0.6929 | | cosine_accuracy@3 | 0.8229 | | cosine_accuracy@5 | 0.8643 | | cosine_accuracy@10 | 0.91 | | cosine_precision@1 | 0.6929 | | cosine_precision@3 | 0.2743 | | cosine_precision@5 | 0.1729 | | cosine_precision@10 | 0.091 | | cosine_recall@1 | 0.6929 | | cosine_recall@3 | 0.8229 | | cosine_recall@5 | 0.8643 | | cosine_recall@10 | 0.91 | | cosine_ndcg@10 | 0.8017 | | cosine_mrr@10 | 0.7668 | | **cosine_map@100** | **0.7701** | #### 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 | 0.6871 | | cosine_accuracy@3 | 0.8186 | | cosine_accuracy@5 | 0.8629 | | cosine_accuracy@10 | 0.9014 | | cosine_precision@1 | 0.6871 | | cosine_precision@3 | 0.2729 | | cosine_precision@5 | 0.1726 | | cosine_precision@10 | 0.0901 | | cosine_recall@1 | 0.6871 | | cosine_recall@3 | 0.8186 | | cosine_recall@5 | 0.8629 | | cosine_recall@10 | 0.9014 | | cosine_ndcg@10 | 0.7963 | | cosine_mrr@10 | 0.7623 | | **cosine_map@100** | **0.7657** | #### 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 | 0.6743 | | cosine_accuracy@3 | 0.8057 | | cosine_accuracy@5 | 0.8529 | | cosine_accuracy@10 | 0.8943 | | cosine_precision@1 | 0.6743 | | cosine_precision@3 | 0.2686 | | cosine_precision@5 | 0.1706 | | cosine_precision@10 | 0.0894 | | cosine_recall@1 | 0.6743 | | cosine_recall@3 | 0.8057 | | cosine_recall@5 | 0.8529 | | cosine_recall@10 | 0.8943 | | cosine_ndcg@10 | 0.7862 | | cosine_mrr@10 | 0.7513 | | **cosine_map@100** | **0.7549** | #### 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 | 0.6429 | | cosine_accuracy@3 | 0.7971 | | cosine_accuracy@5 | 0.8186 | | cosine_accuracy@10 | 0.8686 | | cosine_precision@1 | 0.6429 | | cosine_precision@3 | 0.2657 | | cosine_precision@5 | 0.1637 | | cosine_precision@10 | 0.0869 | | cosine_recall@1 | 0.6429 | | cosine_recall@3 | 0.7971 | | cosine_recall@5 | 0.8186 | | cosine_recall@10 | 0.8686 | | cosine_ndcg@10 | 0.7591 | | cosine_mrr@10 | 0.7237 | | **cosine_map@100** | **0.7283** | ## Training Details ### Training Dataset #### Unnamed Dataset * 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 | |:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------| | Net revenue for fiscal year 2023 increased by $435 million compared to fiscal year 2022. | How did the net revenue for fiscal year 2023 compare to fiscal year 2022? | | Adjusted Free Cash Flow is defined as operating cash flow less capital spending and excluding payments for the transitional tax resulting from the U.S. Tax Act. | How is Adjusted Free Cash Flow defined in the text? | | During 2023, the Company’s net sales through its direct and indirect distribution channels accounted for 37% and 63%, respectively, of total net sales. | During 2023, what percentage of the Company’s net sales came from direct sales channels? | * 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 - `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`: 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`: 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 | 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 | |:----------:|:------:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:| | 0.8122 | 10 | 1.6399 | - | - | - | - | - | | 0.9746 | 12 | - | 0.7441 | 0.7580 | 0.7543 | 0.7068 | 0.7632 | | 1.6244 | 20 | 0.6475 | - | - | - | - | - | | 1.9492 | 24 | - | 0.7530 | 0.7653 | 0.7672 | 0.7244 | 0.7708 | | 2.4365 | 30 | 0.4494 | - | - | - | - | - | | 2.9239 | 36 | - | 0.7548 | 0.7653 | 0.7683 | 0.7297 | 0.7679 | | 3.2487 | 40 | 0.4089 | - | - | - | - | - | | **3.8985** | **48** | **-** | **0.7549** | **0.7657** | **0.7701** | **0.7283** | **0.7697** | * The bold row denotes the saved checkpoint. ### Framework Versions - Python: 3.10.12 - 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} } ```