--- 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:6300 - loss:MatryoshkaLoss - loss:MultipleNegativesRankingLoss widget: - source_sentence: The lawsuits were filed in the wake of media reports that the U.S. Department of Justice had served civil investigative demands upon these carriers seeking documents and information relating to this subject. sentences: - What type of details does Note 15 of the Consolidated Financial Statements provide? - What action did the U.S. Department of Justice take in relation to the antitrust allegations against Delta, American, United, and Southwest airlines? - What does the index in a financial report indicate? - source_sentence: Unearned Revenue comprises mainly unearned revenue related to volume licensing programs, which may include Software Assurance ("SA") and cloud services. sentences: - What was the total number of Starbucks employees worldwide as of October 1, 2023? - What primarily comprises unearned revenue according to the discussed financial statements? - How are impairment charges for the years 2021, 2022, and 2023 recorded for restaurants and offices, and what is their impact on financial statements? - source_sentence: Total sales and revenues for 2023 were $67.060 billion, an increase of $7.633 billion, or 13 percent, compared with $59.427 billion in 2022. sentences: - How much did Caterpillar's total sales and revenues increase by in 2023 compared to 2022? - What is included in the cost of revenues for Google? - What entity audited the company's consolidated financial statements? - source_sentence: 'Weighted average remaining lease term and discount rate at March 31, 2023 and 2022 are as follows: At March 31, 2023 - Lease term: 7.5 years, Discount rate: 3.3%; At March 31, 2022 - Lease term: 6.8 years, Discount rate: 2.5%.' sentences: - What operating system is used for the Company's iPhone line? - What was the SRO's accrued amount as a receivable for CAT implementation expenses as of December 31, 2023? - What were the lease terms and discount rates for operating leases as of March 31, 2023 and 2022? - source_sentence: During 2023, continuing investing activities generated $240 million, significantly influenced by $14.5 billion received from the maturities and sales of investments, with expenditures of $13.9 billion on investments and $456 million on property and equipment. sentences: - What significant financial activity occurred in continuing investing activities in 2023? - What indicates where to find information about legal proceedings in the consolidated financial statements of an Annual Report on Form 10-K? - How much cash, cash equivalents, and unrestricted marketable securities did the company have as of September 30, 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: 0.6871428571428572 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8171428571428572 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8542857142857143 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9042857142857142 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.6871428571428572 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.27238095238095233 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.17085714285714282 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09042857142857141 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.6871428571428572 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.8171428571428572 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8542857142857143 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.9042857142857142 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.7940751364022482 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7589863945578228 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7632147157763912 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 512 type: dim_512 metrics: - type: cosine_accuracy@1 value: 0.6828571428571428 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8142857142857143 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8542857142857143 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9014285714285715 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.6828571428571428 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.2714285714285714 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.17085714285714285 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09014285714285714 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.6828571428571428 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.8142857142857143 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8542857142857143 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.9014285714285715 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.7923306650275913 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7573690476190474 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7616425347398016 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 256 type: dim_256 metrics: - type: cosine_accuracy@1 value: 0.6642857142857143 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8042857142857143 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8557142857142858 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.8971428571428571 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.6642857142857143 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.2680952380952381 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.17114285714285712 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.0897142857142857 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.6642857142857143 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.8042857142857143 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8557142857142858 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.8971428571428571 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.781836757101301 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7447794784580494 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7491639960128558 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 128 type: dim_128 metrics: - type: cosine_accuracy@1 value: 0.6457142857142857 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.7828571428571428 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.83 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.8857142857142857 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.6457142857142857 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.26095238095238094 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.16599999999999998 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.08857142857142856 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.6457142857142857 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.7828571428571428 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.83 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.8857142857142857 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.7638551069830676 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7249971655328794 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7295529486648893 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 64 type: dim_64 metrics: - type: cosine_accuracy@1 value: 0.6171428571428571 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.7385714285714285 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.7928571428571428 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.84 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.6171428571428571 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.24619047619047615 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.15857142857142856 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.08399999999999999 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.6171428571428571 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.7385714285714285 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.7928571428571428 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.84 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.7256498773041486 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.6893407029478454 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.6948404384614005 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("Liu-Xiang/bge-base-financial-matryoshka") # Run inference sentences = [ 'During 2023, continuing investing activities generated $240 million, significantly influenced by $14.5 billion received from the maturities and sales of investments, with expenditures of $13.9 billion on investments and $456 million on property and equipment.', 'What significant financial activity occurred in continuing investing activities in 2023?', 'What indicates where to find information about legal proceedings in the consolidated financial statements of an Annual Report on Form 10-K?', ] 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 | 0.6871 | | cosine_accuracy@3 | 0.8171 | | cosine_accuracy@5 | 0.8543 | | cosine_accuracy@10 | 0.9043 | | cosine_precision@1 | 0.6871 | | cosine_precision@3 | 0.2724 | | cosine_precision@5 | 0.1709 | | cosine_precision@10 | 0.0904 | | cosine_recall@1 | 0.6871 | | cosine_recall@3 | 0.8171 | | cosine_recall@5 | 0.8543 | | cosine_recall@10 | 0.9043 | | cosine_ndcg@10 | 0.7941 | | cosine_mrr@10 | 0.759 | | **cosine_map@100** | **0.7632** | #### 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 | 0.6829 | | cosine_accuracy@3 | 0.8143 | | cosine_accuracy@5 | 0.8543 | | cosine_accuracy@10 | 0.9014 | | cosine_precision@1 | 0.6829 | | cosine_precision@3 | 0.2714 | | cosine_precision@5 | 0.1709 | | cosine_precision@10 | 0.0901 | | cosine_recall@1 | 0.6829 | | cosine_recall@3 | 0.8143 | | cosine_recall@5 | 0.8543 | | cosine_recall@10 | 0.9014 | | cosine_ndcg@10 | 0.7923 | | cosine_mrr@10 | 0.7574 | | **cosine_map@100** | **0.7616** | #### 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 | 0.6643 | | cosine_accuracy@3 | 0.8043 | | cosine_accuracy@5 | 0.8557 | | cosine_accuracy@10 | 0.8971 | | cosine_precision@1 | 0.6643 | | cosine_precision@3 | 0.2681 | | cosine_precision@5 | 0.1711 | | cosine_precision@10 | 0.0897 | | cosine_recall@1 | 0.6643 | | cosine_recall@3 | 0.8043 | | cosine_recall@5 | 0.8557 | | cosine_recall@10 | 0.8971 | | cosine_ndcg@10 | 0.7818 | | cosine_mrr@10 | 0.7448 | | **cosine_map@100** | **0.7492** | #### 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 | 0.6457 | | cosine_accuracy@3 | 0.7829 | | cosine_accuracy@5 | 0.83 | | cosine_accuracy@10 | 0.8857 | | cosine_precision@1 | 0.6457 | | cosine_precision@3 | 0.261 | | cosine_precision@5 | 0.166 | | cosine_precision@10 | 0.0886 | | cosine_recall@1 | 0.6457 | | cosine_recall@3 | 0.7829 | | cosine_recall@5 | 0.83 | | cosine_recall@10 | 0.8857 | | cosine_ndcg@10 | 0.7639 | | cosine_mrr@10 | 0.725 | | **cosine_map@100** | **0.7296** | #### 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 | 0.6171 | | cosine_accuracy@3 | 0.7386 | | cosine_accuracy@5 | 0.7929 | | cosine_accuracy@10 | 0.84 | | cosine_precision@1 | 0.6171 | | cosine_precision@3 | 0.2462 | | cosine_precision@5 | 0.1586 | | cosine_precision@10 | 0.084 | | cosine_recall@1 | 0.6171 | | cosine_recall@3 | 0.7386 | | cosine_recall@5 | 0.7929 | | cosine_recall@10 | 0.84 | | cosine_ndcg@10 | 0.7256 | | cosine_mrr@10 | 0.6893 | | **cosine_map@100** | **0.6948** | ## 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 | |:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | If the discount rate used to calculate the present value of these reserves changed by 25 basis points, net income would have been affected by approximately $1.1 million for fiscal 2023. | By what amount would net income for fiscal 2023 be affected if the discount rate used for calculating the present value of reserves changed by 25 basis points? | | Net revenue | $ | 8,110,518 | | | $ | 6,256,617 | | 100.0 | % | 100.0 | % | $ | 1,853,901 | 29.6 | % | What was the percentage increase in net revenue in 2022 compared to 2021? | | Item 8 covers Financial Statements and Supplementary Data. | What is included in Item 8 of the document? | * 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`: 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`: 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 | 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 | |:--------:|:------:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:| | 0.96 | 3 | - | 0.6943 | 0.7200 | 0.7341 | 0.6337 | 0.7346 | | 1.92 | 6 | - | 0.7178 | 0.7393 | 0.7525 | 0.6764 | 0.7513 | | 2.88 | 9 | - | 0.7280 | 0.7468 | 0.7584 | 0.6926 | 0.7611 | | 3.2 | 10 | 3.3659 | - | - | - | - | - | | **3.84** | **12** | **-** | **0.7296** | **0.7492** | **0.7616** | **0.6948** | **0.7632** | * The bold row denotes the saved checkpoint. ### Framework Versions - Python: 3.9.18 - Sentence Transformers: 3.0.1 - Transformers: 4.41.2 - PyTorch: 2.1.2+cu121 - Accelerate: 0.32.1 - 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} } ```