--- 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: As of December 31, 2023, Hilton franchised 6,679 hotels and resorts, with 914,974 rooms. sentences: - What does Google's new model 'Gemini' aim to achieve? - What is the total number of rooms in Hilton's franchised hotels as of December 31, 2023? - How much is the Company agreed to pay under the opioid settlement to resolve all lawsuits and future claims by government entities nationwide? - source_sentence: Under the Biologics Price Competition and Innovation Act, innovator biologics are granted a regulatory exclusivity period of 12 years. sentences: - What are the primary goals of the asset allocation strategy for USRIP's plan, and what standards must investment managers follow? - How long is the regulatory exclusivity period for innovator biologics under the Biologics Price Competition and Innovation Act? - By what percentage did the office loans increase in exposure during 2023? - source_sentence: Amounts recorded in a business combination may change during the measurement period, which is a period not to exceed one year from the date of acquisition, as additional information about conditions that existed at the acquisition date becomes available. sentences: - What is considered during the measurement period in a business combination? - What was the primary reason for the increase in other costs of $15.3 million reported? - How is the stock-based compensation expense determined for service-based and performance or market condition awards at Hewlett Packard Enterprise? - source_sentence: 'The Be Human pillar of our Impact Agenda sets out our focus areas with respect to human capital, including: •Inclusion, Diversity, Equity, and Action (“IDEA”); •Employee empowerment; and •Fair labor practices and the well-being of the people who make our products.' sentences: - How did Hilton Worldwide Holdings Inc.'s accumulated deficit change from December 31, 2022, to December 31, 2023? - What primarily caused the decrease in the Company's effective income tax rate in 2023? - What is the objective of the Be Human pillar in the company's Impact Agenda? - source_sentence: Our revenue consists of service fees, net of incentives and refunds, charged to our customers. For stays, service fees, which are charged to customers as a percentage of the value of the booking, excluding taxes, vary based on factors specific to the booking, such as booking value, the duration of the booking, geography, and Host type. sentences: - What are some factors that affect the percentage of service fees charged to customers? - What is the PCAOB ID number for PricewaterhouseCoopers LLP concerning the firm's financial statements? - What were the net revenues for Global Banking & Markets 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: 0.6957142857142857 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8485714285714285 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.6957142857142857 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.26666666666666666 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.16971428571428568 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.08999999999999998 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.6957142857142857 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.8 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8485714285714285 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.9 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.7935293220413043 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.759959183673469 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7639893123837201 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 512 type: dim_512 metrics: - type: cosine_accuracy@1 value: 0.7057142857142857 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8014285714285714 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8528571428571429 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9028571428571428 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.7057142857142857 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.2671428571428571 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.17057142857142854 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09028571428571427 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.7057142857142857 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.8014285714285714 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8528571428571429 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.9028571428571428 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.7983926017556883 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7656269841269838 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7693363291720529 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 256 type: dim_256 metrics: - type: cosine_accuracy@1 value: 0.6914285714285714 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.79 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8471428571428572 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.8914285714285715 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.6914285714285714 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.2633333333333333 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.16942857142857143 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.08914285714285713 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.6914285714285714 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.79 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8471428571428572 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.8914285714285715 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.7878064776962901 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7549427437641724 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7595543581664418 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 128 type: dim_128 metrics: - type: cosine_accuracy@1 value: 0.6885714285714286 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.7928571428571428 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8385714285714285 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.8914285714285715 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.6885714285714286 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.2642857142857143 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.1677142857142857 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.08914285714285713 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.6885714285714286 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.7928571428571428 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8385714285714285 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.8914285714285715 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.7855455284623294 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.752206916099773 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7560619398777708 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 64 type: dim_64 metrics: - type: cosine_accuracy@1 value: 0.64 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.7642857142857142 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8114285714285714 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.8671428571428571 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.64 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.25476190476190474 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.16228571428571426 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.0867142857142857 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.64 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.7642857142857142 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8114285714285714 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.8671428571428571 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.7491977147487785 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.711975623582766 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7167882776968978 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("bhlim/bge-base-financial-matryoshka") # Run inference sentences = [ 'Our revenue consists of service fees, net of incentives and refunds, charged to our customers. For stays, service fees, which are charged to customers as a percentage of the value of the booking, excluding taxes, vary based on factors specific to the booking, such as booking value, the duration of the booking, geography, and Host type.', 'What are some factors that affect the percentage of service fees charged to customers?', "What is the PCAOB ID number for PricewaterhouseCoopers LLP concerning the firm's financial statements?", ] 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 | 0.6957 | | cosine_accuracy@3 | 0.8 | | cosine_accuracy@5 | 0.8486 | | cosine_accuracy@10 | 0.9 | | cosine_precision@1 | 0.6957 | | cosine_precision@3 | 0.2667 | | cosine_precision@5 | 0.1697 | | cosine_precision@10 | 0.09 | | cosine_recall@1 | 0.6957 | | cosine_recall@3 | 0.8 | | cosine_recall@5 | 0.8486 | | cosine_recall@10 | 0.9 | | cosine_ndcg@10 | 0.7935 | | cosine_mrr@10 | 0.76 | | **cosine_map@100** | **0.764** | #### 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 | 0.7057 | | cosine_accuracy@3 | 0.8014 | | cosine_accuracy@5 | 0.8529 | | cosine_accuracy@10 | 0.9029 | | cosine_precision@1 | 0.7057 | | cosine_precision@3 | 0.2671 | | cosine_precision@5 | 0.1706 | | cosine_precision@10 | 0.0903 | | cosine_recall@1 | 0.7057 | | cosine_recall@3 | 0.8014 | | cosine_recall@5 | 0.8529 | | cosine_recall@10 | 0.9029 | | cosine_ndcg@10 | 0.7984 | | cosine_mrr@10 | 0.7656 | | **cosine_map@100** | **0.7693** | #### 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 | 0.6914 | | cosine_accuracy@3 | 0.79 | | cosine_accuracy@5 | 0.8471 | | cosine_accuracy@10 | 0.8914 | | cosine_precision@1 | 0.6914 | | cosine_precision@3 | 0.2633 | | cosine_precision@5 | 0.1694 | | cosine_precision@10 | 0.0891 | | cosine_recall@1 | 0.6914 | | cosine_recall@3 | 0.79 | | cosine_recall@5 | 0.8471 | | cosine_recall@10 | 0.8914 | | cosine_ndcg@10 | 0.7878 | | cosine_mrr@10 | 0.7549 | | **cosine_map@100** | **0.7596** | #### 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 | 0.6886 | | cosine_accuracy@3 | 0.7929 | | cosine_accuracy@5 | 0.8386 | | cosine_accuracy@10 | 0.8914 | | cosine_precision@1 | 0.6886 | | cosine_precision@3 | 0.2643 | | cosine_precision@5 | 0.1677 | | cosine_precision@10 | 0.0891 | | cosine_recall@1 | 0.6886 | | cosine_recall@3 | 0.7929 | | cosine_recall@5 | 0.8386 | | cosine_recall@10 | 0.8914 | | cosine_ndcg@10 | 0.7855 | | cosine_mrr@10 | 0.7522 | | **cosine_map@100** | **0.7561** | #### 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 | 0.64 | | cosine_accuracy@3 | 0.7643 | | cosine_accuracy@5 | 0.8114 | | cosine_accuracy@10 | 0.8671 | | cosine_precision@1 | 0.64 | | cosine_precision@3 | 0.2548 | | cosine_precision@5 | 0.1623 | | cosine_precision@10 | 0.0867 | | cosine_recall@1 | 0.64 | | cosine_recall@3 | 0.7643 | | cosine_recall@5 | 0.8114 | | cosine_recall@10 | 0.8671 | | cosine_ndcg@10 | 0.7492 | | cosine_mrr@10 | 0.712 | | **cosine_map@100** | **0.7168** | ## 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 | |:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------| | Within the contiguous U.S., FedEx Freight offers FedEx Freight Priority, when speed is critical to meet a customer’s supply chain needs. | How does FedEx Freight accommodate rapid delivery needs? | | For purposes of our goodwill impairment evaluation, the reporting units are Family Dollar, Dollar Tree and Dollar Tree Canada. | What reporting units are used for the goodwill impairment evaluation? | | In 2024, AT&T Inc. expects a long-term rate of return of 7.75% on pension plan assets, reflecting an increase of 0.25%. This adjustment in expected returns is based on economic forecasts and changes in the asset mix. | What will AT&T Inc.'s expected long-term rate of return be on pension plan assets in 2024? | * 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 | |:----------:|:------:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:| | 0.8122 | 10 | 1.5825 | - | - | - | - | - | | 0.9746 | 12 | - | 0.7349 | 0.7502 | 0.7566 | 0.6910 | 0.7566 | | 1.6244 | 20 | 0.6595 | - | - | - | - | - | | 1.9492 | 24 | - | 0.7508 | 0.7583 | 0.7648 | 0.7142 | 0.7615 | | 2.4365 | 30 | 0.4717 | - | - | - | - | - | | **2.9239** | **36** | **-** | **0.7562** | **0.7616** | **0.7692** | **0.7178** | **0.7622** | | 3.2487 | 40 | 0.4059 | - | - | - | - | - | | 3.8985 | 48 | - | 0.7561 | 0.7596 | 0.7693 | 0.7168 | 0.7640 | * The bold row denotes the saved checkpoint. ### Framework Versions - Python: 3.10.12 - Sentence Transformers: 3.0.1 - Transformers: 4.41.2 - PyTorch: 2.3.1+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} } ```