--- 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: Item 8 in IBM's 2023 Annual Report to Stockholders details the Financial Statements and Supplementary Data, which are included on pages 44 through 121. sentences: - What was the amount gained from the disposal of assets in 2022? - What section of IBM's Annual Report for 2023 contains the Financial Statements and Supplementary Data? - What were the cash outflows for capital expenditures in 2023 and 2022 respectively? - source_sentence: For the fiscal year ended March 31, 2023, Electronic Arts reported a gross margin of 75.9 percent, an increase of 2.5 percentage points from the previous year. sentences: - How did investment banking revenues at Goldman Sachs change in 2023 compared to 2022, and what factors contributed to this change? - What was the gross margin percentage for Electronic Arts in the fiscal year ending March 31, 2023? - What were the risk-free interest rates for the fiscal years 2021, 2022, and 2023? - source_sentence: Cash, cash equivalents, and restricted cash at the beginning of the period totaled $7,013 for a company. sentences: - What was the amount of cash, cash equivalents, and restricted cash at the beginning of the period for the company? - What is the impact of the new $1.25 price point on Dollar Tree’s sales units and profitability? - What was the total amount attributed to Goodwill in the acquisition of Nuance Communications, Inc. as reported by the company? - source_sentence: generate our mall revenue primarily from leases with tenants through base minimum rents, overage rents and reimbursements for common area maintenance (CAM) and other expenditures. sentences: - How does Visa facilitate financial inclusion with their prepaid cards? - What are the main objectives of the economic sanctions imposed by the United States and other international bodies? - What revenue sources does Shoppes at Venetian primarily rely on from its tenants? - source_sentence: For the fiscal year ended August 26, 2023, we reported net sales of $17.5 billion compared with $16.3 billion for the year ended August 27, 2022, a 7.4% increase from fiscal 2022. This growth was driven primarily by a domestic same store sales increase of 3.4% and net sales of $327.8 million from new domestic and international stores. sentences: - What drove the 7.4% increase in AutoZone's net sales for fiscal 2023 compared to fiscal 2022? - What percentage of HP's external U.S. hires in fiscal year 2023 were racially or ethnically diverse? - How much did GameStop Corp's valuation allowances increase during fiscal 2022? 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: 0.6985714285714286 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8271428571428572 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8628571428571429 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.8985714285714286 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.6985714285714286 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.2757142857142857 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.17257142857142854 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.08985714285714284 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.6985714285714286 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.8271428571428572 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8628571428571429 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.8985714285714286 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.8023663256793517 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7712675736961451 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7758522351159084 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 512 type: dim_512 metrics: - type: cosine_accuracy@1 value: 0.69 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8271428571428572 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.86 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9028571428571428 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.69 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.2757142857142857 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.17199999999999996 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09028571428571427 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.69 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.8271428571428572 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.86 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.9028571428571428 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.7998655910794988 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7665912698412698 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7706925401671437 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 256 type: dim_256 metrics: - type: cosine_accuracy@1 value: 0.6957142857142857 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8228571428571428 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.86 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.8914285714285715 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.6957142857142857 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.2742857142857143 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.17199999999999996 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.08914285714285713 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.6957142857142857 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.8228571428571428 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.86 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.8914285714285715 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.7974564108711016 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7669535147392289 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7718155211819018 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 128 type: dim_128 metrics: - type: cosine_accuracy@1 value: 0.6871428571428572 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8128571428571428 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8457142857142858 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.8857142857142857 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.6871428571428572 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.27095238095238094 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.16914285714285712 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.08857142857142856 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.6871428571428572 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.8128571428571428 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8457142857142858 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.8857142857142857 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.787697533881839 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.756192743764172 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7610331995977764 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 64 type: dim_64 metrics: - type: cosine_accuracy@1 value: 0.6328571428571429 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.7771428571428571 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8171428571428572 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.8571428571428571 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.6328571428571429 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.259047619047619 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.16342857142857142 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.08571428571428569 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.6328571428571429 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.7771428571428571 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8171428571428572 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.8571428571428571 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.7482728321357093 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7131224489795914 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7189753431460272 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("NickyNicky/bge-base-financial-matryoshka") # Run inference sentences = [ 'For the fiscal year ended August 26, 2023, we reported net sales of $17.5 billion compared with $16.3 billion for the year ended August 27, 2022, a 7.4% increase from fiscal 2022. This growth was driven primarily by a domestic same store sales increase of 3.4% and net sales of $327.8 million from new domestic and international stores.', "What drove the 7.4% increase in AutoZone's net sales for fiscal 2023 compared to fiscal 2022?", "What percentage of HP's external U.S. hires in fiscal year 2023 were racially or ethnically diverse?", ] 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 | 0.6986 | | cosine_accuracy@3 | 0.8271 | | cosine_accuracy@5 | 0.8629 | | cosine_accuracy@10 | 0.8986 | | cosine_precision@1 | 0.6986 | | cosine_precision@3 | 0.2757 | | cosine_precision@5 | 0.1726 | | cosine_precision@10 | 0.0899 | | cosine_recall@1 | 0.6986 | | cosine_recall@3 | 0.8271 | | cosine_recall@5 | 0.8629 | | cosine_recall@10 | 0.8986 | | cosine_ndcg@10 | 0.8024 | | cosine_mrr@10 | 0.7713 | | **cosine_map@100** | **0.7759** | #### 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 | 0.69 | | cosine_accuracy@3 | 0.8271 | | cosine_accuracy@5 | 0.86 | | cosine_accuracy@10 | 0.9029 | | cosine_precision@1 | 0.69 | | cosine_precision@3 | 0.2757 | | cosine_precision@5 | 0.172 | | cosine_precision@10 | 0.0903 | | cosine_recall@1 | 0.69 | | cosine_recall@3 | 0.8271 | | cosine_recall@5 | 0.86 | | cosine_recall@10 | 0.9029 | | cosine_ndcg@10 | 0.7999 | | cosine_mrr@10 | 0.7666 | | **cosine_map@100** | **0.7707** | #### 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 | 0.6957 | | cosine_accuracy@3 | 0.8229 | | cosine_accuracy@5 | 0.86 | | cosine_accuracy@10 | 0.8914 | | cosine_precision@1 | 0.6957 | | cosine_precision@3 | 0.2743 | | cosine_precision@5 | 0.172 | | cosine_precision@10 | 0.0891 | | cosine_recall@1 | 0.6957 | | cosine_recall@3 | 0.8229 | | cosine_recall@5 | 0.86 | | cosine_recall@10 | 0.8914 | | cosine_ndcg@10 | 0.7975 | | cosine_mrr@10 | 0.767 | | **cosine_map@100** | **0.7718** | #### 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 | 0.6871 | | cosine_accuracy@3 | 0.8129 | | cosine_accuracy@5 | 0.8457 | | cosine_accuracy@10 | 0.8857 | | cosine_precision@1 | 0.6871 | | cosine_precision@3 | 0.271 | | cosine_precision@5 | 0.1691 | | cosine_precision@10 | 0.0886 | | cosine_recall@1 | 0.6871 | | cosine_recall@3 | 0.8129 | | cosine_recall@5 | 0.8457 | | cosine_recall@10 | 0.8857 | | cosine_ndcg@10 | 0.7877 | | cosine_mrr@10 | 0.7562 | | **cosine_map@100** | **0.761** | #### 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 | 0.6329 | | cosine_accuracy@3 | 0.7771 | | cosine_accuracy@5 | 0.8171 | | cosine_accuracy@10 | 0.8571 | | cosine_precision@1 | 0.6329 | | cosine_precision@3 | 0.259 | | cosine_precision@5 | 0.1634 | | cosine_precision@10 | 0.0857 | | cosine_recall@1 | 0.6329 | | cosine_recall@3 | 0.7771 | | cosine_recall@5 | 0.8171 | | cosine_recall@10 | 0.8571 | | cosine_ndcg@10 | 0.7483 | | cosine_mrr@10 | 0.7131 | | **cosine_map@100** | **0.719** | ## 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 | |:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------| | Cash used in financing activities in fiscal 2022 was primarily attributable to settlement of stock-based awards. | Why was there a net outflow of cash in financing activities in fiscal 2022? | | Certain vendors have been impacted by volatility in the supply chain financing market. | How have certain vendors been impacted in the supply chain financing market? | | In the consolidated financial statements for Visa, the net cash provided by operating activities amounted to 20,755 units in the most recent period, 18,849 units in the previous period, and 15,227 units in the period before that. | How much net cash did Visa's operating activities generate in the most recent period according to the financial statements? | * 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 - `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`: False - `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 | 1.5643 | - | - | - | - | - | | 0.9746 | 12 | - | 0.7349 | 0.7494 | 0.7524 | 0.6987 | 0.7569 | | 1.6244 | 20 | 0.6756 | - | - | - | - | - | | 1.9492 | 24 | - | 0.7555 | 0.7659 | 0.7683 | 0.7190 | 0.7700 | | 2.4365 | 30 | 0.4561 | - | - | - | - | - | | 2.9239 | 36 | - | 0.7592 | 0.7698 | 0.7698 | 0.7184 | 0.7741 | | 3.2487 | 40 | 0.3645 | - | - | - | - | - | | 3.8985 | 48 | - | 0.7610 | 0.7718 | 0.7707 | 0.7190 | 0.7759 | ### Framework Versions - Python: 3.10.12 - Sentence Transformers: 3.0.1 - Transformers: 4.41.2 - PyTorch: 2.2.0+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} } ```