--- 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: Item 3—Legal Proceedings See discussion of Legal Proceedings in Note 10 to the consolidated financial statements included in Item 8 of this Report. sentences: - How much did the company's finance lease obligations total as of December 31, 2023? - What do Note 10 and Item 8 of the report encompass? - What was the basic earnings per common share attributable to Comcast Corporation shareholders in 2023? - source_sentence: Our quarterly Insurance segment earnings and operating cash flows are impacted by the Medicare Part D benefit Grant program, the changing membership composition, and the multistage plan period starting annually on January 1. These plan designs generally result in us sharing a greater portion of the responsibility for total prescription drug costs in the early stages and less in the latter stages. sentences: - What are the two main categories into which Ford Motor Company classifies its costs and expenses, excluding those related to Ford Credit? - How does the benefit design of Medicare Part D impact the quarterly insurance segment earnings and operating cash flows? - What basis is used to record HTM investment securities in Schwab's financial statements? - source_sentence: Operating Profit in the Wizards of the Coast and Digital Gaming segment decreased 2% to $538.3 million. sentences: - How much did the Wizards of the Coast and Digital Gaming segment's operating profit change in 2022? - What factors are considered in evaluating the lifetime losses for most loans and receivables? - How did the loss on certain U.S. affiliates impact the Company's effective tax rate in the fiscal fourth quarter of 2021? - source_sentence: In 2023, the net earnings of Johnson & Johnson were $35,153 million. The company also registered cash dividends paid amounting to $11,770 million for the year, priced at $4.70 per share. sentences: - What was the postpaid churn rate for AT&T Inc. in 2023? - What was the GAAP net revenue for the fiscal year ended October 31, 2023? - What were the total net earnings of Johnson & Johnson in the year 2023? - source_sentence: During fiscal 2022, GameStop Corp increased its valuation allowances by approximately $70.2 million in various jurisdictions. sentences: - How much did GameStop Corp's valuation allowances increase during fiscal 2022? - How does Gilead ensure an inclusive and diverse workforce? - What factors are considered in determining the estimated future warranty costs for connected fitness and Precor branded fitness products? 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: 0.7185714285714285 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.83 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8714285714285714 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.91 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.7185714285714285 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.27666666666666667 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.17428571428571427 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.091 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.7185714285714285 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.83 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8714285714285714 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.91 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.8137967516958747 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7830442176870747 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7866777593387027 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 512 type: dim_512 metrics: - type: cosine_accuracy@1 value: 0.7114285714285714 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8314285714285714 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8728571428571429 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9142857142857143 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.7114285714285714 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.27714285714285714 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.17457142857142854 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09142857142857141 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.7114285714285714 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.8314285714285714 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8728571428571429 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.9142857142857143 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.8123538841130576 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7798667800453513 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7831580648041446 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 256 type: dim_256 metrics: - type: cosine_accuracy@1 value: 0.7 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8285714285714286 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8614285714285714 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9042857142857142 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.7 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.2761904761904762 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.17228571428571426 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09042857142857143 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.7 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.8285714285714286 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8614285714285714 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.9042857142857142 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.8043112987059042 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7721706349206346 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7759026470022171 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 128 type: dim_128 metrics: - type: cosine_accuracy@1 value: 0.6857142857142857 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8071428571428572 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8571428571428571 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.8971428571428571 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.6857142857142857 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.26904761904761904 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.1714285714285714 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.0897142857142857 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.6857142857142857 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.8071428571428572 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8571428571428571 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.8971428571428571 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.79087795854059 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7568854875283447 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7608935817550728 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 64 type: dim_64 metrics: - type: cosine_accuracy@1 value: 0.66 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.7757142857142857 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8128571428571428 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.8671428571428571 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.66 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.25857142857142856 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.16257142857142853 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.0867142857142857 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.66 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.7757142857142857 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8128571428571428 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.8671428571428571 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.7616045249840884 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7281247165532877 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7330922421864847 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("cristuf/bge-base-financial-matryoshka") # Run inference sentences = [ 'During fiscal 2022, GameStop Corp increased its valuation allowances by approximately $70.2 million in various jurisdictions.', "How much did GameStop Corp's valuation allowances increase during fiscal 2022?", 'How does Gilead ensure an inclusive and diverse workforce?', ] 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 | 0.7186 | | cosine_accuracy@3 | 0.83 | | cosine_accuracy@5 | 0.8714 | | cosine_accuracy@10 | 0.91 | | cosine_precision@1 | 0.7186 | | cosine_precision@3 | 0.2767 | | cosine_precision@5 | 0.1743 | | cosine_precision@10 | 0.091 | | cosine_recall@1 | 0.7186 | | cosine_recall@3 | 0.83 | | cosine_recall@5 | 0.8714 | | cosine_recall@10 | 0.91 | | cosine_ndcg@10 | 0.8138 | | cosine_mrr@10 | 0.783 | | **cosine_map@100** | **0.7867** | #### 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 | 0.7114 | | cosine_accuracy@3 | 0.8314 | | cosine_accuracy@5 | 0.8729 | | cosine_accuracy@10 | 0.9143 | | cosine_precision@1 | 0.7114 | | cosine_precision@3 | 0.2771 | | cosine_precision@5 | 0.1746 | | cosine_precision@10 | 0.0914 | | cosine_recall@1 | 0.7114 | | cosine_recall@3 | 0.8314 | | cosine_recall@5 | 0.8729 | | cosine_recall@10 | 0.9143 | | cosine_ndcg@10 | 0.8124 | | cosine_mrr@10 | 0.7799 | | **cosine_map@100** | **0.7832** | #### 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 | 0.7 | | cosine_accuracy@3 | 0.8286 | | cosine_accuracy@5 | 0.8614 | | cosine_accuracy@10 | 0.9043 | | cosine_precision@1 | 0.7 | | cosine_precision@3 | 0.2762 | | cosine_precision@5 | 0.1723 | | cosine_precision@10 | 0.0904 | | cosine_recall@1 | 0.7 | | cosine_recall@3 | 0.8286 | | cosine_recall@5 | 0.8614 | | cosine_recall@10 | 0.9043 | | cosine_ndcg@10 | 0.8043 | | cosine_mrr@10 | 0.7722 | | **cosine_map@100** | **0.7759** | #### 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 | 0.6857 | | cosine_accuracy@3 | 0.8071 | | cosine_accuracy@5 | 0.8571 | | cosine_accuracy@10 | 0.8971 | | cosine_precision@1 | 0.6857 | | cosine_precision@3 | 0.269 | | cosine_precision@5 | 0.1714 | | cosine_precision@10 | 0.0897 | | cosine_recall@1 | 0.6857 | | cosine_recall@3 | 0.8071 | | cosine_recall@5 | 0.8571 | | cosine_recall@10 | 0.8971 | | cosine_ndcg@10 | 0.7909 | | cosine_mrr@10 | 0.7569 | | **cosine_map@100** | **0.7609** | #### 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 | 0.66 | | cosine_accuracy@3 | 0.7757 | | cosine_accuracy@5 | 0.8129 | | cosine_accuracy@10 | 0.8671 | | cosine_precision@1 | 0.66 | | cosine_precision@3 | 0.2586 | | cosine_precision@5 | 0.1626 | | cosine_precision@10 | 0.0867 | | cosine_recall@1 | 0.66 | | cosine_recall@3 | 0.7757 | | cosine_recall@5 | 0.8129 | | cosine_recall@10 | 0.8671 | | cosine_ndcg@10 | 0.7616 | | cosine_mrr@10 | 0.7281 | | **cosine_map@100** | **0.7331** | ## 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 | |:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------| | Japan's revenue for the year 2023 reached 2,367.0 million. | What was the revenue attributed to Japan in the year 2023? | | Our four reportable segments are: •the Data Center segment, which primarily includes server CPUs, GPUs, APUs, DPUs, FPGAs, SmartNICs, AI accelerators and Adaptive SoC products for data centers; •the Client segment, which primarily includes CPUs, APUs, and chipsets for desktop, notebook and handheld personal computers; •the Gaming segment, which primarily includes discrete GPUs, semi-custom SoC products and development services; and •the Embedded segment, which primarily includes embedded CPUs, GPUs, APUs, FPGAs, SOMs, and Adaptive SoC products. | What are the different segments that AMD reports financially? | | For detailed information about the company's legal proceedings, see Note 4 to the consolidated financial statements, included under the caption 'Contingencies' in the Annual Report on Form 10-K. | Where can detailed information about the company's legal proceedings be found in its 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 - `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 | 1.5267 | - | - | - | - | - | | 0.9746 | 12 | - | 0.7446 | 0.7639 | 0.7765 | 0.7039 | 0.7725 | | 1.6244 | 20 | 0.6742 | - | - | - | - | - | | 1.9492 | 24 | - | 0.7606 | 0.7795 | 0.7828 | 0.7297 | 0.7839 | | 2.4365 | 30 | 0.4469 | - | - | - | - | - | | **2.9239** | **36** | **-** | **0.7643** | **0.7758** | **0.7834** | **0.7332** | **0.7845** | | 3.2487 | 40 | 0.3712 | - | - | - | - | - | | 3.8985 | 48 | - | 0.7609 | 0.7759 | 0.7832 | 0.7331 | 0.7867 | * The bold row denotes the saved checkpoint. ### Framework Versions - Python: 3.11.8 - Sentence Transformers: 3.0.1 - Transformers: 4.41.2 - PyTorch: 2.3.1+cu121 - Accelerate: 0.30.1 - 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} } ```