--- base_model: BAAI/bge-base-en-v1.5 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 consolidated financial statements and accompanying notes listed in Part IV, Item 15(a)(1) of this Annual Report on Form 10-K are included elsewhere in this Annual Report on Form 10-K. sentences: - What is the carrying value of the indefinite-lived intangible assets related to the Certificate of Needs and Medicare licenses as of December 31, 2023? - What sections of the Annual Report on Form 10-K contain the company's financial statements? - What was the effective tax rate excluding discrete net tax benefits for the year 2022? - source_sentence: Consumers are served through Amazon's online and physical stores with an emphasis on selection, price, and convenience. sentences: - What decision did the European Commission make on July 10, 2023 regarding the United States? - What are the primary offerings to consumers through Amazon's online and physical stores? - What activities are included in the services and other revenue segment of General Motors Company? - source_sentence: Visa has traditionally referred to their structure of facilitating secure, reliable, and efficient money movement among consumers, issuing and acquiring financial institutions, and merchants as the 'four-party' model. sentences: - What model does Visa traditionally refer to regarding their transaction process among consumers, financial institutions, and merchants? - What percentage of Meta's U.S. workforce in 2023 were represented by people with disabilities, veterans, and members of the LGBTQ+ community? - What are the revenue sources for the Company’s Health Care Benefits Segment? - source_sentence: 'In addition to LinkedIn’s free services, LinkedIn offers monetized solutions: Talent Solutions, Marketing Solutions, Premium Subscriptions, and Sales Solutions. Talent Solutions provide insights for workforce planning and tools to hire, nurture, and develop talent. Talent Solutions also includes Learning Solutions, which help businesses close critical skills gaps in times where companies are having to do more with existing talent.' sentences: - What were the major factors contributing to the increased expenses excluding interest for Investor Services and Advisor Services in 2023? - What were the pre-tax earnings of the manufacturing sector in 2023, 2022, and 2021? - What does LinkedIn's Talent Solutions include? - source_sentence: Management assessed the effectiveness of the company’s internal control over financial reporting as of December 31, 2023. In making this assessment, we used the criteria set forth by the Committee of Sponsoring Organizations of the Treadway Commission (COSO) in Internal Control—Integrated Framework (2013). sentences: - What criteria did Caterpillar Inc. use to assess the effectiveness of its internal control over financial reporting as of December 31, 2023? - What are the primary components of U.S. sales volumes for Ford? - What was the percentage increase in Schwab's common stock dividend in 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: 0.6914285714285714 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8242857142857143 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.86 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9057142857142857 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.6914285714285714 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.2747619047619047 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.17199999999999996 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09057142857142855 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.6914285714285714 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.8242857142857143 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.86 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.9057142857142857 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.8001680395387087 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7662573696145122 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.769823052973869 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 512 type: dim_512 metrics: - type: cosine_accuracy@1 value: 0.6857142857142857 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.82 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8642857142857143 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9085714285714286 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.6857142857142857 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.2733333333333333 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.17285714285714285 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09085714285714284 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.6857142857142857 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.82 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8642857142857143 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.9085714285714286 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.7971766161179724 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7614897959183673 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7646090238973834 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 256 type: dim_256 metrics: - type: cosine_accuracy@1 value: 0.6857142857142857 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8142857142857143 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8557142857142858 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.8957142857142857 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.6857142857142857 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.2714285714285714 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.17114285714285715 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.08957142857142855 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.6857142857142857 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.8142857142857143 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8557142857142858 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.8957142857142857 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.7918397113464477 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7584121315192744 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7622442104975016 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 128 type: dim_128 metrics: - type: cosine_accuracy@1 value: 0.6671428571428571 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8042857142857143 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8428571428571429 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.88 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.6671428571428571 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.2680952380952381 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.16857142857142854 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.088 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.6671428571428571 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.8042857142857143 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8428571428571429 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.88 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.7749293718962911 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7410045351473922 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7452488037980776 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 64 type: dim_64 metrics: - type: cosine_accuracy@1 value: 0.6442857142857142 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.78 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8185714285714286 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.8585714285714285 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.6442857142857142 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.26 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.1637142857142857 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.08585714285714285 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.6442857142857142 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.78 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8185714285714286 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.8585714285714285 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.7530016364587485 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7189931972789114 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7238907012576284 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) on the json dataset. 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 - **Training Dataset:** - json - **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("silenthunter25/bge-base-financial-matryoshka") # Run inference sentences = [ 'Management assessed the effectiveness of the company’s internal control over financial reporting as of December 31, 2023. In making this assessment, we used the criteria set forth by the Committee of Sponsoring Organizations of the Treadway Commission (COSO) in Internal Control—Integrated Framework (2013).', 'What criteria did Caterpillar Inc. use to assess the effectiveness of its internal control over financial reporting as of December 31, 2023?', 'What are the primary components of U.S. sales volumes for Ford?', ] 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 | 0.6914 | | cosine_accuracy@3 | 0.8243 | | cosine_accuracy@5 | 0.86 | | cosine_accuracy@10 | 0.9057 | | cosine_precision@1 | 0.6914 | | cosine_precision@3 | 0.2748 | | cosine_precision@5 | 0.172 | | cosine_precision@10 | 0.0906 | | cosine_recall@1 | 0.6914 | | cosine_recall@3 | 0.8243 | | cosine_recall@5 | 0.86 | | cosine_recall@10 | 0.9057 | | cosine_ndcg@10 | 0.8002 | | cosine_mrr@10 | 0.7663 | | **cosine_map@100** | **0.7698** | #### 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 | 0.6857 | | cosine_accuracy@3 | 0.82 | | cosine_accuracy@5 | 0.8643 | | cosine_accuracy@10 | 0.9086 | | cosine_precision@1 | 0.6857 | | cosine_precision@3 | 0.2733 | | cosine_precision@5 | 0.1729 | | cosine_precision@10 | 0.0909 | | cosine_recall@1 | 0.6857 | | cosine_recall@3 | 0.82 | | cosine_recall@5 | 0.8643 | | cosine_recall@10 | 0.9086 | | cosine_ndcg@10 | 0.7972 | | cosine_mrr@10 | 0.7615 | | **cosine_map@100** | **0.7646** | #### 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 | 0.6857 | | cosine_accuracy@3 | 0.8143 | | cosine_accuracy@5 | 0.8557 | | cosine_accuracy@10 | 0.8957 | | cosine_precision@1 | 0.6857 | | cosine_precision@3 | 0.2714 | | cosine_precision@5 | 0.1711 | | cosine_precision@10 | 0.0896 | | cosine_recall@1 | 0.6857 | | cosine_recall@3 | 0.8143 | | cosine_recall@5 | 0.8557 | | cosine_recall@10 | 0.8957 | | cosine_ndcg@10 | 0.7918 | | cosine_mrr@10 | 0.7584 | | **cosine_map@100** | **0.7622** | #### 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 | 0.6671 | | cosine_accuracy@3 | 0.8043 | | cosine_accuracy@5 | 0.8429 | | cosine_accuracy@10 | 0.88 | | cosine_precision@1 | 0.6671 | | cosine_precision@3 | 0.2681 | | cosine_precision@5 | 0.1686 | | cosine_precision@10 | 0.088 | | cosine_recall@1 | 0.6671 | | cosine_recall@3 | 0.8043 | | cosine_recall@5 | 0.8429 | | cosine_recall@10 | 0.88 | | cosine_ndcg@10 | 0.7749 | | cosine_mrr@10 | 0.741 | | **cosine_map@100** | **0.7452** | #### 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 | 0.6443 | | cosine_accuracy@3 | 0.78 | | cosine_accuracy@5 | 0.8186 | | cosine_accuracy@10 | 0.8586 | | cosine_precision@1 | 0.6443 | | cosine_precision@3 | 0.26 | | cosine_precision@5 | 0.1637 | | cosine_precision@10 | 0.0859 | | cosine_recall@1 | 0.6443 | | cosine_recall@3 | 0.78 | | cosine_recall@5 | 0.8186 | | cosine_recall@10 | 0.8586 | | cosine_ndcg@10 | 0.753 | | cosine_mrr@10 | 0.719 | | **cosine_map@100** | **0.7239** | ## Training Details ### Training Dataset #### json * Dataset: json * 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 | |:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | The Company defines fair value as the price received to transfer an asset or paid to transfer a liability in an orderly transaction between market participants at the measurement date. In accordance with ASC 820, Fair Value Measurements and Disclosures, the Company uses the fair value hierarchy which prioritizes the inputs used to measure fair value. The hierarchy gives the highest priority to unadjusted quoted prices in active markets for identical assets or liabilities (Level 1), observable inputs other than quoted prices (Level 2), and unobservable inputs (Level 3). | What is the role of Level 1, Level 2, and Level 3 inputs in the fair value hierarchy according to ASC 820? | | In the event of conversion of the Notes, if shares are delivered to the Company under the Capped Call Transactions, they will offset the dilutive effect of the shares that the Company would issue under the Notes. | What happens to the dilutive effect of shares issued under the Notes if shares are delivered to the Company under the Capped Call Transactions during the conversion? | | Marketing expenses increased $48.8 million to $759.2 million in the year ended December 31, 2023 compared to the year ended December 31, 2022. | How much did the marketing expenses increase in the year ended December 31, 2023? | * 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_768_cosine_map@100 | dim_512_cosine_map@100 | dim_256_cosine_map@100 | dim_128_cosine_map@100 | dim_64_cosine_map@100 | |:----------:|:------:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:| | 0.8122 | 10 | 1.5603 | - | - | - | - | - | | 0.9746 | 12 | - | 0.7554 | 0.7556 | 0.7486 | 0.7288 | 0.6899 | | 1.6244 | 20 | 0.6613 | - | - | - | - | - | | 1.9492 | 24 | - | 0.7653 | 0.7633 | 0.7581 | 0.7424 | 0.7194 | | 2.4365 | 30 | 0.4575 | - | - | - | - | - | | 2.9239 | 36 | - | 0.7689 | 0.7643 | 0.7617 | 0.7448 | 0.7230 | | 3.2487 | 40 | 0.3997 | - | - | - | - | - | | **3.8985** | **48** | **-** | **0.7698** | **0.7646** | **0.7622** | **0.7452** | **0.7239** | * The bold row denotes the saved checkpoint. ### Framework Versions - Python: 3.10.12 - Sentence Transformers: 3.2.0 - Transformers: 4.41.2 - PyTorch: 2.2.0a0+6a974be - Accelerate: 0.27.0 - 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} } ```