--- 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: 0.69 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8385714285714285 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.87 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.92 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.69 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.27952380952380956 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.174 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09199999999999998 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.69 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.8385714285714285 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.87 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.92 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.8078047173747194 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7717607709750567 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7745029834237301 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 512 type: dim_512 metrics: - type: cosine_accuracy@1 value: 0.7014285714285714 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8342857142857143 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8671428571428571 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9171428571428571 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.7014285714285714 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.27809523809523806 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.1734285714285714 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09171428571428569 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.7014285714285714 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.8342857142857143 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8671428571428571 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.9171428571428571 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.8099294101814819 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.775592970521542 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7785490266159816 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 256 type: dim_256 metrics: - type: cosine_accuracy@1 value: 0.6928571428571428 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: 0.91 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.6928571428571428 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: 0.091 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.6928571428571428 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: 0.91 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.8023495466461429 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7679013605442175 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7712468743892164 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 128 type: dim_128 metrics: - type: cosine_accuracy@1 value: 0.6728571428571428 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8171428571428572 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.85 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.8828571428571429 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.6728571428571428 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.2723809523809524 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.16999999999999998 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.08828571428571429 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.6728571428571428 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.8171428571428572 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.85 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.8828571428571429 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.7823204493781594 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7495634920634917 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.75425425293366 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: 0.79 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.83 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.8742857142857143 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.64 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.26333333333333336 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.16599999999999998 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.08742857142857141 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.64 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.79 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.83 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.8742857142857143 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.7602361447545036 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7233747165532877 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7278552309882971 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("sh4796/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 | 0.69 | | cosine_accuracy@3 | 0.8386 | | cosine_accuracy@5 | 0.87 | | cosine_accuracy@10 | 0.92 | | cosine_precision@1 | 0.69 | | cosine_precision@3 | 0.2795 | | cosine_precision@5 | 0.174 | | cosine_precision@10 | 0.092 | | cosine_recall@1 | 0.69 | | cosine_recall@3 | 0.8386 | | cosine_recall@5 | 0.87 | | cosine_recall@10 | 0.92 | | cosine_ndcg@10 | 0.8078 | | cosine_mrr@10 | 0.7718 | | **cosine_map@100** | **0.7745** | #### 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 | 0.7014 | | cosine_accuracy@3 | 0.8343 | | cosine_accuracy@5 | 0.8671 | | cosine_accuracy@10 | 0.9171 | | cosine_precision@1 | 0.7014 | | cosine_precision@3 | 0.2781 | | cosine_precision@5 | 0.1734 | | cosine_precision@10 | 0.0917 | | cosine_recall@1 | 0.7014 | | cosine_recall@3 | 0.8343 | | cosine_recall@5 | 0.8671 | | cosine_recall@10 | 0.9171 | | cosine_ndcg@10 | 0.8099 | | cosine_mrr@10 | 0.7756 | | **cosine_map@100** | **0.7785** | #### 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 | 0.6929 | | cosine_accuracy@3 | 0.8286 | | cosine_accuracy@5 | 0.8614 | | cosine_accuracy@10 | 0.91 | | cosine_precision@1 | 0.6929 | | cosine_precision@3 | 0.2762 | | cosine_precision@5 | 0.1723 | | cosine_precision@10 | 0.091 | | cosine_recall@1 | 0.6929 | | cosine_recall@3 | 0.8286 | | cosine_recall@5 | 0.8614 | | cosine_recall@10 | 0.91 | | cosine_ndcg@10 | 0.8023 | | cosine_mrr@10 | 0.7679 | | **cosine_map@100** | **0.7712** | #### 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 | 0.6729 | | cosine_accuracy@3 | 0.8171 | | cosine_accuracy@5 | 0.85 | | cosine_accuracy@10 | 0.8829 | | cosine_precision@1 | 0.6729 | | cosine_precision@3 | 0.2724 | | cosine_precision@5 | 0.17 | | cosine_precision@10 | 0.0883 | | cosine_recall@1 | 0.6729 | | cosine_recall@3 | 0.8171 | | cosine_recall@5 | 0.85 | | cosine_recall@10 | 0.8829 | | cosine_ndcg@10 | 0.7823 | | cosine_mrr@10 | 0.7496 | | **cosine_map@100** | **0.7543** | #### 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 | 0.79 | | cosine_accuracy@5 | 0.83 | | cosine_accuracy@10 | 0.8743 | | cosine_precision@1 | 0.64 | | cosine_precision@3 | 0.2633 | | cosine_precision@5 | 0.166 | | cosine_precision@10 | 0.0874 | | cosine_recall@1 | 0.64 | | cosine_recall@3 | 0.79 | | cosine_recall@5 | 0.83 | | cosine_recall@10 | 0.8743 | | cosine_ndcg@10 | 0.7602 | | cosine_mrr@10 | 0.7234 | | **cosine_map@100** | **0.7279** | ## 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 | 1.5604 | - | - | - | - | - | | 0.9746 | 12 | - | 0.7538 | 0.7540 | 0.7483 | 0.7284 | 0.6906 | | 1.6244 | 20 | 0.6618 | - | - | - | - | - | | 1.9492 | 24 | - | 0.7654 | 0.7632 | 0.7582 | 0.7424 | 0.7186 | | 2.4365 | 30 | 0.4579 | - | - | - | - | - | | 2.9239 | 36 | - | 0.7686 | 0.7646 | 0.7619 | 0.7459 | 0.7238 | | 3.2487 | 40 | 0.3995 | - | - | - | - | - | | **3.8985** | **48** | **-** | **0.7694** | **0.7633** | **0.7641** | **0.7449** | **0.7225** | | 0.8122 | 10 | 0.3798 | - | - | - | - | - | | 0.9746 | 12 | - | 0.7713 | 0.7685 | 0.7691 | 0.7489 | 0.7249 | | 1.6244 | 20 | 0.2958 | - | - | - | - | - | | 1.9492 | 24 | - | 0.7726 | 0.7699 | 0.7688 | 0.7517 | 0.7283 | | 2.4365 | 30 | 0.2273 | - | - | - | - | - | | 2.9239 | 36 | - | 0.7742 | 0.7761 | 0.7734 | 0.7532 | 0.7276 | | 3.2487 | 40 | 0.2136 | - | - | - | - | - | | **3.8985** | **48** | **-** | **0.7745** | **0.7785** | **0.7712** | **0.7543** | **0.7279** | * 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} } ```