--- 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: Net cash used in financing activities in 2023 was $2,430 million. sentences: - What criteria does Airbnb, Inc. use to assess if an available-for-sale security should be recorded as impaired on their financial statements? - What was the total amount of net cash used in financing activities in 2023? - How much did Visa authorize for its share repurchase program in October 2023? - source_sentence: Microsoft® and Windows® are either registered trademarks or trademarks of Microsoft Corporation in the United States and/or other countries. sentences: - Where does Eli Lilly and Company manufacture and distribute its products? - What is the significance of Microsoft® and Windows® in relation to Microsoft Corporation? - What percentage of total net revenue did the Americas region contribute in 2023? - source_sentence: We make available free of charge on the Investor Relations section of our corporate website all of the reports we file with or furnish to the SEC as soon as reasonably practicable, after the reports are filed or furnished. sentences: - Is there a cost to access reports filed by Intuit Inc. with the SEC? - What amount of cash, cash equivalents, and restricted cash did the company have at the end of the period? - Where in IBM’s 2023 Form 10-K can the Financial Statement Schedule be found? - source_sentence: The U.S. Automobile Information and Disclosure Act also requires manufacturers of motor vehicles to disclose certain information regarding the manufacturer’s suggested retail price, optional equipment and pricing. sentences: - What does the Adjusted Effective Tax Rate measure exclude? - What was the fair value of the total consideration transferred for the acquisition discussed, and how was it composed? - Which act requires U.S. automobile manufacturers to disclose certain pricing and equipment information? - source_sentence: Under the Insurance Act, Chubb's Bermuda domiciled subsidiaries are prohibited from declaring or paying any dividends of more than 25 percent of total statutory capital and surplus, as shown in its previous financial year statutory balance sheet, unless at least seven days before payment of the dividends, it files with the BMA an affidavit signed by at least two directors of the relevant Bermuda domiciled subsidiary (one of whom must be a director resident in Bermuda) and by the relevant Bermuda domiciled subsidiary’s principal representative, that it will continue to meet its required solvency margins. Furthermore, Bermuda domiciled subsidiaries may only declare and pay a dividend from retained earnings and a dividend or distribution from contributed surplus if it has no reasonable grounds for believing that it is, or would after the payment be, unable to pay its liabilities as they become due, or if the realizable value of its assets would be less than the aggregate of its liabilities. In addition, Chubb's Bermuda domiciled subsidiaries must obtain the BMA's prior approval before reducing total statutory capital, as shown in its previous financial year's financial statements, by 15 percent or more. sentences: - What are the restrictions and requirements for Bermuda domiciled subsidiaries regarding the distribution of dividends under the Insurance Act? - What section deals with financial statements and supplementary data? - What measures has the company implemented to ensure workplace safety? pipeline_tag: sentence-similarity model-index: - name: BGE small Financial Matryoshka results: - task: type: information-retrieval name: Information Retrieval dataset: name: dim 768 type: dim_768 metrics: - type: cosine_accuracy@1 value: 0.7042857142857143 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8457142857142858 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.88 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9242857142857143 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.7042857142857143 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.28190476190476194 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.176 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09242857142857142 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.7042857142857143 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.8457142857142858 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.88 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.9242857142857143 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.8153543862763872 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7803667800453513 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7829122109320609 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 512 type: dim_512 metrics: - type: cosine_accuracy@1 value: 0.7057142857142857 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8471428571428572 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8685714285714285 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9242857142857143 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.7057142857142857 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.28238095238095234 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.17371428571428568 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09242857142857142 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.7057142857142857 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.8471428571428572 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8685714285714285 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.9242857142857143 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.815124112835889 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7802040816326532 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7828080021041772 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 384 type: dim_384 metrics: - type: cosine_accuracy@1 value: 0.7071428571428572 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8385714285714285 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8757142857142857 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9228571428571428 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.7071428571428572 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.27952380952380956 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.17514285714285713 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09228571428571428 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.7071428571428572 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.8385714285714285 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8757142857142857 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.9228571428571428 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.815223056195625 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7808248299319727 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7833488292208493 name: Cosine Map@100 --- # BGE small 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("haophancs/bge-base-financial-matryoshka") # Run inference sentences = [ "Under the Insurance Act, Chubb's Bermuda domiciled subsidiaries are prohibited from declaring or paying any dividends of more than 25 percent of total statutory capital and surplus, as shown in its previous financial year statutory balance sheet, unless at least seven days before payment of the dividends, it files with the BMA an affidavit signed by at least two directors of the relevant Bermuda domiciled subsidiary (one of whom must be a director resident in Bermuda) and by the relevant Bermuda domiciled subsidiary’s principal representative, that it will continue to meet its required solvency margins. Furthermore, Bermuda domiciled subsidiaries may only declare and pay a dividend from retained earnings and a dividend or distribution from contributed surplus if it has no reasonable grounds for believing that it is, or would after the payment be, unable to pay its liabilities as they become due, or if the realizable value of its assets would be less than the aggregate of its liabilities. In addition, Chubb's Bermuda domiciled subsidiaries must obtain the BMA's prior approval before reducing total statutory capital, as shown in its previous financial year's financial statements, by 15 percent or more.", 'What are the restrictions and requirements for Bermuda domiciled subsidiaries regarding the distribution of dividends under the Insurance Act?', 'What section deals with financial statements and supplementary data?', ] 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 | 0.7043 | | cosine_accuracy@3 | 0.8457 | | cosine_accuracy@5 | 0.88 | | cosine_accuracy@10 | 0.9243 | | cosine_precision@1 | 0.7043 | | cosine_precision@3 | 0.2819 | | cosine_precision@5 | 0.176 | | cosine_precision@10 | 0.0924 | | cosine_recall@1 | 0.7043 | | cosine_recall@3 | 0.8457 | | cosine_recall@5 | 0.88 | | cosine_recall@10 | 0.9243 | | cosine_ndcg@10 | 0.8154 | | cosine_mrr@10 | 0.7804 | | **cosine_map@100** | **0.7829** | #### Information Retrieval * Dataset: `dim_512` * Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) | Metric | Value | |:--------------------|:-----------| | cosine_accuracy@1 | 0.7057 | | cosine_accuracy@3 | 0.8471 | | cosine_accuracy@5 | 0.8686 | | cosine_accuracy@10 | 0.9243 | | cosine_precision@1 | 0.7057 | | cosine_precision@3 | 0.2824 | | cosine_precision@5 | 0.1737 | | cosine_precision@10 | 0.0924 | | cosine_recall@1 | 0.7057 | | cosine_recall@3 | 0.8471 | | cosine_recall@5 | 0.8686 | | cosine_recall@10 | 0.9243 | | cosine_ndcg@10 | 0.8151 | | cosine_mrr@10 | 0.7802 | | **cosine_map@100** | **0.7828** | #### Information Retrieval * Dataset: `dim_384` * Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) | Metric | Value | |:--------------------|:-----------| | cosine_accuracy@1 | 0.7071 | | cosine_accuracy@3 | 0.8386 | | cosine_accuracy@5 | 0.8757 | | cosine_accuracy@10 | 0.9229 | | cosine_precision@1 | 0.7071 | | cosine_precision@3 | 0.2795 | | cosine_precision@5 | 0.1751 | | cosine_precision@10 | 0.0923 | | cosine_recall@1 | 0.7071 | | cosine_recall@3 | 0.8386 | | cosine_recall@5 | 0.8757 | | cosine_recall@10 | 0.9229 | | cosine_ndcg@10 | 0.8152 | | cosine_mrr@10 | 0.7808 | | **cosine_map@100** | **0.7833** | ## 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 | |:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------| | In 2023, $2.2 billion or 5% was primarily related to patient co-pay assistance, cash discounts for prompt payment, distributor fees, and sales return provisions. | What was the amount of sales return provisions in 2023 as part of gross-to-net deductions? | | Cash and cash equivalents were $21.9 billion at the end of 2023 as compared to $14.1 billion at the end of 2022, showing a $7.8 billion increase. | How much did cash and cash equivalents increase by the end of 2023 compared to the end of 2022? | | The net increase in cash and cash equivalents for UnitedHealthcare in 2023 compared to 2022 was $72 million. | What was the net increase in cash and cash equivalents for UnitedHealthcare in 2023 compared to 2022? | * Loss: [MatryoshkaLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "loss": "MultipleNegativesRankingLoss", "matryoshka_dims": [ 768, 512, 384 ], "matryoshka_weights": [ 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_384_cosine_map@100 | dim_512_cosine_map@100 | dim_768_cosine_map@100 | |:----------:|:------:|:-------------:|:----------------------:|:----------------------:|:----------------------:| | 0.8122 | 10 | 0.8256 | - | - | - | | 0.9746 | 12 | - | 0.7719 | 0.7679 | 0.7652 | | 1.6244 | 20 | 0.2984 | - | - | - | | 1.9492 | 24 | - | 0.7784 | 0.7810 | 0.7791 | | 2.4365 | 30 | 0.201 | - | - | - | | 2.9239 | 36 | - | 0.7835 | 0.7832 | 0.7828 | | 3.2487 | 40 | 0.1705 | - | - | - | | **3.8985** | **48** | **-** | **0.7833** | **0.7828** | **0.7829** | * The bold row denotes the saved checkpoint. ### Framework Versions - Python: 3.12.2 - 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} } ```