--- 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: Item 8 includes Financial Statements and Supplementary Data. sentences: - What does the FDA label update for Yescarta include as of the latest approval? - What information can be found in Item 8 of a document? - When does the Company's fiscal year end? - source_sentence: Item 8 in a financial document is designated for Financial Statements and Supplementary Data. sentences: - What are the primary goals of AutoZone's store management system? - What information is contained in Item 8 of a financial document? - What were the pre-tax earnings of the manufacturing sector in 2023, 2022, and 2021? - source_sentence: of approximately $1.0 billion in IBNR liabilities, producing a corresponding decrease in pre-tax earnings. We believe it is reasonably possible for these assumptions to increase at these rates. sentences: - What was the decrease in pre-tax earnings due to the $1.0 billion in IBNR liabilities? - What was the total long-term debt, including the current portion, for AbbVie as of December 31, 2023? - What feature dedicated AI hardware in an x86 processor and uses the XDNA architecture? - source_sentence: In the year ended December 31, 2023, sellers generated GMS of $13.2 billion, approximately 68% of which came from purchases made on mobile devices. sentences: - What was the change in the total balance of revolving credits from December 31, 2022, to December 31, 2023? - What are the purposes of borrowings under the 2021 credit facility? - What percentage of Etsy's Gross Merchandise Sales (GMS) in 2023 came from mobile purchases? - source_sentence: As of December 31, 2023, approximately $1.80 billion is available to be repatriated from Mainland China to the U.S. sentences: - What is the total amount of unrestricted cash available for repatriation from Mainland China to the U.S. as of the end of 2023? - What is the focus of the company's research and development efforts? - Where does the Report of Independent Registered Public Accounting Firm begin in this report? 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: 0.6771428571428572 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8142857142857143 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8642857142857143 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9142857142857143 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.6771428571428572 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.2714285714285714 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.17285714285714282 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09142857142857141 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.6771428571428572 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.8142857142857143 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8642857142857143 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.9142857142857143 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.7948920706768223 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7568055555555551 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7601580985784901 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 512 type: dim_512 metrics: - type: cosine_accuracy@1 value: 0.6714285714285714 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8157142857142857 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8657142857142858 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.92 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.6714285714285714 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.27190476190476187 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.17314285714285713 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09199999999999998 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.6714285714285714 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.8157142857142857 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8657142857142858 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.92 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.7936366054643341 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7534455782312921 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.756388193211117 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 256 type: dim_256 metrics: - type: cosine_accuracy@1 value: 0.6714285714285714 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8157142857142857 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8585714285714285 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9157142857142857 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.6714285714285714 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.27190476190476187 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.1717142857142857 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09157142857142857 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.6714285714285714 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.8157142857142857 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8585714285714285 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.9157142857142857 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.7926136922070053 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7535062358276641 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7564593466816174 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 128 type: dim_128 metrics: - type: cosine_accuracy@1 value: 0.6614285714285715 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8414285714285714 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.8885714285714286 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.6614285714285715 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.26666666666666666 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.16828571428571426 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.08885714285714286 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.6614285714285715 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.8 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8414285714285714 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.8885714285714286 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.7767052058983972 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7407840136054418 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7454236920389576 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 64 type: dim_64 metrics: - type: cosine_accuracy@1 value: 0.6357142857142857 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.7742857142857142 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8185714285714286 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.8642857142857143 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.6357142857142857 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.2580952380952381 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.1637142857142857 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.08642857142857142 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.6357142857142857 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.7742857142857142 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8185714285714286 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.8642857142857143 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.7511926722277801 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7148713151927435 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7199017346952273 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 dimensions - **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("SMARTICT/bge-base-financial-matryoshka") # Run inference sentences = [ 'As of December 31, 2023, approximately $1.80 billion is available to be repatriated from Mainland China to the U.S.', 'What is the total amount of unrestricted cash available for repatriation from Mainland China to the U.S. as of the end of 2023?', "What is the focus of the company's research and development efforts?", ] 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 * Datasets: `dim_768`, `dim_512`, `dim_256`, `dim_128` and `dim_64` * Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) | Metric | dim_768 | dim_512 | dim_256 | dim_128 | dim_64 | |:--------------------|:-----------|:-----------|:-----------|:-----------|:-----------| | cosine_accuracy@1 | 0.6771 | 0.6714 | 0.6714 | 0.6614 | 0.6357 | | cosine_accuracy@3 | 0.8143 | 0.8157 | 0.8157 | 0.8 | 0.7743 | | cosine_accuracy@5 | 0.8643 | 0.8657 | 0.8586 | 0.8414 | 0.8186 | | cosine_accuracy@10 | 0.9143 | 0.92 | 0.9157 | 0.8886 | 0.8643 | | cosine_precision@1 | 0.6771 | 0.6714 | 0.6714 | 0.6614 | 0.6357 | | cosine_precision@3 | 0.2714 | 0.2719 | 0.2719 | 0.2667 | 0.2581 | | cosine_precision@5 | 0.1729 | 0.1731 | 0.1717 | 0.1683 | 0.1637 | | cosine_precision@10 | 0.0914 | 0.092 | 0.0916 | 0.0889 | 0.0864 | | cosine_recall@1 | 0.6771 | 0.6714 | 0.6714 | 0.6614 | 0.6357 | | cosine_recall@3 | 0.8143 | 0.8157 | 0.8157 | 0.8 | 0.7743 | | cosine_recall@5 | 0.8643 | 0.8657 | 0.8586 | 0.8414 | 0.8186 | | cosine_recall@10 | 0.9143 | 0.92 | 0.9157 | 0.8886 | 0.8643 | | **cosine_ndcg@10** | **0.7949** | **0.7936** | **0.7926** | **0.7767** | **0.7512** | | cosine_mrr@10 | 0.7568 | 0.7534 | 0.7535 | 0.7408 | 0.7149 | | cosine_map@100 | 0.7602 | 0.7564 | 0.7565 | 0.7454 | 0.7199 | ## 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 | |:---------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------| | Information on legal proceedings is included in Note 15 to the Consolidated Financial Statements. | What note in the Consolidated Financial Statements provides details on legal proceedings? | | As of December 31, 2023, approximately $1.80 billion is available to be repatriated from Mainland China to the U.S. | What is the total amount of unrestricted cash available for repatriation from Mainland China to the U.S. as of the end of 2023? | | Bank deposits amounted to $289,953 million as of December 31, 2023. | What was the balance of bank deposits at Charles Schwab Corporation as of 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 - `prompts`: None - `batch_sampler`: no_duplicates - `multi_dataset_batch_sampler`: proportional
### Training Logs | Epoch | Step | Training Loss | dim_768_cosine_ndcg@10 | dim_512_cosine_ndcg@10 | dim_256_cosine_ndcg@10 | dim_128_cosine_ndcg@10 | dim_64_cosine_ndcg@10 | |:----------:|:------:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:| | 0.8122 | 10 | 1.5517 | - | - | - | - | - | | 0.9746 | 12 | - | 0.7830 | 0.7842 | 0.7814 | 0.7623 | 0.7215 | | 1.6244 | 20 | 0.6616 | - | - | - | - | - | | 1.9492 | 24 | - | 0.7918 | 0.7924 | 0.7884 | 0.7737 | 0.7429 | | 2.4365 | 30 | 0.46 | - | - | - | - | - | | 2.9239 | 36 | - | 0.7941 | 0.7920 | 0.7930 | 0.7764 | 0.7482 | | 3.2487 | 40 | 0.3917 | - | - | - | - | - | | **3.8985** | **48** | **-** | **0.7949** | **0.7936** | **0.7926** | **0.7767** | **0.7512** | * The bold row denotes the saved checkpoint. ### Framework Versions - Python: 3.10.12 - Sentence Transformers: 3.3.1 - Transformers: 4.41.2 - PyTorch: 2.1.2+cu121 - Accelerate: 0.34.2 - 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} } ```