--- base_model: BAAI/bge-small-en-v1.5 library_name: sentence-transformers 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:1090 - loss:MultipleNegativesRankingLoss widget: - source_sentence: how do different regions contribute to my returns sentences: - '[{"get_portfolio([''type''],None)": "portfolio"}, {"filter(''portfolio'',''type'',''=='',''ETF'')": "portfolio"}, {"get_attribute(''portfolio'',[''losses''],'''')": "portfolio"}, {"filter(''portfolio'',''losses'',''<'',''0'')": "portfolio"}, {"sort(''portfolio'',''losses'',''asc'')": "portfolio"}]' - '[{"get_portfolio(None,None)": "portfolio"}, {"factor_contribution(''portfolio'','''',''region'',None,''returns'')": "portfolio"}]' - '[{"get_portfolio([''marketValue''],None)": "portfolio"}, {"get_attribute(''portfolio'',[''''],'''')": "portfolio"}, {"calculate(''portfolio'',[''marketValue'', ''''],''multiply'',''expo_'')": "portfolio"}, {"sort(''portfolio'',''expo_'',''desc'')": "portfolio"}, {"aggregate(''portfolio'',''ticker'',''expo_'',''sum'',None)": "port_expo_"}]' - source_sentence: which percent of my portfolio is in single stocks? sentences: - '[{"get_portfolio([''quantity'', ''averageCost'', ''marketValue''],None)": "portfolio"}, {"filter(''portfolio'',''ticker'',''=='','''')": "portfolio"}, {"calculate(''portfolio'',[''quantity'', ''averageCost''],''multiply'',''cost_basis'')": "portfolio"}, {"calculate(''portfolio'',[''marketValue'', ''cost_basis''],''difference'',''profit'')": "profit_"}, {"aggregate(''portfolio'',''ticker'',''profit'',''sum'',None)": "profit_"}]' - '[{"get_portfolio([''type''],None)": "portfolio"}, {"filter(''portfolio'',''type'',''=='',''SHARE'')": "portfolio"}, {"aggregate(''portfolio'',''ticker'',''marketValue'',''sum'',None)": "stocks_amount"}]' - '[{"get_portfolio(None,None)": "portfolio"}, {"get_attribute(''portfolio'',[''dividend yield''],'''')": "portfolio"}, {"filter(''portfolio'',''dividend yield'',''>'',''0'')": "portfolio"}, {"sort(''portfolio'',''dividend yield'',''desc'')": "portfolio"}]' - source_sentence: what is the volatility of each of my holdings? sentences: - '[{"get_portfolio(None,None)": "portfolio"}, {"get_attribute(''portfolio'',[''gains''],'''')": "portfolio"}, {"sort(''portfolio'',''gains'',''desc'')": "portfolio"}, {"get_attribute([''''],[''returns''],'''')": "_performance_data"}]' - '[{"get_portfolio(None,None)": "portfolio"}, {"factor_contribution(''portfolio'','''',''asset_class'',''global bonds'',''returns'')": "portfolio"}]' - '[{"get_portfolio([''type''],None)": "portfolio"}, {"get_attribute(''portfolio'',[''risk''],'''')": "portfolio"}, {"sort(''portfolio'',''risk'',''asc'')": "portfolio"}]' - source_sentence: list all paper trading portfolios sentences: - '[{"get_all_portfolios(''virtual'')": "virtual_portfolios"}]' - '[{"get_portfolio([''averageCost''],None)": "portfolio"}, {"get_attribute(''portfolio'',[''price''],'''')": "portfolio"}, {"calculate(''portfolio'',[''price'', ''averageCost''],''difference'',''price_delta'')": "portfolio"}, {"filter(''portfolio'',''price_delta'',''>'',''0'')": "portfolio"}, {"sort(''portfolio'',''price_delta'',''desc'')": "portfolio"}]' - '[{"get_portfolio(None,)": "portfolio"}, {"get_attribute(''portfolio'',[''gains''],'''')": "portfolio"}, {"filter(''portfolio'',''gains'',''>'',''0'')": "portfolio"}, {"sort(''portfolio'',''gains'',''desc'')": "portfolio"}]' - source_sentence: what is my exposure to US Equities? sentences: - '[{"get_portfolio(None,None)": "portfolio"}, {"factor_contribution(''portfolio'','''',''asset_class'',''us equity'',''portfolio'')": "portfolio"}]' - '[{"get_portfolio(None,None)": "portfolio"}, {"factor_contribution(''portfolio'','''',''sector'',''sector industrials'',''portfolio'')": "portfolio"}]' - '[{"get_portfolio([''type''],None)": "portfolio"}, {"filter(''portfolio'',''type'',''=='',''ETF'')": "portfolio"}, {"get_attribute(''portfolio'',[''gains''],'''')": "portfolio"}, {"filter(''portfolio'',''gains'',''>'',''0'')": "portfolio"}, {"sort(''portfolio'',''gains'',''desc'')": "portfolio"}]' model-index: - name: SentenceTransformer based on BAAI/bge-small-en-v1.5 results: - task: type: information-retrieval name: Information Retrieval dataset: name: Unknown type: unknown metrics: - type: cosine_accuracy@1 value: 0.678082191780822 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8082191780821918 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.863013698630137 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9315068493150684 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.678082191780822 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.2694063926940639 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.17260273972602735 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09315068493150684 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.018835616438356163 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.02245053272450533 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.02397260273972603 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.025875190258751908 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.17595381476268288 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7579120460969775 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.02111814463536371 name: Cosine Map@100 --- # SentenceTransformer based on BAAI/bge-small-en-v1.5 This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5). It maps sentences & paragraphs to a 384-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-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) - **Maximum Sequence Length:** 512 tokens - **Output Dimensionality:** 384 dimensions - **Similarity Function:** Cosine Similarity ### 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': 384, '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("sentence_transformers_model_id") # Run inference sentences = [ 'what is my exposure to US Equities?', '[{"get_portfolio(None,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'\',\'asset_class\',\'us equity\',\'portfolio\')": "portfolio"}]', '[{"get_portfolio(None,None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'\',\'sector\',\'sector industrials\',\'portfolio\')": "portfolio"}]', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 384] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` ## Evaluation ### Metrics #### Information Retrieval * Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) | Metric | Value | |:--------------------|:----------| | cosine_accuracy@1 | 0.6781 | | cosine_accuracy@3 | 0.8082 | | cosine_accuracy@5 | 0.863 | | cosine_accuracy@10 | 0.9315 | | cosine_precision@1 | 0.6781 | | cosine_precision@3 | 0.2694 | | cosine_precision@5 | 0.1726 | | cosine_precision@10 | 0.0932 | | cosine_recall@1 | 0.0188 | | cosine_recall@3 | 0.0225 | | cosine_recall@5 | 0.024 | | cosine_recall@10 | 0.0259 | | **cosine_ndcg@10** | **0.176** | | cosine_mrr@10 | 0.7579 | | cosine_map@100 | 0.0211 | ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 1,090 training samples * Columns: sentence_0 and sentence_1 * Approximate statistics based on the first 1000 samples: | | sentence_0 | sentence_1 | |:--------|:----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------| | type | string | string | | details |
  • min: 5 tokens
  • mean: 13.28 tokens
  • max: 27 tokens
|
  • min: 26 tokens
  • mean: 87.73 tokens
  • max: 196 tokens
| * Samples: | sentence_0 | sentence_1 | |:------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | what is my portfolio [DATES] cagr? | [{"get_portfolio(None,None)": "portfolio"}, {"get_attribute('portfolio',['gains'],'')": "portfolio"}, {"sort('portfolio','gains','desc')": "portfolio"}] | | what is my [DATES] rate of return | [{"get_portfolio(None,None)": "portfolio"}, {"get_attribute('portfolio',['gains'],'')": "portfolio"}, {"sort('portfolio','gains','desc')": "portfolio"}] | | show backtest of my performance [DATES]? | [{"get_portfolio(None,None)": "portfolio"}, {"get_attribute('portfolio',['gains'],'')": "portfolio"}, {"sort('portfolio','gains','desc')": "portfolio"}] | * Loss: [MultipleNegativesRankingLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: ```json { "scale": 20.0, "similarity_fct": "cos_sim" } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `per_device_train_batch_size`: 10 - `per_device_eval_batch_size`: 10 - `num_train_epochs`: 6 - `multi_dataset_batch_sampler`: round_robin #### All Hyperparameters
Click to expand - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: steps - `prediction_loss_only`: True - `per_device_train_batch_size`: 10 - `per_device_eval_batch_size`: 10 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 1 - `eval_accumulation_steps`: None - `torch_empty_cache_steps`: None - `learning_rate`: 5e-05 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1 - `num_train_epochs`: 6 - `max_steps`: -1 - `lr_scheduler_type`: linear - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.0 - `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`: False - `fp16`: False - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: None - `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`: False - `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 - `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 - `eval_on_start`: False - `eval_use_gather_object`: False - `prompts`: None - `batch_sampler`: batch_sampler - `multi_dataset_batch_sampler`: round_robin
### Training Logs
Click to expand | Epoch | Step | cosine_ndcg@10 | |:------:|:----:|:--------------:| | 0.0183 | 2 | 0.1179 | | 0.0367 | 4 | 0.1184 | | 0.0550 | 6 | 0.1193 | | 0.0734 | 8 | 0.1201 | | 0.0917 | 10 | 0.1227 | | 0.1101 | 12 | 0.1235 | | 0.1284 | 14 | 0.1255 | | 0.1468 | 16 | 0.1267 | | 0.1651 | 18 | 0.1299 | | 0.1835 | 20 | 0.1320 | | 0.2018 | 22 | 0.1348 | | 0.2202 | 24 | 0.1367 | | 0.2385 | 26 | 0.1383 | | 0.2569 | 28 | 0.1413 | | 0.2752 | 30 | 0.1420 | | 0.2936 | 32 | 0.1432 | | 0.3119 | 34 | 0.1435 | | 0.3303 | 36 | 0.1451 | | 0.3486 | 38 | 0.1471 | | 0.3670 | 40 | 0.1491 | | 0.3853 | 42 | 0.1503 | | 0.4037 | 44 | 0.1523 | | 0.4220 | 46 | 0.1525 | | 0.4404 | 48 | 0.1531 | | 0.4587 | 50 | 0.1535 | | 0.4771 | 52 | 0.1534 | | 0.4954 | 54 | 0.1529 | | 0.5138 | 56 | 0.1528 | | 0.5321 | 58 | 0.1556 | | 0.5505 | 60 | 0.1568 | | 0.5688 | 62 | 0.1576 | | 0.5872 | 64 | 0.1577 | | 0.6055 | 66 | 0.1577 | | 0.6239 | 68 | 0.1575 | | 0.6422 | 70 | 0.1586 | | 0.6606 | 72 | 0.1596 | | 0.6789 | 74 | 0.1612 | | 0.6972 | 76 | 0.1617 | | 0.7156 | 78 | 0.1637 | | 0.7339 | 80 | 0.1638 | | 0.7523 | 82 | 0.1637 | | 0.7706 | 84 | 0.1635 | | 0.7890 | 86 | 0.1634 | | 0.8073 | 88 | 0.1640 | | 0.8257 | 90 | 0.1641 | | 0.8440 | 92 | 0.1652 | | 0.8624 | 94 | 0.1652 | | 0.8807 | 96 | 0.1657 | | 0.8991 | 98 | 0.1650 | | 0.9174 | 100 | 0.1664 | | 0.9358 | 102 | 0.1668 | | 0.9541 | 104 | 0.1671 | | 0.9725 | 106 | 0.1683 | | 0.9908 | 108 | 0.1689 | | 1.0 | 109 | 0.1684 | | 1.0092 | 110 | 0.1673 | | 1.0275 | 112 | 0.1686 | | 1.0459 | 114 | 0.1680 | | 1.0642 | 116 | 0.1676 | | 1.0826 | 118 | 0.1668 | | 1.1009 | 120 | 0.1668 | | 1.1193 | 122 | 0.1671 | | 1.1376 | 124 | 0.1673 | | 1.1560 | 126 | 0.1666 | | 1.1743 | 128 | 0.1669 | | 1.1927 | 130 | 0.1668 | | 1.2110 | 132 | 0.1669 | | 1.2294 | 134 | 0.1673 | | 1.2477 | 136 | 0.1681 | | 1.2661 | 138 | 0.1683 | | 1.2844 | 140 | 0.1681 | | 1.3028 | 142 | 0.1674 | | 1.3211 | 144 | 0.1672 | | 1.3394 | 146 | 0.1668 | | 1.3578 | 148 | 0.1682 | | 1.3761 | 150 | 0.1689 | | 1.3945 | 152 | 0.1690 | | 1.4128 | 154 | 0.1693 | | 1.4312 | 156 | 0.1683 | | 1.4495 | 158 | 0.1683 | | 1.4679 | 160 | 0.1678 | | 1.4862 | 162 | 0.1695 | | 1.5046 | 164 | 0.1710 | | 1.5229 | 166 | 0.1717 | | 1.5413 | 168 | 0.1715 | | 1.5596 | 170 | 0.1698 | | 1.5780 | 172 | 0.1699 | | 1.5963 | 174 | 0.1694 | | 1.6147 | 176 | 0.1701 | | 1.6330 | 178 | 0.1693 | | 1.6514 | 180 | 0.1683 | | 1.6697 | 182 | 0.1692 | | 1.6881 | 184 | 0.1689 | | 1.7064 | 186 | 0.1696 | | 1.7248 | 188 | 0.1696 | | 1.7431 | 190 | 0.1700 | | 1.7615 | 192 | 0.1705 | | 1.7798 | 194 | 0.1718 | | 1.7982 | 196 | 0.1719 | | 1.8165 | 198 | 0.1723 | | 1.8349 | 200 | 0.1721 | | 1.8532 | 202 | 0.1717 | | 1.8716 | 204 | 0.1722 | | 1.8899 | 206 | 0.1722 | | 1.9083 | 208 | 0.1728 | | 1.9266 | 210 | 0.1734 | | 1.9450 | 212 | 0.1733 | | 1.9633 | 214 | 0.1742 | | 1.9817 | 216 | 0.1749 | | 2.0 | 218 | 0.1750 | | 2.0183 | 220 | 0.1760 |
### Framework Versions - Python: 3.10.9 - Sentence Transformers: 3.3.1 - Transformers: 4.44.0 - PyTorch: 2.4.0+cu121 - Accelerate: 0.33.0 - Datasets: 2.20.0 - 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", } ``` #### 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} } ```