--- base_model: BAAI/bge-small-en-v1.5 datasets: [] language: [] 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 - dot_accuracy@1 - dot_accuracy@3 - dot_accuracy@5 - dot_accuracy@10 - dot_precision@1 - dot_precision@3 - dot_precision@5 - dot_precision@10 - dot_recall@1 - dot_recall@3 - dot_recall@5 - dot_recall@10 - dot_ndcg@10 - dot_mrr@10 - dot_map@100 pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:723 - loss:MultipleNegativesRankingLoss widget: - source_sentence: how do different regions contribute to my returns sentences: - '[{"get_portfolio(None)": "portfolio"}, {"filter(''portfolio'',''ticker'',''=='','''')": "portfolio"}, {"get_attribute(''portfolio'',[''losses''],'''')": "portfolio"}]' - '[{"get_portfolio(None)": "portfolio"}, {"factor_contribution(''portfolio'','''',''region'',None,''returns'')": "portfolio"}]' - '[{"get_portfolio(None)": "portfolio"}, {"get_attribute(''portfolio'',[''gains''],'''')": "portfolio"}, {"sort(''portfolio'',''gains'',''desc'')": "portfolio"}, {"get_attribute([''''],[''returns''],'''')": "_performance_data"}]' - source_sentence: how have I done in US equity this year? sentences: - '[{"get_portfolio([''weight''])": "portfolio"}, {"get_attribute(''portfolio'',[''dividend yield''],'''')": "portfolio"}, {"calculate(''portfolio'',[''dividend yield'',''weight''],''multiply'',''weighted_yield'')": "portfolio"}, {"aggregate(''portfolio'',''ticker'',''weighted_yield'',''sum'',None)": "portfolio_yield"}]' - '[{"get_portfolio(None)": "portfolio"}, {"get_attribute(''portfolio'',[''dividend yield''],'''')": "portfolio"}, {"calculate(''portfolio'',[''dividend yield'', ''marketValue''],''multiply'',''div_income'')": "portfolio"}, {"sort(''portfolio'',''div_income'',''desc'')": "portfolio"}]' - '[{"get_portfolio(None)": "portfolio"}, {"factor_contribution(''portfolio'','''',''asset_class'',''us equity'',''returns'')": "portfolio"}]' - source_sentence: What is the total value of my cash? sentences: - '[{"get_portfolio(None)": "portfolio"}, {"factor_contribution(''portfolio'','''',''sector'',''sector utilities'',''portfolio'')": "portfolio"}]' - '[{"get_portfolio([''type'', ''marketValue''])": "portfolio"}, {"filter(''portfolio'',''type'',''=='',''CASH'')": "portfolio"}, {"aggregate(''portfolio'',''ticker'',''marketValue'',''sum'',None)": "buying_power"}]' - '[{"get_portfolio(None)": "portfolio"}, {"factor_contribution(''portfolio'','''',''sector'',''sector information technology'',''returns'')": "portfolio"}]' - source_sentence: What is the exposure of my account to Chinese market? sentences: - '[{"get_portfolio([''marketValue''])": "portfolio"}, {"sort(''portfolio'',''marketValue'',''asc'')": "portfolio"}]' - '[{"get_portfolio(None)": "portfolio"}, {"factor_contribution(''portfolio'','''',''region'',''china'',''portfolio'')": "portfolio"}]' - '[{"get_portfolio(None)": "portfolio"}, {"factor_contribution(''portfolio'','''',''volatility'',None,''portfolio'')": "portfolio"}]' - source_sentence: Which of my investments are projected to generate the most return? sentences: - '[{"get_portfolio([''marketValue''])": "portfolio"}, {"get_attribute(''portfolio'',[''''],'''')": "portfolio"}, {"calculate(''portfolio'',[''marketValue'', ''''],''multiply'',''expo_'')": "portfolio"}, {"sort(''portfolio'',''expo_'',''desc'')": "portfolio"}, {"aggregate(''portfolio'',''ticker'',''expo_'',''sum'',None)": "port_expo_"}]' - '[{"get_portfolio(None)": "portfolio"}, {"factor_contribution(''portfolio'','''',''asset_class'',''us equity'',''returns'')": "portfolio"}]' - '[{"get_portfolio(None)": "portfolio"}, {"get_expected_attribute(''portfolio'',[''returns''])": "portfolio"}, {"sort(''portfolio'',''returns'',''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: 0.6643835616438356 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8287671232876712 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.863013698630137 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9178082191780822 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.6643835616438356 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.27625570776255703 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.17260273972602735 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.0917808219178082 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.018455098934550992 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.023021308980213092 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.02397260273972603 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.02549467275494673 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.1736543171752474 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7480294629267232 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.020863027954722068 name: Cosine Map@100 - type: dot_accuracy@1 value: 0.6643835616438356 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.8287671232876712 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.863013698630137 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.9178082191780822 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.6643835616438356 name: Dot Precision@1 - type: dot_precision@3 value: 0.27625570776255703 name: Dot Precision@3 - type: dot_precision@5 value: 0.17260273972602735 name: Dot Precision@5 - type: dot_precision@10 value: 0.0917808219178082 name: Dot Precision@10 - type: dot_recall@1 value: 0.018455098934550992 name: Dot Recall@1 - type: dot_recall@3 value: 0.023021308980213092 name: Dot Recall@3 - type: dot_recall@5 value: 0.02397260273972603 name: Dot Recall@5 - type: dot_recall@10 value: 0.02549467275494673 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.1736543171752474 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.7480294629267232 name: Dot Mrr@10 - type: dot_map@100 value: 0.020863027954722068 name: Dot 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 tokens - **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 = [ 'Which of my investments are projected to generate the most return?', '[{"get_portfolio(None)": "portfolio"}, {"get_expected_attribute(\'portfolio\',[\'returns\'])": "portfolio"}, {"sort(\'portfolio\',\'returns\',\'desc\')": "portfolio"}]', '[{"get_portfolio(None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'\',\'asset_class\',\'us equity\',\'returns\')": "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 | 0.6644 | | cosine_accuracy@3 | 0.8288 | | cosine_accuracy@5 | 0.863 | | cosine_accuracy@10 | 0.9178 | | cosine_precision@1 | 0.6644 | | cosine_precision@3 | 0.2763 | | cosine_precision@5 | 0.1726 | | cosine_precision@10 | 0.0918 | | cosine_recall@1 | 0.0185 | | cosine_recall@3 | 0.023 | | cosine_recall@5 | 0.024 | | cosine_recall@10 | 0.0255 | | cosine_ndcg@10 | 0.1737 | | cosine_mrr@10 | 0.748 | | **cosine_map@100** | **0.0209** | | dot_accuracy@1 | 0.6644 | | dot_accuracy@3 | 0.8288 | | dot_accuracy@5 | 0.863 | | dot_accuracy@10 | 0.9178 | | dot_precision@1 | 0.6644 | | dot_precision@3 | 0.2763 | | dot_precision@5 | 0.1726 | | dot_precision@10 | 0.0918 | | dot_recall@1 | 0.0185 | | dot_recall@3 | 0.023 | | dot_recall@5 | 0.024 | | dot_recall@10 | 0.0255 | | dot_ndcg@10 | 0.1737 | | dot_mrr@10 | 0.748 | | dot_map@100 | 0.0209 | ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 723 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: 11.8 tokens
  • max: 26 tokens
|
  • min: 24 tokens
  • mean: 84.41 tokens
  • max: 194 tokens
| * Samples: | sentence_0 | sentence_1 | |:--------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | what is my portfolio 3 year cagr? | [{"get_portfolio(None)": "portfolio"}, {"get_attribute('portfolio',['gains'],'')": "portfolio"}, {"sort('portfolio','gains','desc')": "portfolio"}] | | what is my 1 year rate of return | [{"get_portfolio(None)": "portfolio"}, {"get_attribute('portfolio',['gains'],'')": "portfolio"}, {"sort('portfolio','gains','desc')": "portfolio"}] | | show backtest of my performance this year? | [{"get_portfolio(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 - `batch_sampler`: batch_sampler - `multi_dataset_batch_sampler`: round_robin
### Training Logs | Epoch | Step | cosine_map@100 | |:------:|:----:|:--------------:| | 0.0274 | 2 | 0.0136 | | 0.0548 | 4 | 0.0137 | | 0.0822 | 6 | 0.0139 | | 0.1096 | 8 | 0.0142 | | 0.1370 | 10 | 0.0145 | | 0.1644 | 12 | 0.0144 | | 0.1918 | 14 | 0.0147 | | 0.2192 | 16 | 0.0151 | | 0.2466 | 18 | 0.0153 | | 0.2740 | 20 | 0.0158 | | 0.3014 | 22 | 0.0165 | | 0.3288 | 24 | 0.0163 | | 0.3562 | 26 | 0.0167 | | 0.3836 | 28 | 0.0171 | | 0.4110 | 30 | 0.0175 | | 0.4384 | 32 | 0.0177 | | 0.4658 | 34 | 0.0180 | | 0.4932 | 36 | 0.0183 | | 0.5205 | 38 | 0.0185 | | 0.5479 | 40 | 0.0186 | | 0.5753 | 42 | 0.0186 | | 0.6027 | 44 | 0.0186 | | 0.6301 | 46 | 0.0186 | | 0.6575 | 48 | 0.0187 | | 0.6849 | 50 | 0.0189 | | 0.7123 | 52 | 0.0190 | | 0.7397 | 54 | 0.0189 | | 0.7671 | 56 | 0.0188 | | 0.7945 | 58 | 0.0189 | | 0.8219 | 60 | 0.0192 | | 0.8493 | 62 | 0.0193 | | 0.8767 | 64 | 0.0194 | | 0.9041 | 66 | 0.0194 | | 0.9315 | 68 | 0.0197 | | 0.9589 | 70 | 0.0200 | | 0.9863 | 72 | 0.0201 | | 1.0 | 73 | 0.0202 | | 1.0137 | 74 | 0.0203 | | 1.0411 | 76 | 0.0202 | | 1.0685 | 78 | 0.0203 | | 1.0959 | 80 | 0.0205 | | 1.1233 | 82 | 0.0207 | | 1.1507 | 84 | 0.0207 | | 1.1781 | 86 | 0.0206 | | 1.2055 | 88 | 0.0205 | | 1.2329 | 90 | 0.0205 | | 1.2603 | 92 | 0.0205 | | 1.2877 | 94 | 0.0204 | | 1.3151 | 96 | 0.0204 | | 1.3425 | 98 | 0.0205 | | 1.3699 | 100 | 0.0205 | | 1.3973 | 102 | 0.0205 | | 1.4247 | 104 | 0.0205 | | 1.4521 | 106 | 0.0204 | | 1.4795 | 108 | 0.0205 | | 1.5068 | 110 | 0.0208 | | 1.5342 | 112 | 0.0206 | | 1.5616 | 114 | 0.0205 | | 1.5890 | 116 | 0.0206 | | 1.6164 | 118 | 0.0205 | | 1.6438 | 120 | 0.0205 | | 1.6712 | 122 | 0.0205 | | 1.6986 | 124 | 0.0207 | | 1.7260 | 126 | 0.0207 | | 1.7534 | 128 | 0.0207 | | 1.7808 | 130 | 0.0205 | | 1.8082 | 132 | 0.0206 | | 1.8356 | 134 | 0.0208 | | 1.8630 | 136 | 0.0206 | | 1.8904 | 138 | 0.0206 | | 1.9178 | 140 | 0.0206 | | 1.9452 | 142 | 0.0205 | | 1.9726 | 144 | 0.0206 | | 2.0 | 146 | 0.0207 | | 2.0274 | 148 | 0.0209 | ### Framework Versions - Python: 3.10.9 - Sentence Transformers: 3.0.1 - 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} } ```