--- 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:734 - loss:MultipleNegativesRankingLoss widget: - source_sentence: List of ex-dividend dates in my portfolio sentences: - '[{"get_portfolio(None)": "portfolio"}, {"get_attribute(''portfolio'',[''gains''],'''')": "portfolio"}, {"sort(''portfolio'',''gains'',''desc'')": "portfolio"}]' - '[{"get_portfolio(None)": "portfolio"}, {"get_dividend_history(''portfolio'',''next 6 month'')": "portfolio"}]' - '[{"get_portfolio(None)": "portfolio"}, {"factor_contribution(''portfolio'','''',''asset_class'',''global bonds'',''portfolio'')": "portfolio"}]' - source_sentence: How has the momentum factor impacted my investment gains [DATES]? sentences: - '[{"get_portfolio(None)": "portfolio"}, {"factor_contribution(''portfolio'','''',''sector'',''sector information technology'',''portfolio'')": "portfolio"}]' - '[{"get_portfolio(None)": "portfolio"}, {"factor_contribution(''portfolio'','''',''returns'',None,''returns'')": "portfolio"}, {"filter(''portfolio'',''ticker'',''=='','''')": "portfolio"}]' - '[{"get_portfolio(None)": "portfolio"}, {"factor_contribution(''portfolio'','''',''factor'',''momentum'',''returns'')": "portfolio"}]' - source_sentence: how do different asset types contribute to my portfolio's returns? sentences: - '[{"get_portfolio(None)": "portfolio"}, {"factor_contribution(''portfolio'','''',''asset_class'',None,''returns'')": "portfolio"}]' - '[{"get_portfolio(None)": "portfolio"}, {"factor_contribution(''portfolio'','''',''region'',None,''returns'')": "portfolio"}]' - '[{"get_portfolio(None)": "portfolio"}, {"factor_contribution(''portfolio'','''',''returns'',None,''returns'')": "portfolio"}, {"filter(''portfolio'',''ticker'',''=='','''')": "portfolio"}]' - source_sentence: compare my accounts to market performance sentences: - '[{"get_portfolio(None)": "portfolio"}, {"factor_contribution(''portfolio'','''',''sector'',''sector gold'',''portfolio'')": "portfolio"}]' - '[{"get_portfolio(None)": "portfolio"}, {"get_attribute(''portfolio'',[''gains''],'''')": "portfolio"}, {"sort(''portfolio'',''gains'',''desc'')": "portfolio"}, {"get_attribute([''''],[''returns''],'''')": "_performance_data"}]' - '[{"get_portfolio(None)": "portfolio"}, {"get_attribute(''portfolio'',[''gains''],'''')": "portfolio"}, {"sort(''portfolio'',''gains'',''desc'')": "portfolio"}, {"get_attribute([''SPY''],[''returns''],'''')": "market_performance_data"}]' - source_sentence: how have I done in US equity [DATES]? sentences: - '[{"get_portfolio(None)": "portfolio"}, {"factor_contribution(''portfolio'','''',''asset_class'',''us equity'',''returns'')": "portfolio"}]' - '[{"get_portfolio(None)": "portfolio"}, {"factor_contribution(''portfolio'','''',''sector'',''sector utilities'',''portfolio'')": "portfolio"}]' - '[{"get_portfolio(None)": "portfolio"}, {"factor_contribution(''portfolio'','''',''asset_class'',''us equity'',''returns'')": "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: 0.815068493150685 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8767123287671232 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9315068493150684 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.6643835616438356 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.27168949771689493 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.17534246575342463 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09315068493150684 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.018455098934550992 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.02264079147640792 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.024353120243531208 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.025875190258751908 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.17516985160301582 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7525385953468143 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.020976397907656166 name: Cosine Map@100 - type: dot_accuracy@1 value: 0.6643835616438356 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.815068493150685 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.8767123287671232 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.9315068493150684 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.6643835616438356 name: Dot Precision@1 - type: dot_precision@3 value: 0.27168949771689493 name: Dot Precision@3 - type: dot_precision@5 value: 0.17534246575342463 name: Dot Precision@5 - type: dot_precision@10 value: 0.09315068493150684 name: Dot Precision@10 - type: dot_recall@1 value: 0.018455098934550992 name: Dot Recall@1 - type: dot_recall@3 value: 0.02264079147640792 name: Dot Recall@3 - type: dot_recall@5 value: 0.024353120243531208 name: Dot Recall@5 - type: dot_recall@10 value: 0.025875190258751908 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.17516985160301582 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.7525385953468143 name: Dot Mrr@10 - type: dot_map@100 value: 0.020976397907656166 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 = [ 'how have I done in US equity [DATES]?', '[{"get_portfolio(None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'\',\'asset_class\',\'us equity\',\'returns\')": "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 | 0.8151 | | cosine_accuracy@5 | 0.8767 | | cosine_accuracy@10 | 0.9315 | | cosine_precision@1 | 0.6644 | | cosine_precision@3 | 0.2717 | | cosine_precision@5 | 0.1753 | | cosine_precision@10 | 0.0932 | | cosine_recall@1 | 0.0185 | | cosine_recall@3 | 0.0226 | | cosine_recall@5 | 0.0244 | | cosine_recall@10 | 0.0259 | | cosine_ndcg@10 | 0.1752 | | cosine_mrr@10 | 0.7525 | | **cosine_map@100** | **0.021** | | dot_accuracy@1 | 0.6644 | | dot_accuracy@3 | 0.8151 | | dot_accuracy@5 | 0.8767 | | dot_accuracy@10 | 0.9315 | | dot_precision@1 | 0.6644 | | dot_precision@3 | 0.2717 | | dot_precision@5 | 0.1753 | | dot_precision@10 | 0.0932 | | dot_recall@1 | 0.0185 | | dot_recall@3 | 0.0226 | | dot_recall@5 | 0.0244 | | dot_recall@10 | 0.0259 | | dot_ndcg@10 | 0.1752 | | dot_mrr@10 | 0.7525 | | dot_map@100 | 0.021 | ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 734 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: 11.94 tokens
  • max: 26 tokens
|
  • min: 24 tokens
  • mean: 84.1 tokens
  • max: 194 tokens
| * Samples: | sentence_0 | sentence_1 | |:------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | what is my portfolio [DATES] cagr? | [{"get_portfolio(None)": "portfolio"}, {"get_attribute('portfolio',['gains'],'')": "portfolio"}, {"sort('portfolio','gains','desc')": "portfolio"}] | | what is my [DATES] rate of return | [{"get_portfolio(None)": "portfolio"}, {"get_attribute('portfolio',['gains'],'')": "portfolio"}, {"sort('portfolio','gains','desc')": "portfolio"}] | | show backtest of my performance [DATES]? | [{"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
Click to expand | Epoch | Step | cosine_map@100 | |:------:|:----:|:--------------:| | 0.0270 | 2 | 0.0136 | | 0.0541 | 4 | 0.0138 | | 0.0811 | 6 | 0.0140 | | 0.1081 | 8 | 0.0142 | | 0.1351 | 10 | 0.0144 | | 0.1622 | 12 | 0.0146 | | 0.1892 | 14 | 0.0147 | | 0.2162 | 16 | 0.0150 | | 0.2432 | 18 | 0.0152 | | 0.2703 | 20 | 0.0157 | | 0.2973 | 22 | 0.0165 | | 0.3243 | 24 | 0.0168 | | 0.3514 | 26 | 0.0167 | | 0.3784 | 28 | 0.0170 | | 0.4054 | 30 | 0.0174 | | 0.4324 | 32 | 0.0180 | | 0.4595 | 34 | 0.0181 | | 0.4865 | 36 | 0.0181 | | 0.5135 | 38 | 0.0182 | | 0.5405 | 40 | 0.0182 | | 0.5676 | 42 | 0.0182 | | 0.5946 | 44 | 0.0183 | | 0.6216 | 46 | 0.0183 | | 0.6486 | 48 | 0.0183 | | 0.6757 | 50 | 0.0183 | | 0.7027 | 52 | 0.0182 | | 0.7297 | 54 | 0.0185 | | 0.7568 | 56 | 0.0186 | | 0.7838 | 58 | 0.0189 | | 0.8108 | 60 | 0.0190 | | 0.8378 | 62 | 0.0191 | | 0.8649 | 64 | 0.0193 | | 0.8919 | 66 | 0.0197 | | 0.9189 | 68 | 0.0198 | | 0.9459 | 70 | 0.0196 | | 0.9730 | 72 | 0.0196 | | 1.0 | 74 | 0.0198 | | 1.0270 | 76 | 0.0198 | | 1.0541 | 78 | 0.0198 | | 1.0811 | 80 | 0.0199 | | 1.1081 | 82 | 0.0199 | | 1.1351 | 84 | 0.0199 | | 1.1622 | 86 | 0.0199 | | 1.1892 | 88 | 0.0199 | | 1.2162 | 90 | 0.0199 | | 1.2432 | 92 | 0.0199 | | 1.2703 | 94 | 0.0200 | | 1.2973 | 96 | 0.0199 | | 1.3243 | 98 | 0.0197 | | 1.3514 | 100 | 0.0198 | | 1.3784 | 102 | 0.0198 | | 1.4054 | 104 | 0.0198 | | 1.4324 | 106 | 0.0200 | | 1.4595 | 108 | 0.0201 | | 1.4865 | 110 | 0.0202 | | 1.5135 | 112 | 0.0202 | | 1.5405 | 114 | 0.0203 | | 1.5676 | 116 | 0.0203 | | 1.5946 | 118 | 0.0201 | | 1.6216 | 120 | 0.0201 | | 1.6486 | 122 | 0.0202 | | 1.6757 | 124 | 0.0201 | | 1.7027 | 126 | 0.0201 | | 1.7297 | 128 | 0.0201 | | 1.7568 | 130 | 0.0200 | | 1.7838 | 132 | 0.0200 | | 1.8108 | 134 | 0.0202 | | 1.8378 | 136 | 0.0201 | | 1.8649 | 138 | 0.0202 | | 1.8919 | 140 | 0.0202 | | 1.9189 | 142 | 0.0202 | | 1.9459 | 144 | 0.0201 | | 1.9730 | 146 | 0.0202 | | 2.0 | 148 | 0.0202 | | 2.0270 | 150 | 0.0204 | | 2.0541 | 152 | 0.0204 | | 2.0811 | 154 | 0.0203 | | 2.1081 | 156 | 0.0203 | | 2.1351 | 158 | 0.0204 | | 2.1622 | 160 | 0.0204 | | 2.1892 | 162 | 0.0202 | | 2.2162 | 164 | 0.0202 | | 2.2432 | 166 | 0.0201 | | 2.2703 | 168 | 0.0202 | | 2.2973 | 170 | 0.0202 | | 2.3243 | 172 | 0.0202 | | 2.3514 | 174 | 0.0202 | | 2.3784 | 176 | 0.0202 | | 2.4054 | 178 | 0.0202 | | 2.4324 | 180 | 0.0203 | | 2.4595 | 182 | 0.0203 | | 2.4865 | 184 | 0.0203 | | 2.5135 | 186 | 0.0204 | | 2.5405 | 188 | 0.0204 | | 2.5676 | 190 | 0.0203 | | 2.5946 | 192 | 0.0203 | | 2.6216 | 194 | 0.0203 | | 2.6486 | 196 | 0.0202 | | 2.6757 | 198 | 0.0202 | | 2.7027 | 200 | 0.0202 | | 2.7297 | 202 | 0.0202 | | 2.7568 | 204 | 0.0201 | | 2.7838 | 206 | 0.0201 | | 2.8108 | 208 | 0.0201 | | 2.8378 | 210 | 0.0201 | | 2.8649 | 212 | 0.0202 | | 2.8919 | 214 | 0.0202 | | 2.9189 | 216 | 0.0203 | | 2.9459 | 218 | 0.0203 | | 2.9730 | 220 | 0.0204 | | 3.0 | 222 | 0.0204 | | 3.0270 | 224 | 0.0204 | | 3.0541 | 226 | 0.0206 | | 3.0811 | 228 | 0.0205 | | 3.1081 | 230 | 0.0205 | | 3.1351 | 232 | 0.0205 | | 3.1622 | 234 | 0.0206 | | 3.1892 | 236 | 0.0206 | | 3.2162 | 238 | 0.0206 | | 3.2432 | 240 | 0.0206 | | 3.2703 | 242 | 0.0206 | | 3.2973 | 244 | 0.0205 | | 3.3243 | 246 | 0.0205 | | 3.3514 | 248 | 0.0204 | | 3.3784 | 250 | 0.0204 | | 3.4054 | 252 | 0.0204 | | 3.4324 | 254 | 0.0205 | | 3.4595 | 256 | 0.0205 | | 3.4865 | 258 | 0.0205 | | 3.5135 | 260 | 0.0205 | | 3.5405 | 262 | 0.0204 | | 3.5676 | 264 | 0.0204 | | 3.5946 | 266 | 0.0204 | | 3.6216 | 268 | 0.0203 | | 3.6486 | 270 | 0.0203 | | 3.6757 | 272 | 0.0204 | | 3.7027 | 274 | 0.0204 | | 3.7297 | 276 | 0.0206 | | 3.7568 | 278 | 0.0206 | | 3.7838 | 280 | 0.0206 | | 3.8108 | 282 | 0.0206 | | 3.8378 | 284 | 0.0206 | | 3.8649 | 286 | 0.0205 | | 3.8919 | 288 | 0.0206 | | 3.9189 | 290 | 0.0207 | | 3.9459 | 292 | 0.0206 | | 3.9730 | 294 | 0.0206 | | 4.0 | 296 | 0.0207 | | 4.0270 | 298 | 0.0207 | | 4.0541 | 300 | 0.0207 | | 4.0811 | 302 | 0.0208 | | 4.1081 | 304 | 0.0208 | | 4.1351 | 306 | 0.0207 | | 4.1622 | 308 | 0.0207 | | 4.1892 | 310 | 0.0207 | | 4.2162 | 312 | 0.0208 | | 4.2432 | 314 | 0.0208 | | 4.2703 | 316 | 0.0208 | | 4.2973 | 318 | 0.0208 | | 4.3243 | 320 | 0.0208 | | 4.3514 | 322 | 0.0208 | | 4.3784 | 324 | 0.0208 | | 4.4054 | 326 | 0.0208 | | 4.4324 | 328 | 0.0207 | | 4.4595 | 330 | 0.0207 | | 4.4865 | 332 | 0.0207 | | 4.5135 | 334 | 0.0207 | | 4.5405 | 336 | 0.0207 | | 4.5676 | 338 | 0.0207 | | 4.5946 | 340 | 0.0207 | | 4.6216 | 342 | 0.0208 | | 4.6486 | 344 | 0.0208 | | 4.6757 | 346 | 0.0208 | | 4.7027 | 348 | 0.0208 | | 4.7297 | 350 | 0.0208 | | 4.7568 | 352 | 0.0209 | | 4.7838 | 354 | 0.0209 | | 4.8108 | 356 | 0.0210 |
### 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} } ```