| --- |
| tags: |
| - sentence-transformers |
| - sentence-similarity |
| - feature-extraction |
| - generated_from_trainer |
| - dataset_size:132037 |
| - loss:MultipleNegativesRankingLoss |
| base_model: BAAI/bge-small-en-v1.5 |
| widget: |
| - source_sentence: back pain. I'm not sure what to make of it. |
| sentences: |
| - 'Observed: back pain — musculoskeletal system' |
| - Patient is frustrated with the medical system |
| - 'OPQRST — Severity: fever rated 7/10 by patient' |
| - source_sentence: I'm absolutely terrified. I've had eye redness for a few days and |
| I keep imagining the worst. |
| sentences: |
| - 'Observed: palpitations and irregular heart rate — cardiovascular — physician |
| confirmed: Atrial fibrillation' |
| - 'Patient presented: blood in urine — neurological/cognitive system' |
| - 'Chief complaint: patient expressed concern, reason not yet specified — calm presentation' |
| - source_sentence: I take a pill called clopidogrel — not sure of the dose |
| sentences: |
| - Patient is drug-free by choice — lifestyle management only |
| - 'Observed: fatigue and shortness of breath — haematological — physician confirmed: |
| Anaemia' |
| - 'Current medication: clopidogrel 75mg daily — antiplatelet — for post-MI' |
| - source_sentence: I'm on gabapentin for my neuropathic pain |
| sentences: |
| - Patient takes no regular medications |
| - 'Current medication: gabapentin 300mg three times daily — anticonvulsant — for |
| neuropathic pain' |
| - 'Observed: palpitations — cardiovascular system' |
| - source_sentence: my breathing is fast and I feel disoriented |
| sentences: |
| - Patient is anxious and hyperventilating due to health anxiety |
| - 'OPQRST — Onset: abdominal pain started suddenly without warning' |
| - 'Red flag: tachypnoea + confusion — qSOFA >= 2 — possible sepsis — IMMEDIATE ESCALATION' |
| pipeline_tag: sentence-similarity |
| library_name: sentence-transformers |
| metrics: |
| - cosine_accuracy |
| model-index: |
| - name: SentenceTransformer based on BAAI/bge-small-en-v1.5 |
| results: |
| - task: |
| type: triplet |
| name: Triplet |
| dataset: |
| name: rapha val |
| type: rapha-val |
| metrics: |
| - type: cosine_accuracy |
| value: 1.0 |
| name: Cosine Accuracy |
| --- |
| |
| # 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 retrieval. |
|
|
| ## 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) <!-- at revision 5c38ec7c405ec4b44b94cc5a9bb96e735b38267a --> |
| - **Maximum Sequence Length:** 128 tokens |
| - **Output Dimensionality:** 384 dimensions |
| - **Similarity Function:** Cosine Similarity |
| - **Supported Modality:** Text |
| <!-- - **Training Dataset:** Unknown --> |
| <!-- - **Language:** Unknown --> |
| <!-- - **License:** Unknown --> |
|
|
| ### Model Sources |
|
|
| - **Documentation:** [Sentence Transformers Documentation](https://sbert.net) |
| - **Repository:** [Sentence Transformers on GitHub](https://github.com/huggingface/sentence-transformers) |
| - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) |
|
|
| ### Full Model Architecture |
|
|
| ``` |
| SentenceTransformer( |
| (0): Transformer({'transformer_task': 'feature-extraction', 'modality_config': {'text': {'method': 'forward', 'method_output_name': 'last_hidden_state'}}, 'module_output_name': 'token_embeddings', 'architecture': 'BertModel'}) |
| (1): Pooling({'embedding_dimension': 384, 'pooling_mode': 'cls', '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 = [ |
| 'my breathing is fast and I feel disoriented', |
| 'Red flag: tachypnoea + confusion — qSOFA >= 2 — possible sepsis — IMMEDIATE ESCALATION', |
| 'Patient is anxious and hyperventilating due to health anxiety', |
| ] |
| embeddings = model.encode(sentences) |
| print(embeddings.shape) |
| # [3, 384] |
| |
| # Get the similarity scores for the embeddings |
| similarities = model.similarity(embeddings, embeddings) |
| print(similarities) |
| # tensor([[1.0000, 0.8755, 0.1384], |
| # [0.8755, 1.0000, 0.0772], |
| # [0.1384, 0.0772, 1.0000]]) |
| ``` |
| <!-- |
| ### Direct Usage (Transformers) |
|
|
| <details><summary>Click to see the direct usage in Transformers</summary> |
|
|
| </details> |
| --> |
|
|
| <!-- |
| ### Downstream Usage (Sentence Transformers) |
|
|
| You can finetune this model on your own dataset. |
|
|
| <details><summary>Click to expand</summary> |
|
|
| </details> |
| --> |
|
|
| <!-- |
| ### Out-of-Scope Use |
|
|
| *List how the model may foreseeably be misused and address what users ought not to do with the model.* |
| --> |
|
|
| ## Evaluation |
|
|
| ### Metrics |
|
|
| #### Triplet |
|
|
| * Dataset: `rapha-val` |
| * Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.sentence_transformer.evaluation.TripletEvaluator) |
|
|
| | Metric | Value | |
| |:--------------------|:--------| |
| | **cosine_accuracy** | **1.0** | |
| |
| <!-- |
| ## Bias, Risks and Limitations |
| |
| *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
| --> |
| |
| <!-- |
| ### Recommendations |
| |
| *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
| --> |
| |
| ## Training Details |
| |
| ### Training Dataset |
| |
| #### Unnamed Dataset |
| |
| * Size: 132,037 training samples |
| * Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code> |
| * Approximate statistics based on the first 100 samples: |
| | | anchor | positive | negative | |
| |:---------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| |
| | type | string | string | string | |
| | modality | text | text | text | |
| | details | <ul><li>min: 5 tokens</li><li>mean: 16.2 tokens</li><li>max: 34 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 20.9 tokens</li><li>max: 33 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 12.62 tokens</li><li>max: 19 tokens</li></ul> | |
| * Samples: |
| | anchor | positive | negative | |
| |:-----------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------| |
| | <code>I'm trying to stay calm but I have tremors and I genuinely feel like something is very wrong.</code> | <code>Observed: tremors — neurological system</code> | <code>Patient is defensive, resistant to clinical interview</code> | |
| | <code>neck pain so bad I can't look down and light sensitivity too</code> | <code>Red flag: neck stiffness + photophobia + fever — possible meningitis — IMMEDIATE ESCALATION</code> | <code>Patient reports muscle tension in neck from bad posture</code> | |
| | <code>neck pain so bad I can't look down and light sensitivity too</code> | <code>Red flag: neck stiffness + photophobia + fever — possible meningitis — IMMEDIATE ESCALATION</code> | <code>Patient reports muscle tension in neck from bad posture</code> | |
| * Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: |
| ```json |
| { |
| "scale": 20.0, |
| "similarity_fct": "cos_sim", |
| "gather_across_devices": false, |
| "directions": [ |
| "query_to_doc" |
| ], |
| "partition_mode": "joint", |
| "hardness_mode": null, |
| "hardness_strength": 0.0 |
| } |
| ``` |
| |
| ### Evaluation Dataset |
| |
| #### Unnamed Dataset |
| |
| * Size: 15,000 evaluation samples |
| * Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code> |
| * Approximate statistics based on the first 100 samples: |
| | | anchor | positive | negative | |
| |:---------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| |
| | type | string | string | string | |
| | modality | text | text | text | |
| | details | <ul><li>min: 5 tokens</li><li>mean: 15.39 tokens</li><li>max: 27 tokens</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 18.62 tokens</li><li>max: 33 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 12.74 tokens</li><li>max: 23 tokens</li></ul> | |
| * Samples: |
| | anchor | positive | negative | |
| |:----------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------| |
| | <code>I'm on prednisolone 5mg daily</code> | <code>Current medication: prednisolone 5mg daily — corticosteroid — for inflammatory condition</code> | <code>Patient is not currently prescribed anything</code> | |
| | <code>Diagnosis recorded: Community-acquired pneumonia — ICD-10 code J18.9</code> | <code>Observed: cough, fever, shortness of breath — respiratory/infectious — physician confirmed: Community-acquired pneumonia</code> | <code>Observed: cough — upper respiratory tract infection — self-limiting, no antibiotics</code> | |
| | <code>breast lump.</code> | <code>Session start: patient presented with unspecified concern — Minimal communication style</code> | <code>Patient presented: numbness in limbs — respiratory system</code> | |
| * Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: |
| ```json |
| { |
| "scale": 20.0, |
| "similarity_fct": "cos_sim", |
| "gather_across_devices": false, |
| "directions": [ |
| "query_to_doc" |
| ], |
| "partition_mode": "joint", |
| "hardness_mode": null, |
| "hardness_strength": 0.0 |
| } |
| ``` |
| |
| ### Training Hyperparameters |
| #### Non-Default Hyperparameters |
| |
| - `per_device_train_batch_size`: 256 |
| - `learning_rate`: 2e-05 |
| - `lr_scheduler_type`: cosine |
| - `warmup_steps`: 0.1 |
| - `bf16`: True |
| - `per_device_eval_batch_size`: 256 |
| - `load_best_model_at_end`: True |
| - `dataloader_drop_last`: True |
| |
| #### All Hyperparameters |
| <details><summary>Click to expand</summary> |
| |
| - `per_device_train_batch_size`: 256 |
| - `num_train_epochs`: 3 |
| - `max_steps`: -1 |
| - `learning_rate`: 2e-05 |
| - `lr_scheduler_type`: cosine |
| - `lr_scheduler_kwargs`: None |
| - `warmup_steps`: 0.1 |
| - `optim`: adamw_torch_fused |
| - `optim_args`: None |
| - `weight_decay`: 0.0 |
| - `adam_beta1`: 0.9 |
| - `adam_beta2`: 0.999 |
| - `adam_epsilon`: 1e-08 |
| - `optim_target_modules`: None |
| - `gradient_accumulation_steps`: 1 |
| - `average_tokens_across_devices`: True |
| - `max_grad_norm`: 1.0 |
| - `label_smoothing_factor`: 0.0 |
| - `bf16`: True |
| - `fp16`: False |
| - `bf16_full_eval`: False |
| - `fp16_full_eval`: False |
| - `tf32`: None |
| - `gradient_checkpointing`: False |
| - `gradient_checkpointing_kwargs`: None |
| - `torch_compile`: False |
| - `torch_compile_backend`: None |
| - `torch_compile_mode`: None |
| - `use_liger_kernel`: False |
| - `liger_kernel_config`: None |
| - `use_cache`: False |
| - `neftune_noise_alpha`: None |
| - `torch_empty_cache_steps`: None |
| - `auto_find_batch_size`: False |
| - `log_on_each_node`: True |
| - `logging_nan_inf_filter`: True |
| - `include_num_input_tokens_seen`: no |
| - `log_level`: passive |
| - `log_level_replica`: warning |
| - `disable_tqdm`: False |
| - `project`: huggingface |
| - `trackio_space_id`: None |
| - `trackio_bucket_id`: None |
| - `trackio_static_space_id`: None |
| - `per_device_eval_batch_size`: 256 |
| - `prediction_loss_only`: True |
| - `eval_on_start`: False |
| - `eval_do_concat_batches`: True |
| - `eval_use_gather_object`: False |
| - `eval_accumulation_steps`: None |
| - `include_for_metrics`: [] |
| - `batch_eval_metrics`: False |
| - `save_only_model`: False |
| - `save_on_each_node`: False |
| - `enable_jit_checkpoint`: False |
| - `push_to_hub`: False |
| - `hub_private_repo`: None |
| - `hub_model_id`: None |
| - `hub_strategy`: every_save |
| - `hub_always_push`: False |
| - `hub_revision`: None |
| - `load_best_model_at_end`: True |
| - `ignore_data_skip`: False |
| - `restore_callback_states_from_checkpoint`: False |
| - `full_determinism`: False |
| - `seed`: 42 |
| - `data_seed`: None |
| - `use_cpu`: False |
| - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} |
| - `parallelism_config`: None |
| - `dataloader_drop_last`: True |
| - `dataloader_num_workers`: 0 |
| - `dataloader_pin_memory`: True |
| - `dataloader_persistent_workers`: False |
| - `dataloader_prefetch_factor`: None |
| - `remove_unused_columns`: True |
| - `label_names`: None |
| - `train_sampling_strategy`: random |
| - `length_column_name`: length |
| - `ddp_find_unused_parameters`: None |
| - `ddp_bucket_cap_mb`: None |
| - `ddp_broadcast_buffers`: False |
| - `ddp_static_graph`: None |
| - `ddp_backend`: None |
| - `ddp_timeout`: 1800 |
| - `fsdp`: None |
| - `fsdp_config`: None |
| - `deepspeed`: None |
| - `debug`: [] |
| - `skip_memory_metrics`: True |
| - `do_predict`: False |
| - `resume_from_checkpoint`: None |
| - `warmup_ratio`: None |
| - `local_rank`: -1 |
| - `prompts`: None |
| - `batch_sampler`: batch_sampler |
| - `multi_dataset_batch_sampler`: proportional |
| - `router_mapping`: {} |
| - `learning_rate_mapping`: {} |
| |
| </details> |
| |
| ### Training Logs |
| | Epoch | Step | Training Loss | Validation Loss | rapha-val_cosine_accuracy | |
| |:----------:|:-------:|:-------------:|:---------------:|:-------------------------:| |
| | 0.0971 | 50 | 3.5863 | - | - | |
| | 0.1942 | 100 | 2.3320 | - | - | |
| | 0.2913 | 150 | 1.7194 | - | - | |
| | 0.3883 | 200 | 1.5596 | - | - | |
| | 0.4854 | 250 | 1.4893 | - | - | |
| | 0.5825 | 300 | 1.4520 | - | - | |
| | 0.6796 | 350 | 1.4428 | - | - | |
| | 0.7767 | 400 | 1.4143 | - | - | |
| | 0.8738 | 450 | 1.4227 | - | - | |
| | **0.9709** | **500** | **1.3991** | **1.4893** | **1.0** | |
| | 1.0680 | 550 | 1.3811 | - | - | |
| | 1.1650 | 600 | 1.3818 | - | - | |
| | 1.2621 | 650 | 1.3876 | - | - | |
| | 1.3592 | 700 | 1.3830 | - | - | |
| | 1.4563 | 750 | 1.3690 | - | - | |
| | 1.5534 | 800 | 1.3927 | - | - | |
| | 1.6505 | 850 | 1.3683 | - | - | |
| | 1.7476 | 900 | 1.3772 | - | - | |
| | 1.8447 | 950 | 1.3812 | - | - | |
| | 1.9417 | 1000 | 1.4034 | 1.4836 | 1.0 | |
| | 2.0388 | 1050 | 1.3812 | - | - | |
| | 2.1359 | 1100 | 1.3562 | - | - | |
| | 2.2330 | 1150 | 1.3676 | - | - | |
| | 2.3301 | 1200 | 1.3813 | - | - | |
| | 2.4272 | 1250 | 1.3849 | - | - | |
| | 2.5243 | 1300 | 1.3729 | - | - | |
| | 2.6214 | 1350 | 1.3770 | - | - | |
| | 2.7184 | 1400 | 1.3665 | - | - | |
| | 2.8155 | 1450 | 1.3469 | - | - | |
| | 2.9126 | 1500 | 1.3790 | 1.4779 | 1.0 | |
| | 3.0 | 1545 | - | 1.4779 | 1.0 | |
|
|
| * The bold row denotes the saved checkpoint. |
|
|
| ### Training Time |
| - **Training**: 6.3 minutes |
|
|
| ### Framework Versions |
| - Python: 3.12.13 |
| - Sentence Transformers: 5.6.0 |
| - Transformers: 5.12.1 |
| - PyTorch: 2.11.0+cu128 |
| - Accelerate: 1.14.0 |
| - Datasets: 4.0.0 |
| - Tokenizers: 0.22.2 |
|
|
| ## 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{oord2019representationlearningcontrastivepredictive, |
| title={Representation Learning with Contrastive Predictive Coding}, |
| author={Aaron van den Oord and Yazhe Li and Oriol Vinyals}, |
| year={2019}, |
| eprint={1807.03748}, |
| archivePrefix={arXiv}, |
| primaryClass={cs.LG}, |
| url={https://arxiv.org/abs/1807.03748}, |
| } |
| ``` |
|
|
| <!-- |
| ## Glossary |
|
|
| *Clearly define terms in order to be accessible across audiences.* |
| --> |
|
|
| <!-- |
| ## Model Card Authors |
|
|
| *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.* |
| --> |
|
|
| <!-- |
| ## Model Card Contact |
|
|
| *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.* |
| --> |
