Phora68/rapha-embed-clinical-v1

SentenceTransformer based on BAAI/bge-small-en-v1.5

This is a sentence-transformers model finetuned from 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
• Maximum Sequence Length: 128 tokens
• Output Dimensionality: 384 dimensions
• Similarity Function: Cosine Similarity
• Supported Modality: Text

Model Sources

• Documentation: Sentence Transformers Documentation
• Repository: Sentence Transformers on GitHub
• Hugging Face: Sentence Transformers on Hugging Face

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:

pip install -U sentence-transformers

Then you can load this model and run inference.

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]])

Evaluation

Metrics

Triplet

• Dataset: rapha-val
• Evaluated with TripletEvaluator

Metric	Value
cosine_accuracy	1.0

Training Details

Training Dataset

Unnamed Dataset

• Size: 132,037 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 100 samples:

  	anchor	positive	negative
  type	string	string	string
  modality	text	text	text
  details	min: 5 tokens mean: 16.2 tokens max: 34 tokens	min: 8 tokens mean: 20.9 tokens max: 33 tokens	min: 8 tokens mean: 12.62 tokens max: 19 tokens

• Samples:

  anchor	positive	negative
  I'm trying to stay calm but I have tremors and I genuinely feel like something is very wrong.	Observed: tremors — neurological system	Patient is defensive, resistant to clinical interview
  neck pain so bad I can't look down and light sensitivity too	Red flag: neck stiffness + photophobia + fever — possible meningitis — IMMEDIATE ESCALATION	Patient reports muscle tension in neck from bad posture
  neck pain so bad I can't look down and light sensitivity too	Red flag: neck stiffness + photophobia + fever — possible meningitis — IMMEDIATE ESCALATION	Patient reports muscle tension in neck from bad posture

• Loss: MultipleNegativesRankingLoss with these parameters:

  {
      "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: anchor, positive, and negative
• Approximate statistics based on the first 100 samples:

  	anchor	positive	negative
  type	string	string	string
  modality	text	text	text
  details	min: 5 tokens mean: 15.39 tokens max: 27 tokens	min: 9 tokens mean: 18.62 tokens max: 33 tokens	min: 7 tokens mean: 12.74 tokens max: 23 tokens

• Samples:

  anchor	positive	negative
  I'm on prednisolone 5mg daily	Current medication: prednisolone 5mg daily — corticosteroid — for inflammatory condition	Patient is not currently prescribed anything
  Diagnosis recorded: Community-acquired pneumonia — ICD-10 code J18.9	Observed: cough, fever, shortness of breath — respiratory/infectious — physician confirmed: Community-acquired pneumonia	Observed: cough — upper respiratory tract infection — self-limiting, no antibiotics
  breast lump.	Session start: patient presented with unspecified concern — Minimal communication style	Patient presented: numbness in limbs — respiratory system

• Loss: MultipleNegativesRankingLoss with these parameters:

  {
      "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

Click to expand

• 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: {}

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

@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

@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},
}