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Upload domain-adapted PubMedBERT for healthcare interoperability

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  1. README.md +77 -0
  2. added_tokens.json +72 -0
  3. config.json +25 -0
  4. model.safetensors +3 -0
  5. special_tokens_map.json +37 -0
  6. tokenizer.json +0 -0
  7. tokenizer_config.json +625 -0
  8. training_args.bin +3 -0
  9. vocab.txt +0 -0
README.md ADDED
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+ # Healthcare Interoperability Domain-Adapted PubMedBERT
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+
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+ This model is a fine-tuned version of `pritamdeka/S-PubMedBert-MS-MARCO` that has been domain-adapted for healthcare interoperability concepts and terminology.
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+
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+ ## Model Details
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+ - **Base model:** PubMedBERT-MS-MARCO
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+ - **Task:** Domain adaptation via masked language modeling (MLM)
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+ - **Training data:** Healthcare interoperability literature corpus
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+ - **Focus areas:** FHIR, HL7, medical terminologies, health information exchange
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+
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+ ## Domain-Specific Knowledge
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+
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+ This model has been fine-tuned to better understand healthcare interoperability concepts including:
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+
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+ - Health data exchange standards (FHIR, HL7, DICOM, CCD)
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+ - Terminology systems (SNOMED CT, LOINC, ICD, RxNorm)
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+ - Interoperability frameworks and architectures
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+ - Electronic Health Records (EHR) systems and implementations
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+
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+ ## Evaluation Results
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+
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+ Our domain-adapted model shows significant improvements in understanding healthcare interoperability terminology as demonstrated in these fill-mask examples:
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+
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+ | Prompt | Top Predictions |
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+ |--------|----------------|
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+ | "FHIR is a standard for health [MASK] exchange." | "information" (91.3%), "data" (5.6%) |
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+ | "Health information [MASK] is critical for coordinated care." | "exchange" (58.7%), "sharing" (4.6%) |
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+ | "Blockchain technology can improve data [MASK] in healthcare." | "exchange" (21.2%), "quality" (6.0%) |
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+ | "Electronic Health Records require [MASK] standards for sharing information." | "common" (10.1%), "international" (8.0%) |
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+
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+ ## Usage
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+
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+ ```python
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+ from transformers import AutoModelForMaskedLM, AutoTokenizer
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+
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+ # Load model and tokenizer
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+ tokenizer = AutoTokenizer.from_pretrained("adamleeit/pubmedbert-interop-mlm")
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+ model = AutoModelForMaskedLM.from_pretrained("adamleeit/pubmedbert-interop-mlm")
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+
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+ # Example usage
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+ text = "FHIR enables healthcare [MASK] exchange between systems."
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+ inputs = tokenizer(text, return_tensors="pt")
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+ outputs = model(**inputs)
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+ token_id = inputs.input_ids[0].tolist().index(tokenizer.mask_token_id)
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+ probs = outputs.logits[0, token_id].softmax(dim=0)
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+ top_tokens = torch.topk(probs, k=5)
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+
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+ for i, (score, token_id) in enumerate(zip(top_tokens.values, top_tokens.indices)):
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+ print(f"{i+1}. {tokenizer.decode([token_id])}: {score.item():.3f}")
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+ ```
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+
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+ ## For Embeddings/Sentence Transformers
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+
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+ For generating document embeddings (e.g., for topic modeling), use our sentence-transformer version:
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+
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+ ```python
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+ from sentence_transformers import SentenceTransformer
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+
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+ # Load the model
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+ model = SentenceTransformer("adamleeit/pubmedbert-interop-sentence")
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+
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+ # Generate embeddings
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+ embeddings = model.encode(["FHIR enables healthcare information exchange between systems."])
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+ ```
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+
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+ ## Citation
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+
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+ If you use this model in your research, please cite:
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+ ```
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+ @misc{pubmedbert-interop-mlm,
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+ author = {Lee AM},
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+ title = {Healthcare Interoperability Domain-Adapted PubMedBERT},
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+ year = {2025},
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+ publisher = {Hugging Face},
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+ howpublished = {\url{https://huggingface.co/adamleeit/pubmedbert-interop-mlm}}
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+ }
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+ ```
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