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odin-deus/odin-llama3.1-medical-ner-v14

sourceHugging Faceupdated 7mo agoView on Hugging Face
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odin-llama3.1-medical-ner-v14

A LoRA adapter fine-tuned for medical Named Entity Recognition (NER) and Relation Extraction (RE).

Task

Extracts medical entities and their relationships from clinical text.

  • —Entity types: Disease, Drug, Symptom
  • —Relation types: associatedwith, causes, interactswith, treats

Model Details

  • —Base model: unsloth/meta-llama-3.1-8b-bnb-4bit
  • —LoRA rank (r): 32
  • —LoRA alpha: 32
  • —Target modules: qproj, gateproj, vproj, upproj, downproj, oproj, k_proj
  • —PEFT type: LORA

Evaluation Results

MetricPrecisionRecallF1
Entity (micro)0.9000.9230.911
Relation (micro)0.8140.8510.832

Usage

python
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer

base_model = "unsloth/meta-llama-3.1-8b-bnb-4bit"
adapter_id = "pabloformoso/odin-llama3.1-medical-ner-v14"

tokenizer = AutoTokenizer.from_pretrained(adapter_id)
model = AutoModelForCausalLM.from_pretrained(base_model, device_map="auto")
model = PeftModel.from_pretrained(model, adapter_id)

prompt = """### Instruction:
Extract all medical entities and their relations from the following clinical text.

### Input:
The patient developed acute renal failure after treatment with enalapril.

### Output:"""

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Training Data

Combined dataset from:

  • —ADE Corpus V2: Drug–adverse effect relations
  • —BC5CDR: Chemical–disease relations
  • —BioRED: Biomedical relation extraction