metadata
tags:
- biogpt
- boolean-query
- biomedical
- systematic-review
- pubmed
license: unknown
model-index:
- name: BioGPT-BQF-TMK-Small
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: CLEF TAR
type: biomedical
metrics:
- name: Precision @100
type: precision
value: 0.134
- name: Recall @1000
type: recall
value: 0.2125
BioGPT-BQF-TMK-Small
A fine-tuned BioGPT model for Boolean query formalization in biomedical systematic reviews, incorporating Titles, MeSH Terms, and Keywords to improve PubMed search query generation.
Model Overview
- Base Model: BioGPT
- Fine-tuned on: Semi-synthetic generated data
- Task: Boolean Query Generation for PubMed searches
- Inputs: Research topic title, MeSH terms, and Keywords
- Outputs: Optimized PubMed Boolean search query
Usage
from transformers import BioGptForCausalLM, BioGptTokenizer
model_name = "AI4BSLR/BioGPT-BQF-TMK-Small"
model = BioGptForCausalLM.from_pretrained(model_name)
tokenizer = BioGptTokenizer.from_pretrained(model_name)
input_text = "Title: Heterogeneity in Lung Cancer, MeSH: Biomarkers, Tumor, Genetic Heterogeneity, Keywords: Biomarkers, Query: "
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))