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---
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: 0.1340
- 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](https://huggingface.co/microsoft/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**
```python
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))
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