| --- |
| tags: |
| - biogpt |
| - boolean-query |
| - biomedical |
| - systematic-review |
| - pubmed |
| license: unknown |
| model-index: |
| - name: BioGPT-BQF-TMK-Large |
| results: |
| - task: |
| type: text-generation |
| name: Text Generation |
| dataset: |
| name: CLEF TAR |
| type: biomedical |
| metrics: |
| - name: Precision @100 |
| type: precision |
| value: 0.1455 |
| - name: Recall @1000 |
| type: recall |
| value: 0.2661 |
| --- |
| |
| # BioGPT-BQF-TMK-Large |
| Fine-tuned BioGPT for Biomedical Boolean Query Formalization using Titles only. |
|
|
| ## Model Details |
| - Base Model: BioGPT |
| - Fine-tuned on: Semi-synthetic generated data |
| - Task: Boolean Query Generation for PubMed searches |
|
|
| ## How to Use |
| ```python |
| from transformers import BioGptForCausalLM, BioGptTokenizer |
| |
| model = BioGptForCausalLM.from_pretrained("AI4BSLR/BioGPT-BQF-TMK-Large") |
| tokenizer = BioGptTokenizer.from_pretrained("AI4BSLR/BioGPT-BQF-TMK-Large") |
| |
| 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)) |
| |
