Update README.md

README.md

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----
-license: unknown
----
+---
+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, FASS
+      type: biomedical
+    metrics:
+      - name: Precision @100
+        type: precision
+        value: 0.16
+      - name: Recall @1000
+        type: recall
+        value: 0.28
+      - name: MAP Score
+        type: mean_average_precision
+        value: 0.30
+---
+
+# **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**: CLEF TAR and FASS datasets
+- **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 = "leandrabudau/BioGPT-BQF-TMK-Small"
+model = BioGptForCausalLM.from_pretrained(model_name)
+tokenizer = BioGptTokenizer.from_pretrained(model_name)
+
+input_text = "Best treatments for lung cancer"
+inputs = tokenizer(input_text, return_tensors="pt")
+outputs = model.generate(**inputs)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))