Update README.md

README.md

@@ -1,8 +1,40 @@
+---
+license: apache-2.0                           
+tags:
+  - question-answering
+  - biomedical
+  - yesno-classification
+  - transformer
+  - text-classification
+language:
+  - en                                       
+datasets:
+  - bioasq                                    
+metrics:
+  - type: accuracy
+    value: 91.44                              
+  - type: f1
+    value: 89.36                             
+model-index:
+  - name: BioASQ Yes/No Classifier
+    results:
+      - task:
+          type: question-answering
+          name: Yes/No Question Classification
+        dataset:
+          name: BioASQ Phase B Yes/No Dataset
+          type: bioasq
+        metrics:
+          - type: accuracy
+            value: 91.44                      
+          - type: f1
+            value: 89.36                      
+---
+
 # BioASQ Yes/No Question Classifier
 
 ---
 ## Model Details
-
 - **Model architecture:** BERT
 - **Pretrained base:** `microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext`
 - **Fine-tuned on:** BioASQ Phase B Yes/No question dataset
@@ -10,28 +42,22 @@
 - **Input format:** Concatenated question and supporting context passages
 - **Output:** Probability distribution over two classes ("Yes", "No")
 - **Tokenizer:** Depends on base model (WordPiece or SentencePiece)
-  
 ---
 ## Dataset
-
 - **Name:** BioASQ Task B Phase B Yes/No dataset
 - **Domain:** Biomedical question answering
 - **Data format:** Each sample consists of a yes/no question paired with one or more relevant context snippets extracted from biomedical abstracts
 - **Split:** Standard train/dev split from BioASQ
-
 ---
 ## Performance
-
 | Metric   | Value                   |
 |----------|-----------------------------|
-| Accuracy | 91%      |
-| F1 Score | 89%     |
+| Accuracy | 91.44%      |
+| F1 Score | 89.36%     |
 
 _Evaluation performed on the BioASQ dev set._
-
 ---
 ## Usage Example
-
 ```python
 from transformers import AutoTokenizer, AutoModelForSequenceClassification
 import torch
@@ -51,22 +77,16 @@ question = "Does aspirin reduce inflammation?"
 context = "Aspirin is widely used as an anti-inflammatory medication in clinical practice."
 print(f"Question: {question}\nPredicted answer: {predict_yesno(question, context)}")
 ```
-
 ---
 ## Future Work & Maintenance
-
 * Retrain regularly with updated BioASQ datasets to maintain relevance.
 * Implement uncertainty estimation for safer decision support.
 * Expand to multi-class or multi-label biomedical QA tasks.
 * Optimize for deployment efficiency and latency reduction.
-
 ---
 ## Contact & Support
-
 For questions, issues, or collaboration inquiries, please contact:
-
 * **Author / Maintainer:** \Minh Tien
 * **Email:** \[[[email protected]](mailto:[email protected])]
 * **GitHub:** [TMTien31](https://github.com/TMTien31)
-
 ---