Update README.md

README.md

@@ -1,42 +1,42 @@
----
-language: en
-license: mit
-library_name: transformers
-pipeline_tag: question-answering
-tags:
-- question-answering
-- roberta
-- squad2
-- deepset
-dataset:
-- squad2
----
-
-# CTION-QA: RoBERTa Question Answering Model
-
-## Model Description
-This model is **deepset/roberta-base-squad2** uploaded to a personal repository.
-It is fine-tuned on the **SQuAD 2.0** dataset for extractive question answering.
-
-## Performance
-| Metric | Score |
-|------|------|
-| Exact Match | 76.9 |
-| F1 Score | 79.8 |
-| Context Length | 512 |
-
-## Usage
-```python
-from transformers import pipeline
-
-qa = pipeline(
-    "question-answering",
-    model="hariprabhakaran45/CTION-QA"
-)
-
-result = qa(
-    question="Who is the Eiffel Tower named after?",
-    context="The Eiffel Tower is named after Gustave Eiffel."
-)
-
-print(result["answer"])
+---
+language: en
+license: mit
+library_name: transformers
+pipeline_tag: question-answering
+tags:
+- question-answering
+- roberta
+- squad2
+- deepset
+dataset:
+- squad2
+---
+
+# CTION-QA: Question Answering Model
+
+## Model Description
+A Question Answering (Q&A) model is a transformer-based NLP model trained to understand a given context and accurately extract or generate answers to user questions from that text.
+It is fine-tuned on the **SQuAD 2.0** dataset for extractive question answering.
+
+## Performance
+| Metric | Score |
+|------|------|
+| Exact Match | 76.9 |
+| F1 Score | 79.8 |
+| Context Length | 512 |
+
+## Usage
+```python
+from transformers import pipeline
+
+qa = pipeline(
+    "question-answering",
+    model="hariprabhakaran45/CTION-QA"
+)
+
+result = qa(
+    question="Who is the Eiffel Tower named after?",
+    context="The Eiffel Tower is named after Gustave Eiffel."
+)
+
+print(result["answer"])