Add model card

README.md

@@ -1,55 +1,76 @@
 ---
-library_name: transformers
-license: apache-2.0
-base_model: google-bert/bert-base-cased
+language: en
 tags:
-- generated_from_trainer
+  - question-answering
+  - bert
+  - squad
+  - extractive-qa
+datasets:
+  - squad
+metrics:
+  - exact_match
+  - f1
 model-index:
-- name: Question_Answering
-  results: []
+  - name: Question_Answering
+    results:
+      - task:
+          type: question-answering
+        dataset:
+          name: SQuAD v1.1
+          type: squad
+        metrics:
+          - type: exact_match
+            value: 81.0501
+          - type: f1
+            value: 88.5526
 ---
 
-<!-- This model card has been generated automatically according to the information the Trainer had access to. You
-should probably proofread and complete it, then remove this comment. -->
+# BERT Fine-Tuned on SQuAD (Extractive Question Answering)
 
-# Question_Answering
+This model extracts answers to questions directly from a provided text passage.
+It was fine-tuned from `bert-base-cased` on the SQuAD v1.1 dataset.
 
-This model is a fine-tuned version of [google-bert/bert-base-cased](https://huggingface.co/google-bert/bert-base-cased) on an unknown dataset.
+## How It Works
 
-## Model description
+Given a **context** (a paragraph of text) and a **question**, the model finds
+and returns the exact span of text in the context that answers the question.
 
-More information needed
+## Performance
 
-## Intended uses & limitations
+| Metric | Score |
+|--------|-------|
+| Exact Match | 81.05% |
+| F1 Score | 88.55% |
 
-More information needed
+*(Evaluated on SQuAD v1.1 validation set — 10,570 examples)*
 
-## Training and evaluation data
+## How to Use
 
-More information needed
+```python
+from transformers import pipeline
 
-## Training procedure
+qa = pipeline(
+    "question-answering",
+    model="samandar1105/Question_Answering"
+)
 
-### Training hyperparameters
+result = qa(
+    question="Who designed the Eiffel Tower?",
+    context="The Eiffel Tower was designed by Gustave Eiffel and built between 1887 and 1889 in Paris."
+)
+print(result)
+# {'answer': 'Gustave Eiffel', 'score': 0.99, 'start': 31, 'end': 45}
+```
 
-The following hyperparameters were used during training:
-- learning_rate: 2e-05
-- train_batch_size: 16
-- eval_batch_size: 16
-- seed: 42
-- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
-- lr_scheduler_type: linear
-- lr_scheduler_warmup_steps: 0.1
-- num_epochs: 3
-- mixed_precision_training: Native AMP
+## Training Details
 
-### Training results
-
-
-
-### Framework versions
-
-- Transformers 5.12.0
-- Pytorch 2.11.0+cu128
-- Datasets 4.0.0
-- Tokenizers 0.22.2
+| Parameter | Value |
+|-----------|-------|
+| Base model | bert-base-cased |
+| Dataset | SQuAD v1.1 (87,599 train / 10,570 val) |
+| Learning rate | 2e-5 |
+| Epochs | 3 |
+| Batch size | 16 |
+| Max sequence length | 384 |
+| Stride | 128 |
+| Framework | PyTorch + HuggingFace Transformers |