Update README.md

README.md

@@ -1,13 +1,51 @@
----
-license: mit
-datasets:
-- rajpurkar/squad
-language:
-- en
-metrics:
-- accuracy
-base_model:
-- google-bert/bert-base-uncased
-new_version: google-bert/bert-base-uncased
-library_name: transformers
----
+# Model Card for BERT-based Question Answering Model
+
+## Model Details
+
+- **Model Name**: BERT-QA
+- **Model Type**: Question Answering
+- **Model Architecture**: BERT (Bidirectional Encoder Representations from Transformers)
+- **Pretrained Model**: bert-base-uncased
+- **Training Dataset**: SQuAD (Stanford Question Answering Dataset)
+- **Training Data Size**: Subset (2% of the training data)
+- **License**: [Choose an appropriate license, e.g., MIT, Apache-2.0]
+
+## Model Description
+
+This model is a fine-tuned BERT model designed for the task of question answering. It has been trained on a small subset of the SQuAD dataset, which contains passages of text along with questions and their corresponding answers. The model predicts the start and end positions of the answer within the given context.
+
+## Intended Use
+
+This model is intended for:
+- Academic research in natural language processing (NLP)
+- Building applications that require answering questions based on provided contexts, such as chatbots, virtual assistants, and educational tools.
+
+## Limitations
+
+- **Training Data**: The model has been trained on a very small subset of the SQuAD dataset (2%), which may affect its performance. It may not generalize well to other domains or more complex questions.
+- **Biases**: The model may inherit biases present in the training data. It is essential to evaluate the model on diverse datasets to ensure fairness and reliability.
+- **Complexity**: The model may struggle with complex questions or contexts that require deeper understanding or reasoning.
+
+## Evaluation
+
+The model's performance has been evaluated based on its ability to predict correct answer spans in the SQuAD validation set. Metrics such as Exact Match (EM) and F1 Score can be used to quantify its performance.
+
+## How to Use
+
+You can use the model by loading it through the Hugging Face Transformers library:
+
+```python
+from transformers import AutoTokenizer, AutoModelForQuestionAnswering
+
+# Load model and tokenizer
+model = AutoModelForQuestionAnswering.from_pretrained("path/to/your/model")
+tokenizer = AutoTokenizer.from_pretrained("path/to/your/model")
+
+# Function to answer questions
+def answer_question(question, context):
+    inputs = tokenizer(question, context, return_tensors="pt")
+    outputs = model(**inputs)
+    start_idx = outputs.start_logits.argmax()
+    end_idx = outputs.end_logits.argmax()
+    answer = tokenizer.convert_tokens_to_string(tokenizer.convert_ids_to_tokens(inputs["input_ids"][0][start_idx:end_idx + 1]))
+    return answer