Update model card from MODEL_README.md

README.md

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 ---
-license: mit
+language: en
+license: apache-2.0
+tags:
+- t5
+- question-generation
+- text2text-generation
+- mcq
+pretty_name: T5 for Technical MCQ Generation
 ---
+
+# T5 for Technical MCQ Generation
+
+## Model Description
+
+This is a `t5-base` model fine-tuned for the specific task of generating technical multiple-choice questions (MCQs). Given a context paragraph and a correct answer, the model generates a relevant question.
+
+This model is part of a larger pipeline that also generates distractors for the MCQ. It was developed to assist in creating educational content and assessments for technical topics.
+
+The model was fine-tuned by [Ayush472](https://huggingface.co/Ayush472).
+
+## Intended Uses & Limitations
+
+### How to Use
+
+This model is designed to be used within a larger MCQ generation pipeline but can be used as a standalone question generator. You can use it with the `transformers` library `pipeline` function for text-to-text generation.
+
+First, install the necessary library:
+```bash
+pip install transformers sentencepiece
+```
+
+Then, you can use the following Python code to generate a question:
+
+```python
+from transformers import T5ForConditionalGeneration, T5Tokenizer
+
+model_name = "Ayush472/Technical_mcq_model"
+tokenizer = T5Tokenizer.from_pretrained(model_name)
+model = T5ForConditionalGeneration.from_pretrained(model_name)
+
+# The context from which the question should be generated
+context = "The `await` keyword pauses the execution of an async function until a Promise is settled, making asynchronous code look synchronous."
+# The desired answer to the question
+answer = "It pauses the execution of an async function until a Promise is settled"
+
+# Prepare the input for the model
+input_text = f"generate question: context: {context} answer: {answer}"
+
+inputs = tokenizer(input_text, return_tensors="pt", max_length=512, truncation=True)
+
+# Generate the output
+outputs = model.generate(
+    inputs.input_ids, 
+    attention_mask=inputs.attention_mask,
+    max_length=64,
+    num_beams=4,
+    early_stopping=True
+)
+
+# Decode the generated question
+generated_question = tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+print(f"Context: {context}")
+print(f"Answer: {answer}")
+print(f"Generated Question: {generated_question}")
+
+# Expected Output:
+# Generated Question: What does the `await` keyword do in JavaScript?
+```
+
+### Limitations and Bias
+
+*   The model's knowledge is limited to the data it was trained on. It may not be able to generate questions for highly niche or very new technical topics.
+*   The quality of the generated question is highly dependent on the quality and clarity of the input context and answer.
+*   While the model is designed to generate factually consistent questions, it may occasionally produce questions that are awkwardly phrased or not perfectly aligned with the provided answer.
+*   There is no inherent mechanism to prevent the generation of biased or unfair questions if the training data contained such biases.
+
+## Training Data
+
+The model was fine-tuned on a private, custom-built dataset of technical articles and their corresponding multiple-choice questions. The dataset covered various topics in software development, including programming languages (Python, JavaScript), data structures, algorithms, and machine learning concepts.
+
+## Training Procedure
+
+The model was fine-tuned using the `transformers` library's `Trainer` API on a single NVIDIA T4 GPU. The `t5-base` model was used as the starting checkpoint. The training process involved formatting the dataset into `context: {context} answer: {answer}` inputs and the corresponding question as the target label.
+
+## Citation
+
+If you use this model in your work, please consider citing it:
+
+```bibtex
+@misc{ayush472_t5_mcq_2025,
+  author = {Ayush},
+  title = {T5 for Technical MCQ Generation},
+  year = {2025},
+  publisher = {Hugging Face},
+  journal = {Hugging Face repository},
+  howpublished = {\\url{https://huggingface.co/Ayush472/Technical_mcq_model}}
+}
+```