Update README.md

README.md

@@ -1,37 +1,100 @@
 ---
-base_model: Qwen/Qwen3-0.6B-Base
-datasets: youssefbelghmi/MNLP_M3_mcqa_dataset_support
+license: mit
+language: en
+datasets:
+- youssefbelghmi/MNLP_M3_mcqa_dataset
 library_name: transformers
-model_name: MNLP_M3_mcqa_model_support
+pipeline_tag: text-classification
 tags:
-- generated_from_trainer
-- trl
-- sft
-licence: license
+- mcqa
+- multiple-choice
+- qwen
+- qwen3
+- supervised-fine-tuning
+- mnlp
+- epfl
+- stem
 ---
 
-# Model Card for MNLP_M3_mcqa_model_support
+# MNLP M3 MCQA Model (Qwen3-0.6B fine-tuned)
 
-This model is a fine-tuned version of [Qwen/Qwen3-0.6B-Base](https://huggingface.co/Qwen/Qwen3-0.6B-Base) on the [youssefbelghmi/MNLP_M3_mcqa_dataset_support](https://huggingface.co/datasets/youssefbelghmi/MNLP_M3_mcqa_dataset_support) dataset.
-It has been trained using [TRL](https://github.com/huggingface/trl).
+This model is a fine-tuned version of **Qwen/Qwen3-0.6B-Base** on the [MNLP M3 MCQA dataset](https://huggingface.co/datasets/youssefbelghmi/MNLP_M3_mcqa_dataset), a large-scale collection of multiple-choice questions designed for evaluating and training models in **STEM** domains (science, math, engineering, medicine, etc.).
 
-## Quick start
+It was trained as part of the final milestone of the **CS-552: Modern NLP** course at EPFL (Spring 2025).
 
-```python
-from transformers import pipeline
+## Task
 
-question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
-generator = pipeline("text-generation", model="youssefbelghmi/MNLP_M3_mcqa_model_support", device="cuda")
-output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
-print(output["generated_text"])
+**Multiple-Choice Question Answering (MCQA):** Given a question and four answer options (A–D), the model must complete the prompt with the correct option letter only (e.g., `A`, `B`, `C`, or `D`). It was trained with rationales during supervision but outputs only the letter during inference, making it compatible with evaluation frameworks such as LightEval.
+
+## Training Dataset
+
+- **Dataset:** [`youssefbelghmi/MNLP_M3_mcqa_dataset`](https://huggingface.co/datasets/youssefbelghmi/MNLP_M3_mcqa_dataset).
+- ~30,000 questions from SciQ, OpenBookQA, MathQA, ARC, and MedMCQA.
+- Each sample includes in particular:
+  - question,
+  - four answer choices (A–D),
+  - the correct answer as a letter,
+  - a short explanation (`support`) to guide learning.
+
+## Training Setup
+
+- **Base model:** `Qwen/Qwen3-0.6B-Base`.
+- **Method:** Supervised Fine-Tuning (SFT) with `trl` and `SFTTrainer`.
+- **Tokenizer:** AutoTokenizer (with `eos_token` used as padding).
+
+## Training Prompt Format
+
+During fine-tuning, each training example is converted into a prompt-completion pair. The prompt includes both the question and an explanation to guide the model’s reasoning:
+
+```text
+The following is a multiple-choice question (with answers) about knowledge and skills in advanced master's-level STEM fields.
+You will be provided with an explanation to help you understand the correct answer.
+Select the correct answer by replying with the option letter (A, B, C, or D) only.
+
+Question: <question_text>
+A. <option_A>
+B. <option_B>
+C. <option_C>
+D. <option_D>
+Explanation: <support_text>
+Answer:
 ```
 
-## Training procedure
+The completion is a single token: " A", " B", " C", or " D", corresponding to the correct answer.
 
- 
+## Training hyperparameters
 
+The following hyperparameters were used during training:
+- learning_rate: 2e-5
+- num_train_epochs: 1
+- per_device_train_batch_size: 4
+- per_device_eval_batch_size: 4
+- gradient_accumulation_steps: 4
+- gradient_checkpointing: true
+- eval_strategy: steps
+- eval_steps: 100
+- logging_steps: 100
 
-This model was trained with SFT.
+## Training Results
+
+|   Epoch |   Training Loss |   Validation Loss |
+|--------:|----------------:|------------------:|
+|    0.08 |          0.3461 |            0.2748 |
+|    0.15 |          0.2938 |            0.2661 |
+|    0.23 |          0.2881 |            0.26   |
+|    0.31 |          0.2741 |            0.2666 |
+|    0.38 |          0.2684 |            0.257  |
+|    0.46 |          0.2635 |            0.2539 |
+|    0.54 |          0.2603 |            0.2457 |
+|    0.61 |          0.2555 |            0.2441 |
+|    0.69 |          0.2459 |            0.2414 |
+|    0.77 |          0.2383 |            0.2353 |
+|    0.84 |          0.2266 |            0.2338 |
+|    0.92 |          0.2112 |            0.2337 |
+|    0.99 |          0.211  |            0.2335 |
+
+- **Final training loss:** 0.211  
+- **Final validation accuracy:** ~92.0%  
 
 ### Framework versions
 
@@ -43,8 +106,6 @@ This model was trained with SFT.
 
 ## Citations
 
-
-
 Cite TRL as:
     
 ```bibtex
@@ -56,4 +117,9 @@ Cite TRL as:
 	publisher    = {GitHub},
 	howpublished = {\url{https://github.com/huggingface/trl}}
 }
-```
+```
+
+## Author
+
+Developed by [**Youssef Belghmi**](https://huggingface.co/youssefbelghmi)  
+CS-552: Modern NLP – EPFL, Spring 2025