Update README.md

README.md

@@ -1,3 +1,121 @@
----
-license: mit
----
+---
+language: en
+datasets: ag_news
+tags:
+  - text-classification
+  - topic-classification
+  - ag-news
+  - distilbert
+  - transformers
+  - pytorch
+license: apache-2.0
+model-index:
+  - name: DistilBERT AG News Classifier
+    results:
+      - task:
+          name: Topic Classification
+          type: text-classification
+        dataset:
+          name: AG News
+          type: ag_news
+        metrics:
+          - name: Accuracy
+            type: accuracy
+            value: 0.81
+---
+
+# 📰 DistilBERT Fine-Tuned on AG News with and without Label Smoothing
+
+This repository provides two fine-tuned [DistilBERT](https://huggingface.co/distilbert-base-uncased) models for **topic classification** on the [AG News](https://huggingface.co/datasets/ag_news) dataset:
+
+- ✅ `model_no_smoothing`: Fine-tuned **without label smoothing**
+- 🧪 `model_label_smoothing`: Fine-tuned **with label smoothing** (`smoothing=0.1`)
+
+Both models use the same tokenizer (`distilbert-base-uncased`) and were trained using PyTorch and Hugging Face `Trainer`.
+
+---
+
+## 🧠 Model Details
+
+| Model Name              | Label Smoothing | Validation Loss | Epochs | Learning Rate |
+|------------------------|-----------------|------------------|--------|----------------|
+| `model_no_smoothing`   | ❌ No            | 0.1792           | 1      | 2e-5           |
+| `model_label_smoothing`| ✅ Yes (0.1)     | 0.5413           | 1      | 2e-5           |
+
+- Base model: `distilbert-base-uncased`
+- Task: 4-class topic classification
+- Dataset: AG News (train: 120k, test: 7.6k)
+
+---
+
+## 📦 Repository Structure
+
+```
+
+/
+├── model\_no\_smoothing/         # Model A - no smoothing
+├── model\_label\_smoothing/      # Model B - label smoothing
+├── tokenizer/                  # Tokenizer files (shared)
+└── README.md
+
+````
+
+---
+
+## 🧪 How to Use
+
+### Load Model A (No Smoothing)
+
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+
+model_name = "Koushim/distilbert-agnews/model_no_smoothing"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+
+inputs = tokenizer("Breaking news in the tech world!", return_tensors="pt")
+outputs = model(**inputs)
+pred = outputs.logits.argmax(dim=1).item()
+````
+
+### Load Model B (Label Smoothing)
+
+```python
+model_name = "Koushim/distilbert-agnews/model_label_smoothing"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+```
+
+---
+
+## 🏷️ Class Labels
+
+0. World
+1. Sports
+2. Business
+3. Sci/Tech
+
+---
+
+## ⚙️ Training Configuration
+
+* Framework: PyTorch + 🤗 Transformers
+* Optimizer: AdamW
+* Batch size: 16 (train/eval)
+* Epochs: 1
+* Learning rate: 2e-5
+* Max sequence length: 256
+* Loss: CrossEntropy (custom for smoothing)
+
+---
+
+## 📄 License
+
+Apache 2.0
+
+---
+
+## ✍️ Author
+
+* Hugging Face: [Koushim](https://huggingface.co/Koushim)
+* Trained with `transformers.Trainer`