Enhance README.md with detailed model information for emotion classification using DistilBERT.

README.md

@@ -10,4 +10,168 @@ metrics:
 base_model:
 - distilbert/distilbert-base-uncased
 library_name: transformers
----
+pipeline_tag: text-classification
+tags:
+- emotion-classification
+- text-classification
+- distilbert
+- pytorch
+- transformers
+---
+
+# Emotion Classification with DistilBERT
+
+A fine-tuned DistilBERT model for emotion classification trained on the `dair-ai/emotion` dataset. This model classifies text into 6 emotion categories: sadness, joy, love, anger, fear, and surprise.
+
+## Model Details
+
+- **Model type**: DistilBERT for sequence classification
+- **Base model**: `distilbert-base-uncased`
+- **Language**: English
+- **Number of classes**: 6
+- **Task**: Text Classification (Emotion Recognition)
+- **Architecture**: 6 layers, 12 attention heads, 768 hidden dimensions
+
+### Emotion Categories
+
+The model classifies text into the following 6 emotions:
+- **sadness** (0)
+- **joy** (1)
+- **love** (2)
+- **anger** (3)
+- **fear** (4)
+- **surprise** (5)
+
+## Training Details
+
+### Training Data
+- **Dataset**: `dair-ai/emotion`
+- **Training samples**: ~16,000
+- **Validation samples**: ~2,000
+- **Test samples**: ~2,000
+
+### Training Configuration
+- **Epochs**: 3
+- **Batch size**: 16 (train), 32 (eval)
+- **Learning rate**: 2e-5
+- **Weight decay**: 0.01
+- **Warmup ratio**: 0.06
+- **Evaluation strategy**: epoch
+- **Save strategy**: epoch
+- **Best model metric**: F1 score (macro-averaged)
+
+### Training Results
+- **Best validation F1**: Model saved based on best F1 score during training
+- **Best validation accuracy**: Model performance optimized for F1 metric
+
+## Usage
+
+### Using Transformers
+
+```python
+from transformers import pipeline
+
+# Using the pipeline
+classifier = pipeline("text-classification", model="your-username/emotion-distilbert-finetuned")
+result = classifier("I feel great today!")
+print(result)
+# Output: [{'label': 'joy', 'score': 0.9876}]
+```
+
+### Direct Usage
+
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+
+# Load model and tokenizer
+tokenizer = AutoTokenizer.from_pretrained("your-username/emotion-distilbert-finetuned")
+model = AutoModelForSequenceClassification.from_pretrained("your-username/emotion-distilbert-finetuned")
+
+# Emotion labels
+emotion_labels = {
+    0: "sadness",
+    1: "joy",
+    2: "love",
+    3: "anger",
+    4: "fear",
+    5: "surprise"
+}
+
+# Classify emotion
+text = "I feel great today!"
+inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
+
+with torch.no_grad():
+    outputs = model(**inputs)
+    predicted_class = torch.argmax(outputs.logits, dim=1).item()
+
+print(f"Predicted emotion: {emotion_labels[predicted_class]}")
+```
+
+### Inference API
+
+```python
+# Get all probabilities
+with torch.no_grad():
+    outputs = model(**inputs)
+    probabilities = torch.softmax(outputs.logits, dim=1).squeeze()
+
+for i, prob in enumerate(probabilities):
+    print(f"{emotion_labels[i]}: {prob:.4f}")
+```
+
+## Model Architecture
+
+- **Base Model**: DistilBERT (distilled version of BERT-base)
+- **Hidden Size**: 768
+- **Number of Layers**: 6
+- **Attention Heads**: 12
+- **Vocabulary Size**: 30,522
+- **Max Position Embeddings**: 512
+- **Classification Head**: Linear layer with 6 outputs
+
+## Training Infrastructure
+
+- **Framework**: PyTorch with Hugging Face Transformers
+- **Optimizer**: AdamW
+- **Learning Rate Scheduler**: Linear warmup followed by linear decay
+- **Loss Function**: Cross-entropy loss for multi-class classification
+- **Mixed Precision**: Not used (trained in FP32)
+
+## Performance Notes
+
+- Model was trained on a dataset balanced across emotion categories
+- Best model checkpoint was selected based on validation F1 score
+- Model performs well on various text lengths and domains
+- May require domain adaptation for very specific use cases
+
+## Limitations
+
+- Trained primarily on English text
+- May not generalize well to other languages
+- Performance may vary across different text domains
+- Model predictions should be used with appropriate confidence thresholds
+
+## Citation
+
+If you use this model, please cite the original dataset:
+
+```
+@inproceedings{saravia-etal-2018-carer,
+    title = "{CARER}: Contextualized Affect Representations for Emotion Recognition",
+    author = "Saravia, Elvis  and
+      Liu, Hsien-Chi Toby  and
+      Huang, Yen-Hao  and
+      Wu, Junlin  and
+      Chen, Yi-Shin",
+    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
+    month = oct # "-" # nov,
+    year = "2018",
+    address = "Brussels, Belgium",
+    publisher = "Association for Computational Linguistics",
+    url = "https://www.aclweb.org/anthology/D18-1404",
+    doi = "10.18653/v1/D18-1404",
+    pages = "3687--3697"
+}
+```