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README.md

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+---
+language: en
+license: apache-2.0
+tags:
+- text-classification
+- multi-label-classification
+- emotion-classification
+- deberta-v3
+- pytorch
+---
+
+# Emotion Classification Model
+
+This model classifies text into 5 emotion categories: **anger**, **fear**, **joy**, **sadness**, and **surprise**.
+
+## Model Description
+
+- **Base Model:** roberta-base
+- **Task:** Multi-label text classification
+- **Labels:** anger, fear, joy, sadness, surprise
+- **Training Strategy:** 5-Fold Cross-Validation
+- **Framework:** PyTorch + Transformers
+
+## Performance
+
+### Overall Metrics
+- **Macro F1:** N/A
+- **Cross-Validation:** 0.7684 +/- 0.0099
+
+### Per-Label Performance
+N/A
+
+### Optimized Thresholds
+N/A
+
+## Usage
+
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+import numpy as np
+
+# Load model and tokenizer
+model = AutoModelForSequenceClassification.from_pretrained("hrshlgunjal/emotion-classifier-roberta-base")
+tokenizer = AutoTokenizer.from_pretrained("hrshlgunjal/emotion-classifier-roberta-base")
+
+# Optimized thresholds (use these for best results)
+thresholds = np.array([0.5, 0.5, 0.5, 0.5, 0.5])
+labels = ['anger', 'fear', 'joy', 'sadness', 'surprise']
+
+# Predict emotions
+def predict_emotions(text):
+    inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=128)
+    with torch.no_grad():
+        outputs = model(**inputs)
+    probs = torch.sigmoid(outputs.logits).cpu().numpy()[0]
+    predictions = (probs >= thresholds).astype(int)
+    return {label: (pred, prob) for label, pred, prob in zip(labels, predictions, probs)}
+
+# Example
+text = "I am so excited about this amazing opportunity!"
+result = predict_emotions(text)
+print(result)
+```
+
+## Training Details
+
+- **Optimizer:** AdamW with differential weight decay
+- **Learning Rate:** 2e-05
+- **Batch Size:** 32
+- **Epochs:** 3
+- **Max Sequence Length:** 128
+- **Warmup Ratio:** 0.1
+- **Weight Decay:** 0.01
+- **Mixed Precision:** Enabled (FP16)
+- **Gradient Clipping:** 1.0
+
+## Training Infrastructure
+
+- **Device:** GPU
+- **Training Time:** ~225 minutes (approximate)
+- **Framework Versions:**
+  - PyTorch: 2.6.0+cu124
+  - Transformers: 4.53.3
+
+## Model Card Authors
+
+hrshlgunjal
+
+## Model Card Contact
+
+For questions or feedback, please open an issue in the model repository.