First

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README.md

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----
-title: 22f1000829 T32025
-emoji: 🏆
-colorFrom: yellow
-colorTo: green
-sdk: gradio
-sdk_version: 6.0.1
-app_file: app.py
-pinned: false
-license: apache-2.0
-short_description: Multi-label emotion detection from text
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+---
+title: Emotion Classifier – T3 2025 Project
+emoji: 🎭
+colorFrom: yellow
+colorTo: green
+sdk: gradio
+sdk_version: 6.0.1
+app_file: app.py
+pinned: false
+license: apache-2.0
+short_description: Multi-label emotion detection from text
+---
+
+# Emotion Classifier – DeBERTa-v3-base
+
+This Space hosts a **multi-label emotion classification model** developed as part of the **T3-2025 Deep Learning & GenAI Project** at IIT Madras.
+
+The model predicts **five emotions** from English text:
+- **Anger**
+- **Fear**
+- **Joy**
+- **Sadness**
+- **Surprise**
+
+[![HuggingFace Space](https://img.shields.io/badge/Hosted%20on-HuggingFace-blue)](https://huggingface.co/spaces/abhaynb92/22f1000829-t32025)
+
+---
+
+## 🔧 Model Overview
+
+This project fine-tunes the transformer model  
+**`microsoft/deberta-v3-base`**  
+for multi-label emotion classification.
+
+### Key Features:
+- Multi-label classification  
+- Independent sigmoid outputs  
+- **BCEWithLogitsLoss**  
+- Independent threshold tuning  
+- Stratified train-validation split  
+- Tokenization with DeBERTa tokenizer  
+- Inference served via Gradio
+
+---
+
+## 📊 Training Summary
+
+- **Dataset size:** ~6800 labeled samples  
+- **Average text length:** ~15 tokens  
+- **Model:** DeBERTa-v3-base  
+- **Epochs:** 5  
+- **Optimizer:** AdamW  
+- **Loss Function:** BCEWithLogitsLoss  
+- **Metric:** Macro F1-score  
+- **Best Val Macro F1:** **0.83+**  
+- Kaggle test performance further improved after threshold smoothing  
+
+This model contributed to the **Kaggle Performance (KP)** component worth **30 marks** in the DL & GenAI course.
+
+---
+
+## 🚀 Deployment Details
+
+This HuggingFace Space runs on:
+
+- **CPU Basic** (free)
+- **Gradio** as UI
+- **PyTorch** for inference
+- Direct loading of `best_deberta_base.bin` weights
+
+The Gradio app takes text input and returns a dictionary of emotion probabilities.
+
+Example:
+
+Input:
+"I can’t believe this happened!"
+
+Output:
+{
+"anger": 0.14,
+"fear": 0.31,
+"joy": 0.05,
+"sadness": 0.09,
+"surprise": 0.82
+}
+
+
+---
+
+## 🗂️ Project Structure
+
+app.py # Gradio UI + inference logic
+model.py # PyTorch model architecture
+best_deberta_base.bin # Fine-tuned model weights
+requirements.txt # Python dependencies
+README.md # This documentation
+
+app.py # Gradio UI + inference logic
+model.py # PyTorch model architecture
+best_deberta_base.bin # Fine-tuned model weights
+requirements.txt # Python dependencies
+README.md # This documentation

app.py

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+import torch
+import gradio as gr
+from transformers import AutoTokenizer
+from model import EmotionClassifier
+
+MODEL_NAME = "microsoft/deberta-v3-base"
+LABELS = ["anger", "fear", "joy", "sadness", "surprise"]
+
+# Load tokenizer
+tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
+
+# Load model
+model = EmotionClassifier(MODEL_NAME)
+model.load_state_dict(torch.load("best_deberta_base.bin", map_location="cpu"))
+model.eval()
+
+def predict_emotions(text):
+    inputs = tokenizer(
+        text,
+        return_tensors="pt",
+        truncation=True,
+        padding=True,
+        max_length=256
+    )
+
+    with torch.no_grad():
+        logits = model(
+            inputs["input_ids"],
+            inputs["attention_mask"]
+        )
+        probs = torch.sigmoid(logits).squeeze().tolist()
+
+    # Return label: probability
+    return {label: float(prob) for label, prob in zip(LABELS, probs)}
+
+iface = gr.Interface(
+    fn=predict_emotions,
+    inputs=gr.Textbox(lines=3, label="Enter text"),
+    outputs=gr.Label(label="Emotion Probabilities"),
+    title="Emotion Classifier (DeBERTa-v3-base)",
+    description="Predicts emotions: anger, fear, joy, sadness, surprise."
+)
+
+iface.launch()

best_deberta_base.bin

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+version https://git-lfs.github.com/spec/v1
+oid sha256:7771265ce676543f5160034f3baaee5e4a44de872e9a744bcc9ebf07a6a9530d
+size 735429146

model.py

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+import torch
+import torch.nn as nn
+from transformers import AutoModel
+
+class EmotionClassifier(nn.Module):
+    def __init__(self, model_name, num_labels=5):
+        super().__init__()
+        self.transformer = AutoModel.from_pretrained(model_name)
+        hidden_size = self.transformer.config.hidden_size
+        self.classifier = nn.Linear(hidden_size, num_labels)
+
+    def forward(self, input_ids, attention_mask):
+        outputs = self.transformer(
+            input_ids=input_ids,
+            attention_mask=attention_mask
+        )
+        cls_embeddings = outputs.last_hidden_state[:, 0, :]
+        logits = self.classifier(cls_embeddings)
+        return logits

requirements.txt

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+torch
+transformers==4.36.2
+gradio