Add model card, weights, and tokenizer

README.md

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+---
+license: mit
+language:
+  - en
+  - hi
+library_name: transformers
+pipeline_tag: text-classification
+tags:
+  - emotion-detection
+  - distilbert
+  - sentiment-analysis
+  - mental-health
+  - emotion-classification
+  - text-classification
+  - transformers
+  - pytorch
+  - hinglish
+base_model: distilbert-base-uncased
+datasets:
+  - google-research-datasets/go_emotions
+metrics:
+  - accuracy
+  - f1
+  - precision
+  - recall
+model-index:
+  - name: raven-emotion-distilbert
+    results:
+      - task:
+          type: text-classification
+          name: Emotion Classification
+        dataset:
+          name: Custom Indian + International Dataset
+          type: custom
+        metrics:
+          - name: Accuracy
+            type: accuracy
+            value: 0.9762
+          - name: F1
+            type: f1
+            value: 0.9762
+          - name: Precision
+            type: precision
+            value: 0.9762
+          - name: Recall
+            type: recall
+            value: 0.9762
+      - task:
+          type: text-classification
+          name: Emotion Classification
+        dataset:
+          name: GoEmotions (Balanced 300 samples)
+          type: google-research-datasets/go_emotions
+        metrics:
+          - name: Accuracy
+            type: accuracy
+            value: 0.7733
+          - name: F1
+            type: f1
+            value: 0.7724
+widget:
+  - text: "I'm so stressed about my exam tomorrow, I can't sleep"
+    example_title: Anxious
+  - text: "Just got promoted at work, feeling on top of the world!"
+    example_title: Happy
+  - text: "I don't understand why this code keeps throwing errors"
+    example_title: Confused
+  - text: "I lost my best friend over a stupid argument"
+    example_title: Sad
+  - text: "This is absolutely unacceptable, I'm furious right now"
+    example_title: Angry
+  - text: "Nothing much going on today, just chilling at home"
+    example_title: Neutral
+---
+
+# Raven Emotion DistilBERT
+
+A fine-tuned **DistilBERT** model for 6-class emotion classification, built for [Raven AI](https://raven-ai-new.streamlit.app) — an emotionally aware AI assistant.
+
+This model classifies text into **6 emotions**: `happy`, `sad`, `anxious`, `angry`, `confused`, `neutral`.
+
+## Performance
+
+| Model / Method | Dataset | Accuracy | F1 Score |
+|---|---|---|---|
+| Zero-Shot LLM (LLama 3.3 70B) | GoEmotions | 66.67% | 0.6691 |
+| Few-Shot LLM (LLama 3.3 70B) | GoEmotions | 73.00% | 0.7331 |
+| **This model** (initial training) | GoEmotions | **77.33%** | **0.7724** |
+| **This model** (after domain adaptation) | Custom Dataset | **97.62%** | **0.9762** |
+
+**Key result**: This 67M parameter model outperforms a 70B parameter LLM by +4.33% on emotion classification, proving that task-specific fine-tuning beats general-purpose prompting.
+
+## Quick Start
+
+```python
+from transformers import pipeline
+
+classifier = pipeline("text-classification", model="SoumyaCodes/raven-emotion-distilbert", top_k=None)
+
+result = classifier("I'm so stressed about my exam tomorrow")
+print(result)
+# [[{'label': 'anxious', 'score': 0.95}, {'label': 'sad', 'score': 0.02}, ...]]
+```
+
+Or load the model directly:
+
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+
+tokenizer = AutoTokenizer.from_pretrained("SoumyaCodes/raven-emotion-distilbert")
+model = AutoModelForSequenceClassification.from_pretrained("SoumyaCodes/raven-emotion-distilbert")
+
+EMOTIONS = ["happy", "sad", "anxious", "angry", "confused", "neutral"]
+
+def detect_emotion(text):
+    inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=128, padding=True)
+    with torch.no_grad():
+        outputs = model(**inputs)
+    return EMOTIONS[torch.argmax(outputs.logits, dim=1).item()]
+
+print(detect_emotion("I just cleared my exam!"))  # happy
+print(detect_emotion("I'm furious at this situation"))  # angry
+```
+
+## Labels
+
+| ID | Label | Description |
+|---|---|---|
+| 0 | `happy` | Joy, excitement, gratitude, love, pride, amusement |
+| 1 | `sad` | Sadness, grief, disappointment, remorse |
+| 2 | `anxious` | Fear, nervousness, worry, stress |
+| 3 | `angry` | Anger, annoyance, frustration, disgust |
+| 4 | `confused` | Confusion, surprise, curiosity, realization |
+| 5 | `neutral` | Neutral, calm, indifferent |
+
+## Training Details
+
+### Phase 1: Initial Training on GoEmotions
+
+- **Base model**: `distilbert-base-uncased` (67M parameters)
+- **Dataset**: [GoEmotions](https://huggingface.co/datasets/google-research-datasets/go_emotions) — Google's 28-emotion dataset, mapped to 6 categories
+- **Epochs**: 3 | **Batch size**: 16 | **Learning rate**: 2e-5 | **Optimizer**: AdamW (weight decay 0.01)
+
+| Epoch | Train Loss | Val Accuracy | Val F1 |
+|---|---|---|---|
+| 1 | 1.1599 | 66.93% | 0.6671 |
+| 2 | 0.8031 | 67.37% | 0.6737 |
+| 3 | 0.6494 | 67.64% | 0.6747 |
+
+### Phase 2: Domain Adaptation on Custom Dataset
+
+The model was further trained on ~12,343 samples of Indian English, Hinglish (Hindi-English), American English, and British English conversational text to adapt it for real-world student conversations.
+
+- **Learning rate**: 5e-6 (reduced to prevent catastrophic forgetting)
+- **Early stopping**: Patience of 2 epochs
+- **Warmup**: 10% of total training steps
+- **Gradient clipping**: 1.0
+
+| Epoch | Train Loss | Val Accuracy | Val F1 |
+|---|---|---|---|
+| 1 | 0.6765 | 90.99% | 0.9093 |
+| 2 | 0.2549 | 93.15% | 0.9311 |
+| 3 | 0.1625 | 94.08% | 0.9406 |
+| 4 | 0.1147 | 94.46% | 0.9444 |
+| 5 | 0.0940 | 94.65% | 0.9463 |
+
+**Domain adaptation impact**: Accuracy jumped from 64.38% to 97.62% (+33.24%) on the target domain.
+
+## GoEmotions Label Mapping
+
+The original 28 GoEmotions labels were mapped to 6 categories:
+
+| Raven Label | GoEmotions Labels |
+|---|---|
+| `happy` | joy, amusement, excitement, gratitude, love, optimism, pride, relief, admiration, approval, caring |
+| `sad` | sadness, grief, disappointment, remorse, embarrassment |
+| `anxious` | fear, nervousness |
+| `angry` | anger, annoyance, disgust |
+| `confused` | confusion, surprise, realization, curiosity |
+| `neutral` | neutral, desire |
+
+## Use Cases
+
+- **Emotionally aware chatbots** — Adjust response tone based on user emotion
+- **Mental health applications** — Detect distress, anxiety, or anger in user messages
+- **Customer support** — Route frustrated or confused customers to appropriate agents
+- **Social media monitoring** — Track emotional sentiment across conversations
+- **Education platforms** — Detect student frustration or confusion in real-time
+
+## About Raven AI
+
+This model powers [Raven AI](https://raven-ai-new.streamlit.app), an emotionally aware AI assistant that adapts its tone, persona, and response style based on detected user emotion. Raven includes crisis detection, multi-chat management, image understanding, voice input, document processing, and 20+ other features.
+
+- **Live app**: [raven-ai-new.streamlit.app](https://raven-ai-new.streamlit.app)
+- **GitHub**: [github.com/Soumyacodes1/raven-ai](https://github.com/Soumyacodes1/raven-ai)
+
+## Model Architecture
+
+- **Base**: DistilBERT (6 layers, 12 attention heads, 768 hidden dim)
+- **Parameters**: 67M
+- **Task head**: Sequence classification (6 classes)
+- **Max sequence length**: 128 tokens
+- **Format**: Safetensors (FP32)
+
+## Limitations
+
+- Trained primarily on English and Hinglish text — may not generalize well to other languages
+- Emotion categories are coarse-grained (6 classes) — may miss nuanced emotional states
+- Performance on formal/academic text may differ from conversational text
+- Not a diagnostic tool — should not be used as a substitute for professional mental health assessment
+
+## Citation
+
+```bibtex
+@misc{raha2026raven,
+  title={Raven AI: An Emotionally Aware AI Assistant with Fine-tuned DistilBERT},
+  author={Soumyadip Raha},
+  year={2026},
+  url={https://huggingface.co/SoumyaCodes/raven-emotion-distilbert}
+}
+```
+
+## License
+
+MIT