Hostileic
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emotion-vibecheck-model

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 library_name: transformers
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 ---
 library_name: transformers
+tags: [emotion-detection, text-classification, hinglish, nlp, sentiment, emotion-ai]
 ---
+# 🧠 AI VibeCheck – Hinglish + English Emotion Detection Model
+This is a fine-tuned **BERT-based model** trained on **10,000+ Hinglish + English samples** to detect human emotions from short text messages.
+Unlike most emotion datasets that are purely English, this model was built to understand **real Indian conversational language** including Hinglish words such as:
+- **"udas" → sad**
+- **"gussa" → angry**
+- **"mast" → joy**
+It powers the deployed app 👉 [AI VibeCheck on Hugging Face Spaces](https://huggingface.co/spaces/Hostileic/emotion-vibecheck).
+---
+## 📖 Model Details
+- **Developed by:** Jagrit Chaudhry
+- **Model type:** BERT for Sequence Classification
+- **Languages:** Hinglish + English (code-mixed)
+- **Fine-tuned from:** `bert-base-multilingual-cased`
+- **License:** MIT
+---
+## 🚀 Uses
 ### Direct Use
+- Emotion detection from raw text (English or Hinglish).
+- Can process screenshots of text via OCR (in the web app).
+Example:
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+model_id = "Hostileic/emotion-vibecheck-model"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForSequenceClassification.from_pretrained(model_id)
+inputs = tokenizer("mujhe thoda gussa aa raha hai", return_tensors="pt")
+with torch.no_grad():
+    outputs = model(**inputs)
+    probs = torch.nn.functional.softmax(outputs.logits, dim=1)
+    prediction = torch.argmax(probs, dim=1).item()
+print("Predicted Emotion:", model.config.id2label[prediction])
+Downstream Use
+Chatbots and virtual assistants that adapt to user emotions.
+Emotion-aware analytics for social media or customer support.
+Out-of-Scope
+Long-form documents (works best on short text/snippets).
+Non-Hinglish languages not present in training data.
+⚠️ Bias, Risks, and Limitations
+Model is biased towards Hinglish/English texting style, may underperform on formal text.
+Limited coverage of rare emotions due to dataset size.
+Misclassifications possible with sarcasm, irony, or mixed emotions.
+📊 Training Details
+Dataset: Custom synthetic + extended dataset (~10k samples, 10 emotion labels).
+Training procedure: Fine-tuning bert-base-multilingual-cased with PyTorch + Hugging Face Transformers.
+Hyperparameters:
+Epochs: 5
+Batch size: 32
+Learning rate: 2e-5
+Optimizer: AdamW
+✅ Evaluation
+Validation Accuracy: ~85%
+Best performance on: Joy, Sadness, Anger
+Challenging cases: Neutral and Surprise (overlaps in Hinglish texting).
+⚡ Technical Specs
+Architecture: BERT-base (multilingual)
+Framework: PyTorch + Hugging Face Transformers
+Training Hardware: NVIDIA GPU (single-GPU fine-tuning)
+📌 Citation
+If you use this model, please cite:
+@misc{chaudhry2025emotionvibecheck,
+  author = {Jagrit Chaudhry},
+  title = {AI VibeCheck – Hinglish + English Emotion Detection},
+  year = {2025},
+  publisher = {Hugging Face},
+  howpublished = {\url{https://huggingface.co/Hostileic/emotion-vibecheck-model}}
+}
+📬 Contact
+Author: Jagrit Chaudhry
+Email: jagritworkchaudhry1409@gmail.com
+GitHub: [Jagrit-09](https://github.com/Jagrit-09)
+LinkedIn: [Jagrit Chaudhry](https://www.linkedin.com/in/jagrit-chaudhry-448690309/)