README.md

---
language: en
license: apache-2.0
tags:
- text-classification
- emotion
- emotion-detection
- affective-computing
- distilbert
- transformers
- pytorch
pipeline_tag: text-classification
widget:
- text: "I was skeptical at first, but the results completely blew me away with how good it turned out!"
  example_title: "Joy Example"
- text: "I have been sitting here in complete darkness, feeling utterly exhausted and defeated."
  example_title: "Sadness Example"
- text: "Get out of my face right now before I lose my temper completely!"
  example_title: "Anger Example"
- text: "Every single moment spent with you makes my life feel infinitely more meaningful."
  example_title: "Love Example"
---

# DistilBERT Multiclass Emotion Classifier 🎭

A fine-tuned **DistilBERT (Distilled Bidirectional Encoder Representations from Transformers)** sequence classification model trained to detect nuanced affective and emotional states from unstructured natural language.

- **Author**: [Jayan Gupta](https://huggingface.co/Jayan12321)

---

## 🎯 Model Overview & Classes
The model classifies text across 6 primary emotional dimensions:
* `0`: **Sadness** (grief, sorrow, exhaustion)
* `1`: **Joy** (happiness, triumph, gratitude)
* `2`: **Love** (affection, admiration, intimacy)
* `3`: **Anger** (irritation, frustration, rage)
* `4`: **Fear** (anxiety, dread, panic)
* `5`: **Surprise** (shock, amazement, disbelief)

---

## 💻 Python Quickstart with Transformers Pipeline

```python
from transformers import pipeline

# Load fine-tuned pipeline directly from Hugging Face
classifier = pipeline(
    "text-classification",
    model="Jayan12321/distilbert-emotion-classifier"
)

# Run inference
sample_text = "I received the scholarship offer this morning and I cannot stop smiling!"
prediction = classifier(sample_text)

print(prediction)
# Output: [{'label': 'joy', 'score': 0.987}]
```