AbhishekBhavnani/TweetClassification

BERT-based Emotion Classification Model 🎭

This model is a fine-tuned version of BERT for emotion classification. It predicts one of six emotion categories from a given English text input.

🧠 Model Details

• Architecture: BertForSequenceClassification
• Base Model: bert-base-uncased
• Labels:
  • 0: sadness
  • 1: joy
  • 2: love
  • 3: anger
  • 4: fear
  • 5: surprise
• Problem Type: Single-label classification
• Hidden Size: 768
• Max Sequence Length: 512
• Number of Layers: 12

🚀 How to Use

from transformers import pipeline

classifier = pipeline("text-classification", model="AbhishekBhavnani/TweetClassification")
result = classifier("I'm feeling so happy today!")
print(result)

Example

Input:

I can't stop smiling, this movie is too funny!

Output:

[{'label': 'joy', 'score': 0.9821}]