metadata
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
🎯 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
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}]