tishasetia13/inside-out-emotion-classifier

Inside Out Emotion Classifier

A DistilBERT model fine-tuned to classify a short personal story into five emotions: anger, disgust, fear, joy and sadness. It powers a web app that shows the five Inside Out characters sized by the predicted probabilities.

• Live demo: https://inside-out-emotion-classifier.vercel.app
• Code: https://github.com/tishasetia13/inside-out-emotion-classifier
• API: https://huggingface.co/spaces/tishasetia13/inside-out-api

Intended use

This model is a portfolio and educational project. It is not a clinical tool and must not be used to assess anyone's mental health.

Training

• Base model: distilbert-base-uncased, fully fine-tuned (all weights updated) for 3 epochs with PyTorch.
• Data: the ISEAR dataset of short personal descriptions of emotional events. I kept five of its labels (anger, disgust, fear, joy, sadness), which left 5,395 examples. After cleaning, they were split into stratified train, validation and test sets.
• Input length: the demo feeds the model chunks of up to 64 tokens.

Results

Evaluated on a held-out test set. The baseline uses the same data and split.

Model	Test accuracy
TF-IDF + logistic regression (baseline)	66.8%
This model	76.4%

How to use

import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification

repo_id = "tishasetia13/inside-out-emotion-classifier"
tokenizer = AutoTokenizer.from_pretrained(repo_id)
model = AutoModelForSequenceClassification.from_pretrained(repo_id)
model.eval()

text = "I opened my results and saw I had topped my class."

# Tokenize, keeping the input within the 64-token window used in the demo
inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=64)

# Run the model and turn the raw scores into probabilities that sum to 1
with torch.no_grad():
    logits = model(**inputs).logits
probs = torch.softmax(logits, dim=-1)[0]

# Print each emotion with its probability
for i, p in enumerate(probs):
    print(model.config.id2label[i], f"{p.item() * 100:.1f}%")

For stories longer than 64 tokens, the live demo splits the text into chunks at sentence boundaries, classifies each chunk, and averages the probabilities. The implementation is in predict.py in the GitHub repository.

Limitations

• No neutral class. Calm or factual text is forced into one of the five emotions, often joy.
• Five emotions only. ISEAR also contains shame and guilt, which were left out.
• Mixed feelings are flattened. The model tends to return one dominant emotion with very high confidence, even when a story contains two.
• High confidence is not correctness. The scores are model probabilities, not a measure of accuracy.
• Narrow training data. The model was trained on short ISEAR stories, so informal text, slang and other languages are outside what it learned.