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znafian/
multilabel-text-classifier

pytorch

distilbert

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Multilabel Text Classification Model

This model is fine-tuned from DistilBERT for multilabel text classification.

Model Details

Base Model: distilbert-base-uncased
Parameters: ~774M (under 1B limit)
Task: Multilabel Classification
Number of Labels: 14
Max Sequence Length: 157

Training Details

Partial layer freezing applied
Trained for 3 epochs
Learning rate: 2e-5
Batch size: 16

Usage

python

import torch
from transformers import DistilBertTokenizer

# Load model and tokenizer
tokenizer = DistilBertTokenizer.from_pretrained('multilabel-text-classifier')
model = torch.load('multilabel-text-classifier/pytorch_model.bin')

# Example prediction
text = "Your input text here"
inputs = tokenizer(text, return_tensors="pt", max_length=157, truncation=True, padding=True)
with torch.no_grad():
    outputs = model(**inputs)
    predictions = torch.sigmoid(outputs).numpy()

Labels

['amusement', 'anger', 'annoyance', 'caring', 'confusion', 'disappointment', 'disgust', 'embarrassment', 'excitement', 'fear', 'gratitude', 'joy', 'love', 'sadness']

znafian/multilabel-text-classifier