metadata
license: mit
datasets:
- thesofakillers/jigsaw-toxic-comment-classification-challenge
language:
- en
metrics:
- accuracy
- f1
tags:
- text-classification
- toxic_comment
- nlp
- transformers
- distilbert
pipeline_tag: text-classification
Toxic Comment Classifier (Distil-bert-uncased)
This model is a fine-tuned Distil-bert-uncased model for toxic comment classification.
It classifies comments as either toxic or non-toxic.
Training
The model was trained using Hugging Face Trainer on a labeled toxic comment dataset.
Evaluation metrics:
- Accuracy: ~97%
- F1 score: ~83%
Intended Use
- Detecting toxic or harmful language in text.
- Useful for moderation in forums, social media, and chat systems.
Limitations
- May not capture sarcasm or subtle toxicity.
- Biases in the training dataset may affect predictions.
Usage
from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
model_id = "Youssef-El-SaYed/toxic-comment-classifier"
# Define mapping
id2label = {0: "Non-Toxic", 1: "Toxic"}
label2id = {"Non-Toxic": 0, "Toxic": 1}
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForSequenceClassification.from_pretrained(
model_id,
id2label=id2label,
label2id=label2id
)
nlp = pipeline("text-classification", model=model, tokenizer=tokenizer)
print(nlp("You are so stupid and annoying!"))
print(nlp("I really like your work, keep it up!"))