metadata
license: afl-3.0
datasets:
- jigsaw_toxicity_pred
language:
- en
metrics:
- accuracy
library_name: transformers
pipeline_tag: text-classification
Model description
This model is a fine-tuned version of the bert-base-uncased model to classify toxic comments.
How to use
You can use the model with the following code.
from transformers import BertForSequenceClassification, BertTokenizer, TextClassificationPipeline
model_path = "JungleLee/bert-toxic-comment-classification"
tokenizer = BertTokenizer.from_pretrained(model_path)
model = BertForSequenceClassification.from_pretrained(model_path, num_labels=2)
pipeline = TextClassificationPipeline(model=model, tokenizer=tokenizer)
print(pipeline("You're a fucking nerd."))
Training data
The training data comes this Kaggle competition. We use 90% of the train.csv data to train the model.
Evaluation results
The model achieves 0.95 AUC in a 1500 rows held-out test set.