# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("pt-sk/bert-toxic-classification")
model = AutoModelForSequenceClassification.from_pretrained("pt-sk/bert-toxic-classification")Quick Links
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 = "pt-sk/bert-toxic-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 from 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.
- Downloads last month
- 2
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="pt-sk/bert-toxic-classification")