Instructions to use ZiruiXiong/bert-base-finetuned-toxic-comment-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ZiruiXiong/bert-base-finetuned-toxic-comment-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ZiruiXiong/bert-base-finetuned-toxic-comment-classification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ZiruiXiong/bert-base-finetuned-toxic-comment-classification") model = AutoModelForSequenceClassification.from_pretrained("ZiruiXiong/bert-base-finetuned-toxic-comment-classification") - Notebooks
- Google Colab
- Kaggle
bert-base-cased-finetuned-jigsaw
This model is a fine-tuned version of bert-base-uncased on the Kaggle jigsaw toxic comment classification dataset.
How to use
You can use the model with the following code.
from transformers import AutoModelForSequenceClassification, AutoTokenizer, TextClassificationPipeline
model_path = "ZiruiXiong/bert-base-finetuned-toxic-comment-classification"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForSequenceClassification.from_pretrained(model_path)
pipeline = TextClassificationPipeline(model=model, tokenizer=tokenizer)
print(pipeline('You are also a liar since I hardly spoke with you.'))
Evaluation results
The model achieves 0.95 AUC in a 1500 rows held-out test set.
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