Text Classification
Transformers
Safetensors
Vietnamese
xlm-roberta
socialmedia
toxiccomment
classification_toxic_comment
transformer
text-embeddings-inference
Instructions to use UngLong/cafebert-classification-ft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use UngLong/cafebert-classification-ft with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="UngLong/cafebert-classification-ft")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("UngLong/cafebert-classification-ft") model = AutoModelForSequenceClassification.from_pretrained("UngLong/cafebert-classification-ft") - Notebooks
- Google Colab
- Kaggle
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README.md
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- socialmedia
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- toxiccomment
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- classification_toxic_comment
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---
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# CafeBERT Fine-tuned for Toxic Classification
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This model is a fine-tuned version of CafeBERT on a toxic comment classification task.
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from transformers import AutoTokenizer, AutoModelForSequenceClassification
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tokenizer = AutoTokenizer.from_pretrained("UngLong/cafebert-classification-ft")
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model = AutoModelForSequenceClassification.from_pretrained("UngLong/cafebert-classification-ft")
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- socialmedia
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- toxiccomment
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- classification_toxic_comment
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- transformer
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library_name: transformers
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---
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# CafeBERT Fine-tuned for Toxic Classification
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This model is a fine-tuned version of CafeBERT on a toxic comment classification task.
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from transformers import AutoTokenizer, AutoModelForSequenceClassification
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tokenizer = AutoTokenizer.from_pretrained("UngLong/cafebert-classification-ft")
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model = AutoModelForSequenceClassification.from_pretrained("UngLong/cafebert-classification-ft")
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