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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| license: apache-2.0 | |
| datasets: | |
| - UngLong/ViSIR | |
| language: | |
| - vi | |
| metrics: | |
| - f1 | |
| - recall | |
| - precision | |
| base_model: | |
| - uitnlp/CafeBERT | |
| pipeline_tag: text-classification | |
| tags: | |
| - socialmedia | |
| - toxiccomment | |
| - classification_toxic_comment | |
| - transformer | |
| library_name: transformers | |
| # CafeBERT Fine-tuned for Toxic Classification | |
| This model is a fine-tuned version of CafeBERT on a toxic comment classification task. | |
| ## Usage | |
| ```python | |
| from transformers import AutoTokenizer, AutoModelForSequenceClassification | |
| tokenizer = AutoTokenizer.from_pretrained("UngLong/cafebert-classification-ft") | |
| model = AutoModelForSequenceClassification.from_pretrained("UngLong/cafebert-classification-ft") | |