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
metadata
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
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("UngLong/cafebert-classification-ft")
model = AutoModelForSequenceClassification.from_pretrained("UngLong/cafebert-classification-ft")