Instructions to use textdetox/glot500-toxicity-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use textdetox/glot500-toxicity-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="textdetox/glot500-toxicity-classifier")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("textdetox/glot500-toxicity-classifier") model = AutoModelForSequenceClassification.from_pretrained("textdetox/glot500-toxicity-classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from textdetox/glot500-toxicity-classifier: direct link, hf CLI and curl.
- Browser
- Download file 26.9 MB
-
https://huggingface.co/textdetox/glot500-toxicity-classifier/resolve/main/tokenizer.json
- Command line
-
hf download hf://textdetox/glot500-toxicity-classifier/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/textdetox/glot500-toxicity-classifier/resolve/main/tokenizer.json
26.9 MB
- Xet hash:
- 0da56aa36da77a58c1690d6f79d3edc0e99d529b767c2c4232a98825d0c397bb
- Size of remote file:
- 26.9 MB
- SHA256:
- 1628667eeef083e1ae8fc9db7ebd75542f51ce3524287da23931571285d935cb
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