Instructions to use UMatterr/bad_language_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use UMatterr/bad_language_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="UMatterr/bad_language_classification")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("UMatterr/bad_language_classification") model = AutoModelForSequenceClassification.from_pretrained("UMatterr/bad_language_classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from UMatterr/bad_language_classification: direct link, hf CLI and curl.
- Browser
- Download file 436 MB
-
https://huggingface.co/UMatterr/bad_language_classification/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://UMatterr/bad_language_classification/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/UMatterr/bad_language_classification/resolve/main/pytorch_model.bin
436 MB
- Xet hash:
- 5c837a21169016b7cc4dc75c86fbc376467b239b35d719a89ebb83ddd8d55b2a
- Size of remote file:
- 436 MB
- SHA256:
- 620c2cb51f71502d3945154e2a9cc0a0fdece655e4980d0b5f6b2192333975af
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