Instructions to use Nma/RuleClassify-Textclassify with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Nma/RuleClassify-Textclassify with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Nma/RuleClassify-Textclassify")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Nma/RuleClassify-Textclassify") model = AutoModelForSequenceClassification.from_pretrained("Nma/RuleClassify-Textclassify", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Nma/RuleClassify-Textclassify: direct link, hf CLI and curl.
- Browser
- Download file 1.11 GB
-
https://huggingface.co/Nma/RuleClassify-Textclassify/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Nma/RuleClassify-Textclassify/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Nma/RuleClassify-Textclassify/resolve/main/pytorch_model.bin
1.11 GB
- Xet hash:
- 30a8b37b555b8c108370cf6b5e9f3ad5ea4a3966fd9a9d4e6109f2512cfdedec
- Size of remote file:
- 1.11 GB
- SHA256:
- 50023f8592be352e4aba2c2bc36ba436ff0b02028de7a6c4827958ac68ce1f60
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