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 model.safetensors from Nma/RuleClassify-Textclassify: direct link, hf CLI and curl.
- Browser
- Download file 1.11 GB
-
https://huggingface.co/Nma/RuleClassify-Textclassify/resolve/main/model.safetensors
- Command line
-
hf download hf://Nma/RuleClassify-Textclassify/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/Nma/RuleClassify-Textclassify/resolve/main/model.safetensors
1.11 GB
- Xet hash:
- 678e9f4fab6a1fc1ed256260b8f1f9a83bc77f77b9630c2d928ac72260ca7110
- Size of remote file:
- 1.11 GB
- SHA256:
- 22dd5b2e53fc6c3e0bff58d196354fc751014e23b7f0f8ece7baf7dcbcd972d4
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