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 training_args.bin from Nma/RuleClassify-Textclassify: direct link, hf CLI and curl.
- Browser
- Download file 3.44 kB
-
https://huggingface.co/Nma/RuleClassify-Textclassify/resolve/main/training_args.bin
- Command line
-
hf download hf://Nma/RuleClassify-Textclassify/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Nma/RuleClassify-Textclassify/resolve/main/training_args.bin
3.44 kB
- Xet hash:
- 40a5fc33b7e5d08e112c1880cf7d92d70016dd64ff17a17d07644338c12041e2
- Size of remote file:
- 3.44 kB
- SHA256:
- 3ab83aa5275709a4878cf3106c3dbfb0f00ee283e48ec9aca9726944b526b9df
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.