Instructions to use lowem1/cms-invoice-correction-crossencoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lowem1/cms-invoice-correction-crossencoder with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="lowem1/cms-invoice-correction-crossencoder")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("lowem1/cms-invoice-correction-crossencoder") model = AutoModelForSequenceClassification.from_pretrained("lowem1/cms-invoice-correction-crossencoder", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from lowem1/cms-invoice-correction-crossencoder: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/lowem1/cms-invoice-correction-crossencoder/resolve/refs%2Fpr%2F1/pytorch_model.bin
- Command line
-
hf download hf://lowem1/cms-invoice-correction-crossencoder@refs/pr/1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/lowem1/cms-invoice-correction-crossencoder/resolve/refs%2Fpr%2F1/pytorch_model.bin
438 MB
- Xet hash:
- 5248b2b3e80a49fd4e6b70078b6fbce2439cb3ae9eaa34cb265e7bec188b4c2a
- Size of remote file:
- 438 MB
- SHA256:
- b4af80cf9c686d68df70f85d6430dec99f74fcb49a342e5bad1ca37f4e4f2cd6
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