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")# 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
- Xet hash:
- 5248b2b3e80a49fd4e6b70078b6fbce2439cb3ae9eaa34cb265e7bec188b4c2a
- Size of remote file:
- 438 MB
- SHA256:
- b4af80cf9c686d68df70f85d6430dec99f74fcb49a342e5bad1ca37f4e4f2cd6
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.