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") - Notebooks
- Google Colab
- Kaggle
Adding `safetensors` variant of this model
#1
by SFconvertbot - opened
- model.safetensors +3 -0
model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:bd152860d5d92753b58a69026f0ad72d885ed8669d6baefda00a023214928606
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size 437959756
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