Instructions to use CLTL/binary_icf_classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CLTL/binary_icf_classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="CLTL/binary_icf_classifier")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("CLTL/binary_icf_classifier") model = AutoModelForSequenceClassification.from_pretrained("CLTL/binary_icf_classifier") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 429fa186c04a90430f7346ee05c696f9035e77708e0d5e3616ede3879deca111
- Size of remote file:
- 504 MB
- SHA256:
- ee11f46f090025e2d84e7b1ca696ff6127bc406f92d554fc0b75315f670af611
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.