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")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("CLTL/binary_icf_classifier") model = AutoModelForSequenceClassification.from_pretrained("CLTL/binary_icf_classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from CLTL/binary_icf_classifier: direct link, hf CLI and curl.
- Browser
- Download file 504 MB
-
https://huggingface.co/CLTL/binary_icf_classifier/resolve/refs%2Fpr%2F1/model.safetensors
- Command line
-
hf download hf://CLTL/binary_icf_classifier@refs/pr/1/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/CLTL/binary_icf_classifier/resolve/refs%2Fpr%2F1/model.safetensors
504 MB
- 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.