Instructions to use ctu-aic/FERNET-C5-csfever with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ctu-aic/FERNET-C5-csfever with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ctu-aic/FERNET-C5-csfever")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ctu-aic/FERNET-C5-csfever") model = AutoModelForSequenceClassification.from_pretrained("ctu-aic/FERNET-C5-csfever", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from ctu-aic/FERNET-C5-csfever: direct link, hf CLI and curl.
- Browser
- Download file 651 MB
-
https://huggingface.co/ctu-aic/FERNET-C5-csfever/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://ctu-aic/FERNET-C5-csfever/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/ctu-aic/FERNET-C5-csfever/resolve/main/pytorch_model.bin
651 MB
- Xet hash:
- 168d463e7b63061fc5c1c4b2bf0854eb1ca518aabebb70a02e4ef629678ee59f
- Size of remote file:
- 651 MB
- SHA256:
- 9eac4a39c9cbb820ec767407a55c2de84eafdea62906836167df143ffa2a7f0d
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