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 tf_model.h5 from ctu-aic/FERNET-C5-csfever: direct link, hf CLI and curl.
- Browser
- Download file 652 MB
-
https://huggingface.co/ctu-aic/FERNET-C5-csfever/resolve/main/tf_model.h5
- Command line
-
hf download hf://ctu-aic/FERNET-C5-csfever/tf_model.h5
-
curl -L -o tf_model.h5 https://huggingface.co/ctu-aic/FERNET-C5-csfever/resolve/main/tf_model.h5
652 MB
- Xet hash:
- 7d032193a5987125f489fc7b4448a67e28a69762eac69b45329a5ea07f3e9be1
- Size of remote file:
- 652 MB
- SHA256:
- 16c702c6c88c25e6dc7a81e413d2b021aed802d1d70b8ad0c90eb15997cd02e8
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