Instructions to use rasgaard/luke-base-newsgroups-probe with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rasgaard/luke-base-newsgroups-probe with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="rasgaard/luke-base-newsgroups-probe")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("rasgaard/luke-base-newsgroups-probe") model = AutoModelForSequenceClassification.from_pretrained("rasgaard/luke-base-newsgroups-probe", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from rasgaard/luke-base-newsgroups-probe: direct link, hf CLI and curl.
- Browser
- Download file 1.1 GB
-
https://huggingface.co/rasgaard/luke-base-newsgroups-probe/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://rasgaard/luke-base-newsgroups-probe/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/rasgaard/luke-base-newsgroups-probe/resolve/main/pytorch_model.bin
1.1 GB
- Xet hash:
- bc0cc30289f0c33e3dd778abc246c2c8bdb4eb74ebbf97f3d95f783b052bfdbc
- Size of remote file:
- 1.1 GB
- SHA256:
- 4fa65392656ef9f27491f932cd720ec3102302933aa89641677293b592f6652c
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