Instructions to use rasgaard/bert-newsgroups-probe with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rasgaard/bert-newsgroups-probe with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="rasgaard/bert-newsgroups-probe")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("rasgaard/bert-newsgroups-probe") model = AutoModelForSequenceClassification.from_pretrained("rasgaard/bert-newsgroups-probe", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from rasgaard/bert-newsgroups-probe: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/rasgaard/bert-newsgroups-probe/resolve/refs%2Fpr%2F1/pytorch_model.bin
- Command line
-
hf download hf://rasgaard/bert-newsgroups-probe@refs/pr/1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/rasgaard/bert-newsgroups-probe/resolve/refs%2Fpr%2F1/pytorch_model.bin
438 MB
- Xet hash:
- 15e0b68bc6260854d38f7188906aff6e3d3d18d39299bf20effa79b7dbf31093
- Size of remote file:
- 438 MB
- SHA256:
- d1a7b569271c05fed56a39d61498a22a1bb82107a5eaee2ff17612948259688b
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