Instructions to use ucabqfe/roberta_PER_io with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ucabqfe/roberta_PER_io with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="ucabqfe/roberta_PER_io")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("ucabqfe/roberta_PER_io") model = AutoModelForTokenClassification.from_pretrained("ucabqfe/roberta_PER_io", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from ucabqfe/roberta_PER_io: direct link, hf CLI and curl.
- Browser
- Download file 496 MB
-
https://huggingface.co/ucabqfe/roberta_PER_io/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://ucabqfe/roberta_PER_io/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/ucabqfe/roberta_PER_io/resolve/main/pytorch_model.bin
496 MB
- Xet hash:
- e0a1bb6df4efb33d942be3202a8b7ee70e2fd14b73f76569bee5a40e3d1dc276
- Size of remote file:
- 496 MB
- SHA256:
- fcc1237a941fcd8aaca435937a7026203326da5e05e20a0299da24d25b5ce643
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