Instructions to use amanda-901014/roberta-hard with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use amanda-901014/roberta-hard with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="amanda-901014/roberta-hard")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("amanda-901014/roberta-hard") model = AutoModelForSequenceClassification.from_pretrained("amanda-901014/roberta-hard", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from amanda-901014/roberta-hard: direct link, hf CLI and curl.
- Browser
- Download file 499 MB
-
https://huggingface.co/amanda-901014/roberta-hard/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://amanda-901014/roberta-hard/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/amanda-901014/roberta-hard/resolve/main/pytorch_model.bin
499 MB
- Xet hash:
- 3eabee2845df1d3fdc318dcfbefa9a41d9c90b4f9491225bbc01e7489c517836
- Size of remote file:
- 499 MB
- SHA256:
- f6186db8ab79271a1e084b5a770dbd6aed27462c05d4c1fd1f464080300d14dc
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