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 training_args.bin from amanda-901014/roberta-hard: direct link, hf CLI and curl.
- Browser
- Download file 3.18 kB
-
https://huggingface.co/amanda-901014/roberta-hard/resolve/main/training_args.bin
- Command line
-
hf download hf://amanda-901014/roberta-hard/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/amanda-901014/roberta-hard/resolve/main/training_args.bin
3.18 kB
- Xet hash:
- 19a76fb529fe309638bda6002cc2d4c5701dc9295a628ec331d4e297aade558f
- Size of remote file:
- 3.18 kB
- SHA256:
- 074b348b56cc662fb198633a1d0da2e839f5ca4a102e26755c7c9cc97f729e2b
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