Instructions to use amanda-901014/roberta-easy with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use amanda-901014/roberta-easy with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="amanda-901014/roberta-easy")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("amanda-901014/roberta-easy") model = AutoModelForSequenceClassification.from_pretrained("amanda-901014/roberta-easy", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from amanda-901014/roberta-easy: direct link, hf CLI and curl.
- Browser
- Download file 499 MB
-
https://huggingface.co/amanda-901014/roberta-easy/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://amanda-901014/roberta-easy/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/amanda-901014/roberta-easy/resolve/main/pytorch_model.bin
499 MB
- Xet hash:
- 85c0f914f16c8cb5f3eb195be54e950b53b976f77dbb6a03612a98d0aac0ca8f
- Size of remote file:
- 499 MB
- SHA256:
- dd36dbc980374538e156b39d53eefd64e62c27adbe125422e30ccb089df58a6a
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