Instructions to use YWZBrandon/roberta-large_ds100_upsample1000 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use YWZBrandon/roberta-large_ds100_upsample1000 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="YWZBrandon/roberta-large_ds100_upsample1000")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("YWZBrandon/roberta-large_ds100_upsample1000") model = AutoModelForMaskedLM.from_pretrained("YWZBrandon/roberta-large_ds100_upsample1000", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9e90645fb1bc5eb6967bd0833c6b8d940a8a38b95e1e2b5c31b9bf4a94348732
- Size of remote file:
- 5.37 kB
- SHA256:
- 3b4ff6701ec0170d3ecf8638a70ccb9df6f9ff3db37ed3c06ee3ad1f17c92f02
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