Instructions to use chrishuber/roberta-retrained-kaggledev with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use chrishuber/roberta-retrained-kaggledev with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="chrishuber/roberta-retrained-kaggledev")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("chrishuber/roberta-retrained-kaggledev") model = AutoModelForMaskedLM.from_pretrained("chrishuber/roberta-retrained-kaggledev", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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Upload training_args.bin with git-lfs
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Upload config.json
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Upload pytorch_model.bin with git-lfs
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initial commit
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