Instructions to use chrishuber/roberta-kaggledev-testing with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use chrishuber/roberta-kaggledev-testing with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="chrishuber/roberta-kaggledev-testing")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("chrishuber/roberta-kaggledev-testing") model = AutoModelForMaskedLM.from_pretrained("chrishuber/roberta-kaggledev-testing") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 10aae82683338c25a5353bd56f695d97186d05b6a9ec062dc5cf14e77472876b
- Size of remote file:
- 3.12 kB
- SHA256:
- 2a2e5fbab89565608e6c5b967bcdd22ca8cff519b0348ec0a5a3007981daa1ad
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