Instructions to use hf-tiny-model-private/tiny-random-RoFormerForMaskedLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-tiny-model-private/tiny-random-RoFormerForMaskedLM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="hf-tiny-model-private/tiny-random-RoFormerForMaskedLM")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-RoFormerForMaskedLM") model = AutoModelForMaskedLM.from_pretrained("hf-tiny-model-private/tiny-random-RoFormerForMaskedLM", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 94683528130a126356e05e17d8c7008fa445e2f6c948d2bc906d9d9d9875f22e
- Size of remote file:
- 6.79 MB
- SHA256:
- 0de8551a2b00bfdd7de8682b139b35ac65854a94d859211d5455ef300e596048
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