Instructions to use hf-internal-testing/tiny-random-RoCBertForMaskedLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-RoCBertForMaskedLM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="hf-internal-testing/tiny-random-RoCBertForMaskedLM")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-RoCBertForMaskedLM") model = AutoModelForMaskedLM.from_pretrained("hf-internal-testing/tiny-random-RoCBertForMaskedLM", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5cdb67268f146b5c99c71fa989fc715d25d4492e9224fa0c24215aed3e4637ab
- Size of remote file:
- 3.07 MB
- SHA256:
- ac91c2f763c74590e0ebadd3be35e8ce3d2297e47a03bbb8e2db5b3526b724d9
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