Instructions to use hf-tiny-model-private/tiny-random-DistilBertForMaskedLM 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-DistilBertForMaskedLM 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-DistilBertForMaskedLM")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-DistilBertForMaskedLM") model = AutoModelForMaskedLM.from_pretrained("hf-tiny-model-private/tiny-random-DistilBertForMaskedLM") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6acf60e8ec17cf301075e5d5d11eb1cf7b68f87f40a072c1da161a69bbb3e8d4
- Size of remote file:
- 365 kB
- SHA256:
- 29e73144949e667dd6647e8a5a0ea2ec8810554436d5bf9d2eb4d0526335e32b
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