Instructions to use hf-internal-testing/tiny-random-DistilBertForMaskedLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-DistilBertForMaskedLM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="hf-internal-testing/tiny-random-DistilBertForMaskedLM")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-DistilBertForMaskedLM") model = AutoModelForMaskedLM.from_pretrained("hf-internal-testing/tiny-random-DistilBertForMaskedLM", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1bc1637fd67797946df60a078bf848c854d900893aa3b5edf5d425b37efb710f
- Size of remote file:
- 383 kB
- SHA256:
- 50ee38ae09fe270ac5c3150142b4f3fb61844126133ab7ab69c1a05ddfc7ed27
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