Instructions to use hf-internal-testing/tiny-random-SqueezeBertForMaskedLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-SqueezeBertForMaskedLM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="hf-internal-testing/tiny-random-SqueezeBertForMaskedLM")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-SqueezeBertForMaskedLM") model = AutoModelForMaskedLM.from_pretrained("hf-internal-testing/tiny-random-SqueezeBertForMaskedLM", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b57d64c353b03d26618e50901ff76c6b2449bc89680eef9351537543c378a236
- Size of remote file:
- 337 kB
- SHA256:
- 95dc75bd1197bac2f65fa8b6e3438790d7c5bfc91022e0ebae6b4f0493e71367
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