Instructions to use hf-internal-testing/tiny-random-MobileBertForMaskedLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-MobileBertForMaskedLM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="hf-internal-testing/tiny-random-MobileBertForMaskedLM")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-MobileBertForMaskedLM") model = AutoModelForMaskedLM.from_pretrained("hf-internal-testing/tiny-random-MobileBertForMaskedLM") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3b42c99447e3c2a94620ef2b0f9b7a6a47cdc25ba1989e9cbeebe4a7c18acac7
- Size of remote file:
- 2.97 MB
- SHA256:
- 74667ba0847f30e08985006f9d0b63d062875c0c4fe46b9973f2e8c075dc7b96
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.