Instructions to use hf-tiny-model-private/tiny-random-MobileBertForMaskedLM 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-MobileBertForMaskedLM 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-MobileBertForMaskedLM")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-MobileBertForMaskedLM") model = AutoModelForMaskedLM.from_pretrained("hf-tiny-model-private/tiny-random-MobileBertForMaskedLM") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 23360b265a664ad754c66d74d167c0a4c39c3079359b9dca3389a960241a72f4
- Size of remote file:
- 3.43 MB
- SHA256:
- 8f199728403d021f4575ea90564ba73d4a3579f7d4ded7f4d21cf210245ca151
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.