Instructions to use ydshieh/tiny-random-BertForMaskedLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ydshieh/tiny-random-BertForMaskedLM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="ydshieh/tiny-random-BertForMaskedLM")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("ydshieh/tiny-random-BertForMaskedLM") model = AutoModelForMaskedLM.from_pretrained("ydshieh/tiny-random-BertForMaskedLM") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3f332938d9cf1467edccd968de6d2dbb346c0940e64e521084b38c49d88a9fd3
- Size of remote file:
- 371 kB
- SHA256:
- 367d6050f3408d77159f88896baeb169aee9a7ec911015a9a5181d715d00e29c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.