Instructions to use Turka/dummy-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Turka/dummy-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Turka/dummy-model")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("Turka/dummy-model") model = AutoModelForMaskedLM.from_pretrained("Turka/dummy-model") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d78524c9c5b1d3ccddb8459f9d5d3ceb35f45368d2636c49b413540db7006e24
- Size of remote file:
- 443 MB
- SHA256:
- 9214d7755f2982dd1a9d775509b7421fad9a212c1e0f87c10230be5dcbba5e43
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.