Instructions to use sjwsjw/dummy-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sjwsjw/dummy-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="sjwsjw/dummy-model")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("sjwsjw/dummy-model") model = AutoModelForMaskedLM.from_pretrained("sjwsjw/dummy-model") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- efb43eae1985c184554b13f49b10fd24240145cc636528a55af8fdab2e542d2c
- Size of remote file:
- 443 MB
- SHA256:
- 7ec33e2edeca9c3e2d59609b5b074bfa7d2f8de7757b6de945ea3824c31778c0
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.