Instructions to use codingJacob/dummy-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use codingJacob/dummy-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="codingJacob/dummy-model")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("codingJacob/dummy-model") model = AutoModelForMaskedLM.from_pretrained("codingJacob/dummy-model") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 135e25ff1165246cc1dc628d9e0e1bdca756f55316553a6f81799a05acfbee0c
- Size of remote file:
- 443 MB
- SHA256:
- 42478af2c66bce1b9295d91ae64438f1337268d20524aad22ffa56406f2225cd
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.