Instructions to use NbAiLabArchive/test_w7 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NbAiLabArchive/test_w7 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="NbAiLabArchive/test_w7")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("NbAiLabArchive/test_w7") model = AutoModelForMaskedLM.from_pretrained("NbAiLabArchive/test_w7") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1ca79440b47f8fad57cbb7d354bd367f3d4af4b46113f901958bcc41cfb56273
- Size of remote file:
- 499 MB
- SHA256:
- 64cc28f00dbff799001e038bbbfc574beffc02ec5d2292f8f1917d4f962e7c07
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.