Instructions to use NbAiLabArchive/test_w5_long_dataset with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NbAiLabArchive/test_w5_long_dataset with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="NbAiLabArchive/test_w5_long_dataset")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("NbAiLabArchive/test_w5_long_dataset") model = AutoModelForMaskedLM.from_pretrained("NbAiLabArchive/test_w5_long_dataset") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e647979175482dffce2eb8d60d2dd494648f0e25b809eb30a2daf07995a1af58
- Size of remote file:
- 499 MB
- SHA256:
- ad8f8ee5ce56e6b3aa9f124c6e621e8b6b764b43fd50c1539373bf8d17e78933
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.