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:
- 89083464862a654e3cc701a88130c162e16d504c978c0b643b2a5b2347318637
- Size of remote file:
- 499 MB
- SHA256:
- 827bd20807929a60aa9a4b11b244e38f4cfdf621bbbaffbee01d6a1ced220882
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.