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:
- f20f3d5d632b7f5d62245e2fcf6f8e8f6bb716200844db8c86e8a2a3d9520623
- Size of remote file:
- 10.5 GB
- SHA256:
- b381b8acdc31febfcb36831a7949af80c45f084b9954a808b4b272015dd41b5a
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