Instructions to use NbAiLabArchive/test_w5_long with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NbAiLabArchive/test_w5_long with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="NbAiLabArchive/test_w5_long")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("NbAiLabArchive/test_w5_long") model = AutoModelForMaskedLM.from_pretrained("NbAiLabArchive/test_w5_long", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6a7800756e52c3389b66273fa656742d042a67a8c23c78df275800fb909db7f8
- Size of remote file:
- 499 MB
- SHA256:
- 8528348febf9a1caaa41c00c9e7375f545da28aedef937a76ef338bff694bbb4
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