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:
- f21f957d247887181df599c23db38dbc28c583c36d09f719d8b9c849aa361a0d
- Size of remote file:
- 499 MB
- SHA256:
- 375c69aa63e87fe1ddddd3774d660e3b297eb1039fbad88a48730ba454deaddd
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.