Instructions to use funnel-transformer/small-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use funnel-transformer/small-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="funnel-transformer/small-base")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("funnel-transformer/small-base") model = AutoModel.from_pretrained("funnel-transformer/small-base") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 70d07285789e1da6d650825c38cd60200ed87bc10d2e742416cb061154d526b4
- Size of remote file:
- 462 MB
- SHA256:
- b811d4336334ff3223a30357d9875c700413d32778f613bf11670420608a78cb
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