Instructions to use funnel-transformer/small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use funnel-transformer/small with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="funnel-transformer/small")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("funnel-transformer/small") model = AutoModel.from_pretrained("funnel-transformer/small") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 959c02a3bdb08ada6abd10f4742bf16759ab85cc8abd1d21dc4d6456c85049de
- Size of remote file:
- 524 MB
- SHA256:
- 3e2682aa9aa8f980fe312ed25af5c4bf8badc47b19c6a152ad47e66a4ed41fde
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