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:
- a02fad1beef9c14f685fc5e0525445c0ba7cda25cd5b9bf5a9b14313f0f0d7d6
- Size of remote file:
- 524 MB
- SHA256:
- 38728a530f6fd5b3f1459753ccb743280af0d1b18b44da0e41bf0c159e4f705a
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