Instructions to use huggingface/funnel-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use huggingface/funnel-small with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="huggingface/funnel-small")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("huggingface/funnel-small") model = AutoModel.from_pretrained("huggingface/funnel-small") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 14eab8b74b780539128123dff591c846da5ddbfc79352a48786ad6d65c179969
- Size of remote file:
- 524 MB
- SHA256:
- eb40a3fa50b5d7c7b0c25e7a129fc0c01d09cdb0a68ce06eaf16809137798d27
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