Instructions to use huggingface/funnel-small-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use huggingface/funnel-small-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="huggingface/funnel-small-base")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("huggingface/funnel-small-base") model = AutoModel.from_pretrained("huggingface/funnel-small-base") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 06aec1777841a354e9c71ba8a79861e30fd937863e61ff288567e152d82e7709
- Size of remote file:
- 463 MB
- SHA256:
- edd8205d47b01530894db0d4547c68d4f62006af3ab4799d5604e3e736b7d406
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.