Instructions to use funnel-transformer/intermediate with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use funnel-transformer/intermediate with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="funnel-transformer/intermediate")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("funnel-transformer/intermediate") model = AutoModel.from_pretrained("funnel-transformer/intermediate", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1f7f4a56099281eacb51500c822c474ef3f3dc60f491c27e3f91688a41d0036b
- Size of remote file:
- 709 MB
- SHA256:
- 7a3b2549ddf3071bebd73c52f108279b0ecbae66539a232386401fea3ef83ffb
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