Instructions to use funnel-transformer/large-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use funnel-transformer/large-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="funnel-transformer/large-base")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("funnel-transformer/large-base") model = AutoModel.from_pretrained("funnel-transformer/large-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from funnel-transformer/large-base: direct link, hf CLI and curl.
- Browser
- Download file 1.44 GB
-
https://huggingface.co/funnel-transformer/large-base/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://funnel-transformer/large-base/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/funnel-transformer/large-base/resolve/main/pytorch_model.bin
1.44 GB
- Xet hash:
- c53dc7dba85d43a54b983304fe8b6abc50151d6eab760b9d706e5ece40c958b4
- Size of remote file:
- 1.44 GB
- SHA256:
- 86f88edebf7f49b273b68e03273671b2f414b3d9c32e0cc7f036ec75aaf43de3
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