Instructions to use funnel-transformer/xlarge with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use funnel-transformer/xlarge with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="funnel-transformer/xlarge")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("funnel-transformer/xlarge") model = AutoModel.from_pretrained("funnel-transformer/xlarge", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8c336aaabab1afcaed7b96bf5bb7cfebf641487b9d287a169c7f5611a49f913b
- Size of remote file:
- 1.87 GB
- SHA256:
- b6af7a4876d37a2fdb533df24a3639070d1725a6fb89dcaad855637b7d4c5988
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