Instructions to use funnel-transformer/xlarge-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use funnel-transformer/xlarge-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="funnel-transformer/xlarge-base")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("funnel-transformer/xlarge-base") model = AutoModel.from_pretrained("funnel-transformer/xlarge-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ae4e61b1339e6eebda7d97e02270fd2a04dd1f4b0d9bbb0f2531638c26c2a40e
- Size of remote file:
- 1.76 GB
- SHA256:
- 20ef90f6c3adbe4e0e7a72f06b414c236dec7fe75783d34cdc083715b511f808
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