Instructions to use google/tapas-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/tapas-large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="google/tapas-large")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("google/tapas-large") model = AutoModel.from_pretrained("google/tapas-large", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 57fc43d8a60e4fe0a52fc675ad1576f712423f03d63691fa6771ca935e9b436e
- Size of remote file:
- 1.35 GB
- SHA256:
- 6c204019af3cd0df938a3724ef0a2eb1a0e367a3cf0ba99360265b7940c22c68
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