Instructions to use google/tapas-base-finetuned-tabfact with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/tapas-base-finetuned-tabfact with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="google/tapas-base-finetuned-tabfact")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("google/tapas-base-finetuned-tabfact") model = AutoModelForSequenceClassification.from_pretrained("google/tapas-base-finetuned-tabfact") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3f4ba4e4f695120d1aa058a36175baba920d594ac9630d62e8e4cf2f2bee02d0
- Size of remote file:
- 443 MB
- SHA256:
- 3ad8c0e04f47b57796faf92a91faa899dd9495c35bd157b69ebbbed559a727ac
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