Instructions to use google/tapas-small-finetuned-tabfact with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/tapas-small-finetuned-tabfact with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="google/tapas-small-finetuned-tabfact")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("google/tapas-small-finetuned-tabfact") model = AutoModelForSequenceClassification.from_pretrained("google/tapas-small-finetuned-tabfact") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 55a80a149f076f7ac118aa5c2ae5f3181a6ead300338446eadbe5b5005b53061
- Size of remote file:
- 117 MB
- SHA256:
- a7f90cc4ebba0d1e34c9af43636d36e8a550fd4969a2072425a2bbc086b14ddb
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