Instructions to use google/tapas-tiny-finetuned-tabfact with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/tapas-tiny-finetuned-tabfact with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="google/tapas-tiny-finetuned-tabfact")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("google/tapas-tiny-finetuned-tabfact") model = AutoModelForSequenceClassification.from_pretrained("google/tapas-tiny-finetuned-tabfact", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3a6b28645864cc627d3b3a8bc27cd2942b086274275348972abc7e93729e2d04
- Size of remote file:
- 18.2 MB
- SHA256:
- af4a5986b8d78d1f18631e921e12b676e52bf4b4277a8b7d3f02ae29a98fd4dd
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