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:
- f4ea079f117c8e97d198ad0d0d7bc74084d372db20ca4b9285de05c3eac7160a
- Size of remote file:
- 18.1 MB
- SHA256:
- 000a48028dfee75b01424963c9ebd872d219e0b8f0edc9cabbe5ff9fcd70efb3
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