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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d298c07da465b21ab2a52692307c32944a0d028a7e77658bbf3b72b129bb401d
- Size of remote file:
- 443 MB
- SHA256:
- 4667a07c79d247cbacc2d632f29602a4329f8b4fa9609c92b2a5d4520ccfb1ea
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