Instructions to use google/tapas-mini-finetuned-tabfact with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/tapas-mini-finetuned-tabfact with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="google/tapas-mini-finetuned-tabfact")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("google/tapas-mini-finetuned-tabfact") model = AutoModelForSequenceClassification.from_pretrained("google/tapas-mini-finetuned-tabfact") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2ee62f2a027b3f184a3989f83dd4bb360ecda465ef51d77d6eb731d396c364fc
- Size of remote file:
- 45.8 MB
- SHA256:
- 0d1505fa8a1ebe0527745ce2142b43d5135aee09c48b2abf70cf96bf15dedb5b
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