Instructions to use microsoft/tapex-large-finetuned-tabfact with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use microsoft/tapex-large-finetuned-tabfact with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("table-question-answering", model="microsoft/tapex-large-finetuned-tabfact")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("microsoft/tapex-large-finetuned-tabfact") model = AutoModelForSequenceClassification.from_pretrained("microsoft/tapex-large-finetuned-tabfact", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from microsoft/tapex-large-finetuned-tabfact: direct link, hf CLI and curl.
- Browser
- Download file 1.63 GB
-
https://huggingface.co/microsoft/tapex-large-finetuned-tabfact/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://microsoft/tapex-large-finetuned-tabfact/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/microsoft/tapex-large-finetuned-tabfact/resolve/main/pytorch_model.bin
1.63 GB
- Xet hash:
- 0652caf647619177218b161368c81d16f14a6c61119c94b9b259ae957951c7c7
- Size of remote file:
- 1.63 GB
- SHA256:
- 9c83e07fcd0f954ae751d2faa4fadcf9097f01225ba53c9a14715df11b8d0911
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