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")# 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
- Xet hash:
- 0652caf647619177218b161368c81d16f14a6c61119c94b9b259ae957951c7c7
- Size of remote file:
- 1.63 GB
- SHA256:
- 9c83e07fcd0f954ae751d2faa4fadcf9097f01225ba53c9a14715df11b8d0911
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.