Instructions to use microsoft/tapex-large-finetuned-wtq with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use microsoft/tapex-large-finetuned-wtq with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("table-question-answering", model="microsoft/tapex-large-finetuned-wtq")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("microsoft/tapex-large-finetuned-wtq") model = AutoModelForSeq2SeqLM.from_pretrained("microsoft/tapex-large-finetuned-wtq", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from microsoft/tapex-large-finetuned-wtq: direct link, hf CLI and curl.
- Browser
- Download file 1.63 GB
-
https://huggingface.co/microsoft/tapex-large-finetuned-wtq/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://microsoft/tapex-large-finetuned-wtq/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/microsoft/tapex-large-finetuned-wtq/resolve/main/pytorch_model.bin
1.63 GB
- Xet hash:
- e5f903e47114022839ebb5f0c29a1a21b482c8ea56c1b70a729d56d1072791db
- Size of remote file:
- 1.63 GB
- SHA256:
- 6d9dd92d3ee268740d9790bac260f0fd2fd6f7ad783b0d87769a11e7534c7cb3
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