Instructions to use google/tapas-large-finetuned-wtq with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/tapas-large-finetuned-wtq with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("table-question-answering", model="google/tapas-large-finetuned-wtq")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTableQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("google/tapas-large-finetuned-wtq") model = AutoModelForTableQuestionAnswering.from_pretrained("google/tapas-large-finetuned-wtq", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from google/tapas-large-finetuned-wtq: direct link, hf CLI and curl.
- Browser
- Download file 1.35 GB
-
https://huggingface.co/google/tapas-large-finetuned-wtq/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://google/tapas-large-finetuned-wtq/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/google/tapas-large-finetuned-wtq/resolve/main/pytorch_model.bin
1.35 GB
- Xet hash:
- 5ff7e02a79344371414cf10ec6347d602b76c3248a53eb9f668e3ebe2bd4c895
- Size of remote file:
- 1.35 GB
- SHA256:
- 4f1b5620999629496833aad6c111737ca7acaad44a53a50fb3c1de66ad788f8f
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