Instructions to use google/tapas-mini-finetuned-wtq with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/tapas-mini-finetuned-wtq with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("table-question-answering", model="google/tapas-mini-finetuned-wtq")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTableQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("google/tapas-mini-finetuned-wtq") model = AutoModelForTableQuestionAnswering.from_pretrained("google/tapas-mini-finetuned-wtq", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from google/tapas-mini-finetuned-wtq: direct link, hf CLI and curl.
- Browser
- Download file 45.8 MB
-
https://huggingface.co/google/tapas-mini-finetuned-wtq/resolve/no_reset/pytorch_model.bin
- Command line
-
hf download hf://google/tapas-mini-finetuned-wtq@no_reset/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/google/tapas-mini-finetuned-wtq/resolve/no_reset/pytorch_model.bin
45.8 MB
- Xet hash:
- 245c679ed16f9d448013253ba73b642191cacdd3b038d6c3173261bf68562995
- Size of remote file:
- 45.8 MB
- SHA256:
- c3f32ad0f6f14d2c618a9937fc1dc2a4aead05b4522672877630c9e8b7ed549e
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