Instructions to use google/tapas-medium-finetuned-sqa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/tapas-medium-finetuned-sqa with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("table-question-answering", model="google/tapas-medium-finetuned-sqa")# Load model directly from transformers import AutoTokenizer, AutoModelForTableQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("google/tapas-medium-finetuned-sqa") model = AutoModelForTableQuestionAnswering.from_pretrained("google/tapas-medium-finetuned-sqa", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from google/tapas-medium-finetuned-sqa: direct link, hf CLI and curl.
- Browser
- Download file 168 MB
-
https://huggingface.co/google/tapas-medium-finetuned-sqa/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://google/tapas-medium-finetuned-sqa/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/google/tapas-medium-finetuned-sqa/resolve/main/pytorch_model.bin
168 MB
- Xet hash:
- 4952a48b690b1ffbf1932d101901ee688ffc4ee929a23f8466c40fea195d24d5
- Size of remote file:
- 168 MB
- SHA256:
- 8e3680d73db4e750b9d80c07a5f158db77c76d9128d7e2b7c73e39fbbee8f423
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.