Instructions to use thealper2/tapas-wtq with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use thealper2/tapas-wtq with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("table-question-answering", model="thealper2/tapas-wtq")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTableQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("thealper2/tapas-wtq") model = AutoModelForTableQuestionAnswering.from_pretrained("thealper2/tapas-wtq", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from thealper2/tapas-wtq: direct link, hf CLI and curl.
- Browser
- Download file 5.27 kB
-
https://huggingface.co/thealper2/tapas-wtq/resolve/main/training_args.bin
- Command line
-
hf download hf://thealper2/tapas-wtq/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/thealper2/tapas-wtq/resolve/main/training_args.bin
5.27 kB
- Xet hash:
- b11488c8afff7d72b4eb5446ace2b515042b8d19df9e4cad5c9e6402a3d924e1
- Size of remote file:
- 5.27 kB
- SHA256:
- 24da17144fcb73bc807a8ec1dad7dc98f95483a4094a6ab2ec2212a1007b3988
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