Instructions to use HyoungGeun/TAPAS_Capstone with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HyoungGeun/TAPAS_Capstone with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("table-question-answering", model="HyoungGeun/TAPAS_Capstone")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTableQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("HyoungGeun/TAPAS_Capstone") model = AutoModelForTableQuestionAnswering.from_pretrained("HyoungGeun/TAPAS_Capstone", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from HyoungGeun/TAPAS_Capstone: direct link, hf CLI and curl.
- Browser
- Download file 443 MB
-
https://huggingface.co/HyoungGeun/TAPAS_Capstone/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://HyoungGeun/TAPAS_Capstone/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/HyoungGeun/TAPAS_Capstone/resolve/main/pytorch_model.bin
443 MB
- Xet hash:
- cb71453074d5c34c8cad5963884641b56a961176bb3d136de0f8f81479d726a1
- Size of remote file:
- 443 MB
- SHA256:
- cf7d9f722a3b906b5ef8b60d44b1400a5c29eeea849ce47f404d3affa196d358
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