Instructions to use benschlagman/tapas_fine_tuning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use benschlagman/tapas_fine_tuning with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("table-question-answering", model="benschlagman/tapas_fine_tuning")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTableQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("benschlagman/tapas_fine_tuning") model = AutoModelForTableQuestionAnswering.from_pretrained("benschlagman/tapas_fine_tuning", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tf_model.h5 from benschlagman/tapas_fine_tuning: direct link, hf CLI and curl.
- Browser
- Download file 440 MB
-
https://huggingface.co/benschlagman/tapas_fine_tuning/resolve/main/tf_model.h5
- Command line
-
hf download hf://benschlagman/tapas_fine_tuning/tf_model.h5
-
curl -L -o tf_model.h5 https://huggingface.co/benschlagman/tapas_fine_tuning/resolve/main/tf_model.h5
440 MB
- Xet hash:
- 268bf55e5205e4401eb76e684a05f6e5733d3d7faca090158d8ffda62ec3b16b
- Size of remote file:
- 440 MB
- SHA256:
- a08ecc018b3fe9af475da1bccc5419914cf5ff6bf1ceea09820e97f35cc46455
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