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 pytorch_model.bin from benschlagman/tapas_fine_tuning: direct link, hf CLI and curl.
- Browser
- Download file 1.35 GB
-
https://huggingface.co/benschlagman/tapas_fine_tuning/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://benschlagman/tapas_fine_tuning/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/benschlagman/tapas_fine_tuning/resolve/main/pytorch_model.bin
1.35 GB
- Xet hash:
- 959ebcf96a25088ef0bea7f690f6ff0dc3baadb905c32892d11698933bd61c4c
- Size of remote file:
- 1.35 GB
- SHA256:
- 328e3bb137fef75c245ee28143a8d85b619a325c095627f1b5252cd64ae9ac20
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.