Instructions to use hf-tiny-model-private/tiny-random-TapasForQuestionAnswering with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-tiny-model-private/tiny-random-TapasForQuestionAnswering with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("table-question-answering", model="hf-tiny-model-private/tiny-random-TapasForQuestionAnswering")# Load model directly from transformers import AutoTokenizer, AutoModelForTableQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-TapasForQuestionAnswering") model = AutoModelForTableQuestionAnswering.from_pretrained("hf-tiny-model-private/tiny-random-TapasForQuestionAnswering") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0b2ee0b15ac8b645fa9db95603c069200c421b5084ab1789c21ca9608f945eb1
- Size of remote file:
- 4.28 MB
- SHA256:
- 619656158ade710f57aa1b888b50cedafc10f6d8f16d1c4d8b9d5cbb055f547b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.