Instructions to use deepset/tapas-large-nq-reader with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use deepset/tapas-large-nq-reader with Transformers:
# Load model directly from transformers import AutoTokenizer, TapasForScoredQA tokenizer = AutoTokenizer.from_pretrained("deepset/tapas-large-nq-reader") model = TapasForScoredQA.from_pretrained("deepset/tapas-large-nq-reader", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| language: en | |
| tags: | |
| - tapas | |
| license: apache-2.0 | |
| This model contains the converted PyTorch checkpoint of the original Tensorflow model available in the [TaPas repository](https://github.com/google-research/tapas/blob/master/DENSE_TABLE_RETRIEVER.md#reader-models). | |
| It is described in Herzig et al.'s (2021) [paper](https://aclanthology.org/2021.naacl-main.43/) _Open Domain Question Answering over Tables via Dense Retrieval_. | |
| This model has 2 versions which can be used differing only in the table scoring head. | |
| The default one has an adapted table scoring head in order to be able to generate probabilities out of the logits. | |
| The other (non-default) version corredponds to the original checkpoint from the TaPas repository and can be accessed setting `revision="original"`. | |
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