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This learned regression metric is for evaluating models trained on the TaTA dataset. It was trained as per instructions in [TaTA: A Multilingual Table-to-Text Dataset for African Languages](https://aclanthology.org/2023.findings-emnlp.118/) (StATA-QE variant).
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StATA takes as input a linearized table and an output verbalisation seperated by an " \[output\] " tag, and produces a score between 0 and 1. A score closer to 1 means the output is more understandable and atributable to the source table,
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The original file can be found [here](https://github.com/google-research/url-nlp/tree/main/tata).
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This learned regression metric is for evaluating models trained on the TaTA dataset. It was trained as per instructions in [TaTA: A Multilingual Table-to-Text Dataset for African Languages](https://aclanthology.org/2023.findings-emnlp.118/) (StATA-QE variant).
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StATA takes as input a linearized table and an output verbalisation seperated by an " \[output\] " tag, and produces a score between 0 and 1. A score closer to 1 means the output is more understandable and atributable to the source table, a score closer to 0 is less so.
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The original file can be found [here](https://github.com/google-research/url-nlp/tree/main/tata).
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