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  # NT5, a T5 model trained to perform numerical reasoning
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- T5-small model pre-trained on 3 million (partly synthetic) texts and fine-tuned on DROP. It was introduced in the paper [NT5?! Training T5 to Perform Numerical Reasoning](https://arxiv.org/abs/2104.07307) by Yang et al. and first released in [this repository](https://github.com/lesterpjy/numeric-t5). As the original implementation was in Tensorflow 2, I've converted the weigths to PyTorch. This model corresponds to RC Experiment 1 (see the paper), their best performing model.
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  Disclaimer: The team releasing NT5 did not write a model card for this model so this model card has been written by me.
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  ## Model description
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- The NT5 model is a T5 model, in other words, an encoder-decoder Transformer. In order to encourage numerical reasoning, the model was further pre-trained on three datasets designed to strengthen skills necessary for numerical reasoning over text (NRoT) and general reading comprehension before being fine-tuned on Discrete Reasoning over Text (DROP) dataset.
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  ## Intended uses & limitations
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  biburl = {https://dblp.org/rec/journals/corr/abs-1903-00161.bib},
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  bibsource = {dblp computer science bibliography, https://dblp.org}
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  }
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- a service of Schloss Dagstuhl - Leibniz Center for Informatics\\thomebrowsesearchabout
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  ```
 
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  # NT5, a T5 model trained to perform numerical reasoning
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+ T5-small model pre-trained on 3 million (partly synthetic) texts and fine-tuned on [DROP](https://allennlp.org/drop.html). It was introduced in the paper [NT5?! Training T5 to Perform Numerical Reasoning](https://arxiv.org/abs/2104.07307) by Yang et al. and first released in [this repository](https://github.com/lesterpjy/numeric-t5). As the original implementation was in Tensorflow 2, I've converted the weigths to PyTorch. This model corresponds to RC Experiment 1 (see the paper), their best performing model.
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  Disclaimer: The team releasing NT5 did not write a model card for this model so this model card has been written by me.
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  ## Model description
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+ The NT5 model is a T5 model, in other words, an encoder-decoder Transformer. In order to encourage numerical reasoning, the model was further pre-trained on three datasets designed to strengthen skills necessary for numerical reasoning over text (NRoT) and general reading comprehension before being fine-tuned on the Discrete Reasoning over Text (DROP) dataset.
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  ## Intended uses & limitations
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  biburl = {https://dblp.org/rec/journals/corr/abs-1903-00161.bib},
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  bibsource = {dblp computer science bibliography, https://dblp.org}
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  }
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+ a service of Schloss Dagstuhl - Leibniz Center for Informatics\\\\thomebrowsesearchabout
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  ```