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README.md

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 # t5-small-spanish-nahuatl
-Nahuatl is the most widely spoken indigenous language in Mexico. However, training a neural network for the neural machine translation task is hard due to the lack of structured data. The most popular datasets such as the Axolot dataset and the bible-corpus only consist of ~16,000 and ~7,000 samples respectively. Moreover, there are multiple variants of Nahuatl, which makes this task even more difficult. For example, a single word from the Axolot dataset can be found written in more than three different ways. Therefore, in this work, we leverage the T5 text-to-text prefix training strategy to compensate for the lack of data. We first teach the multilingual model Spanish using English, then we make the transition to Spanish-Nahuatl. The resulting model successfully translates short sentences from Spanish to Nahuatl. We report Chrf and BLEU results.
+Nahuatl is the most widely spoken indigenous language in Mexico. However, training a neural network for the neural machine translation task is challenging due to the lack of structured data. The most popular datasets, such as the Axolot and bible-corpus, only consist of ~16,000 and ~7,000 samples, respectively. Moreover, there are multiple variants of Nahuatl, which makes this task even more difficult. For example, it is possible to find a single word from the Axolot dataset written in more than three different ways. Therefore, we leverage the T5 text-to-text prefix training strategy in this work to compensate for the lack of data. We first teach the multilingual model Spanish using English, then transition to Spanish-Nahuatl. The resulting model successfully translates short sentences from Spanish to Nahuatl. Finally, we report Chrf and BLEU results.
 
 
 ## Model description