Update app.py

app.py

@@ -3,7 +3,7 @@ from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
 
 article='''
 # Spanish Nahuatl Automatic Translation
-Nahuatl is the most widely spoken indigenous language in Mexico. However, training a neural network for the task of neural machine tranlation 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 respectivly. 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 sufix training strategy to compensate 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 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 suffix 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.
 
 ## Motivation