Update app.py

app.py

@@ -54,7 +54,7 @@ Since the Axolotl corpus contains misaligments, we just select the best samples
 Also, to increase the amount of data, we collected 3,000 extra samples from the web.
 
 ### Model and training
-We employ two training-stages using a multilingual T5-small. This model was chosen because it can handle different vocabularies and suffixes. T5-small is pretrained on different tasks and languages (French, Romanian, English, German).
+We employ two training-stages using a multilingual T5-small. We use this model because it can handle different vocabularies and suffixes. T5-small is pretrained on different tasks and languages (French, Romanian, English, German).
 
 ### Training-stage 1 (learning Spanish)
 In training stage 1 we first introduce Spanish to the model. The goal is to learn a new language rich in data (Spanish) and not lose the previous knowledge acquired. We use the English-Spanish [Anki](https://www.manythings.org/anki/) dataset, which consists of 118,964 text pairs. We train the model till convergence adding the suffix "Translate Spanish to English: ".