añadidos cambios retrasados

article_app.py

@@ -18,7 +18,7 @@ Below you can find all the pieces that form the system. This section is minimali
     <li><a href="https://huggingface.co/facebook/tts_transformer-es-css10">Text2Speech</a>: For this we used Meta's text2speech service on Huggingface, as text2speech classes are not yet implemented on the main branch of Transformers. This piece was a must to provide a voice to voice service so that it's almost fully accessible. As a future work, as soon as text2speech classes are implemented on transformers, we will train our own models to replace this piece.</li>
 </ol>
 
-Apart from those, this system could not respond in less than a minute on CPU if we didn't use some indexing tricks on the dataset, by using <a href="https://github.com/facebookresearch/faiss">Faiss</a>. We have to look for relevant passages to answer the questions on over 1.5M of semi-long documents, that means that if we want to compare the question vector as encoded by DPR against all of that vectors, we have to perform over 1.5M comparisons. Instead of that, we index those vectors on clusters of similar documents, therefore the question vector only needs to be compared against the passages of similar subject. With this we improved passages retrieving time to miliseconds. This is key since large generative language models like the ones we use already take too much time on CPU, therefore we alleviate this restriction by reducing the retrieving time.
+Apart from those, this system could not respond in less than a minute on CPU if we didn't use some indexing tricks on the dataset, by using <a href="https://github.com/facebookresearch/faiss">Faiss</a>. We have to look for relevant passages to answer the questions on over 1.5M of semi-long documents, that means that if we want to compare the question vector as encoded by DPR against all of that vectors, we have to perform over 1.5M comparisons. Instead of that, we index those vectors on clusters of similar documents, therefore the question vector only needs to be compared against the passages of similar subject. With this we improved passages retrieving time to miliseconds. This is key since large generative language models like the ones we use already take too much time on CPU, therefore we alleviate this restriction by reducing the retrieving time. At the end of the research we discovered that the string factory  OPQ64_128,IVF4898,PQ64x4fsr was the most effective for building such an index. 
 
 On the other hand, we uploaded, and in some cases created, datasets in Spanish to be able to build such a system.