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StKirill commited on Feb 19, 2024

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@@ -24,7 +24,7 @@ TF-IDF, W2V, BERT algorithms are considered inside. For cleaning and preparing d
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)
 Chatbot with TF-IDF convert "question" to vector, find equivalent or most relevant vector in database and for this vector extract answer which sends to user.
-Experiments shows that TF-IDF gives good results from the boxChatBot with TF-IDF. It works perfect if we have all possible questions and answers.
 Second algorithm is W2V. Experiments shows that better to use pretrained vectors instead of trained on small amount of data.
 This is why "glove-twitter-25" is used. Chatbot with W2V shows, probably, similar results as TF-IDF, but spends less time on processing.

 Chart below shows that length of sentences in data not exceeds 60 words.
 !["Length of sentences in data"](data:image/png;base64,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)
 Chatbot with TF-IDF convert "question" to vector, find equivalent or most relevant vector in database and for this vector extract answer which sends to user.
+Experiments shows that TF-IDF gives good results from the box. ChatBot with TF-IDF works perfect if we have all possible questions and answers.
 Second algorithm is W2V. Experiments shows that better to use pretrained vectors instead of trained on small amount of data.
 This is why "glove-twitter-25" is used. Chatbot with W2V shows, probably, similar results as TF-IDF, but spends less time on processing.