Split large documents into many smaller ones? I want to build a basic language detector for English, French and German.

I went to wikipedia and I downloaded the page of 'Technology' in all these languages.

In all these cases, we are talking for about 10000 words.

So basically I have 3 documents of 10000 words each for each of the 3 languages above.

My question is the following:

Should I split these documents in smaller documents e.g. of 100 words and create in this way more labeled observations in my dataset or should I leave them like this for training my classifier (e.g. with a TF-IDF model)?
I think it should be easy to detect a language based on a standard vocabulary (bag of words).

However, I would split the articles into small pieces (maybe not 100 but a bit more words, eg. 200-500), so you can train and test your model without problems.