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I have a huge data set(More than 1 million data points).My dataset is text. i am doing NER on it to identify few entities. if i randomly choose 100 data points from the total data set and train my model(LSTM), will this yield good results? i will be running for 20k random batches. Does this approximate the data properly or do i need to run for more number of batches than the total number of datapoints?

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Depends entirely on your data, if your variables are mostly numerical then you can get by with small samples. If however you have a lot of categorical variables you need to make sure that each category of every variable is well represented in the subsample. If they are all numerical I would go for 1000 data points repeated 1000 times.

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  • $\begingroup$ i updated my question. my data is mostly text data $\endgroup$ – InAFlash Sep 18 '18 at 9:36
  • $\begingroup$ Then it depends on your number of entities and how un/balanced they are, I would go with number of entities * 100 for sample size. $\endgroup$ – user2974951 Sep 18 '18 at 9:40
  • $\begingroup$ are you sure.... $\endgroup$ – InAFlash Sep 18 '18 at 9:44
  • $\begingroup$ Without more information on your data it's anybodies guess how big the sample needs to be. $\endgroup$ – user2974951 Sep 18 '18 at 9:53
  • $\begingroup$ No, i meant (entities * 100), this is not a category classification. its kind of sequence prediction. $\endgroup$ – InAFlash Sep 18 '18 at 9:56

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