Comments (2)
MihaiSurdeanu
commented on August 30, 2024
One option to save some memory is to create CoreNLPProcessor with internStrings = false. By default, our processors "intern" Java Strings, which saves memory when you process long documents, BUT it keeps memory growing if you keep your program running for a long time.
In any case, unfortunately this package needs a good chunk of RAM to run. I recommend at least 3G. 6G is sufficient in my experience for anything you might parse.
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Dveim
commented on August 30, 2024
Thanks for answer. Seems like internStrings = false helps. Also I increased RAM limit in AWS, so now OOM does not happen.
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Related Issues (20)
- Normalize "grand" HOT 7
- Unreliable tests HOT 2
- The webapp messes up the project in IntelliJ HOT 3
- Preferred size of graphs is not set upon Document deserialization HOT 3
- Hashing is inconsistent HOT 2
- equivalenceHash makes use of hashCode
- pounds as measurements HOT 1
- Paths are written for Mentions that are non-arguments but not read back in
- Update dependency on scala-collection-compat HOT 1
- The Core NLP processors don't understand Easter or Mother's Day and throws an exception HOT 8
- TestLemmatizer failing with BalaurProcessor HOT 23
- Make unit tests compile without dynet HOT 4
- "Due to", the exceptional case handled in PostProcessor HOT 2
- Java interface for processors HOT 5
- Balaur tests need to pass before it can be merged into master. HOT 7
- Date rule doesn't match well with action HOT 3
- WEEK.tsv better handled in code
- Adverse effect of processors v9 on eidos HOT 5
- Quote is shoved to next sentence
- TokenPattern.assignIds never visits the start of MatchLookAhead or MatchLookBehind
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