| Title | : | How to Easily Find Keywords in a Document with KeyBERT in Python |
| Lasting | : | 7.17 |
| Date of publication | : | |
| Views | : | 5,5 rb |
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does it take a long time to extract the keyword i find that it almost takes around 2-3 mins jus to extract the keywords I’m not sure whether if it’s just my computer or the library itself Comment from : @piraveenthsooriyakumar1863 |
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512 tokens max, can’t really call it a document Comment from : @PavanMuppala |
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Hello,sorry to bother you! I want to ask you a question about the usage of Keybert I create a excel file which contains thousands of texts,how can I extract keywords from this excel file?Thanks a lot! Comment from : @Doggy-t1h |
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Hello thanks for thé vidéo dumb question if it IS bert based does it have thé same limitation in term of thé text size? Comment from : @tarik1895 |
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Hi! I kind of curious Is there any context size limit for these kind? keybert, bertopic? If not, isn't they kind of superior vs current GPT? Event though the use case is different, but in someway, it has the upperhand Like this one, getting keyword, or bertopic to get topic model GPT can't even do that because the context size is limited and can forget the middle part of the articlebrbrAnd if there's no context size limit, so it can be like our library? We can collect so much data, and when we need, we can search using this Or explore using topic modelling to find out better what we want Am I right?brbrThanks Comment from : @daryladhityahenry |
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Great explanation! But what are some actual usecases for this? Thanks Comment from : @jakobkristensen2390 |
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Thank you for sharing! Awesome tool, I have trillions of use cases in mind … think I'll start with auto-tagging my pdf library … Comment from : @sebastianpranz1307 |
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I am unable to run even a sample code in my work setup !!brI am facing some SSL errors for which I couldn't find a reliable solution Comment from : @sriram151092 |
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Nice one Comment from : @viorelteodorescu |
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Do you have a video on fine-tuning with sentence transformers? I have text which is very narrow in domain with a lot of typos and weird jargon But I do have identified the key features to extract Thanks Comment from : @jesusmtz29 |
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