KeyBERT
Minimal keyword extraction with BERT (by MaartenGr)
yake
Single-document unsupervised keyword extraction (by LIAAD)
KeyBERT | yake | |
---|---|---|
5 | 5 | |
3,217 | 1,573 | |
- | 0.7% | |
6.1 | 3.0 | |
about 1 month ago | 4 months ago | |
Python | Python | |
MIT License | GNU General Public License v3.0 or later |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
KeyBERT
Posts with mentions or reviews of KeyBERT.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2022-04-28.
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I want to extract important keywords from large documents...
Use something else like KeyBERT or BERTopic: https://github.com/MaartenGr/KeyBERT It's much faster.
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[D]: Predict the most probable document including the answer to a given question
Using keyword similarity using KeyBERT:https://github.com/MaartenGr/KeyBERT (i.e. loading keywords for each of the given documents and compare to the keywords of the question)
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BERT execution time
Would anyone know an equation or a general rule of thumb for how long it would take this BERT algorithm (KeyBERT: https://github.com/MaartenGr/KeyBERT) to select n keywords from a string of character length m on a GPU of certain relevant specs?
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[P] Building model to extract keywords from legal documents
Look into rake, pke, phrasemachine, pyate, keybert.
- Alternate approaches to TF-IDF?
yake
Posts with mentions or reviews of yake.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2021-03-14.
- Show HN: Whisper.cpp and YAKE to Analyse Voice Reflections [iOS]
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Simplest keyword extractor
Personally I prefer using YAKE.
- What method should be used to tag specific texts, when the dataset is too small for training a model?
- Is there any YAKE (yet another keyword extractor) implementation in R? Unsupervised Approach for Automatic Keyword Extraction using Text Statistical Features.
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Alternate approaches to TF-IDF?
You can look for usage here: https://github.com/LIAAD/yake and there is also a reference section with publications for more details of how this works. From what I remember, each keyphrase candidate is assigned an aggregated score based on various features: position in the text, casing, frequency, surrounding text frequency...
What are some alternatives?
When comparing KeyBERT and yake you can also consider the following projects:
RAKE-tutorial - A python implementation of the Rapid Automatic Keyword Extraction
rake-nltk - Python implementation of the Rapid Automatic Keyword Extraction algorithm using NLTK.
flashtext - Extract Keywords from sentence or Replace keywords in sentences.
pke - Python Keyphrase Extraction module
simple_keyword_clusterer - A simple machine learning package to cluster keywords in higher-level groups.
faiss - A library for efficient similarity search and clustering of dense vectors.
spaCy - 💫 Industrial-strength Natural Language Processing (NLP) in Python
scattertext - Beautiful visualizations of how language differs among document types.
phrasemachine - Quickly extract multi-word phrases from a corpus