BLINK
Entity Linker solution (by facebookresearch)
laserembeddings
LASER multilingual sentence embeddings as a pip package (by yannvgn)
BLINK | laserembeddings | |
---|---|---|
2 | 2 | |
1,114 | 223 | |
- | - | |
0.0 | 0.0 | |
8 months ago | 9 months ago | |
Python | Python | |
MIT License | BSD 3-clause "New" or "Revised" License |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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.
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.
BLINK
Posts with mentions or reviews of BLINK.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2021-02-01.
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How to write a Scoring Script
I recently used a machine learning model from Github to predict different sets of documents. But what I am trying to do now is create a scoring script, which takes the same testing datasets but with different models. The output should have precision,recall, f1, etc. (something like that)
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SpaCy v3.0 Released (Python Natural Language Processing)
there is also BLINK by Facebook
https://github.com/facebookresearch/BLINK
laserembeddings
Posts with mentions or reviews of laserembeddings.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2022-11-29.
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Firefox Translations doesn't use the cloud
You're pretty much right on the money. For ParaCrawl[1] (which I worked on) we used fast machine translation systems that were "good enough" to translate one side of each pair to the language of the other, see whether they'd match sufficiently, and then deal with all the false positives through various filtering methods. Other datasets I know of use multilingual sentence embeddings, like LASER[2], to compute the distance between two sentences.
Both of these methods have a bootstrapping problem, but at this point in the MT for many languages we have enough data to get started. Previous iterations of ParaCrawl used things like document structure and overlap of named entities among sentences to identify matching pairs. But this is much less robust. I don't know how they solve this problem today for low-resource languages.
[1] https://paracrawl.eu
[2] https://github.com/yannvgn/laserembeddings
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SpaCy v3.0 Released (Python Natural Language Processing)
I've been using LASER from Facebook Research via https://github.com/yannvgn/laserembeddings to accept multi-lingual input in front of the the domain-specific models for recommendations and stuff (that are trained on English annotated examples).
What are some alternatives?
When comparing BLINK and laserembeddings you can also consider the following projects:
spaCy - 💫 Industrial-strength Natural Language Processing (NLP) in Python
syntaxdot - Neural syntax annotator, supporting sequence labeling, lemmatization, and dependency parsing.