DZDzeeta·Feb 26, 2020Submission
Hi, I'm trying out this library: https://github.com/facebookresearch/InferSent
Has anybody used it? What approach do you suggest for segmenting sentences between three groups?
2 points · Open discussionfacebookresearch
InferSent
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InferSent
InferSent sentence embeddings
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InferSent sentence embeddings
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HN mentions by month4 dated mentions
Observed mentions: 2018-03-30 12:34:10 UTC to 2020-02-26 21:46:59 UTC.
Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.
View mention counts and date ranges
| Period | Start (UTC) | End (exclusive, UTC) | Mentions | Submissions | Comments |
|---|---|---|---|---|---|
| Mar 2018 | 2018-03-01 00:00:00 UTC | 2018-04-01 00:00:00 UTC | 1 | 0 | 1 |
| Apr 2018 | 2018-04-01 00:00:00 UTC | 2018-05-01 00:00:00 UTC | 0 | 0 | 0 |
| May 2018 | 2018-05-01 00:00:00 UTC | 2018-06-01 00:00:00 UTC | 0 | 0 | 0 |
| Jun 2018 | 2018-06-01 00:00:00 UTC | 2018-07-01 00:00:00 UTC | 0 | 0 | 0 |
| Jul 2018 | 2018-07-01 00:00:00 UTC | 2018-08-01 00:00:00 UTC | 0 | 0 | 0 |
| Aug 2018 | 2018-08-01 00:00:00 UTC | 2018-09-01 00:00:00 UTC | 0 | 0 | 0 |
| Sep 2018 | 2018-09-01 00:00:00 UTC | 2018-10-01 00:00:00 UTC | 0 | 0 | 0 |
| Oct 2018 | 2018-10-01 00:00:00 UTC | 2018-11-01 00:00:00 UTC | 0 | 0 | 0 |
| Nov 2018 | 2018-11-01 00:00:00 UTC | 2018-12-01 00:00:00 UTC | 1 | 0 | 1 |
| Dec 2018 | 2018-12-01 00:00:00 UTC | 2019-01-01 00:00:00 UTC | 0 | 0 | 0 |
| Jan 2019 | 2019-01-01 00:00:00 UTC | 2019-02-01 00:00:00 UTC | 1 | 0 | 1 |
| Feb 2019 | 2019-02-01 00:00:00 UTC | 2019-03-01 00:00:00 UTC | 0 | 0 | 0 |
| Mar 2019 | 2019-03-01 00:00:00 UTC | 2019-04-01 00:00:00 UTC | 0 | 0 | 0 |
| Apr 2019 | 2019-04-01 00:00:00 UTC | 2019-05-01 00:00:00 UTC | 0 | 0 | 0 |
| May 2019 | 2019-05-01 00:00:00 UTC | 2019-06-01 00:00:00 UTC | 0 | 0 | 0 |
| Jun 2019 | 2019-06-01 00:00:00 UTC | 2019-07-01 00:00:00 UTC | 0 | 0 | 0 |
| Jul 2019 | 2019-07-01 00:00:00 UTC | 2019-08-01 00:00:00 UTC | 0 | 0 | 0 |
| Aug 2019 | 2019-08-01 00:00:00 UTC | 2019-09-01 00:00:00 UTC | 0 | 0 | 0 |
| Sep 2019 | 2019-09-01 00:00:00 UTC | 2019-10-01 00:00:00 UTC | 0 | 0 | 0 |
| Oct 2019 | 2019-10-01 00:00:00 UTC | 2019-11-01 00:00:00 UTC | 0 | 0 | 0 |
| Nov 2019 | 2019-11-01 00:00:00 UTC | 2019-12-01 00:00:00 UTC | 0 | 0 | 0 |
| Dec 2019 | 2019-12-01 00:00:00 UTC | 2020-01-01 00:00:00 UTC | 0 | 0 | 0 |
| Jan 2020 | 2020-01-01 00:00:00 UTC | 2020-02-01 00:00:00 UTC | 0 | 0 | 0 |
| Feb 2020 | 2020-02-01 00:00:00 UTC | 2020-03-01 00:00:00 UTC | 1 | 1 | 0 |
What Hacker News says
4 sourcesI am a noob at Machine Learning / Deep Learning, started learning recently. I stumbled upon Facebook's Infersent [0] and implemented this project. Infersent works really nice out of the box. For toy projects and experimentations, it is really fun to use it. The actual code which finds the closest match is less than 10 lines of Python.
I also have a demo here - http://35.200.165.252:9999/ I added some questions related to the Udacity's course I was doing. This was a side project for the course. Do note that this demo dataset is very small.
[0] - https://github.com/facebookresearch/InferSent
In Show HN: Kylo – Simple FAQ Bot Built with Facebook's InfersentYou quote a target of 200ms per embedding, not sure if it's one type of embedding in particular. I am using Infersent (a sentence embedding from FAIR https://github.com/facebookresearch/InferSent) for filtering and they quote a number of 1000/sentences per second on generic GPU. That's 200 times faster than your number, but it is a local API so I am comparing apples to oranges. Yet it's hard to imagine you are spending 1ms embedding and 199 on API overhead. I am sure I have missed a 0 here or there, but I don't see where, other than theirs is a batch number (batch size 128) and maybe yours is a single embedding number. Can you please clarify? Thanks
In Show HN: Basilica – word2vec for anythingInteresting. There's a big need for better vector representations of things in-between words (for which Word2Vec/Glove/FastText work well) and documents (which to me seems impossible. Yes I know about Doc2Vec etc, but really.. it works ok for paragraphs).
Facebook's InferSent[1] has worked reasonably well for me for a variety of sentence level tasks, but I don't have anything I can point to to say that it is really substantially better than averaging word embeddings.
More options is good.
(Also, is Kurzweil part of Google Brain or separate. He doesn't really have nay background in NLP does he?)
[1] https://github.com/facebookresearch/InferSent
In Universal Sentence Encoder