Bling Fire: Finite state machine and regular expression manipulation library

by catam • 7 years ago

301 65 7 years ago

make32• 7 years ago

Does it also do BPE (byte pair encoding)? can't tell

sergeio768• 7 years ago

No it does not, we are thinking about it. Extra segmentation of tokens is a great idea, I only don't like (who cares? :-)) inconsistencies that BPE sometimes produce. Looking at how BERT tokenizers for example: "tranformers" is one token but "transformer" is segmented into "transform" "##er". Word "outperforms" becomes "out", "##per", "##forms". It is truly amazing that BERT works so well with such tokenization.

make32• 7 years ago

imho the fact that character level language models work mostly just as well as word level or bpe tokenizers makes it clear that the "embedding" view is wrong nowadays, that the meaning is really built (and stored) in the layers, just fine

lsorber• 7 years ago

Looks like an opportunity to do BPE right. Would love to see your take on it with this engine!

bastawhiz• 7 years ago

The title is wrong. It's not "Bing Fire", it's "Bling Fire"

Lowkeyloki• 7 years ago

Confusingly, Bling Fire is part of the Bing search engine.

This is what happens when you let programmers name projects. I'm sure the product owners love this. /s

Names are hard. ;-)

sjwright• 7 years ago

Names are hard, but that’s no reason to bowl straight into the gutter. Calling it Bling is just confusing. That’s like McDonald’s selling a Big Mc. Oh wait...

Sommer• 7 years ago

Its says Bling stands for "Beyond Language Understanding". They must be really good programmers.

breischl• 7 years ago

This is what happens when you let programmers name projects.

OK, this is pretty OT, but coders don't have a monopoly on bad naming. I worked on an internal project that had a unique and well recognized name within the company. Exec forced us to change it to an acronym that he liked but made no sense to anybody else.

Two years later, after the new name had finally caught on, he realized that the project didn't do what he thought and had been telling people. In order to save face, he created another project with _the same name_ so he could tell people that's what he meant all along.

So now there's one project that's had two different names, one of which overlaps with a separate-but-closely-related project. As you might imagine, this has not been confusing to anyone. But at least that executive only looks like a jackass to people who've been around long enough to watch this farce unfold.

Programmers may suck at naming, but at least they understand the concept of using distinct names for related things...

Abishek_Muthian2• 7 years ago

Thanks, This makes sense. Also anything 'Bing' & Open source might be just the client for an Azure subscription.

This seems to be a true standalone open source project.

ma2rten2• 7 years ago

It sounds like they released the tokenizer that powers bing, which one of the largest search engines in the world. That's pretty significant.

dx034• 7 years ago

From the Readme it appears that it will deliver the same results as NLTK, just much faster. Therefore publishing it won't give away any of the secret sauce that make up Bing's search results.

jiguang98• 7 years ago

The tokenization results are not the same, you can see some examples in readme.

danso• 7 years ago

Love seeing these powerful, production libraries be as easy to install as `pip install blingfire`. They've even included a token-based classifier demo in Jupyter Notebook:

https://github.com/Microsoft/BlingFire/blob/master/doc/Bling...

ShadowBannedUsr2• 7 years ago

Yes, indeed, although Tensorflow and Pytorch have a long way to go before their GPU versions are equally easy to install. Their GPU versions are currently incredibly hard to install correctly, even on Linux. Some users just give up and use prebuilt containers that although good good for production code, can be hard to develop with.

solomatov• 7 years ago

Pytorch install without any problem for quite a long time (they have a whl with all libraries included). This can't be said about tensorflow unfortunately.

ctoth2• 7 years ago

Your name leaked from an identifier to a property of your account in my head and I actually thought this was a dead comment. Weird.

skwb• 7 years ago

In my own experience, I've found it much easier to install tensorflow with GPU support from scratch (including cuDNN...) than it is to install Spark/Hadoop.

ChrisFoster• 7 years ago

This has been vastly improved fairly recently with the advent of the tensorflow-gpu meta package available via conda. See https://towardsdatascience.com/tensorflow-gpu-installation-m...

mgreenleaf• 7 years ago

I've been impressed with several of the Bing projects; I recently found that they open sourced the JIT compiler for C++ they use on Bing [1] I've been more and more impressed with Microsoft's efforts in Open Source and elsewhere. Especially with these under the MIT license.

[1] https://github.com/BitFunnel/NativeJIT

appleflaxen• 7 years ago

Is Bling related to Bing? Reading the repo and the comments I'm really confused by this.

seeekr• 7 years ago

First sentence in the introduction:

Hi, we are a team at Microsoft called Bling (Beyond Language Understanding), we help Bing be smarter.

catam• 7 years ago

Yes it is, it's from the team in Bing building the index, doing document understanding.

rlayton2• 7 years ago

Note that the top level summary shows it is 10x faster than Spacey, which is a more impressive achievement than being 10x faster than NLTK (it is 20x faster).

NLTK is designed for learning, not for production systems, speed or efficiency. The code is often the most straight-forward way to implement an algorithm, especially to read the code later.

linkingday• 7 years ago

To further your point, NLTK is written in pure Python whereas the backend for spaCy, thinc, is written in C/C++/Python. Really no comparison here.

avinium3• 7 years ago

Anyone familiar enough with this library to explain how they've achieved such impressive performance? Otherwise, may dig into it for a blog post when I have time.

lettergram• 7 years ago

One advantage is the core appears to be written in C/C++. NLTK is python and SpaCy is a lot of C/C++ with a significant amount of python.

I’ll have to dig in further, but I suspect there’s a lot of optimization for the task at hand here. SpaCy and NLTK tries to be much more general for NLP.

sergeio768• 7 years ago

There are deterministic finite state machines underneath implemented in lean C++. These automata allow to implement operations with strings optimally or close to that. In the readme file there is a link to how to recompile linguistic resources, if you look inside the makefile that is used for resource compilation you will the steps.

rthy• 7 years ago

I have been experimenting with tokenization in Rust in https://github.com/rth/vtext, mostly relying on Unicode segmentation with a few custom rules for tokenization. The obtained performance was also around 10x faster than spacy for comparable precision (see benchmarks section of the readme).

harrygeez• 7 years ago

Can someone suggest a few practical use cases of this for normal plebs like me?

zone411• 7 years ago

In NLP, tokenization is one of the first steps in the pipeline to break down text into individual words. I don't know about others but for the project I'm doing, the tokenization speed doesn't matter much in the big picture because the following steps take much longer.

atoav• 7 years ago

It matters for anything that should be more or less realtime, so where fast feedback is needed.

pts_2• 7 years ago

If you're talking about Natural Language Processing, think voice operated - everything.

switch2017• 7 years ago

In 2008 when I was on the Bing team, Sergei had just developed this library and was putting it to work on the document understanding pipeline. He was incredibly passionate about it and would evangelize it with anybody that cared to listen. When I saw the headline on HN, I immediately guessed that this was Sergei's work. And of course it is! Sergei, if you are reading this, try to guess who I am ;)

sergeio768• 7 years ago

Thank you for remembering us ;)

natch• 7 years ago

Currently released model supports most of the languages except East Asian (Chinese Simplified, Traditional, Japanese, Korean, Thai). You should expect good results if a language uses space as a main token delimitter.

Any plans to change this?

What does MS fall back on for these languages?

fefe232• 7 years ago

Two things about this baffle me.

1. Why only put perf data in the blurb? To me it would be much more helpful to first see a few API calls this allows that solve problems I have had before, or may have in the future. It remains mostly opaque to me what this software does.

2. If this is mostly about splitting text into words, why is the code dump dozens of directories with many hundreds of source code files in there?

I understand that they are proud of their perf numbers, but those are (to me) not as important as first understanding what I could do with the project.

Regular expression mangling sounds like it could be useful for much more than word splitting in a search engine context.

sergeio768• 7 years ago

The library we have published is a finite state machines manipulation library first of all, also it is developed to support linguistic applications (large number of entries, unicode, compile once use many times, etc.) Tokenization is one of its applications. We needed to start somewhere. In our Deep Learning Era not everything that we have created is relevant, but tokenization is. What we might add to the project is BPE or BPE- variant, support East Asian languages, Multi Word Expressions ("New York" as one token).

fefe232• 7 years ago

Could you please explain what kind of manipulation your library supports on finite state machines?

Does it contain a regex engine to easily create the state machines in the first place?

Does it have a JIT?

Do the state machines operate on char? wchar_t?

These are the kinds of details that I would love to see on the github entry page. It's obvious to you what your library does, but I have no idea :-)

ironfootnz• 7 years ago

That's cool, very powerful lib. But I'm not sure if I still have to use other libraries to remove stop words to make the tokenizer simpler.

misterman03• 7 years ago

> stop words

It's a good thing neither Bing nor Google remove so called stop words, because if they did I wouldn't be able to google them for the lyrics from that great song by "The the".

If you use a system that require you to remove stop words from your lexicon I suggest you find better tech because removing them destroys linguistic context. If you're using a search engine that doesn't care about linguistic context, again, you should find better tech.

Even the most naive search engine should at least be using tf-idf or some other statistical tool to determine both a token's document frequency as well as its lexical frequency. That's very important context to have.

If you insist on using stop words most tokenizers would assume you did the filtering or they'd want you to submit a word list for it to use as filter. Be aware, you are not making tokenization much simpler, because a proper search engine stores each distinct token once and only once in its index, so you hardly saved any space. What you did was to slow down the indexing process and use lots more CPU cycles for each token which translates to energy waste in my mind.

Leave your stop words right where they are, and save the planet.

StreamBright• 7 years ago

Just for the record NLTK is the slowest implementation out there.

jiamguo• 7 years ago

It's also 10X faster than SpaCy :) see the repo benchmarking

bravura3• 7 years ago

Can you explain how you use FSTs and REs for Multi-word expression matching and Unknown word-guessing?

sergeio768• 7 years ago

MWE: 1. given your corpus you run something like word2phrase algorithm on it. 2. Compute the phrases. 3. Compile them as one Min DFA and do left-most-longest match between the input sequence of tokens and the compiled DFA. We will include this as a separate API with phrases computed on some large corpus.

Word-Guessing: 1. We compute a mapping as a finite state machine from a word to its properties. 2. we generalize the finite state machine by shrining some path and creating new finite state with the union of the properties so that a. no mistakes are made wrt the known words, b. the model is as general as possible. The result is a finite state machine that a. does not make mistakes wrt the known set of words and is b. as general as possible for typical unseen words.

We will put description better than this comment once the models are published.

bravura3• 7 years ago

One thing that can be important but tricky with some tokenization libraries is to un-tokenize, i.e. recover the offsets of the tokens in the original string. Is this supported in your library? How?

algorithm314• 7 years ago

How this compares to hfst?

Bling Fire: Finite state machine and regular expression manipulation library

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