Python wrapper of lightning fast Finite State Machine based NLP library.
Hi, we are a team at Microsoft called Bling (Beyond Language Understanding), we help Bing be smarter. Here we wanted to share with all of you our FInite State machine and REgular expression manipulation library (FIRE). We use Fire for many linguistic operations inside Bing such as Tokenization, Multi-word expression matching, Unknown word-guessing, Stemming / Lemmatization just to mention a few.
Bling Fire Tokenizer Overview
Bling Fire Tokenizer provides state of the art performance for Natural Language text tokenization. Bling Fire supports the following tokenization algorithms:
- Pattern-based tokenization
- WordPiece tokenization
- SentencePiece Unigram LM
- SentencePiece BPE
- Induced/learned syllabification patterns (identifies possible hyphenation points within a token)
Bling Fire provides uniform interface for working with all four algorithms so there is no difference for the client whether to use tokenizer for XLNET, BERT or your own custom model.
Model files describe the algorithms they are built for and are loaded on demand from external file. There are also two default models for NLTK-style tokenization and sentence breaking, which does not need to be loaded. The default tokenization model follows logic of NLTK, except hyphenated words are split and a few "errors" are fixed.
Normalization can be added to each model, but is optional.
Diffrences between algorithms are summarized here.
We have precompiled some popular models and listed with the source code reference below:
|File Name||Models it should be used for||Algorithm||Source Code|
|wbd.bin||Default Tokenization Model||Pattern-based||src|
|sbd.bin||Default model for Sentence breaking||Pattern-based||src|
|bert_base_cased_tok.bin||BERT Base/Large Cased||WordPiece||src|
|bert_multi_cased.bin||BERT Multi Lingual Cased||WordPiece||src|
|xlnet.bin||XLNET Tokenization Model||Unigram LM||src|
|xlnet_nonorm.bin||XLNET Tokenization Model /wo normalization||Unigram LM||src|
|bpe_example.bin||A model to test BPE tokenization||BPE||src|
|xlm_roberta_base.bin||XLM Roberta Tokenization||Unigram LM||src|
|laser(100k|250k|500k).bin||Trained on balanced by language WikiMatrix corpus of 80+ languages||Unigram LM||src|
|uri(100k|250k|500k).bin||URL tokenization model trained on a large set of random URLs from the web||Unigram LM||src|
|gpt2.bin||Byte-BPE tokenization model for GPT-2||byte BPE||src|
|roberta.bin||Byte-BPE tokenization model for Roberta model||byte BPE||src|
|syllab.bin||Multi lingual model to identify allowed hyphenation points inside a word.||W2H||src|
Oh yes, it is also the fastest! We did a comparison of Bling Fire with tokenizers from Hugging Face, Bling Fire runs 4-5 times faster than Hugging Face Tokenizers, see also Bing Blog Post. We did comparison of Bling Fire Unigram LM and BPE implementaion to the same one in SentencePiece library and our implementation is ~2x faster, see XLNET benchmark and BPE benchmark. Not to mention our default models are 10x faster than the same functionality from SpaCy, see benchmark wiki and this Bing Blog Post.
So if low latency inference is what you need then you have to try Bling Fire!
Python API Description
If you simply want to use it in Python, you can install the latest release using pip:
pip install -U blingfire
1. Python example, using default pattern-based tokenizer:
from blingfire import * text = 'After reading this post, you will know: What "natural language" is and how it is different from other types of data. What makes working with natural language so challenging. ' print(text_to_sentences(text)) print(text_to_words(text))
After reading this post, you will know: What "natural language" is and how it is different from other types of data. What makes working with natural language so challenging.  After reading this post , you will know : What " natural language " is and how it is different from other types of data . What makes working with natural language so challenging . [ 1 ]
2. Python example, load a custom model for a pattern-based tokenizer:
from blingfire import * # load a custom model from file h = load_model("./wbd_chuni.bin") text = 'This is the Bling-Fire tokenizer. 2007年9月日历表_2007年9月农历阳历一览表-万年历' # custom model output print(text_to_words_with_model(h, text)) # default model output print(text_to_words(text)) free_model(h)
This is the Bling - Fire tokenizer . 2007 年 9 月 日 历 表 _2007 年 9 月 农 历 阳 历 一 览 表 - 万 年 历 This is the Bling - Fire tokenizer . 2007年9月日历表_2007年9月农历阳历一览表 - 万年历
3. Python example, calling BERT BASE tokenizer
On one thread, it works 14x faster than orignal BERT tokenizer written in Python. Given this code is written in C++ it can be called from multiple threads without blocking on global interpreter lock thus achieving higher speed-ups for batch mode.
import os import blingfire s = "Эpple pie. How do I renew my virtual smart card?: /Microsoft IT/ 'virtual' smart card certificates for DirectAccess are valid for one year. In order to get to microsoft.com we need to type firstname.lastname@example.org." # one time load the model (we are using the one that comes with the package) h = blingfire.load_model(os.path.join(os.path.dirname(blingfire.__file__), "bert_base_tok.bin")) print("Model Handle: %s" % h) # use the model from one or more threads print(s) ids = blingfire.text_to_ids(h, s, 128, 100) # sequence length: 128, oov id: 100 print(ids) # returns a numpy array of length 128 (padded or trimmed) # free the model at the end blingfire.free_model(h) print("Model Freed")
Model Handle: 2854016629088 Эpple pie. How do I renew my virtual smart card?: /Microsoft IT/ 'virtual' smart card certificates for DirectAccess are valid for one year. In order to get to microsoft.com we need to type email@example.com. [ 1208 9397 2571 11345 1012 2129 2079 1045 20687 2026 7484 6047 4003 1029 1024 1013 7513 2009 1013 1005 7484 1005 6047 4003 17987 2005 3622 6305 9623 2015 2024 9398 2005 2028 2095 1012 1999 2344 2000 2131 2000 7513 1012 4012 2057 2342 2000 2828 14255 1030 1015 1012 1016 1012 1015 1012 1016 1012 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0] Model Freed
Bling Fire is supported for Windows, Linux and Mac (Thanks to Andrew Kane!)
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