fastBPE
C++ implementation of Neural Machine Translation of Rare Words with Subword Units, with Python API.
Installation
Compile with:
g++ -std=c++11 -pthread -O3 fastBPE/main.cc -IfastBPE -o fast
Usage:
List commands
./fast
usage: fastbpe <command> <args>
The commands supported by fastBPE are:
getvocab input1 [input2] extract the vocabulary from one or two text files
learnbpe nCodes input1 [input2] learn BPE codes from one or two text files
applybpe output input codes [vocab] apply BPE codes to a text file
applybpe_stream codes [vocab] apply BPE codes to stdin and outputs to stdout
fastBPE also supports stdin inputs. For instance, these two commands are equivalent:
./fast getvocab text > vocab
cat text | ./fast getvocab - > vocab
But the first one will memory map the input file to read it efficiently, which can be more than twice faster than stdin on very large files. Similarly, these two commands are equivalent:
./fast applybpe output input codes vocab
cat input | ./fast applybpe_stream codes vocab > output
Although the first one will be significantly faster on large datasets, as it uses multi-threading to pre-compute the BPE splits of all words in the input file.
Learn codes
./fast learnbpe 40000 train.de train.en > codes
Apply codes to train
./fast applybpe train.de.40000 train.de codes
./fast applybpe train.en.40000 train.en codes
Get train vocabulary
./fast getvocab train.de.40000 > vocab.de.40000
./fast getvocab train.en.40000 > vocab.en.40000
Apply codes to valid and test
./fast applybpe valid.de.40000 valid.de codes vocab.de.40000
./fast applybpe valid.en.40000 valid.en codes vocab.en.40000
./fast applybpe test.de.40000 test.de codes vocab.de.40000
./fast applybpe test.en.40000 test.en codes vocab.en.40000
Python API
To install the Python API, simply run:
python setup.py installNote: For Mac OSX Users, add export MACOSX_DEPLOYMENT_TARGET=10.x (x=9 or 10, depending on your version) or -stdlib=libc++ to the extra_compile_args of setup.py before/during the above install command, as appropriate.
Call the API using:
import fastBPE
bpe = fastBPE.fastBPE(codes_path, vocab_path)
bpe.apply(["Roasted barramundi fish", "Centrally managed over a client-server architecture"])
>> ['Ro@@ asted barr@@ am@@ un@@ di fish', 'Centr@@ ally managed over a cli@@ ent-@@ server architecture']
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