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diff --git a/training-basics-sentencepiece/README.md b/training-basics-sentencepiece/README.md new file mode 100644 index 0000000..1e145de --- /dev/null +++ b/training-basics-sentencepiece/README.md @@ -0,0 +1,244 @@ +# Marian with Built-in SentencePiece + +In this example, we modify the Romanian-English example from `examples/training-basics` to use Taku Kudo's +[SentencePiece](https://github.com/google/sentencepiece) instead of a complicated pre/prost-processing pipeline. +We also replace the evaluation scripts with Matt Post's [SacreBLEU](https://github.com/mjpost/sacreBLEU). Both tools greatly simplify the training and evaluation process by providing ways to have reversible hidden preprocessing and repeatable evaluation. + +## Building Marian with SentencePiece Support + +Since version 1.7.0, Marian has built-in support for SentencePiece, +but this needs to be enabled at compile-time. We decided to make the compilation of SentencePiece +optional as SentencePiece has a number of dependencies - especially Google's Protobuf - that +are potentially non-trivial to install. + +Following the the SentencePiece Readme, we list a couple of packages you would need to +install for a coule of Ubuntu versions: + +On Ubuntu 14.04 LTS (Trusty Tahr): + +``` +sudo apt-get install libprotobuf8 protobuf-compiler libprotobuf-dev +``` + +On Ubuntu 16.04 LTS (Xenial Xerus): + +``` +sudo apt-get install libprotobuf9v5 protobuf-compiler libprotobuf-dev +``` + +On Ubuntu 17.10 (Artful Aardvark) and Later: + +``` +sudo apt-get install libprotobuf10 protobuf-compiler libprotobuf-dev +``` + +For more details see the documentation in the SentencePiece repo: +https://github.com/marian-nmt/sentencepiece#c-from-source + +With these dependencies met, you can compile Marian as follows: + +``` +git clone https://github.com/marian-nmt/marian +cd marian +mkdir build +cd build +cmake .. -DCMAKE_BUILD_TYPE=Release -DUSE_SENTENCEPIECE=ON +make -j 8 +``` + +To test if `marian` has been compiled with SentencePiece support run + +``` +./marian --help |& grep sentencepiece +``` + +which should display the following new options: + +``` + --sentencepiece-alphas VECTOR ... Sampling factors for SentencePieceVocab; i-th factor corresponds to i-th vocabulary + --sentencepiece-options TEXT Pass-through command-line options to SentencePiece trainer + --sentencepiece-max-lines UINT=10000000 +``` + +## Execute the Example + +Files and scripts in this folder have been adapted from the Romanian-English +sample from https://github.com/rsennrich/wmt16-scripts. We also add the +back-translated data from +http://data.statmt.org/rsennrich/wmt16_backtranslations/ as desribed in +http://www.aclweb.org/anthology/W16-2323. The resulting system should be +competitive or even slightly better than reported in the Edinburgh WMT2016 +paper. + +Assuming you one GPU, to execute the complete example type: + +``` +./run-me.sh +``` + +which downloads the Romanian-English training files and concatenates them into training files. +No preprocessing is required as the Marian command will train a SentencePiece vocabulary from +the raw text. Next the translation model will be trained and after convergence, the dev and test +sets are translated and evaluated with sacreBLEU. + +To use with a different GPUs than device 0 or more GPUs (here 0 1 2 3) use the command below: + +``` +./run-me.sh 0 1 2 3 +``` + +## Step-by-step Walkthrough + +In this section we repeat the content from the above `run-me.sh` script with explanations. You should be able to copy and paste the commands and follow through all the steps. + +We assume you are running these commands from the examples directory of the main Marian directory tree `marian/examples/training-basics-sentencepiece` and that the Marian binaries have been compiled in `marian/build`. The localization of the Marian binary relative to the current directory is therefore `../../build/marian`. + +### Preparing the test and validation sets + +We can use SacreBLEU to produce the original WMT16 development and test sets for Romanian-English. We first clone the SacreBLEU repository from our fork and then generate the test files. + +``` +# get our fork of sacrebleu +git clone https://github.com/marian-nmt/sacreBLEU.git sacreBLEU + +# create dev set +sacreBLEU/sacrebleu.py -t wmt16/dev -l ro-en --echo src > data/newsdev2016.ro +sacreBLEU/sacrebleu.py -t wmt16/dev -l ro-en --echo ref > data/newsdev2016.en + +# create test set +sacreBLEU/sacrebleu.py -t wmt16 -l ro-en --echo src > data/newstest2016.ro +sacreBLEU/sacrebleu.py -t wmt16 -l ro-en --echo ref > data/newstest2016.en +``` + +### Downloading the training files + +Similarly, we download the training files from different sources and concatenate them into two training files. Note, there is no preprocessing whatsoever. Downloading may take a while, the servers are not particularly fast. + +``` +# change into data directory +cd data + +# get En-Ro training data for WMT16 +wget -nc http://www.statmt.org/europarl/v7/ro-en.tgz +wget -nc http://opus.lingfil.uu.se/download.php?f=SETIMES2/en-ro.txt.zip -O SETIMES2.ro-en.txt.zip +wget -nc http://data.statmt.org/rsennrich/wmt16_backtranslations/ro-en/corpus.bt.ro-en.en.gz +wget -nc http://data.statmt.org/rsennrich/wmt16_backtranslations/ro-en/corpus.bt.ro-en.ro.gz + +# extract data +tar -xf ro-en.tgz +unzip SETIMES2.ro-en.txt.zip +gzip -d corpus.bt.ro-en.en.gz corpus.bt.ro-en.ro.gz + +# create corpus files +cat europarl-v7.ro-en.en SETIMES2.en-ro.en corpus.bt.ro-en.en > corpus.en +cat europarl-v7.ro-en.ro SETIMES2.en-ro.ro corpus.bt.ro-en.ro > corpus.ro + +# clean +rm ro-en.tgz SETIMES2.* corpus.bt.* europarl-* + +# change back into main directory +cd .. +``` + +### Normalization of Romanian diacritics with SentencePiece + +It seems that the training data is quite noisy and multiple similar characters are used in place of the one correct character. +Barry Haddow from Edinburgh who created the original normalization Python scripts noticed that removing diacritics on the Romanian side leads to a significant improvment in translation quality. And indeed we saw gains of up to 2 BLEU points due to normalization versus unnormalized text. The original scripts are located in the old Romanian-English example folder in `marian/examples/training-basics/scripts`. We do not need to use them here. + +SentencePiece allows to specify normalization or replacement tables for character sequences. These replacements are applied before tokenization/segmentation and included in the SentencePiece model. Based on the mentioned preprocessing scripts, we manually create a tab-separated normalization rule file `data/norm_romanian.tsv` like this (see the [SentencePiece documentation on normalization](https://github.com/google/sentencepiece/blob/master/doc/normalization.md) for details): + +``` +015E 53 # Ş => S +015F 73 # ş => s +0162 54 # Ţ => T +0163 74 # ţ => t +0218 53 # Ș => S +0219 73 # ș => s +021A 54 # Ț => T +021B 74 # ț => t +0102 41 # Ă => A +0103 61 # ă => a +00C2 41 # Â => A +00E2 61 # â => a +00CE 49 # Î => I +00EE 69 # î => i +``` + +### Training the NMT model + +Next, we execute a training run with `marian`. Note how the training command is called passing the +raw training and validation data into Marian. A single joint SentencePiece model will be saved to +`model/vocab.roen.spm`. The `*.spm` suffix is required and tells Marian to train a SentencePiece +vocabulary. When the same vocabulary file is specified multiple times - like in this example - a single +vocabulary is built for the union of the corresponding training files. This also enables us to use +tied embeddings (`--tied-embeddings-all`). + +We can pass the Romanian-specific normalizaton rules via the `--sentencepiece-options` command line +argument. The values of this option are passed on to the SentencePiece trainer, note the required single +quotes around the SentencePiece options: `--sentencepiece-options '--normalization_rule_tsv=data/norm_romanian.tsv'`. + +Another new feature is the `bleu-detok` validation metric. When used with SentencePiece this should +give you in-training BLEU scores that are very close to sacreBLEU's scores. Differences may appear +if unexpected SentencePiece normalization rules are used. You should still report only official +sacreBLEU scores for publications. + +We are training of four GPUs defined with `--devices 0 1 2 3`. Change this to the required number of GPUs. + +``` +# create the model directory +mkdir model + +# train the model +../../build/marian \ + --devices 0 1 2 3 \ + --type s2s \ + --model model/model.npz \ + --train-sets data/corpus.ro data/corpus.en \ + --vocabs model/vocab.roen.spm model/vocab.roen.spm \ + --sentencepiece-options '--normalization_rule_tsv=data/norm_romanian.tsv' \ + --dim-vocabs 32000 32000 \ + --mini-batch-fit -w 5000 \ + --layer-normalization --tied-embeddings-all \ + --dropout-rnn 0.2 --dropout-src 0.1 --dropout-trg 0.1 \ + --early-stopping 5 --max-length 100 \ + --valid-freq 10000 --save-freq 10000 --disp-freq 1000 \ + --cost-type ce-mean-words --valid-metrics ce-mean-words bleu-detok \ + --valid-sets data/newsdev2016.ro data/newsdev2016.en \ + --log model/train.log --valid-log model/valid.log --tempdir model \ + --overwrite --keep-best \ + --seed 1111 --exponential-smoothing \ + --normalize=0.6 --beam-size=6 --quiet-translation +``` + +The training should stop if cross-entropy on the validation set +stops improving. Depending on the number of and generation of GPUs you are using that may take a while. + +### Translating the test and validation sets with evaluation + +After training, the model with the highest translation validation score is used +to translate the WMT2016 dev set and test set with `marian-decoder`: + +``` +# translate dev set +cat data/newsdev2016.ro \ + | ../../build/marian-decoder -c model/model.npz.best-bleu-detok.npz.decoder.yml -d 0 1 2 3 -b 6 -n0.6 \ + --mini-batch 64 --maxi-batch 100 --maxi-batch-sort src > data/newsdev2016.ro.output + +# translate test set +cat data/newstest2016.ro \ + | ../../build/marian-decoder -c model/model.npz.best-bleu-detok.npz.decoder.yml -d 0 1 2 3 -b 6 -n0.6 \ + --mini-batch 64 --maxi-batch 100 --maxi-batch-sort src > data/newstest2016.ro.output +``` +after which BLEU scores for the dev and test set are reported. +``` +# calculate bleu scores on dev and test set +sacreBLEU/sacrebleu.py -t wmt16/dev -l ro-en < data/newsdev2016.ro.output +sacreBLEU/sacrebleu.py -t wmt16 -l ro-en < data/newstest2016.ro.output +``` +You should see results somewhere in the area of 36.5 BLEU for the dev set and 35.1 BLEU for the test set. This is actually a bit better than for the BPE version from `marian/examples/training-basics` with the complex preprocessing. +``` +BLEU+case.mixed+lang.ro-en+numrefs.1+smooth.exp+test.wmt16/dev+tok.13a+version.1.2.12 = 36.5 67.9/42.7/29.4/20.9 (BP = 1.000 ratio = 1.006 hyp_len = 49816 ref_len = 49526) +BLEU+case.mixed+lang.ro-en+numrefs.1+smooth.exp+test.wmt16+tok.13a+version.1.2.12 = 35.1 66.6/41.3/28.0/19.6 (BP = 1.000 ratio = 1.005 hyp_len = 47804 ref_len = 47562) +``` + +That's all folks. |