一个无监督的文本分词器和去分词器，用于基于神经网络的文本生成的无监督文本标记器

# SentencePiece Python Wrapper Python wrapper for SentencePiece. This API will offer the encoding, decoding and training of Sentencepiece. ## Build and Install SentencePiece For Linux (x64/i686), macOS, and Windows(win32/x64) environment, you can simply use pip command to install SentencePiece python module. ``` % pip install sentencepiece ``` To build and install the Python wrapper from source, try the following commands to build and install wheel package. ``` % git clone https://github.com/google/sentencepiece.git % cd sentencepiece % mkdir build % cd build % cmake .. -DSPM_ENABLE_SHARED=OFF -DCMAKE_INSTALL_PREFIX=./root % make install % cd ../python % python setup.py bdist_wheel % pip install dist/sentencepiece*.whl ``` If you don’t have write permission to the global site-packages directory or don’t want to install into it, please try: ``` % python setup.py install --user ``` ## Usage See [this google colab page](https://github.com/google/sentencepiece/blob/master/python/sentencepiece_python_module_example.ipynb) to run sentencepiece interactively. ### Segmentation ``` % python >>> import sentencepiece as spm >>> sp = spm.SentencePieceProcessor(model_file='test/test_model.model') >>> sp.encode('This is a test') [284, 47, 11, 4, 15, 400] >>> sp.encode(['This is a test', 'Hello world'], out_type=int) [[284, 47, 11, 4, 15, 400], [151, 88, 21, 887]] >>> sp.encode_as_ids(['This is a test', 'Hello world']) [[284, 47, 11, 4, 15, 400], [151, 88, 21, 887]] >>> sp.encode('This is a test', out_type=str) ['▁This', '▁is', '▁a', '▁', 't', 'est'] >>> sp.encode(['This is a test', 'Hello world'], out_type=str) [['▁This', '▁is', '▁a', '▁', 't', 'est'], ['▁He', 'll', 'o', '▁world']] >>> sp.encode_as_pieces(['This is a test', 'Hello world']) [['▁This', '▁is', '▁a', '▁', 't', 'est'], ['▁He', 'll', 'o', '▁world']] >>> proto = sp.encode('This is a test', out_type='immutable_proto') >>> for n in proto.pieces: ... print('piece="{}" surface="{}" id={} begin={} end={}'.format(n.piece, n.surface, n.id, n.begin, n.end)) ... piece="▁This" surface="This" id=284 begin=0 end=4 piece="▁is" surface=" is" id=47 begin=4 end=7 piece="▁a" surface=" a" id=11 begin=7 end=9 piece="▁" surface=" " id=4 begin=9 end=10 piece="t" surface="t" id=15 begin=10 end=11 piece="est" surface="est" id=400 begin=11 end=14 >>> [[x.id for x in proto.pieces], [x.piece for x in proto.pieces], [x.begin for x in proto.pieces], [x.end for x in proto.pieces]] [[284, 47, 11, 4, 15, 400], ['▁This', '▁is', '▁a', '▁', 't', 'est'], [0, 4, 7, 9, 10, 11], [4, 7, 9, 10, 11, 14]] >>> proto2 = sp.encode_as_immutable_proto('This is a test') >>> proto2 == proto True >>> for _ in range(10): ... sp.encode('This is a test', out_type=str, enable_sampling=True, alpha=0.1, nbest_size=-1) ... ['▁', 'This', '▁', 'is', '▁a', '▁', 't', 'e', 'st'] ['▁T', 'h', 'i', 's', '▁is', '▁a', '▁', 'te', 's', 't'] ['▁T', 'h', 'is', '▁', 'is', '▁', 'a', '▁', 't', 'est'] ['▁', 'This', '▁is', '▁', 'a', '▁', 't', 'e', 'st'] ['▁', 'This', '▁', 'is', '▁', 'a', '▁', 't', 'e', 's', 't'] ['▁This', '▁is', '▁a', '▁', 'te', 's', 't'] ['▁This', '▁is', '▁', 'a', '▁', 't', 'e', 'st'] ['▁', 'T', 'h', 'is', '▁', 'is', '▁', 'a', '▁', 'te', 'st'] ['▁', 'This', '▁', 'i', 's', '▁a', '▁', 't', 'e', 'st'] ['▁This', '▁', 'is', '▁a', '▁', 't', 'est'] >> sp.nbest_encode('This is a test', nbest_size=5, out_type=str) [['▁This', '▁is', '▁a', '▁', 't', 'est'], ['▁This', '▁is', '▁a', '▁', 'te', 'st'], ['▁This', '▁is', '▁a', '▁', 'te', 's', 't'], ['▁This', '▁is', '▁a', '▁', 't', 'e', 'st'], ['▁This', '▁is', '▁a', '▁', 't', 'es', 't']] >>> sp.sample_encode_and_score('This is a test', num_samples=5, alpha=0.1, out_type=str, wor=True) [(['▁This', '▁', 'i', 's', '▁a', '▁', 'te', 's', 't'], -3.043105125427246), (['▁This', '▁', 'i', 's', '▁a', '▁', 'te', 'st'], -2.8475849628448486), (['▁', 'This', '▁is', '▁', 'a', '▁', 'te', 'st'], -3.043248176574707), (['▁', 'This', '▁is', '▁a', '▁', 't', 'e', 'st'], -2.87727689743042), (['▁', 'This', '▁', 'i', 's', '▁', 'a', '▁', 't', 'est'], -3.6284031867980957)] >>> sp.decode([284, 47, 11, 4, 15, 400]) 'This is a test' >>> sp.decode([[284, 47, 11, 4, 15, 400], [151, 88, 21, 887]]) ['This is a test', 'Hello world'] >>> proto = sp.decode([284, 47, 11, 4, 15, 400], out_type='immutable_proto') >>> proto.text 'This is a test' >>> sp.decode(['▁', 'This', '▁', 'is', '▁a', '▁', 't', 'e', 'st']) 'This is a test' >>> sp.decode([['▁This', '▁is', '▁a', '▁', 't', 'est'], ['▁He', 'll', 'o', '▁world']]) ['This is a test', 'Hello world'] >>> sp.get_piece_size() 1000 >>> sp.id_to_piece(2) '</s>' >>> sp.id_to_piece([2, 3, 4]) ['</s>', '\r', '▁'] >>> sp.piece_to_id('<s>') 1 >>> sp.piece_to_id(['</s>', '\r', '▁']) [2, 3, 4] >>> len(sp) 1000 >>> sp['</s>'] 2 ``` ### Model Training Training is performed by passing parameters of [spm_train](https://github.com/google/sentencepiece#train-sentencepiece-model) to SentencePieceTrainer.train() function. ``` >>> import sentencepiece as spm >>> spm.SentencePieceTrainer.train(input='test/botchan.txt', model_prefix='m', vocab_size=1000, user_defined_symbols=['foo', 'bar']) sentencepiece_trainer.cc(73) LOG(INFO) Starts training with : trainer_spec { input: test/botchan.txt .. snip unigram_model_trainer.cc(500) LOG(INFO) EM sub_iter=1 size=1188 obj=10.2839 num_tokens=32182 num_tokens/piece=27.0892 unigram_model_trainer.cc(500) LOG(INFO) EM sub_iter=0 size=1100 obj=10.4269 num_tokens=33001 num_tokens/piece=30.0009 unigram_model_trainer.cc(500) LOG(INFO) EM sub_iter=1 size=1100 obj=10.4069 num_tokens=33002 num_tokens/piece=30.0018 trainer_interface.cc(595) LOG(INFO) Saving model: m.model trainer_interface.cc(619) LOG(INFO) Saving vocabs: m.vocab >>> ``` ### Training without local filesystem Sentencepiece trainer can receive any iterable object to feed training sentences. You can also pass a file object (instance with write() method) to emit the output model to any devices. These features are useful to run sentencepiece on environment that have limited access to the local file system (e.g., Google colab.) ``` import urllib.request import io import sentencepiece as spm # Loads model from URL as iterator and stores the model to BytesIO. model = io.BytesIO() with urllib.request.urlopen( 'https://raw.githubusercontent.com/google/sentencepiece/master/data/botchan.txt' ) as response: spm.SentencePieceTrainer.train( sentence_iterator=response, model_writer=model, vocab_size=1000) # Serialize the model as file. # with open('out.model', 'wb') as f: # f.write(model.getvalue()) # Directly load the model from serialized model. sp = spm.SentencePieceProcessor(model_proto=model.getvalue()) print(sp.encode('this is test')) ```