// Copyright 2016 Google Inc.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.!
syntax = "proto2";
// TODO(taku): Needs to use LITE RUNTIME in OSS release.
option optimize_for = LITE_RUNTIME;
package sentencepiece;
// TrainerSpec encodes a various parameters for SentencePiece training.
// Next id: 54
message TrainerSpec {
///////////////////////////////////////////////////////////////////
// General parameters
//
// Input corpus files.
// Trainer accepts the following two formats:
// A) Monolingual: plain text, one sentence per line.
// B) Bilingual: TSV, source sentence <tab> target sentence
// When bilingual data is passed, shared vocabulary model is built.
// Note that the input file must be raw corpus, not a preprocessed corpus.
// Trainer only loads the first `input_sentence_size` sentences specified
// with this parameter.
repeated string input = 1;
// Input corpus format:
// "text": one-sentence-per-line text format (default)
// "tsv": sentence <tab> freq
optional string input_format = 7;
// Output model file prefix.
// <model_prefix>.model and <model_prefix>.vocab are generated.
optional string model_prefix = 2;
// Model type. only have UNIGRAM now.
enum ModelType {
UNIGRAM = 1; // Unigram language model with dynamic algorithm
BPE = 2; // Byte Pair Encoding
WORD = 3; // Delimitered by whitespace.
CHAR = 4; // tokenizes into character sequence
}
optional ModelType model_type = 3 [default = UNIGRAM];
// Vocabulary size. 8k is the default size.
optional int32 vocab_size = 4 [default = 8000];
// List of the languages this model can accept.
// Since the model is language-agnostic, this field is used as a reference.
repeated string accept_language = 5;
// Size of self-test samples, which are encoded in the model file.
optional int32 self_test_sample_size = 6 [default = 0];
// Whether to use DP version of sentencepiece. Use it with TSV input format
// (requires precomputed word tab counts to work).
optional bool enable_differential_privacy = 50 [default = false];
// Set these parameters if you need DP version of sentencepiece.
// std of noise to add.
optional float differential_privacy_noise_level = 51 [default = 0.0];
// Clipping threshold to apply after adding noise. All the words with
// frequency less than this value are dropped.
optional uint64 differential_privacy_clipping_threshold = 52 [default = 0];
///////////////////////////////////////////////////////////////////
// Training parameters.
//
// Uses characters which cover the corpus with the ratio of `chars_coverage`.
// This parameter determines the set of basic Alphabet of sentence piece.
// 1.0 - `chars_coverage` characters are treated as UNK.
// See also required_chars field.
optional float character_coverage = 10 [default = 0.9995];
// Maximum size of sentences the trainer loads from `input` parameter.
// Trainer simply loads the `input` files in sequence.
// It is better to shuffle the input corpus randomly.
optional uint64 input_sentence_size = 11 [default = 0];
optional bool shuffle_input_sentence = 19 [default = true];
// Maximum size of sentences to make seed sentence pieces.
// Extended suffix array is constructed to extract frequent
// sub-strings from the corpus. This uses 20N working space,
// where N is the size of corpus.
optional int32 mining_sentence_size = 12 [deprecated = true];
// Maximum size of sentences to train sentence pieces.
optional int32 training_sentence_size = 13 [deprecated = true];
// The size of seed sentencepieces.
// `seed_sentencepiece_size` must be larger than `vocab_size`.
optional int32 seed_sentencepiece_size = 14 [default = 1000000];
// In every EM sub-iterations, keeps top
// `shrinking_factor` * `current sentencepieces size` with respect to
// the loss of the sentence piece. This value should be smaller than 1.0.
optional float shrinking_factor = 15 [default = 0.75];
// The maximum sentence length in byte. The sentences with the length
// larger than `max_sentence_length` is simply ignored.
// Longer input tends to bring the following risks:
// * Overflow during EM training (unigram language model only)
// * Performance drop because of O(n log n) cost in BPE.
optional int32 max_sentence_length = 18 [default = 4192];
// Number of threads in the training.
optional int32 num_threads = 16 [default = 16];
// Number of EM sub iterations.
optional int32 num_sub_iterations = 17 [default = 2];
///////////////////////////////////////////////////////////////////
// SentencePiece parameters which control the shapes of sentence piece.
//
// Maximum length of sentencepiece.
optional int32 max_sentencepiece_length = 20 [default = 16];
// Uses Unicode script to split sentence pieces.
// When `split_by_unicode_script` is true, we do not allow sentence piece to
// include multiple Unicode scripts, e.g. "F1" is not a valid piece.
// Exception: CJ characters (Hiragana/Katakana/Han) are all handled
// as one script type, since Japanese word can consist of multiple scripts.
// This exception is always applied regardless of the accept-language
// parameter.
optional bool split_by_unicode_script = 21 [default = true];
// When `split_by_number` is true, put a boundary between number and
// non-number transition. If we want to treat "F1" is one token, set this flag
// to be false.
optional bool split_by_number = 23 [default = true];
// Use a white space to split sentence pieces.
// When `split_by_whitespace` is false, we may have the piece containing
// a white space in the middle. e.g., "in_the".
optional bool split_by_whitespace = 22 [default = true];
// Adds whitespace symbol (_) as a suffix instead of prefix. e.g., _hello =>
// hello_. When `treat_whitespace_as_suffix` is true,
// NormalizerSpec::add_dummy_prefix will add the dummy whitespace to the end
// of sentence.
optional bool treat_whitespace_as_suffix = 24 [default = false];
// Allows pieces that only contain whitespaces instead of appearing only as
// prefix or suffix of other pieces.
optional bool allow_whitespace_only_pieces = 26 [default = false];
// Split all digits (0-9) into separate pieces.
optional bool split_digits = 25 [default = false];
// Defines the pre-tokenization delimiter.
// When specified, no pieces crossing this delimiter is not included
// in the vocab. Then the delimiter string is virtually ignored
// during the training. This field can allows constraints on the vocabulary
// selection. Note that this field is available on unigram mode.
optional string pretokenization_delimiter = 53 [default = ""];
///////////////////////////////////////////////////////////////////
// Vocabulary management
//
// Defines control symbols used as an indicator to
// change the behavior of the decoder. <s> and </s> are pre-defined.
// We can use this field to encode various meta information,
// including language indicator in multilingual model.
// These symbols are not visible to users, but visible to
// the decoder. Note that when the input sentence contains control symbols,
// they are not treated as one token, but segmented into normal pieces.
// Control symbols must be inserted independently from the segmentation.
repeated string control_symbols = 30;
// Defines user defined symbols.
// These symbols are added with extremely high score
// so they are always treated as one unique symbol in any context.
// Typical usage of user_defined_symbols is placeholder for named entities.
repeated string user_defined_symbols = 31;
// Defines required characters. Each UTF8 character in this string is included
// in the character set regardless of character_coverage value. Unlike
// user_defined_symbols, these characters have scores based on the frequency
// on input sentences, and the model can form subwords using characters
// in this field.
optional string required_chars = 36;
// Decomposes unknown pieces into UTF-8 bytes.
optional bool byte_fallback = 35 [default = false];
// When creating the vocabulary file, defines whether or not to additionally
// output the score for each piece.
optional bool vocabulary_output_piece_score = 32 [default = true];
// `vocab_size` is treated as hard limit. Crash if
// the model can not produce the vocab of size `vocab_size`,
// When `hard_vocab_limit` is false, vocab_size is treated
// as soft limit. Note that when model_type=char,
// always assumes hard_vocab_limit = false.
optional bool hard_vocab_limit = 33 [default = true];
// use all symbols for vocab extraction. This flag is valid
// if model type is either CHAR or WORD
optional bool use_all_vocab = 34 [default = false];
///////////////////////////////////////////////////////////////////
// Reserved special meta tokens.
// * -1 is not used.
// * unk_id must not be -1.
// Id must starts with 0 and be contigous.
optional int32 unk_id = 40 [default = 0]; // <unk>
optional int32 bos_id = 41 [default = 1]; // <s>
optional int32 eos_id = 42 [default = 2]; // </s>
optional int32 pad_id = 43 [default = -1]; // <pad> (padding)
optional string unk_piece = 45 [default = "<unk>"];
optional string bos_piece = 46 [default = "<s>"];
optional string eos_piece = 47 [default = "</s>"];
optional string pad_piece = 48 [default = "<pad>"];
// Encodes <unk> into U+2047 (DOUBLE QUESTION MARK),
// since this character can be useful both for user and
// developer. We can easily figure out that <unk> is emitted.
optional string unk_surface = 44 [default = " \xE2\x81\x87 "];
// Increase bit depth to allow unigram model training on large
// (>10M sentences) corpora. A Side-effect of enabling this flag
// is increased memory usage.
optional bool train_extremely_large_corpus = 49 [default = false];
// Customized extensions: the range of field numbers
// are open to third-party extensions.
extensions 200 to max;
}
// NormalizerSpec encodes a various parameters for string normalizaiton
message NormalizerSpec {
// name of normalization rule.
optional string name = 1;
// Pre-compiled normalization rule created by
// Builder::GetPrecompiledCharsMap() or Builder::CompileCharsMap() method.
// Usually this field is set by Builder::GetNormalizerSpec() method.
optional bytes precompiled_charsmap = 2;
// Adds dummy whitespace at the beginning of text in order to
// treat "world" in "world" and "hello world" in the same way.
optional bool add_dummy_prefix = 3 [default = true];
// Removes leading, trailing, and duplicate internal whitespace.
optional bool remove_extra_whitespaces = 4 [default = true];
// Replaces whitespace with meta symbol.
// This field must be true to train sentence piece model.
optional bool escape_whitespaces = 5 [default = true];
// Custom normalization rule file in TSV format.
// https://github.com/google/sentencepiece/blob/master/doc/normalization.md
// This field is only used in SentencePieceTrainer::Train() method, which
// compiles the rule into the binary rule stored in `precompiled_charsmap`.
optional string normalization_rule_tsv = 6;
// Customized extensions: the range of field numbers
// are open to third-party extensions.
extensions 200 to max;
}
// Proto to store samples for self-testing.
message SelfTestData {
message Sample {
optional string input = 1;
optional string expected = 2;
}
repeated Sample samples = 1;
// Customized extensions: the range of field numbers
// are open to third-party extensions.
extensions 200 to max;
}
// ModelProto stores model parameters.
// SentencePieceProcessor is supposed to be self-contained.
// All settings/parameters which may change the behavior must be encoded
// in ModelProto.
message ModelProto {
message SentencePiece {
enum Type {
NORMAL = 1; // normal symbol
UNKNOWN = 2; // unknown symbol. only <unk> for now.
CONTROL = 3; // control symbols. </s>, <s>, <2ja> etc.
USER_DEFINED = 4; // user defined symbols.
// Typical usage of USER_DEFINED symbol
// is placeholder.
BYTE = 6; // byte symbols. Used when `byte_fallback` is true.
UNUSED = 5; // this piece is not used.
}
optional string piece = 1; // piece must not be empty.
optional float score = 2;
optional Type type = 3 [default = NORMAL];
// Customized extensions: the range of field numbers
// are open to third-party extensions.
extensions 200 to max;
}
// Sentence pieces with scores.
repeated SentencePiece pieces = 1;
// Spec used to generate this model file.
optional TrainerSpec trainer_spec = 2;
// Spec for text normalization.
optional NormalizerSpec normalizer_spec = 3;
// Stores sample input and its expected segmentation to verify the model.
optional SelfTestData self_test_data = 4;
// Spec for text de-normalization.
optional NormalizerSpec denormalizer_spec = 5;
// Customized extensions: the range of field numbers
// are open to third-party extensions.
extensions 200 to max;
}
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