Jed Fox
2 years ago
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// SOURCE: https://github.com/google/sentencepiece/blob/9ffb33a14c97c512103be0ee74740099660b39aa/src/sentencepiece_model.proto#L282
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// Copyright 2016 Google Inc.
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//
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.!
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syntax = "proto2";
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// TODO(taku): Needs to use LITE RUNTIME in OSS release.
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option optimize_for = LITE_RUNTIME;
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package sentencepiece;
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// TrainerSpec encodes a various parameters for SentencePiece training.
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// Next id: 53
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message TrainerSpec {
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///////////////////////////////////////////////////////////////////
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// General parameters
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//
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// Input corpus files.
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// Trainer accepts the following two formats:
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// A) Monolingual: plain text, one sentence per line.
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// B) Bilingual: TSV, source sentence <tab> target sentence
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// When bilingual data is passed, shared vocabulary model is built.
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// Note that the input file must be raw corpus, not a preprocessed corpus.
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// Trainer only loads the first `input_sentence_size` sentences specified
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// with this parameter.
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repeated string input = 1;
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// Input corpus format:
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// "text": one-sentence-per-line text format (default)
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// "tsv": sentence <tab> freq
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optional string input_format = 7;
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// Output model file prefix.
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// <model_prefix>.model and <model_prefix>.vocab are generated.
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optional string model_prefix = 2;
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// Model type. only have UNIGRAM now.
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enum ModelType {
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UNIGRAM = 1; // Unigram language model with dynamic algorithm
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BPE = 2; // Byte Pair Encoding
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WORD = 3; // Delimitered by whitespace.
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CHAR = 4; // tokenizes into character sequence
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}
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optional ModelType model_type = 3 [default = UNIGRAM];
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// Vocabulary size. 8k is the default size.
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optional int32 vocab_size = 4 [default = 8000];
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// List of the languages this model can accept.
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// Since the model is language-agnostic, this field is used as a reference.
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repeated string accept_language = 5;
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// Size of self-test samples, which are encoded in the model file.
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optional int32 self_test_sample_size = 6 [default = 0];
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// Whether to use DP version of sentencepiece. Use it with TSV input format
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// (requires precomputed word tab counts to work).
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optional bool enable_differential_privacy = 50 [default = false];
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// Set these parameters if you need DP version of sentencepiece.
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// std of noise to add.
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optional float differential_privacy_noise_level = 51 [default = 0.0];
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// Clipping threshold to apply after adding noise. All the words with
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// frequency less than this value are dropped.
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optional uint64 differential_privacy_clipping_threshold = 52 [default = 0];
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///////////////////////////////////////////////////////////////////
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// Training parameters.
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//
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// Uses characters which cover the corpus with the ratio of `chars_coverage`.
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// This parameter determines the set of basic Alphabet of sentence piece.
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// 1.0 - `chars_coverage` characters are treated as UNK.
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// See also required_chars field.
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optional float character_coverage = 10 [default = 0.9995];
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// Maximum size of sentences the trainer loads from `input` parameter.
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// Trainer simply loads the `input` files in sequence.
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// It is better to shuffle the input corpus randomly.
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optional uint64 input_sentence_size = 11 [default = 0];
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optional bool shuffle_input_sentence = 19 [default = true];
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// Maximum size of sentences to make seed sentence pieces.
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// Extended suffix array is constructed to extract frequent
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// sub-strings from the corpus. This uses 20N working space,
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// where N is the size of corpus.
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optional int32 mining_sentence_size = 12 [deprecated = true];
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// Maximum size of sentences to train sentence pieces.
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optional int32 training_sentence_size = 13 [deprecated = true];
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// The size of seed sentencepieces.
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// `seed_sentencepiece_size` must be larger than `vocab_size`.
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optional int32 seed_sentencepiece_size = 14 [default = 1000000];
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// In every EM sub-iterations, keeps top
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// `shrinking_factor` * `current sentencepieces size` with respect to
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// the loss of the sentence piece. This value should be smaller than 1.0.
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optional float shrinking_factor = 15 [default = 0.75];
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// The maximum sentence length in byte. The sentences with the length
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// larger than `max_sentence_length` is simply ignored.
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// Longer input tends to bring the following risks:
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// * Overflow during EM training (unigram language model only)
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// * Performance drop because of O(n log n) cost in BPE.
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optional int32 max_sentence_length = 18 [default = 4192];
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// Number of threads in the training.
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optional int32 num_threads = 16 [default = 16];
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// Number of EM sub iterations.
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optional int32 num_sub_iterations = 17 [default = 2];
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///////////////////////////////////////////////////////////////////
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// SentencePiece parameters which control the shapes of sentence piece.
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//
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// Maximum length of sentencepiece.
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optional int32 max_sentencepiece_length = 20 [default = 16];
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// Uses Unicode script to split sentence pieces.
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// When `split_by_unicode_script` is true, we do not allow sentence piece to
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// include multiple Unicode scripts, e.g. "F1" is not a valid piece.
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// Exception: CJ characters (Hiragana/Katakana/Han) are all handled
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// as one script type, since Japanese word can consist of multiple scripts.
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// This exception is always applied regardless of the accept-language
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// parameter.
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optional bool split_by_unicode_script = 21 [default = true];
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// When `split_by_number` is true, put a boundary between number and
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// non-number transition. If we want to treat "F1" is one token, set this flag
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// to be false.
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optional bool split_by_number = 23 [default = true];
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// Use a white space to split sentence pieces.
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// When `split_by_whitespace` is false, we may have the piece containing
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// a white space in the middle. e.g., "in_the".
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optional bool split_by_whitespace = 22 [default = true];
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// Adds whitespace symbol (_) as a suffix instead of prefix. e.g., _hello =>
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// hello_. When `treat_whitespace_as_suffix` is true,
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// NormalizerSpec::add_dummy_prefix will add the dummy whitespace to the end
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// of sentence.
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optional bool treat_whitespace_as_suffix = 24 [default = false];
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// Allows pieces that only contain whitespaces instead of appearing only as
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// prefix or suffix of other pieces.
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optional bool allow_whitespace_only_pieces = 26 [default = false];
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// Split all digits (0-9) into separate pieces.
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optional bool split_digits = 25 [default = false];
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///////////////////////////////////////////////////////////////////
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// Vocabulary management
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//
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// Defines control symbols used as an indicator to
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// change the behavior of the decoder. <s> and </s> are pre-defined.
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// We can use this field to encode various meta information,
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// including language indicator in multilingual model.
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// These symbols are not visible to users, but visible to
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// the decoder. Note that when the input sentence contains control symbols,
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// they are not treated as one token, but segmented into normal pieces.
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// Control symbols must be inserted independently from the segmentation.
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repeated string control_symbols = 30;
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// Defines user defined symbols.
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// These symbols are added with extremely high score
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// so they are always treated as one unique symbol in any context.
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// Typical usage of user_defined_symbols is placeholder for named entities.
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repeated string user_defined_symbols = 31;
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// Defines required characters. Each UTF8 character in this string is included
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// in the character set regardless of character_coverage value. Unlike
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// user_defined_symbols, these characters have scores based on the frequency
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// on input sentences, and the model can form subwords using characters
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// in this field.
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optional string required_chars = 36;
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// Decomposes unknown pieces into UTF-8 bytes.
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optional bool byte_fallback = 35 [default = false];
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// When creating the vocabulary file, defines whether or not to additionally
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// output the score for each piece.
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optional bool vocabulary_output_piece_score = 32 [default = true];
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// `vocab_size` is treated as hard limit. Crash if
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// the model can not produce the vocab of size `vocab_size`,
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// When `hard_vocab_limit` is false, vocab_size is treated
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// as soft limit. Note that when model_type=char,
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// always assumes hard_vocab_limit = false.
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optional bool hard_vocab_limit = 33 [default = true];
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// use all symbols for vocab extraction. This flag is valid
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// if model type is either CHAR or WORD
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optional bool use_all_vocab = 34 [default = false];
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///////////////////////////////////////////////////////////////////
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// Reserved special meta tokens.
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// * -1 is not used.
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// * unk_id must not be -1.
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// Id must starts with 0 and be contigous.
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optional int32 unk_id = 40 [default = 0]; // <unk>
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optional int32 bos_id = 41 [default = 1]; // <s>
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optional int32 eos_id = 42 [default = 2]; // </s>
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optional int32 pad_id = 43 [default = -1]; // <pad> (padding)
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optional string unk_piece = 45 [default = "<unk>"];
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optional string bos_piece = 46 [default = "<s>"];
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optional string eos_piece = 47 [default = "</s>"];
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optional string pad_piece = 48 [default = "<pad>"];
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// Encodes <unk> into U+2047 (DOUBLE QUESTION MARK),
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// since this character can be useful both for user and
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// developer. We can easily figure out that <unk> is emitted.
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optional string unk_surface = 44 [default = " \xE2\x81\x87 "];
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// Increase bit depth to allow unigram model training on large
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// (>10M sentences) corpora. A Side-effect of enabling this flag
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// is increased memory usage.
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optional bool train_extremely_large_corpus = 49 [default = false];
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// Customized extensions: the range of field numbers
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// are open to third-party extensions.
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extensions 200 to max;
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}
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// NormalizerSpec encodes a various parameters for string normalizaiton
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message NormalizerSpec {
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// name of normalization rule.
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optional string name = 1;
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// Pre-compiled normalization rule created by
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// Builder::GetPrecompiledCharsMap() or Builder::CompileCharsMap() method.
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// Usually this field is set by Builder::GetNormalizerSpec() method.
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optional bytes precompiled_charsmap = 2;
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// Adds dummy whitespace at the beginning of text in order to
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// treat "world" in "world" and "hello world" in the same way.
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optional bool add_dummy_prefix = 3 [default = true];
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// Removes leading, trailing, and duplicate internal whitespace.
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optional bool remove_extra_whitespaces = 4 [default = true];
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// Replaces whitespace with meta symbol.
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// This field must be true to train sentence piece model.
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optional bool escape_whitespaces = 5 [default = true];
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// Custom normalization rule file in TSV format.
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// https://github.com/google/sentencepiece/blob/master/doc/normalization.md
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// This field is only used in SentencePieceTrainer::Train() method, which
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// compiles the rule into the binary rule stored in `precompiled_charsmap`.
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optional string normalization_rule_tsv = 6;
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// Customized extensions: the range of field numbers
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// are open to third-party extensions.
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extensions 200 to max;
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}
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// Proto to store samples for self-testing.
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message SelfTestData {
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message Sample {
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optional string input = 1;
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optional string expected = 2;
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}
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repeated Sample samples = 1;
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// Customized extensions: the range of field numbers
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// are open to third-party extensions.
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extensions 200 to max;
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}
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// ModelProto stores model parameters.
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// SentencePieceProcessor is supposed to be self-contained.
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// All settings/parameters which may change the behavior must be encoded
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// in ModelProto.
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message ModelProto {
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message SentencePiece {
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enum Type {
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NORMAL = 1; // normal symbol
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UNKNOWN = 2; // unknown symbol. only <unk> for now.
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CONTROL = 3; // control symbols. </s>, <s>, <2ja> etc.
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USER_DEFINED = 4; // user defined symbols.
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// Typical usage of USER_DEFINED symbol
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// is placeholder.
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BYTE = 6; // byte symbols. Used when `byte_fallback` is true.
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UNUSED = 5; // this piece is not used.
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}
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optional string piece = 1; // piece must not be empty.
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optional float score = 2;
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optional Type type = 3 [default = NORMAL];
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// Customized extensions: the range of field numbers
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// are open to third-party extensions.
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extensions 200 to max;
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}
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// Sentence pieces with scores.
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repeated SentencePiece pieces = 1;
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// Spec used to generate this model file.
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optional TrainerSpec trainer_spec = 2;
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// Spec for text normalization.
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optional NormalizerSpec normalizer_spec = 3;
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// Stores sample input and its expected segmentation to verify the model.
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optional SelfTestData self_test_data = 4;
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// Spec for text de-normalization.
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optional NormalizerSpec denormalizer_spec = 5;
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// Customized extensions: the range of field numbers
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// are open to third-party extensions.
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extensions 200 to max;
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}
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@ -0,0 +1,44 @@
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# -*- coding: utf-8 -*-
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# Generated by the protocol buffer compiler. DO NOT EDIT!
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# source: sentencepiece_model.proto
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"""Generated protocol buffer code."""
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from google.protobuf.internal import builder as _builder
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from google.protobuf import descriptor as _descriptor
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from google.protobuf import descriptor_pool as _descriptor_pool
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from google.protobuf import symbol_database as _symbol_database
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# @@protoc_insertion_point(imports)
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_sym_db = _symbol_database.Default()
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|
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DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n\x19sentencepiece_model.proto\x12\rsentencepiece\"\xdb\x0b\n\x0bTrainerSpec\x12\r\n\x05input\x18\x01 \x03(\t\x12\x14\n\x0cinput_format\x18\x07 \x01(\t\x12\x14\n\x0cmodel_prefix\x18\x02 \x01(\t\x12\x41\n\nmodel_type\x18\x03 \x01(\x0e\x32$.sentencepiece.TrainerSpec.ModelType:\x07UNIGRAM\x12\x18\n\nvocab_size\x18\x04 \x01(\x05:\x04\x38\x30\x30\x30\x12\x17\n\x0f\x61\x63\x63\x65pt_language\x18\x05 \x03(\t\x12 \n\x15self_test_sample_size\x18\x06 \x01(\x05:\x01\x30\x12*\n\x1b\x65nable_differential_privacy\x18\x32 \x01(\x08:\x05\x66\x61lse\x12+\n differential_privacy_noise_level\x18\x33 \x01(\x02:\x01\x30\x12\x32\n\'differential_privacy_clipping_threshold\x18\x34 \x01(\x04:\x01\x30\x12\"\n\x12\x63haracter_coverage\x18\n \x01(\x02:\x06\x30.9995\x12\x1e\n\x13input_sentence_size\x18\x0b \x01(\x04:\x01\x30\x12$\n\x16shuffle_input_sentence\x18\x13 \x01(\x08:\x04true\x12 \n\x14mining_sentence_size\x18\x0c \x01(\x05\x42\x02\x18\x01\x12\"\n\x16training_sentence_size\x18\r \x01(\x05\x42\x02\x18\x01\x12(\n\x17seed_sentencepiece_size\x18\x0e \x01(\x05:\x07\x31\x30\x30\x30\x30\x30\x30\x12\x1e\n\x10shrinking_factor\x18\x0f \x01(\x02:\x04\x30.75\x12!\n\x13max_sentence_length\x18\x12 \x01(\x05:\x04\x34\x31\x39\x32\x12\x17\n\x0bnum_threads\x18\x10 \x01(\x05:\x02\x31\x36\x12\x1d\n\x12num_sub_iterations\x18\x11 \x01(\x05:\x01\x32\x12$\n\x18max_sentencepiece_length\x18\x14 \x01(\x05:\x02\x31\x36\x12%\n\x17split_by_unicode_script\x18\x15 \x01(\x08:\x04true\x12\x1d\n\x0fsplit_by_number\x18\x17 \x01(\x08:\x04true\x12!\n\x13split_by_whitespace\x18\x16 \x01(\x08:\x04true\x12)\n\x1atreat_whitespace_as_suffix\x18\x18 \x01(\x08:\x05\x66\x61lse\x12+\n\x1c\x61llow_whitespace_only_pieces\x18\x1a \x01(\x08:\x05\x66\x61lse\x12\x1b\n\x0csplit_digits\x18\x19 \x01(\x08:\x05\x66\x61lse\x12\x17\n\x0f\x63ontrol_symbols\x18\x1e \x03(\t\x12\x1c\n\x14user_defined_symbols\x18\x1f \x03(\t\x12\x16\n\x0erequired_chars\x18$ \x01(\t\x12\x1c\n\rbyte_fallback\x18# \x01(\x08:\x05\x66\x61lse\x12+\n\x1dvocabulary_output_piece_score\x18 \x01(\x08:\x04true\x12\x1e\n\x10hard_vocab_limit\x18! \x01(\x08:\x04true\x12\x1c\n\ruse_all_vocab\x18\" \x01(\x08:\x05\x66\x61lse\x12\x11\n\x06unk_id\x18( \x01(\x05:\x01\x30\x12\x11\n\x06\x62os_id\x18) \x01(\x05:\x01\x31\x12\x11\n\x06\x65os_id\x18* \x01(\x05:\x01\x32\x12\x12\n\x06pad_id\x18+ \x01(\x05:\x02-1\x12\x18\n\tunk_piece\x18- \x01(\t:\x05<unk>\x12\x16\n\tbos_piece\x18. \x01(\t:\x03<s>\x12\x17\n\teos_piece\x18/ \x01(\t:\x04</s>\x12\x18\n\tpad_piece\x18\x30 \x01(\t:\x05<pad>\x12\x1a\n\x0bunk_surface\x18, \x01(\t:\x05 \xe2\x81\x87 \x12+\n\x1ctrain_extremely_large_corpus\x18\x31 \x01(\x08:\x05\x66\x61lse\"5\n\tModelType\x12\x0b\n\x07UNIGRAM\x10\x01\x12\x07\n\x03\x42PE\x10\x02\x12\x08\n\x04WORD\x10\x03\x12\x08\n\x04\x43HAR\x10\x04*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02\"\xd1\x01\n\x0eNormalizerSpec\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\x1c\n\x14precompiled_charsmap\x18\x02 \x01(\x0c\x12\x1e\n\x10\x61\x64\x64_dummy_prefix\x18\x03 \x01(\x08:\x04true\x12&\n\x18remove_extra_whitespaces\x18\x04 \x01(\x08:\x04true\x12 \n\x12\x65scape_whitespaces\x18\x05 \x01(\x08:\x04true\x12\x1e\n\x16normalization_rule_tsv\x18\x06 \x01(\t*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02\"y\n\x0cSelfTestData\x12\x33\n\x07samples\x18\x01 \x03(\x0b\x32\".sentencepiece.SelfTestData.Sample\x1a)\n\x06Sample\x12\r\n\x05input\x18\x01 \x01(\t\x12\x10\n\x08\x65xpected\x18\x02 \x01(\t*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02\"\xfe\x03\n\nModelProto\x12\x37\n\x06pieces\x18\x01 \x03(\x0b\x32\'.sentencepiece.ModelProto.SentencePiece\x12\x30\n\x0ctrainer_spec\x18\x02 \x01(\x0b\x32\x1a.sentencepiece.TrainerSpec\x12\x36\n\x0fnormalizer_spec\x18\x03 \x01(\x0b\x32\x1d.sentencepiece.NormalizerSpec\x12\x33\n\x0eself_test_data\x18\x04 \x01(\x0b\x32\x1b.sentencepiece.SelfTestData\x12\x38\n\x11\x64\x65normalizer_spec\x18\x05 \x01(\x0b\x32\x1d.sentencepiece.NormalizerSpec\x1a\xd2\x01\n\rSentencePiece\x12\r\n\x05piece\x18\x01 \x01(\t\x12\r\n\x05score\x18\x02 \x01(\x02\x12\x42\n\x04type\x18\x03 \x01(\x0e\x32,.sentencepiece.ModelProto.SentencePiece.Type:\x06NORMAL\"T\n\x04Type\x12\n\n\x06NORMAL\x10\x01\x12\x0b\n\x07UNKNOWN\x10\x02\x12\x0b\n\x07\x43ONTROL\x10\x03\x12\x10\n\x0cUSER_DEFINED\x10\x04\x12\x08\n\x04\x42YTE\x10\x06\x12\n\n\x06UNUSED\x10\x05*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02\x42\x02H\x03')
|
||||
|
||||
_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, globals())
|
||||
_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'sentencepiece_model_pb2', globals())
|
||||
if _descriptor._USE_C_DESCRIPTORS == False:
|
||||
|
||||
DESCRIPTOR._options = None
|
||||
DESCRIPTOR._serialized_options = b'H\003'
|
||||
_TRAINERSPEC.fields_by_name['mining_sentence_size']._options = None
|
||||
_TRAINERSPEC.fields_by_name['mining_sentence_size']._serialized_options = b'\030\001'
|
||||
_TRAINERSPEC.fields_by_name['training_sentence_size']._options = None
|
||||
_TRAINERSPEC.fields_by_name['training_sentence_size']._serialized_options = b'\030\001'
|
||||
_TRAINERSPEC._serialized_start=45
|
||||
_TRAINERSPEC._serialized_end=1544
|
||||
_TRAINERSPEC_MODELTYPE._serialized_start=1480
|
||||
_TRAINERSPEC_MODELTYPE._serialized_end=1533
|
||||
_NORMALIZERSPEC._serialized_start=1547
|
||||
_NORMALIZERSPEC._serialized_end=1756
|
||||
_SELFTESTDATA._serialized_start=1758
|
||||
_SELFTESTDATA._serialized_end=1879
|
||||
_SELFTESTDATA_SAMPLE._serialized_start=1827
|
||||
_SELFTESTDATA_SAMPLE._serialized_end=1868
|
||||
_MODELPROTO._serialized_start=1882
|
||||
_MODELPROTO._serialized_end=2392
|
||||
_MODELPROTO_SENTENCEPIECE._serialized_start=2171
|
||||
_MODELPROTO_SENTENCEPIECE._serialized_end=2381
|
||||
_MODELPROTO_SENTENCEPIECE_TYPE._serialized_start=2286
|
||||
_MODELPROTO_SENTENCEPIECE_TYPE._serialized_end=2370
|
||||
# @@protoc_insertion_point(module_scope)
|
||||
Loading…
Reference in new issue