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// 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.!
#ifndef UNIGRAM_MODEL_TRAINER_H_
#define UNIGRAM_MODEL_TRAINER_H_
#include <memory>
#include <string>
#include <utility>
#include <vector>
#include "builtin_pb/sentencepiece_model.pb.h"
#include "third_party/absl/strings/string_view.h"
#include "trainer_interface.h"
#include "unigram_model.h"
#include "util.h"
namespace sentencepiece {
namespace unigram {
using string_util::UnicodeText;
class TrainerModel : public Model {
public:
using SentencePieces = std::vector<std::pair<std::string, float>>;
TrainerModel() {}
TrainerModel(const ModelProto &model_proto) = delete;
TrainerModel(const TrainerSpec &trainer_spec,
const NormalizerSpec &normalizaiton_spec);
~TrainerModel() override;
// Returns the sentencepieces.
// The meta symbols, e.g., </s> are NOT included.
const SentencePieces &GetSentencePieces() const;
// Sets sentencepieces. The sentencepieces are moved.
// The meta symbols, e.g., </s> are NOT included.
void SetSentencePieces(SentencePieces &&sentencepieces);
EncodeResult Encode(absl::string_view normalized) const override {
return {};
}
private:
SentencePieces sentencepieces_;
TrainerSpec trainer_spec_;
NormalizerSpec normalizer_spec_;
ModelProto model_proto_data_;
};
class Trainer : public TrainerInterface {
public:
Trainer(const TrainerSpec &trainer_spec,
const NormalizerSpec &normalizer_spec,
const NormalizerSpec &denormalizer_spec)
: TrainerInterface::TrainerInterface(trainer_spec, normalizer_spec,
denormalizer_spec) {}
util::Status Train() override;
private:
FRIEND_TEST(TrainerTest, IsValidSentencePieceTest);
// Makes seed pieces from the training corpus.
// The size of seed pieces is determined by seed_sentencepiece_size.
// node_int_type should be of integer type (int32 or int64),
// determined by train_extremely_large_corpus.
template <typename node_int_type>
TrainerModel::SentencePieces MakeSeedSentencePieces() const;
// Executes the E step of EM and returns expected count.
// The index of return array is the vocab id.
// |objective| is a negative likelihood of the current model.
// |num_token| is the number of total tokens to tokenize
// training corpus.
std::vector<float> RunEStep(const TrainerModel &model, float *objective,
int64 *num_tokens) const;
// Executes the M step of EM with the expected frequency and
// returns new pieces.
TrainerModel::SentencePieces RunMStep(
const TrainerModel &model, const std::vector<float> &expected) const;
// Heuristically prunes the current pieces.
// This is called after each EM sub-iteration.
TrainerModel::SentencePieces PruneSentencePieces(
const TrainerModel &model) const;
// Makes the final sentence pieces by incorporating the required characters
// and control/user defined symbols.
TrainerModel::SentencePieces FinalizeSentencePieces(
const TrainerModel &model) const;
// When the size of SentencePieces becomes less than desired_vocab_size_,
// break the main training loop. desired_vocab_size_ = 1.1 * vocab_size_
// for now.
int desired_vocab_size_;
};
} // namespace unigram
} // namespace sentencepiece
#endif // UNIGRAM_MODEL_TRAINER_H_
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