/*
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* Copyright (C) 2018 The Android Open Source Project
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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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*/
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#include "actions/ngram-model.h"
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#include <algorithm>
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#include "actions/feature-processor.h"
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#include "utils/hash/farmhash.h"
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#include "utils/strings/stringpiece.h"
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namespace libtextclassifier3 {
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namespace {
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// An iterator to iterate over the initial tokens of the n-grams of a model.
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class FirstTokenIterator
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: public std::iterator<std::random_access_iterator_tag,
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/*value_type=*/uint32, /*difference_type=*/ptrdiff_t,
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/*pointer=*/const uint32*,
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/*reference=*/uint32&> {
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public:
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explicit FirstTokenIterator(const NGramLinearRegressionModel* model,
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int index)
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: model_(model), index_(index) {}
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FirstTokenIterator& operator++() {
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index_++;
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return *this;
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}
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FirstTokenIterator& operator+=(ptrdiff_t dist) {
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index_ += dist;
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return *this;
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}
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ptrdiff_t operator-(const FirstTokenIterator& other_it) const {
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return index_ - other_it.index_;
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}
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uint32 operator*() const {
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const uint32 token_offset = (*model_->ngram_start_offsets())[index_];
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return (*model_->hashed_ngram_tokens())[token_offset];
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}
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int index() const { return index_; }
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private:
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const NGramLinearRegressionModel* model_;
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int index_;
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};
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} // anonymous namespace
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std::unique_ptr<NGramModel> NGramModel::Create(
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const NGramLinearRegressionModel* model, const Tokenizer* tokenizer,
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const UniLib* unilib) {
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if (model == nullptr) {
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return nullptr;
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}
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if (tokenizer == nullptr && model->tokenizer_options() == nullptr) {
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TC3_LOG(ERROR) << "No tokenizer options specified.";
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return nullptr;
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}
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return std::unique_ptr<NGramModel>(new NGramModel(model, tokenizer, unilib));
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}
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NGramModel::NGramModel(const NGramLinearRegressionModel* model,
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const Tokenizer* tokenizer, const UniLib* unilib)
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: model_(model) {
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// Create new tokenizer if options are specified, reuse feature processor
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// tokenizer otherwise.
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if (model->tokenizer_options() != nullptr) {
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owned_tokenizer_ = CreateTokenizer(model->tokenizer_options(), unilib);
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tokenizer_ = owned_tokenizer_.get();
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} else {
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tokenizer_ = tokenizer;
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}
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}
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// Returns whether a given n-gram matches the token stream.
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bool NGramModel::IsNGramMatch(const uint32* tokens, size_t num_tokens,
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const uint32* ngram_tokens,
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size_t num_ngram_tokens, int max_skips) const {
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int token_idx = 0, ngram_token_idx = 0, skip_remain = 0;
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for (; token_idx < num_tokens && ngram_token_idx < num_ngram_tokens;) {
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if (tokens[token_idx] == ngram_tokens[ngram_token_idx]) {
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// Token matches. Advance both and reset the skip budget.
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++token_idx;
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++ngram_token_idx;
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skip_remain = max_skips;
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} else if (skip_remain > 0) {
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// No match, but we have skips left, so just advance over the token.
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++token_idx;
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skip_remain--;
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} else {
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// No match and we're out of skips. Reject.
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return false;
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}
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}
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return ngram_token_idx == num_ngram_tokens;
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}
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// Calculates the total number of skip-grams that can be created for a stream
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// with the given number of tokens.
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uint64 NGramModel::GetNumSkipGrams(int num_tokens, int max_ngram_length,
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int max_skips) {
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// Start with unigrams.
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uint64 total = num_tokens;
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for (int ngram_len = 2;
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ngram_len <= max_ngram_length && ngram_len <= num_tokens; ++ngram_len) {
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// We can easily compute the expected length of the n-gram (with skips),
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// but it doesn't account for the fact that they may be longer than the
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// input and should be pruned.
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// Instead, we iterate over the distribution of effective n-gram lengths
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// and add each length individually.
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const int num_gaps = ngram_len - 1;
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const int len_min = ngram_len;
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const int len_max = ngram_len + num_gaps * max_skips;
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const int len_mid = (len_max + len_min) / 2;
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for (int len_i = len_min; len_i <= len_max; ++len_i) {
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if (len_i > num_tokens) continue;
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const int num_configs_of_len_i =
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len_i <= len_mid ? len_i - len_min + 1 : len_max - len_i + 1;
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const int num_start_offsets = num_tokens - len_i + 1;
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total += num_configs_of_len_i * num_start_offsets;
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}
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}
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return total;
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}
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std::pair<int, int> NGramModel::GetFirstTokenMatches(uint32 token_hash) const {
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const int num_ngrams = model_->ngram_weights()->size();
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const auto start_it = FirstTokenIterator(model_, 0);
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const auto end_it = FirstTokenIterator(model_, num_ngrams);
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const int start = std::lower_bound(start_it, end_it, token_hash).index();
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const int end = std::upper_bound(start_it, end_it, token_hash).index();
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return std::make_pair(start, end);
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}
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bool NGramModel::Eval(const UnicodeText& text, float* score) const {
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const std::vector<Token> raw_tokens = tokenizer_->Tokenize(text);
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// If we have no tokens, then just bail early.
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if (raw_tokens.empty()) {
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if (score != nullptr) {
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*score = model_->default_token_weight();
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}
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return false;
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}
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// Hash the tokens.
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std::vector<uint32> tokens;
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tokens.reserve(raw_tokens.size());
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for (const Token& raw_token : raw_tokens) {
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tokens.push_back(tc3farmhash::Fingerprint32(raw_token.value.data(),
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raw_token.value.length()));
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}
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// Calculate the total number of skip-grams that can be generated for the
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// input text.
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const uint64 num_candidates = GetNumSkipGrams(
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tokens.size(), model_->max_denom_ngram_length(), model_->max_skips());
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// For each token, see whether it denotes the start of an n-gram in the model.
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int num_matches = 0;
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float weight_matches = 0.f;
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for (size_t start_i = 0; start_i < tokens.size(); ++start_i) {
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const std::pair<int, int> ngram_range =
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GetFirstTokenMatches(tokens[start_i]);
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for (int ngram_idx = ngram_range.first; ngram_idx < ngram_range.second;
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++ngram_idx) {
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const uint16 ngram_tokens_begin =
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(*model_->ngram_start_offsets())[ngram_idx];
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const uint16 ngram_tokens_end =
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(*model_->ngram_start_offsets())[ngram_idx + 1];
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if (IsNGramMatch(
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/*tokens=*/tokens.data() + start_i,
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/*num_tokens=*/tokens.size() - start_i,
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/*ngram_tokens=*/model_->hashed_ngram_tokens()->data() +
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ngram_tokens_begin,
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/*num_ngram_tokens=*/ngram_tokens_end - ngram_tokens_begin,
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/*max_skips=*/model_->max_skips())) {
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++num_matches;
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weight_matches += (*model_->ngram_weights())[ngram_idx];
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}
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}
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}
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// Calculate the score.
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const int num_misses = num_candidates - num_matches;
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const float internal_score =
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(weight_matches + (model_->default_token_weight() * num_misses)) /
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num_candidates;
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if (score != nullptr) {
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*score = internal_score;
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}
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return internal_score > model_->threshold();
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}
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} // namespace libtextclassifier3
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