chromium/third_party/tflite_support/src/tensorflow_lite_support/cc/task/text/text_embedder.h

/* Copyright 2022 The TensorFlow Authors. All Rights Reserved.

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 TENSORFLOW_LITE_SUPPORT_CC_TASK_TEXT_TEXT_EMBEDDER_H_
#define TENSORFLOW_LITE_SUPPORT_CC_TASK_TEXT_TEXT_EMBEDDER_H_

#include <string>
#include <vector>

#include "absl/status/status.h"  // from @com_google_absl
#include "tensorflow/lite/core/api/op_resolver.h"
#include "tensorflow/lite/kernels/register.h"
#include "tensorflow_lite_support/cc/port/statusor.h"
#include "tensorflow_lite_support/cc/task/core/base_task_api.h"
#include "tensorflow_lite_support/cc/task/processor/embedding_postprocessor.h"
#include "tensorflow_lite_support/cc/task/processor/proto/embedding.pb.h"
#include "tensorflow_lite_support/cc/task/processor/proto/embedding_options.pb.h"
#include "tensorflow_lite_support/cc/task/processor/text_preprocessor.h"
#include "tensorflow_lite_support/cc/task/text/proto/text_embedder_options.pb.h"

namespace tflite {
namespace task {
namespace text {

// Performs dense feature vector extraction on text.
//
// The API expects a TFLite model with metadata populated. The metadata should
// contain the following information:
// 1. For Bert based TFLite model:
//   - 3 input tensors of type kTfLiteString with names "ids", "mask" and
//   "segment_ids".
//   - input_process_units for Wordpiece/Sentencepiece Tokenizer
//   - one or more output tensors of type kTfLiteFloat32
// 2. For Regex based TFLite model:
//   - 1 input tensor.
//   - input_process_units for RegexTokenizer Tokenizer
//   - one or more output tensors of type kTfLiteFloat32
// 3. For Universal Sentence Encoder based TFLite model:
//   - 3 input tensors with names "inp_text", "res_context" and "res_text"
//   - 2 output tensors with names "query_encoding" and "response_encoding" of
//     type kTfLiteFloat32
class TextEmbedder
    : public core::BaseTaskApi<processor::EmbeddingResult, const std::string&> { … };

}  // namespace text
}  // namespace task
}  // namespace tflite

#endif  // TENSORFLOW_LITE_SUPPORT_CC_TASK_TEXT_TEXT_EMBEDDER_H_