94 lines
3.9 KiB
Objective-C
94 lines
3.9 KiB
Objective-C
// Copyright 2023 The MediaPipe Authors.
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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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#import <Foundation/Foundation.h>
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#import "mediapipe/tasks/ios/core/sources/MPPTaskOptions.h"
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#import "mediapipe/tasks/ios/text/text_embedder/sources/MPPTextEmbedderOptions.h"
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#import "mediapipe/tasks/ios/text/text_embedder/sources/MPPTextEmbedderResult.h"
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NS_ASSUME_NONNULL_BEGIN
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/**
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* @brief Performs embedding extraction on text.
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*
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* This API expects a TFLite model with (optional) [TFLite Model
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* Metadata](https://www.tensorflow.org/lite/convert/metadata").
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*
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* Metadata is required for models with int32 input tensors because it contains the input process
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* unit for the model's Tokenizer. No metadata is required for models with string input tensors.
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*
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* Input tensors
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* - Three input tensors `kTfLiteInt32` of shape `[batch_size x bert_max_seq_len]`
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* representing the input ids, mask ids, and segment ids. This input signature requires
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* a Bert Tokenizer process unit in the model metadata.
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* - Or one input tensor `kTfLiteInt32` of shape `[batch_size x max_seq_len]` representing
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* the input ids. This input signature requires a Regex Tokenizer process unit in the
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* model metadata.
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* - Or one input tensor (`kTfLiteString`) that is shapeless or has shape `[1]` containing
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* the input string.
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*
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* At least one output tensor (`kTfLiteFloat32`/`kTfLiteUint8`) with shape `[1 x N]` where `N` is
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* the number of dimensions in the produced embeddings.
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*/
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NS_SWIFT_NAME(TextEmbedder)
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@interface MPPTextEmbedder : NSObject
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/**
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* Creates a new instance of `MPPTextEmbedder` from an absolute path to a TensorFlow Lite
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* model file stored locally on the device and the default `MPPTextEmbedderOptions`.
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*
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* @param modelPath An absolute path to a TensorFlow Lite model file stored locally on the device.
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* @param error An optional error parameter populated when there is an error in initializing the
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* text embedder.
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*
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* @return A new instance of `MPPTextEmbedder` with the given model path. `nil` if there is an
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* error in initializing the text embedder.
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*/
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- (nullable instancetype)initWithModelPath:(NSString *)modelPath error:(NSError **)error;
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/**
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* Creates a new instance of `MPPTextEmbedder` from the given `MPPTextEmbedderOptions`.
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*
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* @param options The options of type `MPPTextEmbedderOptions` to use for configuring the
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* `MPPTextEmbedder.
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* @param error An optional error parameter populated when there is an error in initializing the
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* text embedder.
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*
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* @return A new instance of `MPPTextEmbedder` with the given options. `nil` if there is an
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* error in initializing the text embedder.
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*/
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- (nullable instancetype)initWithOptions:(MPPTextEmbedderOptions *)options
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error:(NSError **)error NS_DESIGNATED_INITIALIZER;
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/**
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* Performs embedding extraction on the input text.
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*
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* @param text The `NSString` on which embedding extraction is to be performed.
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* @param error An optional error parameter populated when there is an error in performing
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* embedding extraction on the input text.
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*
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* @return A `MPPTextEmbedderResult` object that contains a list of embeddings.
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*/
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- (nullable MPPTextEmbedderResult *)embedText:(NSString *)text
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error:(NSError **)error NS_SWIFT_NAME(embed(text:));
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- (instancetype)init NS_UNAVAILABLE;
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+ (instancetype)new NS_UNAVAILABLE;
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@end
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NS_ASSUME_NONNULL_END
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