Holds the base options that is used for creation of any type of task. It has fields with important information acceleration configuration, TFLite model source etc.

Category is a util class that contains a label, its display name, a float value as score, and the index of the label in the corresponding label file. Typically it’s used as the result of classification tasks.

Represents the list of classification for a given classifier head. Typically used as a result for classification tasks.

Represents the classification results of a model. Typically used as a result for classification tasks.

Represents the embedding for a given embedder head. Typically used in embedding tasks.

One and only one of the two ‘floatEmbedding’ and ‘quantizedEmbedding’ will contain data, based on whether or not the embedder was configured to perform scala quantization.

Represents the embedding results of a model. Typically used as a result for embedding tasks.

@brief Predicts the language of an input text.

This API expects a TFLite model with TFLite Model Metadatathat contains the mandatory (described below) input tensor, output tensor, and the language codes in an AssociatedFile.

Metadata is required for models with int32 input tensors because it contains the input process unit for the model’s Tokenizer. No metadata is required for models with string input tensors.

Input tensor

• One input tensor (kTfLiteString) of shape [1] containing the input string.

Output tensor

• One output tensor (kTfLiteFloat32) of shape [1 x N] where N is the number of languages.

Options for setting up a LanguageDetector.

Undocumented

Represents the results generated by LanguageDetector. *

MediaPipe Tasks options base class. Any MediaPipe task-specific options class should extend this class.

MediaPipe Tasks result base class. Any MediaPipe task result class should extend this class.

@brief Performs classification on text.

This API expects a TFLite model with (optional) TFLite Model Metadatathat contains the mandatory (described below) input tensors, output tensor, and the optional (but recommended) label items as AssociatedFiles with type TENSOR_AXIS_LABELS per output classification tensor.

Metadata is required for models with int32 input tensors because it contains the input process unit for the model’s Tokenizer. No metadata is required for models with string input tensors.

Input tensors

• Three input tensors kTfLiteInt32 of shape [batch_size xbert_max_seq_len] representing the input ids, mask ids, and segment ids. This input signature requires a Bert Tokenizer process unit in the model metadata.
• Or one input tensor kTfLiteInt32 of shape [batch_size xmax_seq_len] representing the input ids. This input signature requires a Regex Tokenizer process unit in the model metadata.
• Or one input tensor (kTfLiteString) that is shapeless or has shape [1] containing the input string.

At least one output tensor (kTfLiteFloat32/kBool) with:

• N classes and shape [1 x N]
• optional (but recommended) label map(s) as AssociatedFiles with type TENSOR_AXIS_LABELS, containing one label per line. The first such AssociatedFile (if any) is used to fill the categoryName field of the results. The displayName field is filled from the AssociatedFile (if any) whose locale matches the displayNamesLocale field of the MPPTextClassifierOptions used at creation time (“en” by default, i.e. English). If none of these are available, only the index field of the results will be filled.

Options for setting up a MPPTextClassifier.

Represents the classification results generated by MPPTextClassifier. *

@brief Performs embedding extraction on text.

This API expects a TFLite model with (optional) TFLite Model Metadata.

Metadata is required for models with int32 input tensors because it contains the input process unit for the model’s Tokenizer. No metadata is required for models with string input tensors.

Input tensors:

• Three input tensors kTfLiteInt32 of shape [batch_size x bert_max_seq_len] representing the input ids, mask ids, and segment ids. This input signature requires a Bert Tokenizer process unit in the model metadata.
• Or one input tensor kTfLiteInt32 of shape [batch_size x max_seq_len] representing the input ids. This input signature requires a Regex Tokenizer process unit in the model metadata.
• Or one input tensor (kTfLiteString) that is shapeless or has shape [1] containing the input string.

At least one output tensor (kTfLiteFloat32/kTfLiteUint8) with shape [1 x N] where N is the number of dimensions in the produced embeddings.

Options for setting up a MPPTextEmbedder.

Represents the embedding results generated by MPPTextEmbedder. *