page_type: reference description: QuestionAnswer class for inference and exporting to tflite.

tflite_model_maker.question_answer.QuestionAnswer

<table class="tfo-notebook-buttons tfo-api nocontent" align="left"> <td> <a target="_blank" href="https://github.com/tensorflow/examples/blob/master/tensorflow_examples/lite/model_maker/core/task/question_answer.py#L51-L232">

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QuestionAnswer class for inference and exporting to tflite.

<pre class="devsite-click-to-copy prettyprint lang-py tfo-signature-link"> <code>tflite_model_maker.question_answer.QuestionAnswer( model_spec, shuffle ) </code></pre>   <table class="responsive fixed orange"> <colgroup><col width="214px"><col></colgroup> <tr><th colspan="2"><h2 class="add-link">Args</h2></th></tr> <tr> <td> `model_spec`<a id="model_spec"></a> </td> <td> Specification for the model. </td> </tr><tr> <td> `shuffle`<a id="shuffle"></a> </td> <td> Whether the training data should be shuffled. </td> </tr> </table>

Methods

<h3 id="create"><code>create</code></h3>

<a target="_blank" class="external" href="https://github.com/tensorflow/examples/blob/master/tensorflow_examples/lite/model_maker/core/task/question_answer.py#L193-L232">View source</a>

<pre class="devsite-click-to-copy prettyprint lang-py tfo-signature-link"> <code>@classmethod</code> <code>create( train_data, model_spec, batch_size=None, epochs=2, steps_per_epoch=None, shuffle=False, do_train=True ) </code></pre>

Loads data and train the model for question answer.

<table class="responsive fixed orange"> <colgroup><col width="214px"><col></colgroup> <tr><th colspan="2">Args</th></tr> <tr> <td> `train_data` </td> <td> Training data. </td> </tr><tr> <td> `model_spec` </td> <td> Specification for the model. </td> </tr><tr> <td> `batch_size` </td> <td> Batch size for training. </td> </tr><tr> <td> `epochs` </td> <td> Number of epochs for training. </td> </tr><tr> <td> `steps_per_epoch` </td> <td> Integer or None. Total number of steps (batches of samples) before declaring one epoch finished and starting the next epoch. If `steps_per_epoch` is None, the epoch will run until the input dataset is exhausted. </td> </tr><tr> <td> `shuffle` </td> <td> Whether the data should be shuffled. </td> </tr><tr> <td> `do_train` </td> <td> Whether to run training. </td> </tr> </table>  <table class="responsive fixed orange"> <colgroup><col width="214px"><col></colgroup> <tr><th colspan="2">Returns</th></tr> <tr class="alt"> <td colspan="2"> An instance based on QuestionAnswer. </td> </tr> </table> <h3 id="create_model"><code>create_model</code></h3>

<a target="_blank" class="external" href="https://github.com/tensorflow/examples/blob/master/tensorflow_examples/lite/model_maker/core/task/question_answer.py#L84-L85">View source</a>

<pre class="devsite-click-to-copy prettyprint lang-py tfo-signature-link"> <code>create_model() </code></pre> <h3 id="create_serving_model"><code>create_serving_model</code></h3>

<a target="_blank" class="external" href="https://github.com/tensorflow/examples/blob/master/tensorflow_examples/lite/model_maker/core/task/custom_model.py#L170-L176">View source</a>

<pre class="devsite-click-to-copy prettyprint lang-py tfo-signature-link"> <code>create_serving_model() </code></pre>

Returns the underlining Keras model for serving.

<h3 id="evaluate"><code>evaluate</code></h3>

<a target="_blank" class="external" href="https://github.com/tensorflow/examples/blob/master/tensorflow_examples/lite/model_maker/core/task/question_answer.py#L87-L118">View source</a>

<pre class="devsite-click-to-copy prettyprint lang-py tfo-signature-link"> <code>evaluate( data, max_answer_length=30, null_score_diff_threshold=0.0, verbose_logging=False, output_dir=None ) </code></pre>

Evaluate the model.

<table class="responsive fixed orange"> <colgroup><col width="214px"><col></colgroup> <tr><th colspan="2">Args</th></tr> <tr> <td> `data` </td> <td> Data to be evaluated. </td> </tr><tr> <td> `max_answer_length` </td> <td> The maximum length of an answer that can be generated. This is needed because the start and end predictions are not conditioned on one another. </td> </tr><tr> <td> `null_score_diff_threshold` </td> <td> If null_score - best_non_null is greater than the threshold, predict null. This is only used for SQuAD v2. </td> </tr><tr> <td> `verbose_logging` </td> <td> If true, all of the warnings related to data processing will be printed. A number of warnings are expected for a normal SQuAD evaluation. </td> </tr><tr> <td> `output_dir` </td> <td> The output directory to save output to json files: predictions.json, nbest_predictions.json, null_odds.json. If None, skip saving to json files. </td> </tr> </table>  <table class="responsive fixed orange"> <colgroup><col width="214px"><col></colgroup> <tr><th colspan="2">Returns</th></tr> <tr class="alt"> <td colspan="2"> A dict contains two metrics: Exact match rate and F1 score. </td> </tr> </table> <h3 id="evaluate_tflite"><code>evaluate_tflite</code></h3>

<a target="_blank" class="external" href="https://github.com/tensorflow/examples/blob/master/tensorflow_examples/lite/model_maker/core/task/question_answer.py#L120-L151">View source</a>

<pre class="devsite-click-to-copy prettyprint lang-py tfo-signature-link"> <code>evaluate_tflite( tflite_filepath, data, max_answer_length=30, null_score_diff_threshold=0.0, verbose_logging=False, output_dir=None ) </code></pre>

Evaluate the model.

<table class="responsive fixed orange"> <colgroup><col width="214px"><col></colgroup> <tr><th colspan="2">Args</th></tr> <tr> <td> `tflite_filepath` </td> <td> File path to the TFLite model. </td> </tr><tr> <td> `data` </td> <td> Data to be evaluated. </td> </tr><tr> <td> `max_answer_length` </td> <td> The maximum length of an answer that can be generated. This is needed because the start and end predictions are not conditioned on one another. </td> </tr><tr> <td> `null_score_diff_threshold` </td> <td> If null_score - best_non_null is greater than the threshold, predict null. This is only used for SQuAD v2. </td> </tr><tr> <td> `verbose_logging` </td> <td> If true, all of the warnings related to data processing will be printed. A number of warnings are expected for a normal SQuAD evaluation. </td> </tr><tr> <td> `output_dir` </td> <td> The output directory to save output to json files: predictions.json, nbest_predictions.json, null_odds.json. If None, skip saving to json files. </td> </tr> </table>  <table class="responsive fixed orange"> <colgroup><col width="214px"><col></colgroup> <tr><th colspan="2">Returns</th></tr> <tr class="alt"> <td colspan="2"> A dict contains two metrics: Exact match rate and F1 score. </td> </tr> </table> <h3 id="export"><code>export</code></h3>

<a target="_blank" class="external" href="https://github.com/tensorflow/examples/blob/master/tensorflow_examples/lite/model_maker/core/task/custom_model.py#L95-L168">View source</a>

<pre class="devsite-click-to-copy prettyprint lang-py tfo-signature-link"> <code>export( export_dir, tflite_filename='model.tflite', label_filename='labels.txt', vocab_filename='vocab.txt', saved_model_filename='saved_model', tfjs_folder_name='tfjs', export_format=None, **kwargs ) </code></pre>

Converts the retrained model based on export_format.

<table class="responsive fixed orange"> <colgroup><col width="214px"><col></colgroup> <tr><th colspan="2">Args</th></tr> <tr> <td> `export_dir` </td> <td> The directory to save exported files. </td> </tr><tr> <td> `tflite_filename` </td> <td> File name to save tflite model. The full export path is {export_dir}/{tflite_filename}. </td> </tr><tr> <td> `label_filename` </td> <td> File name to save labels. The full export path is {export_dir}/{label_filename}. </td> </tr><tr> <td> `vocab_filename` </td> <td> File name to save vocabulary. The full export path is {export_dir}/{vocab_filename}. </td> </tr><tr> <td> `saved_model_filename` </td> <td> Path to SavedModel or H5 file to save the model. The full export path is {export_dir}/{saved_model_filename}/{saved_model.pb|assets|variables}. </td> </tr><tr> <td> `tfjs_folder_name` </td> <td> Folder name to save tfjs model. The full export path is {export_dir}/{tfjs_folder_name}. </td> </tr><tr> <td> `export_format` </td> <td> List of export format that could be saved_model, tflite, label, vocab. </td> </tr><tr> <td> `**kwargs` </td> <td> Other parameters like `quantized_config` for TFLITE model. </td> </tr> </table> <h3 id="summary"><code>summary</code></h3>

<a target="_blank" class="external" href="https://github.com/tensorflow/examples/blob/master/tensorflow_examples/lite/model_maker/core/task/custom_model.py#L65-L66">View source</a>

<pre class="devsite-click-to-copy prettyprint lang-py tfo-signature-link"> <code>summary() </code></pre> <h3 id="train"><code>train</code></h3>

<a target="_blank" class="external" href="https://github.com/tensorflow/examples/blob/master/tensorflow_examples/lite/model_maker/core/task/question_answer.py#L59-L82">View source</a>

<pre class="devsite-click-to-copy prettyprint lang-py tfo-signature-link"> <code>train( train_data, epochs=None, batch_size=None, steps_per_epoch=None ) </code></pre>

Feeds the training data for training.

<table class="responsive fixed orange"> <colgroup><col width="214px"><col></colgroup> <tr><th colspan="2"><h2 class="add-link">Class Variables</h2></th></tr> <tr> <td> ALLOWED_EXPORT_FORMAT<a id="ALLOWED_EXPORT_FORMAT"></a> </td> <td> `(<ExportFormat.TFLITE: 'TFLITE'>, <ExportFormat.VOCAB: 'VOCAB'>, <ExportFormat.SAVED_MODEL: 'SAVED_MODEL'>)` </td> </tr><tr> <td> DEFAULT_EXPORT_FORMAT<a id="DEFAULT_EXPORT_FORMAT"></a> </td> <td> `(<ExportFormat.TFLITE: 'TFLITE'>, <ExportFormat.VOCAB: 'VOCAB'>)` </td> </tr> </table>