Question Answering Model

QuestionAnsweringModel

The QuestionAnsweringModel class is used for Question Answering.

To create a QuestionAnsweringModel, you must specify a model_type and a model_name.

  • model_type should be one of the model types from the supported models (e.g. bert, electra, xlnet)
  • model_name specifies the exact architecture and trained weights to use. This may be a Hugging Face Transformers compatible pre-trained model, a community model, or the path to a directory containing model files.

    Note: For a list of standard pre-trained models, see here.

    Note: For a list of community models, see here.

    You may use any of these models provided the model_type is supported.

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from simpletransformers.question_answering import QuestionAnsweringModel


model = QuestionAnsweringModel(
    "roberta", "roberta-base"
)

Note: For more information on working with Simple Transformers models, please refer to the General Usage section.

Configuring a QuestionAnsweringModel

QuestionAnsweringModel has several task-specific configuration options.

Argument Type Default Description
doc_stride int 384 When splitting up a long document into chunks, how much stride to take between chunks.
max_query_length int 64 Maximum token length for questions. Any questions longer than this will be truncated to this length.
n_best_size int 20 The number of predictions given per question.
max_answer_length int 100 The maximum token length of an answer that can be generated.
null_score_diff_threshold float 0.0 If (null_score - best_non_null) is greater than the threshold predict null.
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from simpletransformers.question_answering import QuestionAnsweringModel, QuestionAnsweringArgs


model_args = QuestionAnsweringModel(n_best_size=2)

model = QuestionAnsweringModel(
    "roberta",
    "roberta-base",
    args=model_args,
)

Note: For configuration options common to all Simple Transformers models, please refer to the Configuring a Simple Transformers Model section.

Class QuestionAnsweringModel

simpletransformers.question_answering.QuestionAnsweringModel(self, model_type, model_name, args=None, use_cuda=True, cuda_device=-1, **kwargs,)

Initializes a QuestionAnsweringModel model.

Parameters

  • model_type (str) - The type of model to use (model types)

  • model_name (str) - The exact architecture and trained weights to use. This may be a Hugging Face Transformers compatible pre-trained model, a community model, or the path to a directory containing model files.

  • args (dict, optional) - Default args will be used if this parameter is not provided. If provided, it should be a dict containing the args that should be changed in the default args.

  • use_cuda (bool, optional) - Use GPU if available. Setting to False will force model to use CPU only. (See here)

  • cuda_device (int, optional) - Specific GPU that should be used. Will use the first available GPU by default. (See here)

  • kwargs (optional) - For providing proxies, force_download, resume_download, cache_dir and other options specific to the ‘from_pretrained’ implementation where this will be supplied. (See here)

Returns

  • None

Note: For configuration options common to all Simple Transformers models, please refer to the Configuring a Simple Transformers Model section.

Training a QuestionAnsweringModel

The train_model() method is used to train the model.

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model.train_model(train_data)

simpletransformers.question_answering.QuestionAnsweringModel(self, train_data, output_dir=None, show_running_loss=True, args=None, eval_data=None, verbose=True, **kwargs)

Trains the model using ‘train_data’

Parameters

  • train_data - Path to JSON file containing training data OR list of Python dicts in the correct format. The model will be trained on this data. Refer to the Question Answering Data Formats section for the correct formats.

  • output_dir (str, optional) - The directory where model files will be saved. If not given, self.args['output_dir'] will be used.

  • show_running_loss (bool, optional) - If True, the running loss (training loss at current step) will be logged to the console.

  • args (dict, optional) - A dict of configuration options for the QuestionAnsweringModel. Any changes made will persist for the model.

  • eval_data (optional) - Evaluation data (same format as train_data) against which evaluation will be performed when evaluate_during_training is enabled. Is required if evaluate_during_training is enabled.

  • kwargs (optional) - Additional metrics that should be calculated. Pass in the metrics as keyword arguments (name of metric: function to calculate metric). Refer to the additional metrics section. E.g. f1=sklearn.metrics.f1_score. A metric function should take in two parameters. The first parameter will be the true labels, and the second parameter will be the predictions.

Returns

  • None

Note: For more details on training models with Simple Transformers, please refer to the Tips and Tricks section.

Evaluating a QuestionAnsweringModel

The eval_model() method is used to evaluate the model.

The following metrics will be calculated by default:

  • correct - Number of predicted answers matching the true answer exactly.
  • similar - Number of predicted answers that are a substring of the true answer or vice versa.
  • incorrect - Number of predicted answers that does not meet the criteria for correct or similar.
  • eval_loss - Cross Entropy Loss for eval_data
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result, model_outputs, wrong_preds = model.eval_model(eval_data)

simpletransformers.question_answering.QuestionAnsweringModel.eval_model(self, eval_data, output_dir=None, verbose=True, silent=False, **kwargs)

Evaluates the model using ‘eval_data’

Parameters

  • eval_data - Path to JSON file containing evaluation data OR list of Python dicts in the correct format. The model will be evaluated on this data. Refer to the Question Answering Data Formats section for the correct formats.

  • output_dir (str, optional) - The directory where model files will be saved. If not given, self.args['output_dir'] will be used.

  • verbose (bool, optional) - If verbose, results will be printed to the console on completion of evaluation.

  • verbose_logging (bool, optional) - Log info related to feature conversion and writing predictions.

  • silent (bool, optional) - If silent, tqdm progress bars will be hidden.

  • kwargs (optional) - Additional metrics that should be calculated. Pass in the metrics as keyword arguments (name of metric: function to calculate metric). Refer to the additional metrics section. E.g. f1=sklearn.metrics.f1_score. A metric function should take in two parameters. The first parameter will be the true labels, and the second parameter will be the predictions.

Returns

  • result (dict) - Dictionary containing evaluation results.

  • texts (list) - A dictionary containing the 3 dictionaries correct_text, similar_text, and incorrect_text.

Note: For more details on evaluating models with Simple Transformers, please refer to the Tips and Tricks section.

Making Predictions With a QuestionAnsweringModel

The predict() method is used to make predictions with the model.

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context_text = "Mistborn is a series of epic fantasy novels written by American author Brandon Sanderson."

predictions, raw_outputs = model.predict(
    [
        {
            "context": context_text,
            "qas": [
                {
                    "question": "Who was the author of Mistborn?",
                    "id": "0",
                }
            ],
        }
    ]
)

Note: The input must be a List even if there is only one sentence.

simpletransformers.question_answering.QuestionAnsweringModel.predict(to_predict, n_best_size=None)

Performs predictions on a list of text to_predict.

Parameters

  • to_predict - A python list of python dicts in the correct format to be sent to the model for prediction. Refer to the Question Answering Data Formats section for the correct formats.

  • n_best_size (int, optional) - Number of predictions to return. args[‘n_best_size’] will be used if not specified.

Returns

  • answer_list (list) - A Python list of dicts containing each question id mapped to its answer (or a list of answers if n_best_size > 1).
  • probability_list (list) - A Python list of dicts containing each question id mapped to the probability score for the answer (or a list of probability scores if n_best_size > 1).

Tip: You can also make predictions using the Simple Viewer web app. Please refer to the Simple Viewer section.

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