Datasets documentation

Evaluate predictions

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# Evaluate predictions

Metrics is deprecated in 🤗 Datasets. To learn more about how to use metrics, take a look at the library 🤗 Evaluate! In addition to metrics, you can find more tools for evaluating models and datasets.

🤗 Datasets provides various common and NLP-specific metrics for you to measure your models performance. In this section of the tutorials, you will load a metric and use it to evaluate your models predictions.

You can see what metrics are available with list_metrics():

>>> from datasets import list_metrics
>>> metrics_list = list_metrics()
>>> len(metrics_list)
28
>>> print(metrics_list)
['accuracy', 'bertscore', 'bleu', 'bleurt', 'cer', 'comet', 'coval', 'cuad', 'f1', 'gleu', 'glue', 'indic_glue', 'matthews_correlation', 'meteor', 'pearsonr', 'precision', 'recall', 'rouge', 'sacrebleu', 'sari', 'seqeval', 'spearmanr', 'squad', 'squad_v2', 'super_glue', 'wer', 'wiki_split', 'xnli']

It is very easy to load a metric with 🤗 Datasets. In fact, you will notice that it is very similar to loading a dataset! Load a metric from the Hub with load_metric():

>>> from datasets import load_metric
>>> metric = load_metric('glue', 'mrpc')

This will load the metric associated with the MRPC dataset from the GLUE benchmark.

## Select a configuration

If you are using a benchmark dataset, you need to select a metric that is associated with the configuration you are using. Select a metric configuration by providing the configuration name:

>>> metric = load_metric('glue', 'mrpc')

## Metrics object

Before you begin using a Metric object, you should get to know it a little better. As with a dataset, you can return some basic information about a metric. For example, access the inputs_description parameter in datasets.MetricInfo to get more information about a metrics expected input format and some usage examples:

>>> print(metric.inputs_description)
Compute GLUE evaluation metric associated to each GLUE dataset.
Args:
predictions: list of predictions to score.
Each translation should be tokenized into a list of tokens.
references: list of lists of references for each translation.
Each reference should be tokenized into a list of tokens.
Returns: depending on the GLUE subset, one or several of:
"accuracy": Accuracy
"f1": F1 score
"pearson": Pearson Correlation
"spearmanr": Spearman Correlation
"matthews_correlation": Matthew Correlation
Examples:
>>> glue_metric = datasets.load_metric('glue', 'sst2')  # 'sst2' or any of ["mnli", "mnli_mismatched", "mnli_matched", "qnli", "rte", "wnli", "hans"]
>>> references = [0, 1]
>>> predictions = [0, 1]
>>> results = glue_metric.compute(predictions=predictions, references=references)
>>> print(results)
{'accuracy': 1.0}
...
>>> glue_metric = datasets.load_metric('glue', 'mrpc')  # 'mrpc' or 'qqp'
>>> references = [0, 1]
>>> predictions = [0, 1]
>>> results = glue_metric.compute(predictions=predictions, references=references)
>>> print(results)
{'accuracy': 1.0, 'f1': 1.0}
...

Notice for the MRPC configuration, the metric expects the input format to be zero or one. For a complete list of attributes you can return with your metric, take a look at MetricInfo.

## Compute metric

Once you have loaded a metric, you are ready to use it to evaluate a models predictions. Provide the model predictions and references to compute():

>>> model_predictions = model(model_inputs)
>>> final_score = metric.compute(predictions=model_predictions, references=gold_references)