SQuAD¶

Module Interface¶

class torchmetrics.SQuAD(**kwargs)[source]

Calculate SQuAD Metric which corresponds to the scoring script for version 1 of the Stanford Question Answering Dataset (SQuAD).

As input to forward and update the metric accepts the following input:

preds (Dict): A Dictionary or List of Dictionary-s that map id and prediction_text to the respective values

Example prediction:
{"prediction_text": "TorchMetrics is awesome", "id": "123"}

target (Dict): A Dictionary or List of Dictionary-s that contain the answers and id in the SQuAD Format.

Example target:

{
    'answers': [{'answer_start': [1], 'text': ['This is a test answer']}],
    'id': '1',
}

Reference SQuAD Format:

{
    'answers': {'answer_start': [1], 'text': ['This is a test text']},
    'context': 'This is a test context.',
    'id': '1',
    'question': 'Is this a test?',
    'title': 'train test'
}

As output of forward and compute the metric returns the following output:

squad (Dict): A dictionary containing the F1 score (key: “f1”),
and Exact match score (key: “exact_match”) for the batch.

Parameters: kwargs¶ (Any) – Additional keyword arguments, see Advanced metric settings for more info.

Example

>>> from torchmetrics import SQuAD
>>> preds = [{"prediction_text": "1976", "id": "56e10a3be3433e1400422b22"}]
>>> target = [{"answers": {"answer_start": [97], "text": ["1976"]}, "id": "56e10a3be3433e1400422b22"}]
>>> squad = SQuAD()
>>> squad(preds, target)
{'exact_match': tensor(100.), 'f1': tensor(100.)}

Initializes internal Module state, shared by both nn.Module and ScriptModule.

Functional Interface¶

torchmetrics.functional.squad(preds, target)[source]

Calculate SQuAD Metric .

Parameters

preds¶ (Union[Dict[str, str], List[Dict[str, str]]]) –
A Dictionary or List of Dictionary-s that map id and prediction_text to the respective values.

Example prediction:
```
{"prediction_text": "TorchMetrics is awesome", "id": "123"}
```

target¶ (Union[Dict[str, Union[str, Dict[str, Union[List[str], List[int]]]]], List[Dict[str, Union[str, Dict[str, Union[List[str], List[int]]]]]]]) –

A Dictionary or List of Dictionary-s that contain the answers and id in the SQuAD Format.

Example target:

{
    'answers': [{'answer_start': [1], 'text': ['This is a test answer']}],
    'id': '1',
}

Reference SQuAD Format:

{
    'answers': {'answer_start': [1], 'text': ['This is a test text']},
    'context': 'This is a test context.',
    'id': '1',
    'question': 'Is this a test?',
    'title': 'train test'
}

Return type

Dict[str, Tensor]

Returns

Dictionary containing the F1 score, Exact match score for the batch.

Example

>>> from torchmetrics.functional.text.squad import squad
>>> preds = [{"prediction_text": "1976", "id": "56e10a3be3433e1400422b22"}]
>>> target = [{"answers": {"answer_start": [97], "text": ["1976"]},"id": "56e10a3be3433e1400422b22"}]
>>> squad(preds, target)
{'exact_match': tensor(100.), 'f1': tensor(100.)}

Raises: KeyError – If the required keys are missing in either predictions or targets.

References

[1] SQuAD: 100,000+ Questions for Machine Comprehension of Text by Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, Percy Liang SQuAD Metric .