BERT Score¶

Module Interface¶

class torchmetrics.text.bert.BERTScore(model_name_or_path=None, num_layers=None, all_layers=False, model=None, user_tokenizer=None, user_forward_fn=None, verbose=False, idf=False, device=None, max_length=512, batch_size=64, num_threads=0, return_hash=False, lang='en', rescale_with_baseline=False, baseline_path=None, baseline_url=None, truncation=False, **kwargs)[source]¶

Bert_score Evaluating Text Generation for measuring text similarity.

BERT leverages the pre-trained contextual embeddings from BERT and matches words in candidate and reference sentences by cosine similarity. It has been shown to correlate with human judgment on sentence-level and system-level evaluation. Moreover, BERTScore computes precision, recall, and F1 measure, which can be useful for evaluating different language generation tasks. This implementation follows the original implementation from BERT_score.

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

preds (List): An iterable of predicted sentences
target (List): An iterable of reference sentences

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

score (Dict): A dictionary containing the keys precision, recall and f1 with corresponding values

Parameters:

preds¶ – An iterable of predicted sentences.
target¶ – An iterable of target sentences.
model_type¶ – A name or a model path used to load transformers pretrained model.
num_layers¶ (Optional[int]) – A layer of representation to use.
all_layers¶ (bool) – An indication of whether the representation from all model’s layers should be used. If all_layers=True, the argument num_layers is ignored.
model¶ (Optional[Module]) – A user’s own model. Must be of torch.nn.Module instance.
user_tokenizer¶ (Optional[Any]) – A user’s own tokenizer used with the own model. This must be an instance with the __call__ method. This method must take an iterable of sentences (List[str]) and must return a python dictionary containing “input_ids” and “attention_mask” represented by Tensor. It is up to the user’s model of whether “input_ids” is a Tensor of input ids or embedding vectors. This tokenizer must prepend an equivalent of [CLS] token and append an equivalent of [SEP] token as transformers tokenizer does.
user_forward_fn¶ (Optional[Callable[[Module, Dict[str, Tensor]], Tensor]]) – A user’s own forward function used in a combination with user_model. This function must take user_model and a python dictionary of containing "input_ids" and "attention_mask" represented by Tensor as an input and return the model’s output represented by the single Tensor.
verbose¶ (bool) – An indication of whether a progress bar to be displayed during the embeddings’ calculation.
idf¶ (bool) – An indication whether normalization using inverse document frequencies should be used.
device¶ (Union[str, device, None]) – A device to be used for calculation.
max_length¶ (int) – A maximum length of input sequences. Sequences longer than max_length are to be trimmed.
batch_size¶ (int) – A batch size used for model processing.
num_threads¶ (int) – A number of threads to use for a dataloader.
return_hash¶ (bool) – An indication of whether the correspodning hash_code should be returned.
lang¶ (str) – A language of input sentences.
rescale_with_baseline¶ (bool) – An indication of whether bertscore should be rescaled with a pre-computed baseline. When a pretrained model from transformers model is used, the corresponding baseline is downloaded from the original bert-score package from BERT_score if available. In other cases, please specify a path to the baseline csv/tsv file, which must follow the formatting of the files from BERT_score.
baseline_path¶ (Optional[str]) – A path to the user’s own local csv/tsv file with the baseline scale.
baseline_url¶ (Optional[str]) – A url path to the user’s own csv/tsv file with the baseline scale.
truncation¶ (bool) – An indication of whether the input sequences should be truncated to the max_length.
kwargs¶ (Any) – Additional keyword arguments, see Advanced metric settings for more info.

Example

>>> from pprint import pprint
>>> from torchmetrics.text.bert import BERTScore
>>> preds = ["hello there", "general kenobi"]
>>> target = ["hello there", "master kenobi"]
>>> bertscore = BERTScore()
>>> pprint(bertscore(preds, target))
{'f1': tensor([1.0000, 0.9961]), 'precision': tensor([1.0000, 0.9961]), 'recall': tensor([1.0000, 0.9961])}

plot(val=None, ax=None)[source]¶

Plot a single or multiple values from the metric.

Parameters:

val¶ (Union[Tensor, Sequence[Tensor], None]) – Either a single result from calling metric.forward or metric.compute or a list of these results. If no value is provided, will automatically call metric.compute and plot that result.
ax¶ (Optional[Axes]) – An matplotlib axis object. If provided will add plot to that axis

Return type:

Tuple[Figure, Union[Axes, ndarray]]

Returns:

Figure and Axes object

Raises:

ModuleNotFoundError – If matplotlib is not installed

>>> # Example plotting a single value
>>> from torchmetrics.text.bert import BERTScore
>>> preds = ["hello there", "general kenobi"]
>>> target = ["hello there", "master kenobi"]
>>> metric = BERTScore()
>>> metric.update(preds, target)
>>> fig_, ax_ = metric.plot()

>>> # Example plotting multiple values
>>> from torch import tensor
>>> from torchmetrics.text.bert import BERTScore
>>> preds = ["hello there", "general kenobi"]
>>> target = ["hello there", "master kenobi"]
>>> metric = BERTScore()
>>> values = []
>>> for _ in range(10):
...     val = metric(preds, target)
...     val = {k: tensor(v).mean() for k,v in val.items()}  # convert into single value per key
...     values.append(val)
>>> fig_, ax_ = metric.plot(values)

Functional Interface¶

torchmetrics.functional.text.bert.bert_score(preds, target, model_name_or_path=None, num_layers=None, all_layers=False, model=None, user_tokenizer=None, user_forward_fn=None, verbose=False, idf=False, device=None, max_length=512, batch_size=64, num_threads=0, return_hash=False, lang='en', rescale_with_baseline=False, baseline_path=None, baseline_url=None, truncation=False)[source]¶

Bert_score Evaluating Text Generation for text similirity matching.

This metric leverages the pre-trained contextual embeddings from BERT and matches words in candidate and reference sentences by cosine similarity. It has been shown to correlate with human judgment on sentence-level and system-level evaluation. Moreover, BERTScore computes precision, recall, and F1 measure, which can be useful for evaluating different language generation tasks.

This implementation follows the original implementation from BERT_score.

Parameters:

preds¶ (Union[str, Sequence[str], Dict[str, Tensor]]) – Either an iterable of predicted sentences or a Dict[input_ids, attention_mask].
target¶ (Union[str, Sequence[str], Dict[str, Tensor]]) – Either an iterable of target sentences or a Dict[input_ids, attention_mask].
model_name_or_path¶ (Optional[str]) – A name or a model path used to load transformers pretrained model.
num_layers¶ (Optional[int]) – A layer of representation to use.
all_layers¶ (bool) – An indication of whether the representation from all model’s layers should be used. If all_layers = True, the argument num_layers is ignored.
model¶ (Optional[Module]) – A user’s own model.
user_tokenizer¶ (Optional[Any]) – A user’s own tokenizer used with the own model. This must be an instance with the __call__ method. This method must take an iterable of sentences (List[str]) and must return a python dictionary containing "input_ids" and "attention_mask" represented by Tensor. It is up to the user’s model of whether "input_ids" is a Tensor of input ids or embedding vectors. his tokenizer must prepend an equivalent of [CLS] token and append an equivalent of [SEP] token as transformers tokenizer does.
user_forward_fn¶ (Optional[Callable[[Module, Dict[str, Tensor]], Tensor]]) – A user’s own forward function used in a combination with user_model. This function must take user_model and a python dictionary of containing "input_ids" and "attention_mask" represented by Tensor as an input and return the model’s output represented by the single Tensor.
verbose¶ (bool) – An indication of whether a progress bar to be displayed during the embeddings’ calculation.
idf¶ (bool) – An indication of whether normalization using inverse document frequencies should be used.
device¶ (Union[str, device, None]) – A device to be used for calculation.
max_length¶ (int) – A maximum length of input sequences. Sequences longer than max_length are to be trimmed.
batch_size¶ (int) – A batch size used for model processing.
num_threads¶ (int) – A number of threads to use for a dataloader.
return_hash¶ (bool) – An indication of whether the correspodning hash_code should be returned.
lang¶ (str) – A language of input sentences. It is used when the scores are rescaled with a baseline.
rescale_with_baseline¶ (bool) – An indication of whether bertscore should be rescaled with a pre-computed baseline. When a pretrained model from transformers model is used, the corresponding baseline is downloaded from the original bert-score package from BERT_score if available. In other cases, please specify a path to the baseline csv/tsv file, which must follow the formatting of the files from BERT_score
baseline_path¶ (Optional[str]) – A path to the user’s own local csv/tsv file with the baseline scale.
baseline_url¶ (Optional[str]) – A url path to the user’s own csv/tsv file with the baseline scale.
truncation¶ (bool) – An indication of whether the input sequences should be truncated to the maximum length.

Return type:

Dict[str, Union[Tensor, List[float], str]]

Returns:

Python dictionary containing the keys precision, recall and f1 with corresponding values.

Raises:

ValueError – If len(preds) != len(target).
ModuleNotFoundError – If tqdm package is required and not installed.
ModuleNotFoundError – If transformers package is required and not installed.
ValueError – If num_layer is larger than the number of the model layers.
ValueError – If invalid input is provided.

Example

>>> from pprint import pprint
>>> from torchmetrics.functional.text.bert import bert_score
>>> preds = ["hello there", "general kenobi"]
>>> target = ["hello there", "master kenobi"]
>>> pprint(bert_score(preds, target))
{'f1': tensor([1.0000, 0.9961]), 'precision': tensor([1.0000, 0.9961]), 'recall': tensor([1.0000, 0.9961])}