o yi,@sddlmZmZmZmZmZmZddlmZddl Z ddl m Z ddl m Z ddl mZddlmZddlmZdd lmZmZdd lmZd Zeradd lmZmZdddZer`ees`dgZndgZdee dee d eee ffddZGdddeZ dS))AnyCallableDictListOptionalUnion)warnN)Tensor)Module) bert_score)_preprocess_text)Metric)_SKIP_SLOW_DOCTEST_try_proceed_with_timeout)_TRANSFORMERS_AVAILABLEz roberta-large) AutoModel AutoTokenizerreturncCsttttdS)zDownload intensive operations.N)rfrom_pretrained_DEFAULT_MODELrrrJ/home/ubuntu/.local/lib/python3.10/site-packages/torchmetrics/text/bert.py_download_model!s r BERTScore input_idsattention_maskcCst|t|d}|S)z]Create an input dictionary of ``input_ids`` and ``attention_mask`` for BERTScore calculation.rr)torchcat)rr output_dictrrr_get_input_dict,sr c%s^eZdZUdZdZeed<dZeed<dZeed<e e ed<e e ed<e e ed <e e ed <        d+de e de e dede ede edeeee e fge fdedede ee ejfde de de dede dede e d e e d!ef$fd"d# Zd$e e d%e e d&d fd'd(Zd&ee ee ee fffd)d*ZZS),ra7`Bert_score Evaluating Text Generation`_ 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 implemenation follows the original implementation from `BERT_score`_. As input to ``forward`` and ``update`` the metric accepts the following input: - ``preds`` (:class:`~List`): An iterable of predicted sentences - ``target`` (:class:`~List`): An iterable of reference sentences As output of ``forward`` and ``compute`` the metric returns the following output: - ``score`` (:class:`~Dict`): A dictionary containing the keys ``precision``, ``recall`` and ``f1`` with corresponding values Args: 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: A layer of representation to use. all_layers: 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: A user's own model. Must be of `torch.nn.Module` instance. user_tokenizer: 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 :class:`~torch.Tensor`. It is up to the user's model of whether `"input_ids"` is a :class:`~torch.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: 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 :class:`~torch.Tensor` as an input and return the model's output represented by the single :class:`~torch.Tensor`. verbose: An indication of whether a progress bar to be displayed during the embeddings' calculation. idf: An indication whether normalization using inverse document frequencies should be used. device: A device to be used for calculation. max_length: A maximum length of input sequences. Sequences longer than ``max_length`` are to be trimmed. batch_size: A batch size used for model processing. num_threads: A number of threads to use for a dataloader. return_hash: An indication of whether the correspodning ``hash_code`` should be returned. lang: A language of input sentences. rescale_with_baseline: 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: A path to the user's own local csv/tsv file with the baseline scale. baseline_url: A url path to the user's own csv/tsv file with the baseline scale. kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info. Example: >>> from torchmetrics.text.bert import BERTScore >>> preds = ["hello there", "general kenobi"] >>> target = ["hello there", "master kenobi"] >>> bertscore = BERTScore() >>> score = bertscore(preds, target) >>> from pprint import pprint >>> rounded_score = {k: [round(v, 3) for v in vv] for k, vv in score.items()} >>> pprint(rounded_score) {'f1': [1.0, 0.996], 'precision': [1.0, 0.996], 'recall': [1.0, 0.996]} Fis_differentiableThigher_is_betterfull_state_updatepreds_input_idspreds_attention_masktarget_input_idstarget_attention_maskN@enmodel_name_or_path num_layers all_layersmodeluser_tokenizeruser_forward_fnverboseidfdevice max_length batch_size num_threads return_hashlangrescale_with_baseline baseline_path baseline_urlkwargsc stjd i||p t|_||_||_||_||_||_||_ | |_ | |_ | |_ | |_ | |_||_||_||_||_ggd|_ggd|_|rP||_d|_ntsVtd|durbtdtdt|j|_d|_|jdgdd |jd gdd |jd gdd |jd gdd dS)NrTz`BERTScore` metric with default tokenizers requires `transformers` package be installed. Either install with `pip install transformers>=4.0` or `pip install torchmetrics[text]`.zThe argument `model_name_or_path` was not specified while it is required when the default `transformers` model is used. It will use the default recommended model - .Fr$r)dist_reduce_fxr%r&r'r)super__init__rr,r-r.r/r1r2r3embedding_devicer5r6r7r8r9r:r;r<predstarget tokenizerr0rModuleNotFoundErrorrrr add_state)selfr,r-r.r/r0r1r2r3r4r5r6r7r8r9r:r;r<r= __class__rrrAsL   zBERTScore.__init__rCrDrcCst||j|jdd|jd\}}t||j|jdd|jd\}}|j|d|j|d|j|d|j|ddS)zStore predictions/references for computing BERT scores. It is necessary to store sentences in a tokenized form to ensure the DDP mode working. F) truncationsort_according_length own_tokenizerrrN) r rEr5r0r$appendr%r&r')rHrCrD preds_dict_ target_dictrrrupdates(   zBERTScore.updatecCs"tdidt|j|jdt|j|jd|jd|jd|jd|j d|j r*|j n4dd |j d |j d |jd |jd |jd|jd|jd|jd|jd|jd|jd|jSd |j d |j d |jd |jd |jd|jd|jd|jd|jd|jd|jd|jS)zCalculate BERT scores.rCrDr,r-r.r/r0Nr1r2r3r4r5r6r7r8r9r:r;r<r)r r r$r%r&r'r,r-r.r/r0rEr1r2r3rBr5r6r7r8r9r:r;r<)rHrrrcomputes          zBERTScore.compute)NNFNNNFFNr(r)r*Fr+FNN)__name__ __module__ __qualname____doc__r!bool__annotations__r"r#rr rstrintr rrrrrr4rArRfloatrS __classcell__rrrIrr2s  D           @*)rN)!typingrrrrrrwarningsrrr torch.nnr !torchmetrics.functional.text.bertr 4torchmetrics.functional.text.helper_embedding_metricr torchmetrics.metricr torchmetrics.utilities.checksrrtorchmetrics.utilities.importsrr transformersrrr__doctest_skip__rZr rrrrrs(         &