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| # Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor. | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, software | |
| # distributed under the License is distributed on an "AS IS" BASIS, | |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| # See the License for the specific language governing permissions and | |
| # limitations under the License. | |
| """TODO: Add a description here.""" | |
| import evaluate | |
| import datasets | |
| import numpy as np | |
| from transformers import AutoModelForSequenceClassification, AutoTokenizer | |
| import getpass | |
| import pdb | |
| import os | |
| import torch | |
| from rouge_score import scoring | |
| from contextlib import contextmanager | |
| # TODO: Add BibTeX citation | |
| _CITATION = """\ | |
| @InProceedings{huggingface:module, | |
| title = {A great new module}, | |
| authors={huggingface, Inc.}, | |
| year={2020} | |
| } | |
| """ | |
| # TODO: Add description of the module here | |
| _DESCRIPTION = """\ | |
| local coherecence with classifier trained on the shuffle task, window=3 sentences | |
| """ | |
| # TODO: Add description of the arguments of the module here | |
| _KWARGS_DESCRIPTION = """ | |
| Calculates how good are predictions given some references, using certain scores | |
| Args: | |
| predictions: list of predictions to score. Each predictions | |
| should be a string with tokens separated by spaces. | |
| references: list of reference for each prediction. Each | |
| reference should be a string with tokens separated by spaces. | |
| Returns: | |
| accuracy: description of the first score, | |
| another_score: description of the second score, | |
| Examples: | |
| Examples should be written in doctest format, and should illustrate how | |
| to use the function. | |
| >>> my_new_module = evaluate.load("my_new_module") | |
| >>> results = my_new_module.compute(references=[0, 1], predictions=[0, 1]) | |
| >>> print(results) | |
| {'accuracy': 1.0} | |
| """ | |
| WINDOW_SIZE = 3 | |
| def filter_logging_context(): | |
| def filter_log(record): | |
| return False if "This IS expected if you are initializing" in record.msg else True | |
| logger = datasets.utils.logging.get_logger("transformers.modeling_utils") | |
| logger.addFilter(filter_log) | |
| try: | |
| yield | |
| finally: | |
| logger.removeFilter(filter_log) | |
| class Scorer: | |
| def __init__( | |
| self, | |
| model_type=None, | |
| batch_size=64, | |
| device=None, | |
| use_fast_tokenizer=False): | |
| if device is not None: | |
| # assert device in ["gpu", "cpu", "cuda"], "device should be either gpu or cpu." | |
| if device == "gpu": | |
| device = "cuda" | |
| else: | |
| device = "cuda" if torch.cuda.is_available() else "cpu" | |
| self.device = device | |
| self.model_type = model_type | |
| self.batch_size = batch_size | |
| self._tokenizer = AutoTokenizer.from_pretrained("roberta-large") | |
| self._model = AutoModelForSequenceClassification.from_pretrained(f"ronaldahmed/ccl_win-{model_type}") | |
| self._model.to(device) | |
| self._model.eval() | |
| def hash(self): | |
| return self.model_type | |
| def preprocess_adjacent_window(self,preds): | |
| pred_list = [] | |
| lens = [] | |
| for pred in preds: | |
| sents = pred.split("\n") | |
| ns = len(sents) | |
| if ns <= WINDOW_SIZE: | |
| pred_list.append(pred) | |
| lens.append(1) | |
| else: | |
| llen = 0 | |
| for i in range(0,ns-WINDOW_SIZE+1): | |
| sss = sents[i:i+WINDOW_SIZE] | |
| ss = "\n".join(sss) | |
| pred_list.append(ss) | |
| llen += 1 | |
| lens.append(llen) | |
| # | |
| return pred_list,lens | |
| def score(self,predictions): | |
| sent_lens = [len(x.split("\n")) for x in predictions] | |
| pred_list,len_by_sample = self.preprocess_adjacent_window(predictions) | |
| scores = [] | |
| n_preds = len(pred_list) | |
| with torch.no_grad(): | |
| for b in range(0,n_preds,self.batch_size): | |
| strides = [x.lower() for x in pred_list[b:b+self.batch_size]] | |
| tinput = self._tokenizer(strides,padding=True,truncation=True,max_length=512,return_tensors="pt") | |
| tinput = {k:v.to(self.device) for k,v in tinput.items()} | |
| output = self._model(**tinput) | |
| probs = torch.softmax(output.logits,dim=-1).detach().cpu().numpy() | |
| scores.extend(probs[:,0].tolist()) | |
| # | |
| results = [] | |
| offset = 0 | |
| for i,_len in enumerate(len_by_sample): | |
| score = float(np.mean(scores[offset:offset+_len])) if sent_lens[i]>1 else 0. | |
| results.append(score) | |
| offset += _len | |
| # | |
| return results | |
| class ccl_win(evaluate.Measurement): | |
| """TODO: Short description of my evaluation module.""" | |
| def _info(self): | |
| # TODO: Specifies the evaluate.EvaluationModuleInfo object | |
| return evaluate.MeasurementInfo( | |
| # This is the description that will appear on the modules page. | |
| module_type="measurement", | |
| description=_DESCRIPTION, | |
| citation=_CITATION, | |
| inputs_description=_KWARGS_DESCRIPTION, | |
| # This defines the format of each prediction and reference | |
| features=datasets.Features({ | |
| 'predictions': datasets.Value('string'), | |
| }), | |
| # Homepage of the module for documentation | |
| homepage="http://module.homepage", | |
| # Additional links to the codebase or references | |
| codebase_urls=["http://github.com/path/to/codebase/of/new_module"], | |
| reference_urls=["http://path.to.reference.url/new_module"] | |
| ) | |
| def _download_and_prepare(self, dl_manager): | |
| """Optional: download external resources useful to compute the scores""" | |
| # TODO: Download external resources if needed | |
| pass | |
| def _compute(self, predictions, dataset="arxiv", batch_size: int = 16, device=None, use_aggregator=True): | |
| """Returns the scores""" | |
| hashcode = dataset | |
| with filter_logging_context(): | |
| if not hasattr(self, "cached_scorer") or self.cached_scorer.hash != hashcode: | |
| self.cached_scorer = Scorer( | |
| model_type=dataset, | |
| batch_size=batch_size, | |
| device=device, | |
| ) | |
| results = self.cached_scorer.score(predictions) | |
| outres = {} | |
| aggregator = None | |
| if use_aggregator: | |
| np.random.seed(42) | |
| aggregator = scoring.BootstrapAggregator() | |
| for score in results: | |
| aggregator.add_scores({"loc_coh_ccl": score}) | |
| # | |
| res = aggregator.aggregate() | |
| for k in res: outres[k] = res[k].mid | |
| else: | |
| outres = {"loc_coh_ccl": results} | |
| return outres | |