# 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 @contextmanager 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() @property 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 @evaluate.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION) 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