# coding=utf-8 # Copyright 2021 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors. # # 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. # Lint as: python3 """Dynabench.DynaSent""" from __future__ import absolute_import, division, print_function import json import os from collections import OrderedDict import datasets logger = datasets.logging.get_logger(__name__) _VERSION = datasets.Version("1.1.0") # v1.1 fixed for example uid. _NUM_ROUNDS = 2 _DESCRIPTION = """\ Dynabench.DynaSent is a Sentiment Analysis dataset collected using a human-and-model-in-the-loop. """.strip() class DynabenchRoundDetails: """Round details for Dynabench.DynaSent datasets.""" def __init__( self, citation, description, homepage, data_license, data_url, data_features, data_subset_map=None ): self.citation = citation self.description = description self.homepage = homepage self.data_license = data_license self.data_url = data_url self.data_features = data_features self.data_subset_map = data_subset_map # Provide the details for each round _ROUND_DETAILS = { 1: DynabenchRoundDetails( citation="""\ @article{ potts-etal-2020-dynasent, title={{DynaSent}: A Dynamic Benchmark for Sentiment Analysis}, author={Potts, Christopher and Wu, Zhengxuan and Geiger, Atticus and Kiela, Douwe}, journal={arXiv preprint arXiv:2012.15349}, url={https://arxiv.org/abs/2012.15349}, year={2020} } """.strip(), description="""\ DynaSent is an English-language benchmark task for ternary (positive/negative/neutral) sentiment analysis. For more details on the dataset construction process, see https://github.com/cgpotts/dynasent. """.strip(), homepage="https://dynabench.org/tasks/3", data_license="CC BY 4.0", data_url="https://github.com/cgpotts/dynasent/raw/main/dynasent-v1.1.zip", data_features=datasets.Features( { "id": datasets.Value("string"), "hit_ids": datasets.features.Sequence( datasets.Value("string") ), "sentence": datasets.Value("string"), "indices_into_review_text": datasets.features.Sequence( datasets.Value("int32") ), "model_0_label": datasets.Value("string"), "model_0_probs": { "negative": datasets.Value("float32"), "positive": datasets.Value("float32"), "neutral": datasets.Value("float32") }, "text_id": datasets.Value("string"), "review_id": datasets.Value("string"), "review_rating": datasets.Value("int32"), "label_distribution": { "positive": datasets.features.Sequence( datasets.Value("string") ), "negative": datasets.features.Sequence( datasets.Value("string") ), "neutral": datasets.features.Sequence( datasets.Value("string") ), "mixed": datasets.features.Sequence( datasets.Value("string") ) }, "gold_label": datasets.Value("string"), "metadata": { "split": datasets.Value("string"), "round": datasets.Value("int32"), "subset": datasets.Value("string"), "model_in_the_loop": datasets.Value("string"), } } ), data_subset_map=OrderedDict({ "all": { "dir": "dynasent-v1.1", "file_prefix": "dynasent-v1.1-round01-yelp-", "model": "RoBERTa" } }), ), 2: DynabenchRoundDetails( citation="""\ @article{ potts-etal-2020-dynasent, title={{DynaSent}: A Dynamic Benchmark for Sentiment Analysis}, author={Potts, Christopher and Wu, Zhengxuan and Geiger, Atticus and Kiela, Douwe}, journal={arXiv preprint arXiv:2012.15349}, url={https://arxiv.org/abs/2012.15349}, year={2020} } """.strip(), description="""\ DynaSent is an English-language benchmark task for ternary (positive/negative/neutral) sentiment analysis. For more details on the dataset construction process, see https://github.com/cgpotts/dynasent. """.strip(), homepage="https://dynabench.org/tasks/3", data_license="CC BY 4.0", data_url="https://github.com/cgpotts/dynasent/raw/main/dynasent-v1.1.zip", data_features=datasets.Features( { "id": datasets.Value("string"), "hit_ids": datasets.features.Sequence( datasets.Value("string") ), "sentence": datasets.Value("string"), "sentence_author": datasets.Value("string"), "has_prompt": datasets.Value("bool"), "prompt_data": { "indices_into_review_text": datasets.features.Sequence( datasets.Value("int32") ), "review_rating": datasets.Value("int32"), "prompt_sentence": datasets.Value("string"), "review_id": datasets.Value("string") }, "model_1_label": datasets.Value("string"), "model_1_probs": { "negative": datasets.Value("float32"), "positive": datasets.Value("float32"), "neutral": datasets.Value("float32") }, "text_id": datasets.Value("string"), "label_distribution": { "positive": datasets.features.Sequence( datasets.Value("string") ), "negative": datasets.features.Sequence( datasets.Value("string") ), "neutral": datasets.features.Sequence( datasets.Value("string") ), "mixed": datasets.features.Sequence( datasets.Value("string") ) }, "gold_label": datasets.Value("string"), "metadata": { "split": datasets.Value("string"), "round": datasets.Value("int32"), "subset": datasets.Value("string"), "model_in_the_loop": datasets.Value("string"), } } ), data_subset_map=OrderedDict({ "all": { "dir": "dynasent-v1.1", "file_prefix": "dynasent-v1.1-round02-dynabench-", "model": "RoBERTa" } }), ) } class DynabenchDynaSentConfig(datasets.BuilderConfig): """BuilderConfig for Dynabench.DynaSent datasets.""" def __init__(self, round, subset='all', **kwargs): """BuilderConfig for Dynabench.DynaSent. Args: round: integer, the dynabench round to load. subset: string, the subset of that round's data to load or 'all'. **kwargs: keyword arguments forwarded to super. """ assert isinstance(round, int), "round ({}) must be set and of type integer".format(round) assert 0 < round <= _NUM_ROUNDS, \ "round (received {}) must be between 1 and {}".format(round, _NUM_ROUNDS) super(DynabenchDynaSentConfig, self).__init__( name="dynabench.dynasent.r{}.{}".format(round, subset), description="Dynabench DynaSent dataset for round {}, showing dataset selection: {}.".format(round, subset), **kwargs, ) self.round = round self.subset = subset class DynabenchDynaSent(datasets.GeneratorBasedBuilder): """Dynabench.DynaSent""" BUILDER_CONFIG_CLASS = DynabenchDynaSentConfig BUILDER_CONFIGS = [ DynabenchDynaSentConfig( version=_VERSION, round=round, subset=subset, ) # pylint:disable=g-complex-comprehension for round in range(1, _NUM_ROUNDS+1) for subset in _ROUND_DETAILS[round].data_subset_map ] def _info(self): round_details = _ROUND_DETAILS[self.config.round] return datasets.DatasetInfo( description=round_details.description, features=round_details.data_features, homepage=round_details.homepage, citation=round_details.citation, supervised_keys=None ) @staticmethod def _get_filepath(dl_dir, round, subset, split): round_details = _ROUND_DETAILS[round] return os.path.join( dl_dir, round_details.data_subset_map[subset]["dir"], round_details.data_subset_map[subset]["file_prefix"] + split + ".jsonl" ) def _split_generators(self, dl_manager): round_details = _ROUND_DETAILS[self.config.round] dl_dir = dl_manager.download_and_extract(round_details.data_url) return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={ "filepath": self._get_filepath( dl_dir, self.config.round, self.config.subset, "train" ), "split": "train", "round": self.config.round, "subset": self.config.subset, "model_in_the_loop": round_details.data_subset_map[self.config.subset]["model"], }, ), datasets.SplitGenerator( name=datasets.Split.VALIDATION, gen_kwargs={ "filepath": self._get_filepath( dl_dir, self.config.round, self.config.subset, "dev" ), "split": "validation", "round": self.config.round, "subset": self.config.subset, "model_in_the_loop": round_details.data_subset_map[self.config.subset]["model"], }, ), datasets.SplitGenerator( name=datasets.Split.TEST, gen_kwargs={ "filepath": self._get_filepath( dl_dir, self.config.round, self.config.subset, "test" ), "split": "test", "round": self.config.round, "subset": self.config.subset, "model_in_the_loop": round_details.data_subset_map[self.config.subset]["model"], }, ), ] def _generate_examples(self, filepath, split, round, subset, model_in_the_loop): """This function returns the examples in the raw (text) form.""" ternary_labels = ('positive', 'negative', 'neutral') # Enforce to be the tenary version now. logger.info("generating examples from = %s", filepath) with open(filepath, encoding="utf-8") as f: for line in f: d = json.loads(line) if d['gold_label'] in ternary_labels: if round == 1: # Construct DynaSent features. yield d["text_id"], { "id": d["text_id"], # DynaSent Example. "hit_ids": d["hit_ids"], "sentence": d["sentence"], "indices_into_review_text": d["indices_into_review_text"], "model_0_label": d["model_0_label"], "model_0_probs": d["model_0_probs"], "text_id": d["text_id"], "review_id": d["review_id"], "review_rating": d["review_rating"], "label_distribution": d["label_distribution"], "gold_label": d["gold_label"], # Metadata. "metadata": { "split": split, "round": round, "subset": subset, "model_in_the_loop": model_in_the_loop } } elif round == 2: # Construct DynaSent features. if d["has_prompt"]: if "indices_into_review_text" in d["prompt_data"]: indices_into_review_text = d["prompt_data"]["indices_into_review_text"] else: indices_into_review_text = [] if "review_rating" in d["prompt_data"]: review_rating = d["prompt_data"]["review_rating"] else: review_rating = -1 # -1 means unknown. if "review_id" in d["prompt_data"]: review_id = d["prompt_data"]["review_id"] else: review_id = "" if "prompt_sentence" in d["prompt_data"]: prompt_sentence = d["prompt_data"]["prompt_sentence"] else: prompt_sentence = "" prompt_data = { "indices_into_review_text": indices_into_review_text, "review_rating": review_rating, "prompt_sentence": prompt_sentence, "review_id": review_id, } else: prompt_data = { "indices_into_review_text": [], "review_rating": -1, # -1 means unknown. "prompt_sentence": "", "review_id": "", } yield d["text_id"], { "id": d["text_id"], # DynaSent Example. "hit_ids": d["hit_ids"], "sentence": d["sentence"], "sentence_author": d["sentence_author"], "has_prompt": d["has_prompt"], "prompt_data": prompt_data, "model_1_label": d["model_1_label"], "model_1_probs": d["model_1_probs"], "text_id": d["text_id"], "label_distribution": d["label_distribution"], "gold_label": d["gold_label"], # Metadata. "metadata": { "split": split, "round": round, "subset": subset, "model_in_the_loop": model_in_the_loop } }