import glob import json import os from io import BytesIO import ijson import more_itertools import pandas as pd import datasets from datasets import Dataset, DatasetDict, DatasetInfo, Features, Sequence, Value logger = datasets.logging.get_logger(__name__) # _URL = "https://www.cs.tau.ac.il/~ohadr/NatQuestions.zip" # RERANKING_URLS = { # "train": "https://dl.fbaipublicfiles.com/dpr/data/retriever/biencoder-nq-train.json.gz", # "validation": "https://dl.fbaipublicfiles.com/dpr/data/retriever/biencoder-nq-dev.json.gz", # # "test": "https://dl.fbaipublicfiles.com/dpr/data/retriever/nq-test.qa.csv", # } from tqdm.auto import tqdm _CITATION = """ """ _DESCRIPTION = """ """ # def # def read_glob(paths): # paths = glob.glob(paths) # data = [] # for path in paths: # with open(path) as f: # if path.endswith(".json"): # data.extend(json.load(f)) # elif path.endswith(".jsonl"): # for line in f: # data.append(json.loads(line)) # return data def to_dict_element(el, cols): bucked_fields = more_itertools.bucket(cols, key=lambda x: x.split(".")[0]) final_dict = {} for parent_name in list(bucked_fields): fields = [y.split(".")[-1] for y in list(bucked_fields[parent_name])] if len(fields) == 1 and fields[0] == parent_name: final_dict[parent_name] = el[fields[0]] else: parent_list = [] zipped_fields = list(zip(*[el[f"{parent_name}.{child}"] for child in fields])) for x in zipped_fields: parent_list.append({k: v for k, v in zip(fields, x)}) final_dict[parent_name] = parent_list return final_dict def get_json_dataset(dataset): flat_dataset = dataset.flatten() json_dataset = dataset_to_json(flat_dataset) return [to_dict_element(el, cols=flat_dataset.column_names) for el in json_dataset] def dataset_to_json(dataset): new_str = BytesIO() dataset.to_json(new_str) new_str.seek(0) return [json.loads(line.decode()) for line in new_str] # inference_features = datasets.Features( # { # "source": Value(dtype="string"), # "meta": { # "question": Value(dtype="string"), # "text": Value(dtype="string"), # "title": Value(dtype="string"), # "qid": Value(dtype="string"), # "id": Value(dtype="string"), # }, # } # ) class NatQuestionsConfig(datasets.BuilderConfig): """BuilderConfig for NatQuestionsDPR.""" def __init__(self, features, retriever, feature_format, url, **kwargs): """BuilderConfig for NatQuestions. Args: **kwargs: keyword arguments forwarded to super. """ super(NatQuestionsConfig, self).__init__(**kwargs) self.features = features self.retriever = retriever self.feature_format = feature_format self.url = url RETBM25_RERANKING_URLS = { split: f"https://dl.fbaipublicfiles.com/dpr/data/retriever/biencoder-nq-{split}.json.gz" for split in ["train", "dev"] } RETDPR_RERANKING_URLS = { split: f"https://dl.fbaipublicfiles.com/dpr/data/retriever/biencoder-nq-adv-hn-{split}.json.gz" for split in ["train"] } RETDPR_INF_URLS = { split: f"https://dl.fbaipublicfiles.com/dpr/data/retriever_results/single/nq-{split}.json.gz" for split in ["train", "dev", "test"] } RETBM25_INF_URLS = { split:f"https://www.cs.tau.ac.il/~ohadr/nq-{split}.json.gz" for split in ["dev","test"] } RETBM25_RERANKING_features = Features( { "dataset": Value(dtype="string"), "qid": Value(dtype="string"), "question": Value(dtype="string"), "answers": Sequence(feature=Value(dtype="string")), "positive_ctxs": Sequence( feature={ "title": Value(dtype="string"), "text": Value(dtype="string"), "score": Value(dtype="float32"), # 'title_score': Value(dtype='int32'), "passage_id": Value(dtype="string"), } ), # 'negative_ctxs': Sequence(feature={'title': Value(dtype='string'), # 'text': Value(dtype='string'), # 'score': Value(dtype='float32'), # # 'title_score': Value(dtype='int32'), # 'passage_id': Value(dtype='string')}), "hard_negative_ctxs": Sequence( feature={ "title": Value(dtype="string"), "text": Value(dtype="string"), "score": Value(dtype="float32"), # 'title_score': Value(dtype='int32'), "passage_id": Value(dtype="string"), } ), } ) RETDPR_RERANKING_features = Features( { "qid": Value(dtype="string"), "question": Value(dtype="string"), "answers": Sequence(feature=Value(dtype="string")), # 'negative_ctxs': Sequence(feature=[]), "hard_negative_ctxs": Sequence( feature={ "passage_id": Value(dtype="string"), "title": Value(dtype="string"), "text": Value(dtype="string"), "score": Value(dtype="string"), # 'has_answer': Value(dtype='int32') } ), "positive_ctxs": Sequence( feature={ "title": Value(dtype="string"), "text": Value(dtype="string"), "score": Value(dtype="float32"), # 'title_score': Value(dtype='int32'), # 'has_answer': Value(dtype='int32'), "passage_id": Value(dtype="string"), } ), } ) RETDPR_INF_features = Features( { "question": Value(dtype="string"), "qid": Value(dtype="string"), "answers": Sequence(feature=Value(dtype="string")), "ctxs": Sequence( feature={ "id": Value(dtype="string"), "title": Value(dtype="string"), "text": Value(dtype="string"), "score": Value(dtype="float32"), # "has_answer": Value(dtype="int32"), } ), } ) URL_DICT = {"reranking_dprnq":RETDPR_RERANKING_URLS, "reranking_bm25":RETBM25_RERANKING_URLS, "inference_dprnq":RETDPR_INF_URLS} class NatQuestions(datasets.GeneratorBasedBuilder): BUILDER_CONFIGS = [ NatQuestionsConfig( name="reranking_dprnq", version=datasets.Version("1.0.1", ""), description="NatQuestions dataset in DPR format with the dprnq retrieval results", features=RETDPR_RERANKING_features, retriever="dprnq", feature_format="dpr", url=URL_DICT, ), NatQuestionsConfig( name="reranking_bm25", version=datasets.Version("1.0.1", ""), description="NatQuestions dataset in DPR format with the bm25 retrieval results", features=RETBM25_RERANKING_features, retriever="bm25", feature_format="dpr", url=URL_DICT, ), NatQuestionsConfig( name="inference_dprnq", version=datasets.Version("1.0.1", ""), description="NatQuestions dataset in a format accepted by the inference model, performing reranking on the dprnq retrieval results", features=RETDPR_INF_features, retriever="dprnq", feature_format="inference", url=URL_DICT, ), NatQuestionsConfig( name="inference_bm25", version=datasets.Version("1.0.1", ""), description="NatQuestions dataset in a format accepted by the inference model, performing reranking on the bm25 retrieval results", features=RETDPR_INF_features, retriever="bm25", feature_format="inference", url=URL_DICT, ), ] def _info(self): self.features = self.config.features self.retriever = self.config.retriever self.feature_format = self.config.feature_format self.url = self.config.url return datasets.DatasetInfo( description=_DESCRIPTION, features=self.config.features, supervised_keys=None, homepage="", citation=_CITATION, ) def _split_generators(self, dl_manager): print(self.url) if len(self.url) > 0: filepath = dl_manager.download_and_extract(self.url) else: filepath = "" # filepath = "/home/joberant/home/ohadr/testbed/notebooks/NatQuestions_retrievers" result = [] if "train" in filepath[self.info.config_name]: result.append( datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={"filepath": filepath, "split": "train"}, ) ) if "dev" in filepath[self.info.config_name] or self.info.config_name=="reranking_dprnq": result.append( datasets.SplitGenerator( name=datasets.Split.VALIDATION, gen_kwargs={"filepath": filepath, "split": "dev"}, ) ) if "test" in filepath[self.info.config_name] or self.info.config_name=="reranking_dprnq": result.append( datasets.SplitGenerator( name=datasets.Split.TEST, gen_kwargs={"filepath": filepath, "split": "test"}, ) ) return result def _prepare_split(self, split_generator, **kwargs): self.info.features = self.config.features super()._prepare_split(split_generator, **kwargs) def _generate_examples(self, filepath, split): if self.info.config_name=="reranking_dprnq" and split in ["dev","test"]: for i,dict_element in new_method(split, "inference_dprnq", f"{filepath['inference_dprnq'][split]}"): dict_element['positive_ctxs'] = [] answers = dict_element['answers'] any_true = False for x in dict_element['ctxs']: x['passage_id'] = x.pop('id') x['has_answer'] = False for ans in answers: if ans in x['title'] or ans in x['text']: if 'id' in x: x['passage_id'] = x.pop('id') x['has_answer'] = True dict_element['positive_ctxs'].append(x) any_true = True negative_candidates = [x for x in dict_element['ctxs'] if not x['has_answer']] dict_element['hard_negative_ctxs'] = negative_candidates[:len(dict_element['positive_ctxs'])] dict_element['ctxs'] = dict_element.pop("ctxs") for name in ['positive_ctxs',"hard_negative_ctxs"]: for x in dict_element[name]: x.pop("has_answer",None) if any_true: dict_element.pop("ctxs") yield i,dict_element else: yield from new_method(split, self.info.config_name, f"{filepath[self.info.config_name][split]}") def new_method(split, config_name, object_path): count = 0 with open(object_path) as f: items = ijson.items(f, "item") for element in items: element.pop("negative_ctxs",None) for name in ["positive_ctxs","hard_negative_ctxs","ctxs"]: for x in element.get(name,[]): x.pop("title_score",None) x.pop("has_answer", None) if "reranking" in config_name and "id" in x: x["passage_id"] = x.pop("id") element["qid"] = f"{count}_{split}" yield count, element count += 1 # def single_inference_format_example(ctx, question, qid): # datum = {} # datum["source"] = f"Title: {ctx['meta']['title']}\nText: {ctx['meta']['content']}\nQuestion: {question}\n" # datum["meta"] = {} # datum["meta"]["question"] = question # datum["meta"]["qid"] = qid # datum["meta"]["title"] = ctx["meta"]["title"] # datum["meta"]["text"] = ctx["meta"]["content"] # datum["meta"]["id"] = ctx["id"] # return datum # def inference_format_example(element): # return [ # single_inference_format_example(ctx, element["proof"], element["pid"]) for ctx in element["query_res"] # ] # def inference_example(example):