import datasets import csv import requests import pandas as pd import inspect import copy from .process_underscores import run key_to_entry = requests.get('https://www.dropbox.com/scl/fi/85pnc7n6e4puoureavtzo/filtered_disrpt.json?rlkey=6cbgbe9vn2549eths7ah8gm7u&dl=1').json() citation="\n".join(key_to_entry.values()) datasets_and_citations = { "deu.rst.pcc": "stede-neumann-2014-potsdam", "eng.dep.covdtb": "nishida-matsumoto-2022-domain", "eng.dep.scidtb": "yang-li-2018-scidtb", "eng.rst.gum": "Zeldes2017", "eng.rst.rstdt": "carlson-etal-2001-building", "eng.sdrt.stac": "asher-etal-2016-discourse", "eus.rst.ert": "IruskietaAranzabeIlarrazaEtAl2013", "fas.rst.prstc": "shahmohammadi2021persian", "fra.sdrt.annodis": "afantenos-etal-2012-empirical", "nld.rst.nldt": "redeker-etal-2012-multi", "por.rst.cstn": "CardosoMazieroRosarioCastroJorgeEtAl2011", "rus.rst.rrt": "toldova-etal-2017-rhetorical", "spa.rst.rststb": "da-cunha-etal-2011-development", "spa.rst.sctb": "cao-etal-2018-rst", "zho.dep.scidtb": "yi-etal-2021-unifying,cheng-li-2019-zero", "zho.rst.gcdt": "peng_gcdt_2022,peng_chinese_2022", "zho.rst.sctb": "cao-etal-2018-rst", "eng.pdtb.pdtb": "prasad-etal-2014-reflections", "eng.pdtb.tedm": "zeyrek-etal-2018-multilingual,zeyrek2019ted", "ita.pdtb.luna": "tonelli-etal-2010-annotation,RiccardiStepanovChowdhury2016", "por.pdtb.crpc": "CRPC-DB-Portuguese,genereux-etal-2012-introducing", "por.pdtb.tedm": "zeyrek-etal-2018-multilingual,zeyrek2019ted", "tha.pdtb.tdtb": "", "tur.pdtb.tdb": "zeyrek-webber-2008-discourse,zeyrek-kurfali-2017-tdb", "tur.pdtb.tedm": "zeyrek-etal-2018-multilingual,zeyrek2019ted", "zho.pdtb.cdtb": "Zhou2014" } class Config(datasets.BuilderConfig): citation=citation files = [ "eng.dep.covdtb", "eng.dep.scidtb", "eng.pdtb.pdtb", "eng.pdtb.tedm", "eng.rst.gum", "eng.rst.rstdt", "eng.sdrt.stac", "deu.rst.pcc", "eus.rst.ert", "fas.rst.prstc", "fra.sdrt.annodis", "ita.pdtb.luna", "nld.rst.nldt", "por.pdtb.crpc", "por.pdtb.tedm", "por.rst.cstn", "rus.rst.rrt", "spa.rst.rststb", "spa.rst.sctb", "tha.pdtb.tdtb", "tur.pdtb.tdb", "tur.pdtb.tedm", "zho.dep.scidtb", "zho.pdtb.cdtb", "zho.rst.gcdt", "zho.rst.sctb", ] def parse_conll_stream(file_stream): names = ['id', 'form', 'lemma', 'upos', 'xpos', 'feats', 'head', 'deprel', 'deps', 'misc'] sentence = {name: [] for name in names} for line in file_stream: line = line.strip() if line.startswith("#"): continue if not line: if sentence['id']: yield sentence sentence = {name: [] for name in names} continue token_data = line.split('\t') for name, value in zip(names, token_data): sentence[name].append(value) def get_kwarg_names(func): return [k for k, v in inspect.signature(func).parameters.items() if v.default != v.empty] _URLs = {f'{task}-{split}.{type}':f"https://raw.githubusercontent.com/disrpt/sharedtask2023/main/data/{task}/{task}_{split}.{type}" \ for task in files for split in 'train dev test'.split() for type in ['rels','conllu']} #_URLs = {k:v for k,v in _URLs.items() if requests.get(v).status_code!=404} conllu_features = ['id', 'form', 'lemma', 'upos', 'xpos', 'feats', 'head', 'deprel', 'deps', 'misc', 'seg'] feature_type = {"seg":datasets.features.Sequence( datasets.features.ClassLabel(names=["O","B-Segment"])), 'id':datasets.Value("string")} conllu_features = datasets.Features({x:feature_type.get(x,datasets.Sequence(datasets.Value("string"))) for x in conllu_features}) def map_seg(x): return [("B-Segment" if "beginseg=yes" in a.lower() else "O") for a in x] def remove_type(x): return x.replace(".rels","").replace(".conllu","") class Dataset(datasets.GeneratorBasedBuilder): BUILDER_CONFIGS = [ Config( name=f"{n}.{type}", data_dir=f"{n}.{type}", ) for n in files for type in ["rels","conllu"] ] def __init__(self,*args,**kwargs): self.BUILDER_CONFIG_CLASS.__post_init__=lambda x:x base_kwargs_names=get_kwarg_names(super().__init__) gen_kwargs={} self.files={} self.preprocessed_underscores=dict() for k,v in copy.deepcopy(kwargs).items(): if k not in base_kwargs_names: gen_kwargs[k]=v del kwargs[k] self.gen_kwargs=gen_kwargs return super().__init__(*args,**kwargs) def _split_generators(self, dl_manager: datasets.DownloadManager): cfg_name = self.config.name.rsplit('.', 1)[0] data_dir = remove_type(self.config.data_dir) print("datadir:",data_dir) type = self.config.name.split('.')[-1] urls={k:v for (k,v) in _URLs.items() if cfg_name in k and requests.get(v).status_code!=404} data_file = dl_manager.download(urls) self.files = {**self.files, **data_file} train_key = data_dir+'-train' print("datafile:",data_file, self.config.data_dir) if train_key in data_file: train=[datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": data_file[train_key]})] else: train=[] return train+[ datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": data_file[data_dir+'-dev.'+type]}), datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": data_file[data_dir+'-test.'+type]}), ] def _info(self): return datasets.DatasetInfo( citation=key_to_entry.get(datasets_and_citations.get(remove_type(self.config.name)),None), features=(None if ".rels" in self.config.name else conllu_features) ) def _generate_examples(self, filepath): print(filepath) corpus=self.config.name.split('.')[2] run_args={ 'corpus':corpus 'rel_files': [v for k, v in self.files.items() if 'rels' in k], 'dep_files': [v for k, v in self.files.items() if 'conllu' in k] } print('run_args',run_args) if corpus in ['rstdt','pdtb','cdtb','gum','tdb'] and not self.preprocessed_underscores[corpus]: run(**run_args) self.preprocessed_underscores[corpus]=True with open(filepath, encoding="utf-8") as f: if "conllu" in self.config.name: stream=parse_conll_stream(f) for i, row in enumerate(stream): row['seg']=map_seg(row['misc']) yield i,row reader = csv.DictReader(f,delimiter='\t',quoting=csv.QUOTE_NONE) for id_, row in enumerate(reader): if id_ == 0: continue yield id_, row