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