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from curses.ascii import isalpha |
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import os |
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import csv |
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import re |
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from typing import Sequence |
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import json |
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import ast |
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import datasets |
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_DESCRIPTION = """\ |
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Example dataset toxic |
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""" |
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_DATA_URL = "https://drive.google.com/uc?id=1Ldnn3YYt_ErYq4ZGSon1MvcP3uJO0_PX" |
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_DATA_ENG = "https://drive.google.com/uc?id=1p-iyKTRhUXaDmqsx69Zvb4ivjaCmVVr8" |
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_TEXT = { |
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"sen_vi": [" thất vọng", " bình thường", " hài lòng"], |
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"sen_en": [" negative", " neutral", " positive"], |
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"top_vi": [" giảng viên", " môn học", " phòng học", " tổng thể"], |
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"top_en": [" lecturer", " curriculum", " facility", " general"], |
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"top_en_": ["lecturer", "curriculum", "facility", "general"], |
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"sen_en_": ["negative", "neutral", "positive"], |
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"sen_vi_": ["thất vọng", "bình thường", "hài lòng"], |
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"top_vi_": ["giảng viên", "môn học", "phòng học", "tổng thể"], |
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} |
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class Config(datasets.BuilderConfig): |
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"""BuilderConfig for GLUE.""" |
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def __init__(self, data_url, **kwargs): |
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"""BuilderConfig |
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Args: |
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data_url: `string`, url to the dataset (word or raw level) |
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**kwargs: keyword arguments forwarded to super. |
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""" |
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super(Config, self).__init__( |
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version=datasets.Version( |
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"1.0.0", |
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), |
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**kwargs, |
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) |
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self.data_url = data_url |
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class Guess(datasets.GeneratorBasedBuilder): |
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VERSION = datasets.Version("0.1.0") |
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BUILDER_CONFIGS = [ |
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Config( |
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name="top_vi", |
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data_url=_DATA_URL, |
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description="data", |
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), |
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Config( |
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name="top_en", |
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data_url=_DATA_ENG, |
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description="data", |
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), |
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Config( |
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name="sen_vi", |
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data_url=_DATA_URL, |
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description="data", |
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), |
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Config( |
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name="sen_en", |
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data_url=_DATA_ENG, |
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description="data", |
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), |
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Config( |
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name="sen_en_", |
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data_url=_DATA_ENG, |
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description="data", |
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), |
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Config( |
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name="top_en_", |
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data_url=_DATA_ENG, |
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description="data", |
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), |
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Config( |
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name="top_vi_", |
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data_url=_DATA_URL, |
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description="data", |
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), |
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Config( |
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name="sen_vi_", |
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data_url=_DATA_URL, |
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description="data", |
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), |
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] |
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def _info(self): |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=datasets.Features( |
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{ |
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"text": datasets.Value("string"), |
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"classes": datasets.Sequence(datasets.Value("string")), |
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"target": datasets.Value("int8") |
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} |
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), |
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supervised_keys=None, |
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) |
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def _split_generators(self, dl_manager): |
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"""Returns SplitGenerators.""" |
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data_file = dl_manager.download(self.config.data_url) |
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return [ |
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datasets.SplitGenerator( |
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name=datasets.Split.TEST, |
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gen_kwargs={"data_file": data_file, "type": self.config.name}, |
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), |
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] |
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def _generate_examples(self, data_file, type): |
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"""Yields examples.""" |
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with open(data_file, 'r') as f: |
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lines = list(f) |
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if type[:3] == 'sen': |
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_CLASS = { |
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"negative": 0, |
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"neutral": 1, |
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"positive": 2, |
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} |
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else: |
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_CLASS = { |
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"lecturer": 0, |
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"curriculum": 1, |
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"facility": 2, |
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"others": 3 |
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} |
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TEXT_ = _TEXT[type] |
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for idx, line in enumerate(lines): |
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json_object = ast.literal_eval(line) |
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if type[:3] == 'top': |
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label = json_object['topic'] |
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else: |
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label = json_object['sentiment'] |
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if label not in _CLASS: |
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continue |
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_text = json_object['text'] |
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_classes = [] |
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_PROMPT = { |
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"sen_vi": f'{_text} Cảm thấy ', |
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"sen_en": f'{_text} The sentiment of this sentence is ', |
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"top_vi": f'Nói về ', |
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"top_en": f'Comment about ', |
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"sen_en_": f'{_text} The sentiment of this sentence is ', |
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"top_en_": f'Comment about ', |
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"sen_vi_": f'{_text} Cảm thấy ', |
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"top_vi_": f'Nói về ', |
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} |
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for _cl in TEXT_: |
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if type[:3] == 'sen': |
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_classes.append(_cl) |
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else: |
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_classes.append(f'{_cl}. {_text}') |
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yield idx, { |
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"text" : _PROMPT[type], |
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"classes" : _classes, |
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"target" : _CLASS[label] |
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} |
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