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import os |
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from typing import Union, Dict |
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from ...core.const import Const |
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from ...core.vocabulary import Vocabulary |
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from ...io.base_loader import DataInfo, DataSetLoader |
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from ...io.dataset_loader import JsonLoader, CSVLoader |
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from ...io.file_utils import _get_base_url, cached_path, PRETRAINED_BERT_MODEL_DIR |
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from ...modules.encoder._bert import BertTokenizer |
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class MatchingLoader(DataSetLoader): |
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""" |
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别名：:class:`fastNLP.io.MatchingLoader` :class:`fastNLP.io.dataset_loader.MatchingLoader` |
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读取Matching任务的数据集 |
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:param dict paths: key是数据集名称（如train、dev、test），value是对应的文件名 |
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""" |
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def __init__(self, paths: dict = None): |
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self.paths = paths |
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def _load(self, path): |
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""" |
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:param str path: 待读取数据集的路径名 |
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:return: fastNLP.DataSet ds: 返回一个DataSet对象，里面必须包含3个field：其中两个分别为两个句子 |
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的原始字符串文本，第三个为标签 |
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""" |
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raise NotImplementedError |
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def process(self, paths: Union[str, Dict[str, str]], dataset_name: str = None, |
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to_lower=False, seq_len_type: str = None, bert_tokenizer: str = None, |
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cut_text: int = None, get_index=True, auto_pad_length: int = None, |
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auto_pad_token: str = '<pad>', set_input: Union[list, str, bool] = True, |
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set_target: Union[list, str, bool] = True, concat: Union[str, list, bool] = None, ) -> DataInfo: |
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""" |
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:param paths: str或者Dict[str, str]。如果是str，则为数据集所在的文件夹或者是全路径文件名：如果是文件夹， |
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则会从self.paths里面找对应的数据集名称与文件名。如果是Dict，则为数据集名称（如train、dev、test）和 |
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对应的全路径文件名。 |
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:param str dataset_name: 如果在paths里传入的是一个数据集的全路径文件名，那么可以用dataset_name来定义 |
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这个数据集的名字，如果不定义则默认为train。 |
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:param bool to_lower: 是否将文本自动转为小写。默认值为False。 |
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:param str seq_len_type: 提供的seq_len类型，支持 ``seq_len`` ：提供一个数字作为句子长度； ``mask`` : |
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提供一个0/1的mask矩阵作为句子长度； ``bert`` ：提供segment_type_id（第一个句子为0，第二个句子为1）和 |
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attention mask矩阵（0/1的mask矩阵）。默认值为None，即不提供seq_len |
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:param str bert_tokenizer: bert tokenizer所使用的词表所在的文件夹路径 |
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:param int cut_text: 将长于cut_text的内容截掉。默认为None，即不截。 |
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:param bool get_index: 是否需要根据词表将文本转为index |
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:param int auto_pad_length: 是否需要将文本自动pad到一定长度（超过这个长度的文本将会被截掉），默认为不会自动pad |
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:param str auto_pad_token: 自动pad的内容 |
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:param set_input: 如果为True，则会自动将相关的field（名字里含有Const.INPUT的）设置为input，如果为False |
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则不会将任何field设置为input。如果传入str或者List[str]，则会根据传入的内容将相对应的field设置为input， |
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于此同时其他field不会被设置为input。默认值为True。 |
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:param set_target: set_target将控制哪些field可以被设置为target，用法与set_input一致。默认值为True。 |
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:param concat: 是否需要将两个句子拼接起来。如果为False则不会拼接。如果为True则会在两个句子之间插入一个<sep>。 |
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如果传入一个长度为4的list，则分别表示插在第一句开始前、第一句结束后、第二句开始前、第二句结束后的标识符。如果 |
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传入字符串 ``bert`` ，则会采用bert的拼接方式，等价于['[CLS]', '[SEP]', '', '[SEP]']. |
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:return: |
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""" |
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if isinstance(set_input, str): |
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set_input = [set_input] |
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if isinstance(set_target, str): |
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set_target = [set_target] |
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if isinstance(set_input, bool): |
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auto_set_input = set_input |
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else: |
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auto_set_input = False |
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if isinstance(set_target, bool): |
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auto_set_target = set_target |
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else: |
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auto_set_target = False |
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if isinstance(paths, str): |
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if os.path.isdir(paths): |
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path = {n: os.path.join(paths, self.paths[n]) for n in self.paths.keys()} |
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else: |
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path = {dataset_name if dataset_name is not None else 'train': paths} |
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else: |
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path = paths |
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data_info = DataInfo() |
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for data_name in path.keys(): |
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data_info.datasets[data_name] = self._load(path[data_name]) |
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for data_name, data_set in data_info.datasets.items(): |
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if auto_set_input: |
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data_set.set_input(Const.INPUTS(0), Const.INPUTS(1)) |
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if auto_set_target: |
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if Const.TARGET in data_set.get_field_names(): |
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data_set.set_target(Const.TARGET) |
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if to_lower: |
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for data_name, data_set in data_info.datasets.items(): |
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data_set.apply(lambda x: [w.lower() for w in x[Const.INPUTS(0)]], new_field_name=Const.INPUTS(0), |
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is_input=auto_set_input) |
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data_set.apply(lambda x: [w.lower() for w in x[Const.INPUTS(1)]], new_field_name=Const.INPUTS(1), |
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is_input=auto_set_input) |
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if bert_tokenizer is not None: |
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if bert_tokenizer.lower() in PRETRAINED_BERT_MODEL_DIR: |
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PRETRAIN_URL = _get_base_url('bert') |
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model_name = PRETRAINED_BERT_MODEL_DIR[bert_tokenizer] |
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model_url = PRETRAIN_URL + model_name |
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model_dir = cached_path(model_url) |
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# 检查是否存在 |
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elif os.path.isdir(bert_tokenizer): |
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model_dir = bert_tokenizer |
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else: |
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raise ValueError(f"Cannot recognize BERT tokenizer from {bert_tokenizer}.") |
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words_vocab = Vocabulary(padding='[PAD]', unknown='[UNK]') |
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with open(os.path.join(model_dir, 'vocab.txt'), 'r') as f: |
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lines = f.readlines() |
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lines = [line.strip() for line in lines] |
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words_vocab.add_word_lst(lines) |
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words_vocab.build_vocab() |
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tokenizer = BertTokenizer.from_pretrained(model_dir) |
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for data_name, data_set in data_info.datasets.items(): |
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for fields in data_set.get_field_names(): |
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if Const.INPUT in fields: |
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data_set.apply(lambda x: tokenizer.tokenize(' '.join(x[fields])), new_field_name=fields, |
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is_input=auto_set_input) |
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if isinstance(concat, bool): |
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concat = 'default' if concat else None |
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if concat is not None: |
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if isinstance(concat, str): |
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CONCAT_MAP = {'bert': ['[CLS]', '[SEP]', '', '[SEP]'], |
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'default': ['', '<sep>', '', '']} |
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if concat.lower() in CONCAT_MAP: |
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concat = CONCAT_MAP[concat] |
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else: |
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concat = 4 * [concat] |
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assert len(concat) == 4, \ |
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f'Please choose a list with 4 symbols which at the beginning of first sentence ' \ |
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f'the end of first sentence, the begin of second sentence, and the end of second' \ |
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f'sentence. Your input is {concat}' |
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for data_name, data_set in data_info.datasets.items(): |
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data_set.apply(lambda x: [concat[0]] + x[Const.INPUTS(0)] + [concat[1]] + [concat[2]] + |
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x[Const.INPUTS(1)] + [concat[3]], new_field_name=Const.INPUT) |
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data_set.apply(lambda x: [w for w in x[Const.INPUT] if len(w) > 0], new_field_name=Const.INPUT, |
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is_input=auto_set_input) |
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if seq_len_type is not None: |
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if seq_len_type == 'seq_len': # |
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for data_name, data_set in data_info.datasets.items(): |
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for fields in data_set.get_field_names(): |
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if Const.INPUT in fields: |
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data_set.apply(lambda x: len(x[fields]), |
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new_field_name=fields.replace(Const.INPUT, Const.INPUT_LEN), |
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is_input=auto_set_input) |
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elif seq_len_type == 'mask': |
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for data_name, data_set in data_info.datasets.items(): |
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for fields in data_set.get_field_names(): |
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if Const.INPUT in fields: |
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data_set.apply(lambda x: [1] * len(x[fields]), |
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new_field_name=fields.replace(Const.INPUT, Const.INPUT_LEN), |
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is_input=auto_set_input) |
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elif seq_len_type == 'bert': |
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for data_name, data_set in data_info.datasets.items(): |
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if Const.INPUT not in data_set.get_field_names(): |
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raise KeyError(f'Field ``{Const.INPUT}`` not in {data_name} data set: ' |
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f'got {data_set.get_field_names()}') |
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data_set.apply(lambda x: [0] * (len(x[Const.INPUTS(0)]) + 2) + [1] * (len(x[Const.INPUTS(1)]) + 1), |
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new_field_name=Const.INPUT_LENS(0), is_input=auto_set_input) |
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data_set.apply(lambda x: [1] * len(x[Const.INPUT_LENS(0)]), |
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new_field_name=Const.INPUT_LENS(1), is_input=auto_set_input) |
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if auto_pad_length is not None: |
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cut_text = min(auto_pad_length, cut_text if cut_text is not None else 0) |
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if cut_text is not None: |
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for data_name, data_set in data_info.datasets.items(): |
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for fields in data_set.get_field_names(): |
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if (Const.INPUT in fields) or ((Const.INPUT_LEN in fields) and (seq_len_type != 'seq_len')): |
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data_set.apply(lambda x: x[fields][: cut_text], new_field_name=fields, |
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is_input=auto_set_input) |
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data_set_list = [d for n, d in data_info.datasets.items()] |
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assert len(data_set_list) > 0, f'There are NO data sets in data info!' |
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if bert_tokenizer is None: |
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words_vocab = Vocabulary(padding=auto_pad_token) |
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words_vocab = words_vocab.from_dataset(*[d for n, d in data_info.datasets.items() if 'train' in n], |
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field_name=[n for n in data_set_list[0].get_field_names() |
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if (Const.INPUT in n)], |
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no_create_entry_dataset=[d for n, d in data_info.datasets.items() |
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if 'train' not in n]) |
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target_vocab = Vocabulary(padding=None, unknown=None) |
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target_vocab = target_vocab.from_dataset(*[d for n, d in data_info.datasets.items() if 'train' in n], |
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field_name=Const.TARGET) |
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data_info.vocabs = {Const.INPUT: words_vocab, Const.TARGET: target_vocab} |
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if get_index: |
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for data_name, data_set in data_info.datasets.items(): |
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for fields in data_set.get_field_names(): |
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if Const.INPUT in fields: |
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data_set.apply(lambda x: [words_vocab.to_index(w) for w in x[fields]], new_field_name=fields, |
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is_input=auto_set_input) |
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if Const.TARGET in data_set.get_field_names(): |
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data_set.apply(lambda x: target_vocab.to_index(x[Const.TARGET]), new_field_name=Const.TARGET, |
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is_input=auto_set_input, is_target=auto_set_target) |
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if auto_pad_length is not None: |
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for data_name, data_set in data_info.datasets.items(): |
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if seq_len_type == 'seq_len': |
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raise RuntimeError(f'sequence will be padded with the length {auto_pad_length},' |
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f'the seq_len_type cannot be `{seq_len_type}`!') |
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for fields in data_set.get_field_names(): |
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if Const.INPUT in fields: |
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data_set.apply(lambda x: x[fields] + [words_vocab.to_index(words_vocab.padding)] * |
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(auto_pad_length - len(x[fields])), new_field_name=fields, |
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is_input=auto_set_input) |
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elif (Const.INPUT_LEN in fields) and (seq_len_type != 'seq_len'): |
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data_set.apply(lambda x: x[fields] + [0] * (auto_pad_length - len(x[fields])), |
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new_field_name=fields, is_input=auto_set_input) |
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for data_name, data_set in data_info.datasets.items(): |
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if isinstance(set_input, list): |
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data_set.set_input(*[inputs for inputs in set_input if inputs in data_set.get_field_names()]) |
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if isinstance(set_target, list): |
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data_set.set_target(*[target for target in set_target if target in data_set.get_field_names()]) |
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return data_info |
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class SNLILoader(MatchingLoader, JsonLoader): |
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""" |
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别名：:class:`fastNLP.io.SNLILoader` :class:`fastNLP.io.dataset_loader.SNLILoader` |
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读取SNLI数据集，读取的DataSet包含fields:: |
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words1: list(str)，第一句文本, premise |
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words2: list(str), 第二句文本, hypothesis |
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target: str, 真实标签 |
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数据来源: https://nlp.stanford.edu/projects/snli/snli_1.0.zip |
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""" |
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def __init__(self, paths: dict = None): |
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fields = { |
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'sentence1_binary_parse': Const.INPUTS(0), |
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'sentence2_binary_parse': Const.INPUTS(1), |
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'gold_label': Const.TARGET, |
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} |
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paths = paths if paths is not None else { |
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'train': 'snli_1.0_train.jsonl', |
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'dev': 'snli_1.0_dev.jsonl', |
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'test': 'snli_1.0_test.jsonl'} |
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MatchingLoader.__init__(self, paths=paths) |
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JsonLoader.__init__(self, fields=fields) |
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def _load(self, path): |
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ds = JsonLoader._load(self, path) |
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parentheses_table = str.maketrans({'(': None, ')': None}) |
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ds.apply(lambda ins: ins[Const.INPUTS(0)].translate(parentheses_table).strip().split(), |
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new_field_name=Const.INPUTS(0)) |
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ds.apply(lambda ins: ins[Const.INPUTS(1)].translate(parentheses_table).strip().split(), |
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new_field_name=Const.INPUTS(1)) |
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ds.drop(lambda x: x[Const.TARGET] == '-') |
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return ds |
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class RTELoader(MatchingLoader, CSVLoader): |
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""" |
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别名：:class:`fastNLP.io.RTELoader` :class:`fastNLP.io.dataset_loader.RTELoader` |
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读取RTE数据集，读取的DataSet包含fields:: |
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words1: list(str)，第一句文本, premise |
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words2: list(str), 第二句文本, hypothesis |
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target: str, 真实标签 |
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数据来源: |
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""" |
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def __init__(self, paths: dict = None): |
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paths = paths if paths is not None else { |
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'train': 'train.tsv', |
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'dev': 'dev.tsv', |
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'test': 'test.tsv' # test set has not label |
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} |
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MatchingLoader.__init__(self, paths=paths) |
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self.fields = { |
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'sentence1': Const.INPUTS(0), |
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'sentence2': Const.INPUTS(1), |
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'label': Const.TARGET, |
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} |
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CSVLoader.__init__(self, sep='\t') |
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def _load(self, path): |
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ds = CSVLoader._load(self, path) |
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for k, v in self.fields.items(): |
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ds.rename_field(k, v) |
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for fields in ds.get_all_fields(): |
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if Const.INPUT in fields: |
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ds.apply(lambda x: x[fields].strip().split(), new_field_name=fields) |
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return ds |
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class QNLILoader(MatchingLoader, CSVLoader): |
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""" |
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别名：:class:`fastNLP.io.QNLILoader` :class:`fastNLP.io.dataset_loader.QNLILoader` |
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读取QNLI数据集，读取的DataSet包含fields:: |
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words1: list(str)，第一句文本, premise |
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words2: list(str), 第二句文本, hypothesis |
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target: str, 真实标签 |
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数据来源: |
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""" |
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def __init__(self, paths: dict = None): |
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paths = paths if paths is not None else { |
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'train': 'train.tsv', |
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'dev': 'dev.tsv', |
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'test': 'test.tsv' # test set has not label |
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} |
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MatchingLoader.__init__(self, paths=paths) |
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self.fields = { |
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'question': Const.INPUTS(0), |
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'sentence': Const.INPUTS(1), |
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'label': Const.TARGET, |
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} |
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CSVLoader.__init__(self, sep='\t') |
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def _load(self, path): |
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ds = CSVLoader._load(self, path) |
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for k, v in self.fields.items(): |
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ds.rename_field(k, v) |
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for fields in ds.get_all_fields(): |
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if Const.INPUT in fields: |
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ds.apply(lambda x: x[fields].strip().split(), new_field_name=fields) |
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return ds |
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class MNLILoader(MatchingLoader, CSVLoader): |
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""" |
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别名：:class:`fastNLP.io.MNLILoader` :class:`fastNLP.io.dataset_loader.MNLILoader` |
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读取MNLI数据集，读取的DataSet包含fields:: |
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words1: list(str)，第一句文本, premise |
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words2: list(str), 第二句文本, hypothesis |
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target: str, 真实标签 |
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数据来源: |
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""" |
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def __init__(self, paths: dict = None): |
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paths = paths if paths is not None else { |
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'train': 'train.tsv', |
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'dev_matched': 'dev_matched.tsv', |
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'dev_mismatched': 'dev_mismatched.tsv', |
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'test_matched': 'test_matched.tsv', |
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'test_mismatched': 'test_mismatched.tsv', |
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# 'test_0.9_matched': 'multinli_0.9_test_matched_unlabeled.txt', |
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# 'test_0.9_mismatched': 'multinli_0.9_test_mismatched_unlabeled.txt', |
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# test_0.9_mathed与mismatched是MNLI0.9版本的（数据来源：kaggle） |
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} |
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MatchingLoader.__init__(self, paths=paths) |
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CSVLoader.__init__(self, sep='\t') |
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self.fields = { |
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'sentence1_binary_parse': Const.INPUTS(0), |
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'sentence2_binary_parse': Const.INPUTS(1), |
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'gold_label': Const.TARGET, |
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} |
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def _load(self, path): |
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ds = CSVLoader._load(self, path) |
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for k, v in self.fields.items(): |
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if k in ds.get_field_names(): |
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ds.rename_field(k, v) |
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if Const.TARGET in ds.get_field_names(): |
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if ds[0][Const.TARGET] == 'hidden': |
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ds.delete_field(Const.TARGET) |
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parentheses_table = str.maketrans({'(': None, ')': None}) |
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ds.apply(lambda ins: ins[Const.INPUTS(0)].translate(parentheses_table).strip().split(), |
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new_field_name=Const.INPUTS(0)) |
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ds.apply(lambda ins: ins[Const.INPUTS(1)].translate(parentheses_table).strip().split(), |
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new_field_name=Const.INPUTS(1)) |
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if Const.TARGET in ds.get_field_names(): |
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ds.drop(lambda x: x[Const.TARGET] == '-') |
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return ds |
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class QuoraLoader(MatchingLoader, CSVLoader): |
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""" |
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别名：:class:`fastNLP.io.QuoraLoader` :class:`fastNLP.io.dataset_loader.QuoraLoader` |
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读取MNLI数据集，读取的DataSet包含fields:: |
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words1: list(str)，第一句文本, premise |
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words2: list(str), 第二句文本, hypothesis |
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target: str, 真实标签 |
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数据来源: |
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""" |
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def __init__(self, paths: dict = None): |
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paths = paths if paths is not None else { |
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'train': 'train.tsv', |
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'dev': 'dev.tsv', |
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'test': 'test.tsv', |
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} |
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MatchingLoader.__init__(self, paths=paths) |
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CSVLoader.__init__(self, sep='\t', headers=(Const.TARGET, Const.INPUTS(0), Const.INPUTS(1), 'pairID')) |
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def _load(self, path): |
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ds = CSVLoader._load(self, path) |
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return ds |
xuyige
6 years ago