fastNLP/reproduction/text_classification/data/MTL16Loader.py

from fastNLP.io.embed_loader import EmbeddingOption, EmbedLoader
from fastNLP.core.vocabulary import VocabularyOption
from fastNLP.io.data_bundle import DataSetLoader, DataBundle
from typing import Union, Dict, List, Iterator
from fastNLP import DataSet
from fastNLP import Instance
from fastNLP import Vocabulary
from fastNLP import Const
from reproduction.utils import check_dataloader_paths
from functools import partial

class MTL16Loader(DataSetLoader):
    """
    读取MTL16数据集，DataSet包含以下fields:

        words: list(str), 需要分类的文本
        target: str, 文本的标签

    数据来源：https://pan.baidu.com/s/1c2L6vdA

    """

    def __init__(self):
        super(MTL16Loader, self).__init__()

    def _load(self, path):
        dataset = DataSet()
        with open(path, 'r', encoding="utf-8") as f:
            for line in f:
                line = line.strip()
                if not line:
                    continue
                parts = line.split('\t')
                target = parts[0]
                words = parts[1].lower().split()
                dataset.append(Instance(words=words, target=target))
        if len(dataset)==0:
            raise RuntimeError(f"{path} has no valid data.")

        return dataset
    
    def process(self,
                paths: Union[str, Dict[str, str]],
                src_vocab_opt: VocabularyOption = None,
                tgt_vocab_opt: VocabularyOption = None,
                src_embed_opt: EmbeddingOption = None):
        
        paths = check_dataloader_paths(paths)
        datasets = {}
        info = DataBundle()
        for name, path in paths.items():
            dataset = self.load(path)
            datasets[name] = dataset

        src_vocab = Vocabulary() if src_vocab_opt is None else Vocabulary(**src_vocab_opt)
        src_vocab.from_dataset(datasets['train'], field_name='words')
        src_vocab.index_dataset(*datasets.values(), field_name='words')

        tgt_vocab = Vocabulary(unknown=None, padding=None) \
            if tgt_vocab_opt is None else Vocabulary(**tgt_vocab_opt)
        tgt_vocab.from_dataset(datasets['train'], field_name='target')
        tgt_vocab.index_dataset(*datasets.values(), field_name='target')

        info.vocabs = {
            "words": src_vocab,
            "target": tgt_vocab
        }

        info.datasets = datasets

        if src_embed_opt is not None:
            embed = EmbedLoader.load_with_vocab(**src_embed_opt, vocab=src_vocab)
            info.embeddings['words'] = embed

        for name, dataset in info.datasets.items():
            dataset.set_input("words")
            dataset.set_target("target")

        return info