[update] add Loader and Pipe for AG's News dataset

fastNLP/io/loader/classification.py

@@ -4,6 +4,7 @@ __all__ = [
     "YelpLoader",
     "YelpFullLoader",
     "YelpPolarityLoader",
+    "AGsNewsLoader",
     "IMDBLoader",
     "SSTLoader",
     "SST2Loader",
@@ -161,6 +162,20 @@ class YelpPolarityLoader(YelpLoader):
         return data_dir
 
 
+class AGsNewsLoader(YelpLoader):
+    def download(self):
+        """
+        自动下载数据集，如果你使用了这个数据集，请引用以下的文章
+
+        Xiang Zhang, Junbo Zhao, Yann LeCun. Character-level Convolutional Networks for Text Classification. Advances
+        in Neural Information Processing Systems 28 (NIPS 2015)
+
+        :return: str, 数据集的目录地址
+        """
+
+        return self._get_dataset_path(dataset_name='ag-news')
+
+
 class IMDBLoader(Loader):
     """
     原始数据中内容应该为, 每一行为一个sample，制表符之前为target，制表符之后为文本内容。

fastNLP/io/loader/matching.py

@@ -57,6 +57,7 @@ class MNLILoader(Loader):
             f.readline()  # 跳过header
             if path.endswith("test_matched.tsv") or path.endswith('test_mismatched.tsv'):
                 warnings.warn("RTE's test file has no target.")
+                warnings.warn("MNLI's test file has no target.")
                 for line in f:
                     line = line.strip()
                     if line:

fastNLP/io/pipe/classification.py

@@ -21,6 +21,8 @@ from .utils import get_tokenizer, _indexize, _add_words_field, _drop_empty_insta
 from ..data_bundle import DataBundle
 from ..loader.classification import ChnSentiCorpLoader, THUCNewsLoader, WeiboSenti100kLoader
 from ..loader.classification import IMDBLoader, YelpFullLoader, SSTLoader, SST2Loader, YelpPolarityLoader
+from ..loader.classification import IMDBLoader, YelpFullLoader, SSTLoader, SST2Loader, YelpPolarityLoader, \
+    AGsNewsLoader
 from ...core._logger import logger
 from ...core.const import Const
 from ...core.dataset import DataSet
@@ -272,6 +274,49 @@ class YelpPolarityPipe(_CLSPipe):
         return self.process(data_bundle=data_bundle)
 
 
+class AGsNewsPipe(YelpFullPipe):
+    """
+    处理AG's News的数据, 处理之后DataSet中的内容如下
+
+    .. csv-table:: 下面是使用AGsNewsPipe处理后的DataSet所具备的field
+        :header: "raw_words", "target", "words", "seq_len"
+
+        "I got 'new' tires from them and within...", 0 ,"[7, 110, 22, 107, 22, 499, 59, 140, 3,...]", 160
+        " Don't waste your time.  We had two dif... ", 0, "[277, 17, 278, 38, 30, 112, 24, 85, 27...", 40
+        "...", ., "[...]", .
+
+    dataset的print_field_meta()函数输出的各个field的被设置成input和target的情况为::
+
+        +-------------+-----------+--------+-------+---------+
+        | field_names | raw_words | target | words | seq_len |
+        +-------------+-----------+--------+-------+---------+
+        |   is_input  |   False   | False  |  True |   True  |
+        |  is_target  |   False   |  True  | False |  False  |
+        | ignore_type |           | False  | False |  False  |
+        |  pad_value  |           |   0    |   0   |    0    |
+        +-------------+-----------+--------+-------+---------+
+
+    """
+
+    def __init__(self, lower: bool = False, tokenizer: str = 'spacy'):
+        """
+
+        :param bool lower: 是否对输入进行小写化。
+        :param str tokenizer: 使用哪种tokenize方式将数据切成单词。支持'spacy'和'raw'。raw使用空格作为切分。
+        """
+        super().__init__(lower=lower, tokenizer=tokenizer)
+        self.tag_map = {"1": 0, "2": 1, "3": 2, "4": 3}
+
+    def process_from_file(self, paths=None):
+        """
+
+        :param str paths:
+        :return: DataBundle
+        """
+        data_bundle = AGsNewsLoader().load(paths)
+        return self.process(data_bundle=data_bundle)
+
+
 class SSTPipe(_CLSPipe):
     """
     经过该Pipe之后，DataSet中具备的field如下所示