增加中文分类Pipe;使用矩阵加速BertEmbedding部分pool_method;调整部分测试用例名称;修复metric中对warning的误报

fastNLP/core/losses.py

@@ -238,8 +238,8 @@ class CrossEntropyLoss(LossBase):
                 pred = pred.tranpose(-1, pred)
             pred = pred.reshape(-1, pred.size(-1))
             target = target.reshape(-1)
-        if seq_len is not None:
-            mask = seq_len_to_mask(seq_len).reshape(-1).eq(0)
+        if seq_len is not None and target.dim()>1:
+            mask = seq_len_to_mask(seq_len, max_len=target.size(1)).reshape(-1).eq(0)
             target = target.masked_fill(mask, self.padding_idx)
 
         return F.cross_entropy(input=pred, target=target,

fastNLP/core/metrics.py

@@ -347,7 +347,7 @@ class AccuracyMetric(MetricBase):
             pass
         elif pred.dim() == target.dim() + 1:
             pred = pred.argmax(dim=-1)
-            if seq_len is None:
+            if seq_len is None and target.dim()>1:
                 warnings.warn("You are not passing `seq_len` to exclude pad when calculate accuracy.")
         else:
             raise RuntimeError(f"In {_get_func_signature(self.evaluate)}, when pred have "

fastNLP/embeddings/bert_embedding.py

@@ -68,7 +68,7 @@ class BertEmbedding(ContextualEmbedding):
     
     def __init__(self, vocab: Vocabulary, model_dir_or_name: str = 'en-base-uncased', layers: str = '-1',
                  pool_method: str = 'first', word_dropout=0, dropout=0, include_cls_sep: bool = False,
-                 pooled_cls=True, requires_grad: bool = False, auto_truncate: bool = False):
+                 pooled_cls=True, requires_grad: bool = True, auto_truncate: bool = False):
         super(BertEmbedding, self).__init__(vocab, word_dropout=word_dropout, dropout=dropout)
 
         if model_dir_or_name.lower() in PRETRAINED_BERT_MODEL_DIR:
@@ -165,7 +165,7 @@ class BertWordPieceEncoder(nn.Module):
     """
     
     def __init__(self, model_dir_or_name: str = 'en-base-uncased', layers: str = '-1', pooled_cls: bool = False,
-                 word_dropout=0, dropout=0, requires_grad: bool = False):
+                 word_dropout=0, dropout=0, requires_grad: bool = True):
         super().__init__()
         
         self.model = _WordPieceBertModel(model_dir_or_name=model_dir_or_name, layers=layers, pooled_cls=pooled_cls)
@@ -288,7 +288,7 @@ class _WordBertModel(nn.Module):
         self.auto_truncate = auto_truncate
         
         # 将所有vocab中word的wordpiece计算出来, 需要额外考虑[CLS]和[SEP]
-        logger.info("Start to generating word pieces for word.")
+        logger.info("Start to generate word pieces for word.")
         # 第一步统计出需要的word_piece, 然后创建新的embed和word_piece_vocab, 然后填入值
         word_piece_dict = {'[CLS]': 1, '[SEP]': 1}  # 用到的word_piece以及新增的
         found_count = 0
@@ -374,7 +374,8 @@ class _WordBertModel(nn.Module):
                 else:
                     raise RuntimeError(
                         "After split words into word pieces, the lengths of word pieces are longer than the "
-                        f"maximum allowed sequence length:{self._max_position_embeddings} of bert.")
+                        f"maximum allowed sequence length:{self._max_position_embeddings} of bert. You can set "
+                        f"`auto_truncate=True` for BertEmbedding to automatically truncate overlong input.")
             
             # +2是由于需要加入[CLS]与[SEP]
             word_pieces = words.new_full((batch_size, min(word_piece_length + 2, self._max_position_embeddings)),
@@ -407,15 +408,26 @@ class _WordBertModel(nn.Module):
         # output_layers = [self.layers]  # len(self.layers) x batch_size x real_word_piece_length x hidden_size
         
         if self.include_cls_sep:
-            outputs = bert_outputs[-1].new_zeros(len(self.layers), batch_size, max_word_len + 2,
-                                                 bert_outputs[-1].size(-1))
             s_shift = 1
+            outputs = bert_outputs[-1].new_zeros(len(self.layers), batch_size, max_word_len + 2,
+                                                     bert_outputs[-1].size(-1))
+
         else:
+            s_shift = 0
             outputs = bert_outputs[-1].new_zeros(len(self.layers), batch_size, max_word_len,
                                                  bert_outputs[-1].size(-1))
-            s_shift = 0
         batch_word_pieces_cum_length = batch_word_pieces_length.new_zeros(batch_size, max_word_len + 1)
         batch_word_pieces_cum_length[:, 1:] = batch_word_pieces_length.cumsum(dim=-1)  # batch_size x max_len
+
+        if self.pool_method == 'first':
+            batch_word_pieces_cum_length = batch_word_pieces_cum_length[:, :seq_len.max()]
+            batch_word_pieces_cum_length.masked_fill_(batch_word_pieces_cum_length.ge(word_piece_length), 0)
+            batch_indexes = batch_indexes[:, None].expand((batch_size, batch_word_pieces_cum_length.size(1)))
+        elif self.pool_method == 'last':
+            batch_word_pieces_cum_length = batch_word_pieces_cum_length[:, 1:seq_len.max()+1] - 1
+            batch_word_pieces_cum_length.masked_fill_(batch_word_pieces_cum_length.ge(word_piece_length), 0)
+            batch_indexes = batch_indexes[:, None].expand((batch_size, batch_word_pieces_cum_length.size(1)))
+
         for l_index, l in enumerate(self.layers):
             output_layer = bert_outputs[l]
             real_word_piece_length = output_layer.size(1) - 2
@@ -426,16 +438,15 @@ class _WordBertModel(nn.Module):
                 output_layer = torch.cat((output_layer, paddings), dim=1).contiguous()
             # 从word_piece collapse到word的表示
             truncate_output_layer = output_layer[:, 1:-1]  # 删除[CLS]与[SEP] batch_size x len x hidden_size
-            outputs_seq_len = seq_len + s_shift
             if self.pool_method == 'first':
-                for i in range(batch_size):
-                    i_word_pieces_cum_length = batch_word_pieces_cum_length[i, :seq_len[i]]  # 每个word的start位置
-                    outputs[l_index, i, s_shift:outputs_seq_len[i]] = truncate_output_layer[
-                        i, i_word_pieces_cum_length]  # num_layer x batch_size x len x hidden_size
+                tmp = truncate_output_layer[batch_indexes, batch_word_pieces_cum_length]
+                tmp = tmp.masked_fill(word_mask[:, :batch_word_pieces_cum_length.size(1), None].eq(0), 0)
+                outputs[l_index, :, s_shift:batch_word_pieces_cum_length.size(1)+s_shift] = tmp
+
             elif self.pool_method == 'last':
-                for i in range(batch_size):
-                    i_word_pieces_cum_length = batch_word_pieces_cum_length[i, 1:seq_len[i] + 1] - 1  # 每个word的end
-                    outputs[l_index, i, s_shift:outputs_seq_len[i]] = truncate_output_layer[i, i_word_pieces_cum_length]
+                tmp = truncate_output_layer[batch_indexes, batch_word_pieces_cum_length]
+                tmp = tmp.masked_fill(word_mask[:, :batch_word_pieces_cum_length.size(1), None].eq(0), 0)
+                outputs[l_index, :, s_shift:batch_word_pieces_cum_length.size(1)+s_shift] = tmp
             elif self.pool_method == 'max':
                 for i in range(batch_size):
                     for j in range(seq_len[i]):
@@ -452,5 +463,6 @@ class _WordBertModel(nn.Module):
                 else:
                     outputs[l_index, :, 0] = output_layer[:, 0]
                 outputs[l_index, batch_indexes, seq_len + s_shift] = output_layer[batch_indexes, seq_len + s_shift]
+
         # 3. 最终的embedding结果
         return outputs

fastNLP/io/__init__.py

@@ -24,6 +24,7 @@ __all__ = [
     'IMDBLoader',
     'SSTLoader',
     'SST2Loader',
+    "ChnSentiCorpLoader",
 
     'ConllLoader',
     'Conll2003Loader',
@@ -52,8 +53,9 @@ __all__ = [
     "SSTPipe",
     "SST2Pipe",
     "IMDBPipe",
-    "Conll2003Pipe",
+    "ChnSentiCorpPipe",
 
+    "Conll2003Pipe",
     "Conll2003NERPipe",
     "OntoNotesNERPipe",
     "MsraNERPipe",

fastNLP/io/data_bundle.py

@@ -306,12 +306,15 @@ class DataBundle:
         return self
 
     def __repr__(self):
-        _str = 'In total {} datasets:\n'.format(len(self.datasets))
-        for name, dataset in self.datasets.items():
-            _str += '\t{} has {} instances.\n'.format(name, len(dataset))
-        _str += 'In total {} vocabs:\n'.format(len(self.vocabs))
-        for name, vocab in self.vocabs.items():
-            _str += '\t{} has {} entries.\n'.format(name, len(vocab))
+        _str = ''
+        if len(self.datasets):
+            _str += 'In total {} datasets:\n'.format(len(self.datasets))
+            for name, dataset in self.datasets.items():
+                _str += '\t{} has {} instances.\n'.format(name, len(dataset))
+        if len(self.vocabs):
+            _str += 'In total {} vocabs:\n'.format(len(self.vocabs))
+            for name, vocab in self.vocabs.items():
+                _str += '\t{} has {} entries.\n'.format(name, len(vocab))
         return _str
 
 

fastNLP/io/file_utils.py

@@ -77,6 +77,9 @@ PRETRAIN_STATIC_FILES = {
     'cn-tencent': "tencent_cn.zip",
     'cn-fasttext': "cc.zh.300.vec.gz",
     'cn-sgns-literature-word': 'sgns.literature.word.txt.zip',
+    'cn-char-fastnlp-100d': "cn_char_fastnlp_100d.zip",
+    'cn-bi-fastnlp-100d': "cn_bi_fastnlp_100d.zip",
+    "cn-tri-fastnlp-100d": "cn_tri_fastnlp_100d.zip"
 }
 
 DATASET_DIR = {
@@ -96,7 +99,9 @@ DATASET_DIR = {
     "cws-pku": 'cws_pku.zip',
     "cws-cityu": "cws_cityu.zip",
     "cws-as": 'cws_as.zip',
-    "cws-msra": 'cws_msra.zip'
+    "cws-msra": 'cws_msra.zip',
+
+    "chn-senti-corp":"chn_senti_corp.zip"
 }
 
 PRETRAIN_MAP = {'elmo': PRETRAINED_ELMO_MODEL_DIR,

fastNLP/io/loader/__init__.py

@@ -52,6 +52,7 @@ __all__ = [
     'IMDBLoader',
     'SSTLoader',
     'SST2Loader',
+    "ChnSentiCorpLoader",
 
     'ConllLoader',
     'Conll2003Loader',
@@ -73,7 +74,7 @@ __all__ = [
     "QNLILoader",
     "RTELoader"
 ]
-from .classification import YelpLoader, YelpFullLoader, YelpPolarityLoader, IMDBLoader, SSTLoader, SST2Loader
+from .classification import YelpLoader, YelpFullLoader, YelpPolarityLoader, IMDBLoader, SSTLoader, SST2Loader, ChnSentiCorpLoader
 from .conll import ConllLoader, Conll2003Loader, Conll2003NERLoader, OntoNotesNERLoader, CTBLoader
 from .csv import CSVLoader
 from .cws import CWSLoader

fastNLP/io/loader/classification.py

@@ -7,6 +7,7 @@ __all__ = [
     "IMDBLoader",
     "SSTLoader",
     "SST2Loader",
+    "ChnSentiCorpLoader"
 ]
 
 import glob
@@ -346,3 +347,59 @@ class SST2Loader(Loader):
         """
         output_dir = self._get_dataset_path(dataset_name='sst-2')
         return output_dir
+
+
+class ChnSentiCorpLoader(Loader):
+    """
+    支持读取的数据的格式为，第一行为标题(具体内容会被忽略)，之后一行为一个sample，第一个制表符之前被认为是label，第
+    一个制表符及之后认为是句子
+
+    Example::
+
+        label	raw_chars
+        1	這間酒店環境和服務態度亦算不錯,但房間空間太小~~
+        1	<荐书> 推荐所有喜欢<红楼>的红迷们一定要收藏这本书,要知道...
+        0	商品的不足暂时还没发现，京东的订单处理速度实在.......周二就打包完成，周五才发货...
+
+    读取后的DataSet具有以下的field
+
+    .. csv-table::
+        :header: "raw_chars", "target"
+
+        "這間酒店環境和服務態度亦算不錯,但房間空間太小~~", "1"
+        "<荐书> 推荐所有喜欢<红楼>...", "1"
+        "..."
+
+    """
+    def __init__(self):
+        super().__init__()
+
+    def _load(self, path:str):
+        """
+        从path中读取数据
+
+        :param path:
+        :return:
+        """
+        ds = DataSet()
+        with open(path, 'r', encoding='utf-8') as f:
+            f.readline()
+            for line in f:
+                line = line.strip()
+                tab_index = line.index('\t')
+                if tab_index!=-1:
+                    target = line[:tab_index]
+                    raw_chars = line[tab_index+1:]
+                    if raw_chars:
+                        ds.append(Instance(raw_chars=raw_chars, target=target))
+        return ds
+
+    def download(self)->str:
+        """
+        自动下载数据，该数据取自https://github.com/pengming617/bert_classification/tree/master/data，在
+        https://arxiv.org/pdf/1904.09223.pdf与https://arxiv.org/pdf/1906.08101.pdf有使用
+
+        :return:
+        """
+        output_dir = self._get_dataset_path('chn-senti-corp')
+        return output_dir

fastNLP/io/pipe/__init__.py

@@ -17,6 +17,7 @@ __all__ = [
     "SSTPipe",
     "SST2Pipe",
     "IMDBPipe",
+    "ChnSentiCorpPipe",
 
     "Conll2003NERPipe",
     "OntoNotesNERPipe",
@@ -39,7 +40,7 @@ __all__ = [
     "MNLIPipe",
 ]
 
-from .classification import YelpFullPipe, YelpPolarityPipe, SSTPipe, SST2Pipe, IMDBPipe
+from .classification import YelpFullPipe, YelpPolarityPipe, SSTPipe, SST2Pipe, IMDBPipe, ChnSentiCorpPipe
 from .conll import Conll2003NERPipe, OntoNotesNERPipe, MsraNERPipe, WeiboNERPipe, PeopleDailyPipe
 from .matching import MatchingBertPipe, RTEBertPipe, SNLIBertPipe, QuoraBertPipe, QNLIBertPipe, MNLIBertPipe, \
     MatchingPipe, RTEPipe, SNLIPipe, QuoraPipe, QNLIPipe, MNLIPipe

fastNLP/io/pipe/classification.py

@@ -5,7 +5,8 @@ __all__ = [
     "YelpPolarityPipe",
     "SSTPipe",
     "SST2Pipe",
-    'IMDBPipe'
+    'IMDBPipe',
+    "ChnSentiCorpPipe"
 ]
 
 import re
@@ -13,18 +14,18 @@ import re
 from nltk import Tree
 
 from .pipe import Pipe
-from .utils import get_tokenizer, _indexize, _add_words_field, _drop_empty_instance
+from .utils import get_tokenizer, _indexize, _add_words_field, _drop_empty_instance, _add_chars_field
 from ..data_bundle import DataBundle
 from ..loader.classification import IMDBLoader, YelpFullLoader, SSTLoader, SST2Loader, YelpPolarityLoader
 from ...core.const import Const
 from ...core.dataset import DataSet
 from ...core.instance import Instance
 from ...core.vocabulary import Vocabulary
+from ..loader.classification import ChnSentiCorpLoader
 
 nonalpnum = re.compile('[^0-9a-zA-Z?!\']+')
 
 
-
 class _CLSPipe(Pipe):
     """
     分类问题的基类，负责对classification的数据进行tokenize操作。默认是对raw_words列操作，然后生成words列
@@ -457,3 +458,97 @@ class IMDBPipe(_CLSPipe):
         data_bundle = self.process(data_bundle)
         
         return data_bundle
+
+
+class ChnSentiCorpPipe(Pipe):
+    """
+    处理之后的DataSet有以下的结构
+
+    .. csv-table::
+        :header: "raw_chars", "chars", "target", "seq_len"
+
+        "這間酒店環境和服務態度亦算不錯,但房間空間太小~~", "[2, 3, 4, 5, ...]", 1, 31
+        "<荐书> 推荐所有喜欢<红楼>...", "[10, 21, ....]", 1, 25
+        "..."
+
+    其中chars, seq_len是input，target是target
+
+    :param bool bigrams: 是否增加一列bigrams. bigrams的构成是['复', '旦', '大', '学', ...]->["复旦", "旦大", ...]。如果
+        设置为True，返回的DataSet将有一列名为bigrams, 且已经转换为了index并设置为input，对应的vocab可以通过
+        data_bundle.get_vocab('bigrams')获取.
+    :param bool trigrams: 是否增加一列trigrams. trigrams的构成是 ['复', '旦', '大', '学', ...]->["复旦大", "旦大学", ...]
+        。如果设置为True，返回的DataSet将有一列名为trigrams, 且已经转换为了index并设置为input，对应的vocab可以通过
+        data_bundle.get_vocab('trigrams')获取.
+    """
+    def __init__(self, bigrams=False, trigrams=False):
+        super().__init__()
+
+        self.bigrams = bigrams
+        self.trigrams = trigrams
+
+    def _tokenize(self, data_bundle):
+        """
+        将DataSet中的"复旦大学"拆分为["复", "旦", "大", "学"]. 未来可以通过扩展这个函数实现分词。
+
+        :param data_bundle:
+        :return:
+        """
+        data_bundle.apply_field(list, field_name=Const.CHAR_INPUT, new_field_name=Const.CHAR_INPUT)
+        return data_bundle
+
+    def process(self, data_bundle:DataBundle):
+        """
+        可以处理的DataSet应该具备以下的field
+
+        .. csv-table::
+            :header: "raw_chars", "target"
+
+            "這間酒店環境和服務態度亦算不錯,但房間空間太小~~", "1"
+            "<荐书> 推荐所有喜欢<红楼>...", "1"
+            "..."
+
+        :param data_bundle:
+        :return:
+        """
+        _add_chars_field(data_bundle, lower=False)
+
+        data_bundle = self._tokenize(data_bundle)
+
+        input_field_names = [Const.CHAR_INPUT]
+        if self.bigrams:
+            for name, dataset in data_bundle.iter_datasets():
+                dataset.apply_field(lambda chars: [c1 + c2 for c1, c2 in zip(chars, chars[1:] + ['<eos>'])],
+                                    field_name=Const.CHAR_INPUT, new_field_name='bigrams')
+            input_field_names.append('bigrams')
+        if self.trigrams:
+            for name, dataset in data_bundle.iter_datasets():
+                dataset.apply_field(lambda chars: [c1 + c2 + c3 for c1, c2, c3 in
+                                                   zip(chars, chars[1:] + ['<eos>'], chars[2:] + ['<eos>'] * 2)],
+                                    field_name=Const.CHAR_INPUT, new_field_name='trigrams')
+            input_field_names.append('trigrams')
+
+        # index
+        _indexize(data_bundle, input_field_names, Const.TARGET)
+
+        input_fields = [Const.TARGET, Const.INPUT_LEN] + input_field_names
+        target_fields = [Const.TARGET]
+
+        for name, dataset in data_bundle.datasets.items():
+            dataset.add_seq_len(Const.CHAR_INPUT)
+
+        data_bundle.set_input(*input_fields)
+        data_bundle.set_target(*target_fields)
+
+        return data_bundle
+
+    def process_from_file(self, paths=None):
+        """
+
+        :param paths: 支持路径类型参见 :class:`fastNLP.io.loader.Loader` 的load函数。
+        :return: DataBundle
+        """
+        # 读取数据
+        data_bundle = ChnSentiCorpLoader().load(paths)
+        data_bundle = self.process(data_bundle)
+
+        return data_bundle

fastNLP/io/pipe/conll.py

@@ -222,14 +222,23 @@ class _CNNERPipe(Pipe):
     target。返回的DataSet中被设置为input有chars, target, seq_len; 设置为target有target, seq_len。
 
     :param: str encoding_type: target列使用什么类型的encoding方式，支持bioes, bio两种。
+    :param bool bigrams: 是否增加一列bigrams. bigrams的构成是['复', '旦', '大', '学', ...]->["复旦", "旦大", ...]。如果
+        设置为True，返回的DataSet将有一列名为bigrams, 且已经转换为了index并设置为input，对应的vocab可以通过
+        data_bundle.get_vocab('bigrams')获取.
+    :param bool trigrams: 是否增加一列trigrams. trigrams的构成是 ['复', '旦', '大', '学', ...]->["复旦大", "旦大学", ...]
+        。如果设置为True，返回的DataSet将有一列名为trigrams, 且已经转换为了index并设置为input，对应的vocab可以通过
+        data_bundle.get_vocab('trigrams')获取.
     """
     
-    def __init__(self, encoding_type: str = 'bio'):
+    def __init__(self, encoding_type: str = 'bio', bigrams=False, trigrams=False):
         if encoding_type == 'bio':
             self.convert_tag = iob2
         else:
             self.convert_tag = lambda words: iob2bioes(iob2(words))
-    
+
+        self.bigrams = bigrams
+        self.trigrams = trigrams
+
     def process(self, data_bundle: DataBundle) -> DataBundle:
         """
         支持的DataSet的field为
@@ -241,11 +250,11 @@ class _CNNERPipe(Pipe):
            "[青, 岛, 海, 牛, 队, 和, ...]", "[B-ORG, I-ORG, I-ORG, ...]"
            "[...]", "[...]"
 
-        raw_chars列为List[str], 是未转换的原始数据; chars列为List[int]，是转换为index的输入数据; target列是List[int]，是转换为index的
-        target。返回的DataSet中被设置为input有chars, target, seq_len; 设置为target有target。
+        raw_chars列为List[str], 是未转换的原始数据; chars列为List[int]，是转换为index的输入数据; target列是List[int]，
+        是转换为index的target。返回的DataSet中被设置为input有chars, target, seq_len; 设置为target有target。
 
-        :param ~fastNLP.DataBundle data_bundle: 传入的DataBundle中的DataSet必须包含raw_words和ner两个field，且两个field的内容均为List[str]。
-            在传入DataBundle基础上原位修改。
+        :param ~fastNLP.DataBundle data_bundle: 传入的DataBundle中的DataSet必须包含raw_words和ner两个field，且两个field
+        的内容均为List[str]。在传入DataBundle基础上原位修改。
         :return: DataBundle
         """
         # 转换tag
@@ -253,11 +262,24 @@ class _CNNERPipe(Pipe):
             dataset.apply_field(self.convert_tag, field_name=Const.TARGET, new_field_name=Const.TARGET)
         
         _add_chars_field(data_bundle, lower=False)
-        
+
+        input_field_names = [Const.CHAR_INPUT]
+        if self.bigrams:
+            for name, dataset in data_bundle.datasets.items():
+                dataset.apply_field(lambda chars: [c1 + c2 for c1, c2 in zip(chars, chars[1:] + ['<eos>'])],
+                                    field_name=Const.CHAR_INPUT, new_field_name='bigrams')
+            input_field_names.append('bigrams')
+        if self.trigrams:
+            for name, dataset in data_bundle.datasets.items():
+                dataset.apply_field(lambda chars: [c1 + c2 + c3 for c1, c2, c3 in
+                                                   zip(chars, chars[1:] + ['<eos>'], chars[2:] + ['<eos>'] * 2)],
+                                    field_name=Const.CHAR_INPUT, new_field_name='trigrams')
+            input_field_names.append('trigrams')
+
         # index
-        _indexize(data_bundle, input_field_names=Const.CHAR_INPUT, target_field_names=Const.TARGET)
+        _indexize(data_bundle, input_field_names, Const.TARGET)
         
-        input_fields = [Const.TARGET, Const.CHAR_INPUT, Const.INPUT_LEN]
+        input_fields = [Const.TARGET, Const.INPUT_LEN] + input_field_names
         target_fields = [Const.TARGET, Const.INPUT_LEN]
         
         for name, dataset in data_bundle.datasets.items():

test/embeddings/test_bert_embedding.py

@@ -13,6 +13,12 @@ class TestDownload(unittest.TestCase):
         words = torch.LongTensor([[2, 3, 4, 0]])
         print(embed(words).size())
 
+        for pool_method in ['first', 'last', 'max', 'avg']:
+            for include_cls_sep in [True, False]:
+                embed = BertEmbedding(vocab, model_dir_or_name='en', pool_method=pool_method,
+                                      include_cls_sep=include_cls_sep)
+                print(embed(words).size())
+
     def test_word_drop(self):
         vocab = Vocabulary().add_word_lst("This is a test .".split())
         embed = BertEmbedding(vocab, model_dir_or_name='en', dropout=0.1, word_dropout=0.2)

test/io/loader/test_classification_loader.py

@@ -5,22 +5,22 @@ from fastNLP.io.loader.classification import YelpPolarityLoader
 from fastNLP.io.loader.classification import IMDBLoader
 from fastNLP.io.loader.classification import SST2Loader
 from fastNLP.io.loader.classification import SSTLoader
+from fastNLP.io.loader.classification import ChnSentiCorpLoader
 import os
 
 @unittest.skipIf('TRAVIS' in os.environ, "Skip in travis")
 class TestDownload(unittest.TestCase):
     def test_download(self):
-        for loader in [YelpFullLoader, YelpPolarityLoader, IMDBLoader, SST2Loader, SSTLoader]:
+        for loader in [YelpFullLoader, YelpPolarityLoader, IMDBLoader, SST2Loader, SSTLoader, ChnSentiCorpLoader]:
             loader().download()
 
     def test_load(self):
-        for loader in [YelpFullLoader, YelpPolarityLoader, IMDBLoader, SST2Loader, SSTLoader]:
+        for loader in [YelpFullLoader, YelpPolarityLoader, IMDBLoader, SST2Loader, SSTLoader, ChnSentiCorpLoader]:
             data_bundle = loader().load()
             print(data_bundle)
 
 
 class TestLoad(unittest.TestCase):
-
     def test_load(self):
         for loader in [IMDBLoader]:
             data_bundle = loader().load('test/data_for_tests/io/imdb')

test/io/loader/test_conll_loader.py

@@ -5,7 +5,7 @@ from fastNLP.io.loader.conll import MsraNERLoader, PeopleDailyNERLoader, WeiboNE
     Conll2003Loader
 
 
-class MSRANERTest(unittest.TestCase):
+class TestMSRANER(unittest.TestCase):
     @unittest.skipIf('TRAVIS' in os.environ, "Skip in travis")
     def test_download(self):
         MsraNERLoader().download(re_download=False)
@@ -13,13 +13,13 @@ class MSRANERTest(unittest.TestCase):
         print(data_bundle)
 
 
-class PeopleDailyTest(unittest.TestCase):
+class TestPeopleDaily(unittest.TestCase):
     @unittest.skipIf('TRAVIS' in os.environ, "Skip in travis")
     def test_download(self):
         PeopleDailyNERLoader().download()
 
 
-class WeiboNERTest(unittest.TestCase):
+class TestWeiboNER(unittest.TestCase):
     @unittest.skipIf('TRAVIS' in os.environ, "Skip in travis")
     def test_download(self):
         WeiboNERLoader().download()

test/io/loader/test_cws_loader.py

@@ -3,7 +3,7 @@ import os
 from fastNLP.io.loader import CWSLoader
 
 
-class CWSLoaderTest(unittest.TestCase):
+class TestCWSLoader(unittest.TestCase):
     @unittest.skipIf('TRAVIS' in os.environ, "Skip in travis")
     def test_download(self):
         dataset_names = ['pku', 'cityu', 'as', 'msra']
@@ -13,7 +13,7 @@ class CWSLoaderTest(unittest.TestCase):
                 print(data_bundle)
 
 
-class RunCWSLoaderTest(unittest.TestCase):
+class TestRunCWSLoader(unittest.TestCase):
     def test_cws_loader(self):
         dataset_names = ['msra']
         for dataset_name in dataset_names:

test/io/loader/test_matching_loader.py

@@ -8,7 +8,7 @@ from fastNLP.io.loader.matching import MNLILoader
 import os
 
 @unittest.skipIf('TRAVIS' in os.environ, "Skip in travis")
-class TestDownload(unittest.TestCase):
+class TestMatchingDownload(unittest.TestCase):
     def test_download(self):
         for loader in [RTELoader, QNLILoader, SNLILoader, MNLILoader]:
             loader().download()
@@ -21,8 +21,7 @@ class TestDownload(unittest.TestCase):
             print(data_bundle)
 
 
-class TestLoad(unittest.TestCase):
-
+class TestMatchingLoad(unittest.TestCase):
     def test_load(self):
         for loader in [RTELoader]:
             data_bundle = loader().load('test/data_for_tests/io/rte')

test/io/pipe/test_classification.py

@@ -2,9 +2,10 @@ import unittest
 import os
 
 from fastNLP.io.pipe.classification import SSTPipe, SST2Pipe, IMDBPipe, YelpFullPipe, YelpPolarityPipe
+from fastNLP.io.pipe.classification import ChnSentiCorpPipe
 
 @unittest.skipIf('TRAVIS' in os.environ, "Skip in travis")
-class TestPipe(unittest.TestCase):
+class TestClassificationPipe(unittest.TestCase):
     def test_process_from_file(self):
         for pipe in [YelpPolarityPipe, SST2Pipe, IMDBPipe, YelpFullPipe,  SSTPipe]:
             with self.subTest(pipe=pipe):
@@ -14,8 +15,16 @@ class TestPipe(unittest.TestCase):
 
 
 class TestRunPipe(unittest.TestCase):
-
     def test_load(self):
         for pipe in [IMDBPipe]:
             data_bundle = pipe(tokenizer='raw').process_from_file('test/data_for_tests/io/imdb')
             print(data_bundle)
+
+
+@unittest.skipIf('TRAVIS' in os.environ, "Skip in travis")
+class TestCNClassificationPipe(unittest.TestCase):
+    def test_process_from_file(self):
+        for pipe in [ChnSentiCorpPipe]:
+            with self.subTest(pipe=pipe):
+                data_bundle = pipe(bigrams=True, trigrams=True).process_from_file()
+                print(data_bundle)

test/io/pipe/test_conll.py

@@ -4,12 +4,14 @@ from fastNLP.io import MsraNERPipe, PeopleDailyPipe, WeiboNERPipe, Conll2003Pipe
 
 
 @unittest.skipIf('TRAVIS' in os.environ, "Skip in travis")
-class TestPipe(unittest.TestCase):
+class TestConllPipe(unittest.TestCase):
     def test_process_from_file(self):
         for pipe in [MsraNERPipe, PeopleDailyPipe, WeiboNERPipe]:
             with self.subTest(pipe=pipe):
                 print(pipe)
-                data_bundle = pipe().process_from_file()
+                data_bundle = pipe(bigrams=True, trigrams=True).process_from_file()
+                print(data_bundle)
+                data_bundle = pipe(encoding_type='bioes').process_from_file()
                 print(data_bundle)
 
 

test/io/pipe/test_cws.py

@@ -4,7 +4,7 @@ import os
 from fastNLP.io.pipe.cws import CWSPipe
 
 
-class CWSPipeTest(unittest.TestCase):
+class TestCWSPipe(unittest.TestCase):
     @unittest.skipIf('TRAVIS' in os.environ, "Skip in travis")
     def test_process_from_file(self):
         dataset_names = ['pku', 'cityu', 'as', 'msra']
@@ -14,7 +14,7 @@ class CWSPipeTest(unittest.TestCase):
                 print(data_bundle)
 
 
-class RunCWSPipeTest(unittest.TestCase):
+class TestRunCWSPipe(unittest.TestCase):
     def test_process_from_file(self):
         dataset_names = ['msra']
         for dataset_name in dataset_names:

test/io/pipe/test_matching.py

@@ -7,7 +7,7 @@ from fastNLP.io.pipe.matching import SNLIBertPipe, RTEBertPipe, QNLIBertPipe, MN
 
 
 @unittest.skipIf('TRAVIS' in os.environ, "Skip in travis")
-class TestPipe(unittest.TestCase):
+class TestMatchingPipe(unittest.TestCase):
     def test_process_from_file(self):
         for pipe in [SNLIPipe, RTEPipe, QNLIPipe, MNLIPipe]:
             with self.subTest(pipe=pipe):
@@ -17,7 +17,7 @@ class TestPipe(unittest.TestCase):
 
 
 @unittest.skipIf('TRAVIS' in os.environ, "Skip in travis")
-class TestBertPipe(unittest.TestCase):
+class TestMatchingBertPipe(unittest.TestCase):
     def test_process_from_file(self):
         for pipe in [SNLIBertPipe, RTEBertPipe, QNLIBertPipe, MNLIBertPipe]:
             with self.subTest(pipe=pipe):
@@ -26,7 +26,7 @@ class TestBertPipe(unittest.TestCase):
                 print(data_bundle)
 
 
-class TestRunPipe(unittest.TestCase):
+class TestRunMatchingPipe(unittest.TestCase):
 
     def test_load(self):
         for pipe in [RTEPipe, RTEBertPipe]: