Ready for V0.3.1

* 升级parser API和模型
* update docs: add new pages for tutorials
* upgrade CWS api download source
* add a new method for dataset field access
* add introduction for bert
* add more unit tests for api/processor
* remove unused test data. Add new test data.

docs/source/tutorials/fastnlp_10tmin_tutorial.rst

@@ -1,7 +1,8 @@
-
-fastNLP上手教程
+fastNLP 10分钟上手教程
 ===============
 
+教程原文见 https://github.com/fastnlp/fastNLP/blob/master/tutorials/fastnlp_10min_tutorial.ipynb
+
 fastNLP提供方便的数据预处理，训练和测试模型的功能
 
 DataSet & Instance

docs/source/tutorials/fastnlp_1_minute_tutorial.rst

@@ -2,6 +2,8 @@
 FastNLP 1分钟上手教程
 =====================
 
+教程原文见 https://github.com/fastnlp/fastNLP/blob/master/tutorials/fastnlp_1min_tutorial.ipynb
+
 step 1
 ------
 

docs/source/tutorials/fastnlp_advanced_tutorial.rst

@@ -0,0 +1,5 @@
+fastNLP 进阶教程
+===============
+
+教程原文见 https://github.com/fastnlp/fastNLP/blob/master/tutorials/fastnlp_advanced_tutorial/advance_tutorial.ipynb
+

docs/source/tutorials/fastnlp_developer_guide.rst

@@ -0,0 +1,5 @@
+fastNLP 开发者指南
+===============
+
+原文见 https://github.com/fastnlp/fastNLP/blob/master/tutorials/tutorial_for_developer.md
+

docs/source/user/installation.rst

@@ -5,6 +5,7 @@ Installation
 .. contents::
    :local:
 
+Make sure your environment satisfies https://github.com/fastnlp/fastNLP/blob/master/requirements.txt .
 
 Run the following commands to install fastNLP package:
 

docs/source/user/quickstart.rst

@@ -6,4 +6,6 @@ Quickstart
 
    ../tutorials/fastnlp_1_minute_tutorial
    ../tutorials/fastnlp_10tmin_tutorial
+   ../tutorials/fastnlp_advanced_tutorial
+   ../tutorials/fastnlp_developer_guide
 

fastNLP/api/api.py

@@ -9,7 +9,7 @@ from fastNLP.core.dataset import DataSet
 
 from fastNLP.api.utils import load_url
 from fastNLP.api.processor import ModelProcessor
-from fastNLP.io.dataset_loader import ConllCWSReader, ConllxDataLoader, add_seg_tag
+from fastNLP.io.dataset_loader import ConllCWSReader, ConllxDataLoader
 from fastNLP.core.instance import Instance
 from fastNLP.api.pipeline import Pipeline
 from fastNLP.core.metrics import SpanFPreRecMetric
@@ -17,9 +17,9 @@ from fastNLP.api.processor import IndexerProcessor
 
 # TODO add pretrain urls
 model_urls = {
-    "cws": "http://123.206.98.91:8888/download/cws_crf_1_11-457fc899.pkl",
+    "cws": "http://123.206.98.91:8888/download/cws_lstm_ctb9_1_20-09908656.pkl",
     "pos": "http://123.206.98.91:8888/download/pos_tag_model_20190119-43f8b435.pkl",
-    "parser": "http://123.206.98.91:8888/download/biaffine_parser-3a2f052c.pkl"
+    "parser": "http://123.206.98.91:8888/download/parser_20190204-c72ca5c0.pkl"
 }
 
 
@@ -90,38 +90,28 @@ class POS(API):
         # 3. 使用pipeline
         self.pipeline(dataset)
 
-        # def decode_tags(ins):
-        #     pred_tags = ins["tag"]
-        #     chars = ins["words"]
-        #     words = []
-        #     start_idx = 0
-        #     for idx, tag in enumerate(pred_tags):
-        #         if tag[0] == "S":
-        #             words.append(chars[start_idx:idx + 1] + "/" + tag[2:])
-        #             start_idx = idx + 1
-        #         elif tag[0] == "E":
-        #             words.append("".join(chars[start_idx:idx + 1]) + "/" + tag[2:])
-        #             start_idx = idx + 1
-        #     return words
-        #
-        # dataset.apply(decode_tags, new_field_name="tag_output")
+        def merge_tag(words_list, tags_list):
+            rtn = []
+            for words, tags in zip(words_list, tags_list):
+                rtn.append([w + "/" + t for w, t in zip(words, tags)])
+            return rtn
 
         output = dataset.field_arrays["tag"].content
         if isinstance(content, str):
             return output[0]
         elif isinstance(content, list):
-            return output
+            return merge_tag(content, output)
 
     def test(self, file_path):
         test_data = ConllxDataLoader().load(file_path)
 
-        with open("model_pp_0117.pkl", "rb") as f:
-            save_dict = torch.load(f)
+        save_dict = self._dict
         tag_vocab = save_dict["tag_vocab"]
         pipeline = save_dict["pipeline"]
         index_tag = IndexerProcessor(vocab=tag_vocab, field_name="tag", new_added_field_name="truth", is_input=False)
         pipeline.pipeline = [index_tag] + pipeline.pipeline
 
+        test_data.rename_field("pos_tags", "tag")
         pipeline(test_data)
         test_data.set_target("truth")
         prediction = test_data.field_arrays["predict"].content
@@ -235,7 +225,7 @@ class CWS(API):
         rec = eval_res['BMESF1PreRecMetric']['rec']
         # print("f1:{:.2f}, pre:{:.2f}, rec:{:.2f}".format(f1, pre, rec))
 
-        return f1, pre, rec
+        return {"F1": f1, "precision": pre, "recall": rec}
 
 
 class Parser(API):
@@ -260,6 +250,7 @@ class Parser(API):
         dataset.add_field('wp', pos_out)
         dataset.apply(lambda x: ['<BOS>'] + [w.split('/')[0] for w in x['wp']], new_field_name='words')
         dataset.apply(lambda x: ['<BOS>'] + [w.split('/')[1] for w in x['wp']], new_field_name='pos')
+        dataset.rename_field("words", "raw_words")
 
         # 3. 使用pipeline
         self.pipeline(dataset)
@@ -269,31 +260,74 @@ class Parser(API):
         # output like: [['2/top', '0/root', '4/nn', '2/dep']]
         return dataset.field_arrays['output'].content
 
-    def test(self, filepath):
-        data = ConllxDataLoader().load(filepath)
-        ds = DataSet()
-        for ins1, ins2 in zip(add_seg_tag(data), data):
-            ds.append(Instance(words=ins1[0], tag=ins1[1],
-                               gold_words=ins2[0], gold_pos=ins2[1],
-                               gold_heads=ins2[2], gold_head_tags=ins2[3]))
+    def load_test_file(self, path):
+        def get_one(sample):
+            sample = list(map(list, zip(*sample)))
+            if len(sample) == 0:
+                return None
+            for w in sample[7]:
+                if w == '_':
+                    print('Error Sample {}'.format(sample))
+                    return None
+            # return word_seq, pos_seq, head_seq, head_tag_seq
+            return sample[1], sample[3], list(map(int, sample[6])), sample[7]
+
+        datalist = []
+        with open(path, 'r', encoding='utf-8') as f:
+            sample = []
+            for line in f:
+                if line.startswith('\n'):
+                    datalist.append(sample)
+                    sample = []
+                elif line.startswith('#'):
+                    continue
+                else:
+                    sample.append(line.split('\t'))
+            if len(sample) > 0:
+                datalist.append(sample)
+
+        data = [get_one(sample) for sample in datalist]
+        data_list = list(filter(lambda x: x is not None, data))
+        return data_list
 
+    def test(self, filepath):
+        data = self.load_test_file(filepath)
+
+        def convert(data):
+            BOS = '<BOS>'
+            dataset = DataSet()
+            for sample in data:
+                word_seq = [BOS] + sample[0]
+                pos_seq = [BOS] + sample[1]
+                heads = [0] + sample[2]
+                head_tags = [BOS] + sample[3]
+                dataset.append(Instance(raw_words=word_seq,
+                                        pos=pos_seq,
+                                        gold_heads=heads,
+                                        arc_true=heads,
+                                        tags=head_tags))
+            return dataset
+
+        ds = convert(data)
         pp = self.pipeline
         for p in pp:
             if p.field_name == 'word_list':
                 p.field_name = 'gold_words'
             elif p.field_name == 'pos_list':
                 p.field_name = 'gold_pos'
+        # ds.rename_field("words", "raw_words")
+        # ds.rename_field("tag", "pos")
         pp(ds)
         head_cor, label_cor, total = 0, 0, 0
         for ins in ds:
             head_gold = ins['gold_heads']
-            head_pred = ins['heads']
+            head_pred = ins['arc_pred']
             length = len(head_gold)
             total += length
             for i in range(length):
                 head_cor += 1 if head_pred[i] == head_gold[i] else 0
         uas = head_cor / total
-        print('uas:{:.2f}'.format(uas))
+        # print('uas:{:.2f}'.format(uas))
 
         for p in pp:
             if p.field_name == 'gold_words':
@@ -301,7 +335,7 @@ class Parser(API):
             elif p.field_name == 'gold_pos':
                 p.field_name = 'pos_list'
 
-        return uas
+        return {"USA": round(uas, 5)}
 
 
 class Analyzer:

fastNLP/api/examples.py

@@ -15,19 +15,40 @@ def chinese_word_segmentation():
     print(cws.predict(text))
 
 
+def chinese_word_segmentation_test():
+    cws = CWS(device='cpu')
+    print(cws.test("../../test/data_for_tests/zh_sample.conllx"))
+
+
 def pos_tagging():
     # 输入已分词序列
     text = ['编者 按： 7月 12日 ， 英国 航空 航天 系统 公司 公布 了 该 公司 研制 的 第一款 高科技 隐形 无人机 雷电之神 。']
     text = [text[0].split()]
-    print(text)
     pos = POS(device='cpu')
     print(pos.predict(text))
 
 
+def pos_tagging_test():
+    pos = POS(device='cpu')
+    print(pos.test("../../test/data_for_tests/zh_sample.conllx"))
+
+
 def syntactic_parsing():
+    text = ['编者 按： 7月 12日 ， 英国 航空 航天 系统 公司 公布 了 该 公司 研制 的 第一款 高科技 隐形 无人机 雷电之神 。']
+    text = [text[0].split()]
     parser = Parser(device='cpu')
     print(parser.predict(text))
 
 
+def syntactic_parsing_test():
+    parser = Parser(device='cpu')
+    print(parser.test("../../test/data_for_tests/zh_sample.conllx"))
+
+
 if __name__ == "__main__":
+    chinese_word_segmentation()
+    chinese_word_segmentation_test()
     pos_tagging()
+    pos_tagging_test()
+    syntactic_parsing()
+    syntactic_parsing_test()

fastNLP/api/processor.py

@@ -102,6 +102,7 @@ class PreAppendProcessor(Processor):
         [data] + instance[field_name]
 
     """
+
     def __init__(self, data, field_name, new_added_field_name=None):
         super(PreAppendProcessor, self).__init__(field_name, new_added_field_name)
         self.data = data
@@ -116,6 +117,7 @@ class SliceProcessor(Processor):
     从某个field中只取部分内容。等价于instance[field_name][start:end:step]
 
     """
+
     def __init__(self, start, end, step, field_name, new_added_field_name=None):
         super(SliceProcessor, self).__init__(field_name, new_added_field_name)
         for o in (start, end, step):
@@ -132,6 +134,7 @@ class Num2TagProcessor(Processor):
     将一句话中的数字转换为某个tag。
 
     """
+
     def __init__(self, tag, field_name, new_added_field_name=None):
         """
 
@@ -163,6 +166,7 @@ class IndexerProcessor(Processor):
     给定一个vocabulary , 将指定field转换为index形式。指定field应该是一维的list，比如
         ['我', '是', xxx]
     """
+
     def __init__(self, vocab, field_name, new_added_field_name, delete_old_field=False, is_input=True):
 
         assert isinstance(vocab, Vocabulary), "Only Vocabulary class is allowed, not {}.".format(type(vocab))
@@ -215,6 +219,7 @@ class SeqLenProcessor(Processor):
     根据某个field新增一个sequence length的field。取该field的第一维
 
     """
+
     def __init__(self, field_name, new_added_field_name='seq_lens', is_input=True):
         super(SeqLenProcessor, self).__init__(field_name, new_added_field_name)
         self.is_input = is_input
@@ -229,6 +234,7 @@ class SeqLenProcessor(Processor):
 
 from fastNLP.core.utils import _build_args
 
+
 class ModelProcessor(Processor):
     def __init__(self, model, seq_len_field_name='seq_lens', batch_size=32):
         """
@@ -292,6 +298,7 @@ class Index2WordProcessor(Processor):
     将DataSet中某个为index的field根据vocab转换为str
 
     """
+
     def __init__(self, vocab, field_name, new_added_field_name):
         super(Index2WordProcessor, self).__init__(field_name, new_added_field_name)
         self.vocab = vocab
@@ -303,7 +310,6 @@ class Index2WordProcessor(Processor):
 
 
 class SetTargetProcessor(Processor):
-    # TODO; remove it.
     def __init__(self, *fields, flag=True):
         super(SetTargetProcessor, self).__init__(None, None)
         self.fields = fields
@@ -313,6 +319,7 @@ class SetTargetProcessor(Processor):
         dataset.set_target(*self.fields, flag=self.flag)
         return dataset
 
+
 class SetInputProcessor(Processor):
     def __init__(self, *fields, flag=True):
         super(SetInputProcessor, self).__init__(None, None)

fastNLP/core/dataset.py

@@ -92,6 +92,10 @@ class DataSet(object):
                 data_set.add_field(name=field.name, fields=field.content[idx], padder=field.padder,
                                    is_input=field.is_input, is_target=field.is_target)
             return data_set
+        elif isinstance(idx, str):
+            if idx not in self:
+                raise KeyError("No such field called {} in DataSet.".format(idx))
+            return self.field_arrays[idx]
         else:
             raise KeyError("Unrecognized type {} for idx in __getitem__ method".format(type(idx)))
 

fastNLP/models/bert.py

@@ -1,3 +1,7 @@
+"""
+bert.py is modified from huggingface/pytorch-pretrained-BERT, which is licensed under the Apache License 2.0.
+
+"""
 import copy
 import json
 import math
@@ -220,7 +224,23 @@ class BertPooler(nn.Module):
 
 
 class BertModel(nn.Module):
-    """BERT model ("Bidirectional Embedding Representations from a Transformer").
+    """Bidirectional Embedding Representations from Transformers.
+
+    If you want to use pre-trained weights, please download from the following sources provided by pytorch-pretrained-BERT.
+    sources::
+
+    'bert-base-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-uncased.tar.gz",
+    'bert-large-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-uncased.tar.gz",
+    'bert-base-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-cased.tar.gz",
+    'bert-large-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-cased.tar.gz",
+    'bert-base-multilingual-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-multilingual-uncased.tar.gz",
+    'bert-base-multilingual-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-multilingual-cased.tar.gz",
+    'bert-base-chinese': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-chinese.tar.gz",
+
+
+    Construct a BERT model with pre-trained weights::
+
+        model = BertModel.from_pretrained("path/to/weights/directory")
 
     """
 

reproduction/Biaffine_parser/cfg.cfg

@@ -1,5 +1,5 @@
 [train]
-n_epochs = 1
+n_epochs = 20
 batch_size = 32
 use_cuda = true
 use_tqdm=true

test/api/test_processor.py

@@ -1,9 +1,12 @@
 import random
 import unittest
 
-from fastNLP import Vocabulary
+import numpy as np
+
+from fastNLP import Vocabulary, Instance
 from fastNLP.api.processor import FullSpaceToHalfSpaceProcessor, PreAppendProcessor, SliceProcessor, Num2TagProcessor, \
-    IndexerProcessor, VocabProcessor, SeqLenProcessor
+    IndexerProcessor, VocabProcessor, SeqLenProcessor, ModelProcessor, Index2WordProcessor, SetTargetProcessor, \
+    SetInputProcessor, VocabIndexerProcessor
 from fastNLP.core.dataset import DataSet
 
 
@@ -53,3 +56,46 @@ class TestProcessor(unittest.TestCase):
         ds = proc(ds)
         for data in ds.field_arrays["len"].content:
             self.assertEqual(data, 30)
+
+    def test_ModelProcessor(self):
+        from fastNLP.models.cnn_text_classification import CNNText
+        model = CNNText(100, 100, 5)
+        ins_list = []
+        for _ in range(64):
+            seq_len = np.random.randint(5, 30)
+            ins_list.append(Instance(word_seq=[np.random.randint(0, 100) for _ in range(seq_len)], seq_lens=seq_len))
+        data_set = DataSet(ins_list)
+        data_set.set_input("word_seq", "seq_lens")
+        proc = ModelProcessor(model)
+        data_set = proc(data_set)
+        self.assertTrue("pred" in data_set)
+
+    def test_Index2WordProcessor(self):
+        vocab = Vocabulary()
+        vocab.add_word_lst(["a", "b", "c", "d", "e"])
+        proc = Index2WordProcessor(vocab, "tag_id", "tag")
+        data_set = DataSet([Instance(tag_id=[np.random.randint(0, 7) for _ in range(32)])])
+        data_set = proc(data_set)
+        self.assertTrue("tag" in data_set)
+
+    def test_SetTargetProcessor(self):
+        proc = SetTargetProcessor("a", "b", "c")
+        data_set = DataSet({"a": [1, 2, 3], "b": [1, 2, 3], "c": [1, 2, 3]})
+        data_set = proc(data_set)
+        self.assertTrue(data_set["a"].is_target)
+        self.assertTrue(data_set["b"].is_target)
+        self.assertTrue(data_set["c"].is_target)
+
+    def test_SetInputProcessor(self):
+        proc = SetInputProcessor("a", "b", "c")
+        data_set = DataSet({"a": [1, 2, 3], "b": [1, 2, 3], "c": [1, 2, 3]})
+        data_set = proc(data_set)
+        self.assertTrue(data_set["a"].is_input)
+        self.assertTrue(data_set["b"].is_input)
+        self.assertTrue(data_set["c"].is_input)
+
+    def test_VocabIndexerProcessor(self):
+        proc = VocabIndexerProcessor("word_seq", "word_ids")
+        data_set = DataSet([Instance(word_seq=["a", "b", "c", "d", "e"])])
+        data_set = proc(data_set)
+        self.assertTrue("word_ids" in data_set)

test/data_for_tests/people_infer.txt

@@ -1,2 +0,0 @@
-迈向充满希望的新世纪——一九九八年新年讲话
-（附图片1张）

test/data_for_tests/zh_sample.conllx

@@ -0,0 +1,100 @@
+1	上海	_	NR	NR	_	3	nsubj	_	_
+2	积极	_	AD	AD	_	3	advmod	_	_
+3	准备	_	VV	VV	_	0	root	_	_
+4	迎接	_	VV	VV	_	3	ccomp	_	_
+5	欧元	_	NN	NN	_	6	nn	_	_
+6	诞生	_	NN	NN	_	4	dobj	_	_
+
+1	新华社	_	NR	NR	_	7	dep	_	_
+2	上海	_	NR	NR	_	7	dep	_	_
+3	十二月	_	NT	NT	_	7	dep	_	_
+4	三十日	_	NT	NT	_	7	dep	_	_
+5	电	_	NN	NN	_	7	dep	_	_
+6	（	_	PU	PU	_	7	punct	_	_
+7	记者	_	NN	NN	_	0	root	_	_
+8	潘清	_	NR	NR	_	7	dep	_	_
+9	）	_	PU	PU	_	7	punct	_	_
+
+1	即将	_	AD	AD	_	2	advmod	_	_
+2	诞生	_	VV	VV	_	4	rcmod	_	_
+3	的	_	DEC	DEC	_	2	cpm	_	_
+4	欧元	_	NN	NN	_	6	nsubj	_	_
+5	，	_	PU	PU	_	6	punct	_	_
+6	引起	_	VV	VV	_	0	root	_	_
+7	了	_	AS	AS	_	6	asp	_	_
+8	上海	_	NR	NR	_	14	nn	_	_
+9	这	_	DT	DT	_	14	det	_	_
+10	个	_	M	M	_	9	clf	_	_
+11	中国	_	NR	NR	_	13	nn	_	_
+12	金融	_	NN	NN	_	13	nn	_	_
+13	中心	_	NN	NN	_	14	nn	_	_
+14	城市	_	NN	NN	_	16	assmod	_	_
+15	的	_	DEG	DEG	_	14	assm	_	_
+16	关注	_	NN	NN	_	6	dobj	_	_
+17	。	_	PU	PU	_	6	punct	_	_
+
+1	上海	_	NR	NR	_	2	nn	_	_
+2	银行界	_	NN	NN	_	4	nsubj	_	_
+3	纷纷	_	AD	AD	_	4	advmod	_	_
+4	推出	_	VV	VV	_	0	root	_	_
+5	了	_	AS	AS	_	4	asp	_	_
+6	与	_	P	P	_	8	prep	_	_
+7	之	_	PN	PN	_	6	pobj	_	_
+8	相关	_	VA	VA	_	15	rcmod	_	_
+9	的	_	DEC	DEC	_	8	cpm	_	_
+10	外汇	_	NN	NN	_	15	nn	_	_
+11	业务	_	NN	NN	_	15	nn	_	_
+12	品种	_	NN	NN	_	15	conj	_	_
+13	和	_	CC	CC	_	15	cc	_	_
+14	服务	_	NN	NN	_	15	nn	_	_
+15	举措	_	NN	NN	_	4	dobj	_	_
+16	，	_	PU	PU	_	4	punct	_	_
+17	积极	_	AD	AD	_	18	advmod	_	_
+18	准备	_	VV	VV	_	4	dep	_	_
+19	启动	_	VV	VV	_	18	ccomp	_	_
+20	欧元	_	NN	NN	_	21	nn	_	_
+21	业务	_	NN	NN	_	19	dobj	_	_
+22	。	_	PU	PU	_	4	punct	_	_
+
+1	一些	_	CD	CD	_	8	nummod	_	_
+2	热衷于	_	VV	VV	_	8	rcmod	_	_
+3	个人	_	NN	NN	_	5	nn	_	_
+4	外汇	_	NN	NN	_	5	nn	_	_
+5	交易	_	NN	NN	_	2	dobj	_	_
+6	的	_	DEC	DEC	_	2	cpm	_	_
+7	上海	_	NR	NR	_	8	nn	_	_
+8	市民	_	NN	NN	_	13	nsubj	_	_
+9	，	_	PU	PU	_	13	punct	_	_
+10	也	_	AD	AD	_	13	advmod	_	_
+11	对	_	P	P	_	13	prep	_	_
+12	欧元	_	NN	NN	_	11	pobj	_	_
+13	表示	_	VV	VV	_	0	root	_	_
+14	出	_	VV	VV	_	13	rcomp	_	_
+15	极	_	AD	AD	_	16	advmod	_	_
+16	大	_	VA	VA	_	18	rcmod	_	_
+17	的	_	DEC	DEC	_	16	cpm	_	_
+18	兴趣	_	NN	NN	_	13	dobj	_	_
+19	。	_	PU	PU	_	13	punct	_	_
+
+1	继	_	P	P	_	38	prep	_	_
+2	上海	_	NR	NR	_	6	nn	_	_
+3	大众	_	NR	NR	_	6	nn	_	_
+4	汽车	_	NN	NN	_	6	nn	_	_
+5	有限	_	JJ	JJ	_	6	amod	_	_
+6	公司	_	NN	NN	_	13	nsubj	_	_
+7	十八日	_	NT	NT	_	13	tmod	_	_
+8	在	_	P	P	_	13	prep	_	_
+9	中国	_	NR	NR	_	10	nn	_	_
+10	银行	_	NN	NN	_	12	nn	_	_
+11	上海	_	NR	NR	_	12	nn	_	_
+12	分行	_	NN	NN	_	8	pobj	_	_
+13	开立	_	VV	VV	_	19	lccomp	_	_
+14	上海	_	NR	NR	_	16	dep	_	_
+15	第一	_	OD	OD	_	16	ordmod	_	_
+16	个	_	M	M	_	18	clf	_	_
+17	欧元	_	NN	NN	_	18	nn	_	_
+18	帐户	_	NN	NN	_	13	dobj	_	_
+19	后	_	LC	LC	_	1	plmod	_	_
+20	，	_	PU	PU	_	38	punct	_	_
+21	工商	_	NN	NN	_	28	nn	_	_
+22	银行	_	NN	NN	_	28	conj	_	_