Merge branch 'dataset' of github.com:yhcc/fastNLP into dataset

fastNLP/api/converter.py

@@ -0,0 +1,182 @@
+import re
+
+
+class SpanConverter:
+    def __init__(self, replace_tag, pattern):
+        super(SpanConverter, self).__init__()
+
+        self.replace_tag = replace_tag
+        self.pattern = pattern
+
+    def find_certain_span_and_replace(self, sentence):
+        replaced_sentence = ''
+        prev_end = 0
+        for match in re.finditer(self.pattern, sentence):
+            start, end = match.span()
+            span = sentence[start:end]
+            replaced_sentence += sentence[prev_end:start] + \
+                                 self.span_to_special_tag(span)
+            prev_end = end
+        replaced_sentence += sentence[prev_end:]
+
+        return replaced_sentence
+
+    def span_to_special_tag(self, span):
+
+        return self.replace_tag
+
+    def find_certain_span(self, sentence):
+        spans = []
+        for match in re.finditer(self.pattern, sentence):
+            spans.append(match.span())
+        return spans
+
+
+class AlphaSpanConverter(SpanConverter):
+    def __init__(self):
+        replace_tag = '<ALPHA>'
+        # 理想状态下仅处理纯为字母的情况, 但不处理<[a-zA-Z]+>(因为这应该是特殊的tag).
+        pattern = '[a-zA-Z]+(?=[\u4e00-\u9fff ,%.!<\\-"])'
+
+        super(AlphaSpanConverter, self).__init__(replace_tag, pattern)
+
+
+class DigitSpanConverter(SpanConverter):
+    def __init__(self):
+        replace_tag = '<NUM>'
+        pattern = '\d[\d\\.]*(?=[\u4e00-\u9fff  ,%.!<-])'
+
+        super(DigitSpanConverter, self).__init__(replace_tag, pattern)
+
+    def span_to_special_tag(self, span):
+        # return self.special_tag
+        if span[0] == '0' and len(span) > 2:
+            return '<NUM>'
+        decimal_point_count = 0  # one might have more than one decimal pointers
+        for idx, char in enumerate(span):
+            if char == '.' or char == '﹒' or char == '·':
+                decimal_point_count += 1
+        if span[-1] == '.' or span[-1] == '﹒' or span[
+            -1] == '·':  # last digit being decimal point means this is not a number
+            if decimal_point_count == 1:
+                return span
+            else:
+                return '<UNKDGT>'
+        if decimal_point_count == 1:
+            return '<DEC>'
+        elif decimal_point_count > 1:
+            return '<UNKDGT>'
+        else:
+            return '<NUM>'
+
+
+class TimeConverter(SpanConverter):
+    def __init__(self):
+        replace_tag = '<TOC>'
+        pattern = '\d+[:：∶][\d:：∶]+(?=[\u4e00-\u9fff  ,%.!<-])'
+
+        super().__init__(replace_tag, pattern)
+
+
+class MixNumAlphaConverter(SpanConverter):
+    def __init__(self):
+        replace_tag = '<MIX>'
+        pattern = None
+
+        super().__init__(replace_tag, pattern)
+
+    def find_certain_span_and_replace(self, sentence):
+        replaced_sentence = ''
+        start = 0
+        matching_flag = False
+        number_flag = False
+        alpha_flag = False
+        link_flag = False
+        slash_flag = False
+        bracket_flag = False
+        for idx in range(len(sentence)):
+            if re.match('[0-9a-zA-Z/\\(\\)\'′&\\-]', sentence[idx]):
+                if not matching_flag:
+                    replaced_sentence += sentence[start:idx]
+                    start = idx
+                if re.match('[0-9]', sentence[idx]):
+                    number_flag = True
+                elif re.match('[\'′&\\-]', sentence[idx]):
+                    link_flag = True
+                elif re.match('/', sentence[idx]):
+                    slash_flag = True
+                elif re.match('[\\(\\)]', sentence[idx]):
+                    bracket_flag = True
+                else:
+                    alpha_flag = True
+                matching_flag = True
+            elif re.match('[\\.]', sentence[idx]):
+                pass
+            else:
+                if matching_flag:
+                    if (number_flag and alpha_flag) or (link_flag and alpha_flag) \
+                            or (slash_flag and alpha_flag) or (link_flag and number_flag) \
+                            or (number_flag and bracket_flag) or (bracket_flag and alpha_flag):
+                        span = sentence[start:idx]
+                        start = idx
+                        replaced_sentence += self.span_to_special_tag(span)
+                    matching_flag = False
+                    number_flag = False
+                    alpha_flag = False
+                    link_flag = False
+                    slash_flag = False
+                    bracket_flag = False
+
+        replaced_sentence += sentence[start:]
+        return replaced_sentence
+
+    def find_certain_span(self, sentence):
+        spans = []
+        start = 0
+        matching_flag = False
+        number_flag = False
+        alpha_flag = False
+        link_flag = False
+        slash_flag = False
+        bracket_flag = False
+        for idx in range(len(sentence)):
+            if re.match('[0-9a-zA-Z/\\(\\)\'′&\\-]', sentence[idx]):
+                if not matching_flag:
+                    start = idx
+                if re.match('[0-9]', sentence[idx]):
+                    number_flag = True
+                elif re.match('[\'′&\\-]', sentence[idx]):
+                    link_flag = True
+                elif re.match('/', sentence[idx]):
+                    slash_flag = True
+                elif re.match('[\\(\\)]', sentence[idx]):
+                    bracket_flag = True
+                else:
+                    alpha_flag = True
+                matching_flag = True
+            elif re.match('[\\.]', sentence[idx]):
+                pass
+            else:
+                if matching_flag:
+                    if (number_flag and alpha_flag) or (link_flag and alpha_flag) \
+                            or (slash_flag and alpha_flag) or (link_flag and number_flag) \
+                            or (number_flag and bracket_flag) or (bracket_flag and alpha_flag):
+                        spans.append((start, idx))
+                        start = idx
+
+                    matching_flag = False
+                    number_flag = False
+                    alpha_flag = False
+                    link_flag = False
+                    slash_flag = False
+                    bracket_flag = False
+
+        return spans
+
+
+class EmailConverter(SpanConverter):
+    def __init__(self):
+        replaced_tag = "<EML>"
+        pattern = '[0-9a-zA-Z]+[@][.﹒0-9a-zA-Z@]+(?=[\u4e00-\u9fff  ,%.!<\\-"$])'
+
+        super(EmailConverter, self).__init__(replaced_tag, pattern)

fastNLP/api/pipeline.py

@@ -1,17 +1,25 @@
 from fastNLP.api.processor import Processor
 
 
-
 class Pipeline:
-    def __init__(self):
+    """
+        Pipeline takes a DataSet object as input, runs multiple processors sequentially, and
+        outputs a DataSet object.
+    """
+
+    def __init__(self, processors=None):
         self.pipeline = []
+        if isinstance(processors, list):
+            for proc in processors:
+                assert isinstance(proc, Processor), "Must be a Processor, not {}.".format(type(processor))
+            self.pipeline = processors
 
     def add_processor(self, processor):
         assert isinstance(processor, Processor), "Must be a Processor, not {}.".format(type(processor))
         self.pipeline.append(processor)
 
     def process(self, dataset):
-        assert len(self.pipeline)!=0, "You need to add some processor first."
+        assert len(self.pipeline) != 0, "You need to add some processor first."
 
         for proc in self.pipeline:
             dataset = proc(dataset)

fastNLP/api/pos_tagger.py

@@ -1,45 +0,0 @@
-import pickle
-
-import numpy as np
-
-from fastNLP.core.dataset import DataSet
-from fastNLP.loader.model_loader import ModelLoader
-from fastNLP.core.predictor import Predictor
-from fastNLP.api.api import API
-
-
-class POS_tagger(API):
-    def __init__(self):
-        pass
-
-    def predict(self, query):
-        """ 
-        :param query: List[str]
-        :return answer: List[str]
-
-        """
-        # TODO: 根据query 构建DataSet
-        pos_dataset = DataSet()
-        pos_dataset["text_field"] = np.array(query)
-
-        # 加载pipeline和model
-        pipeline = self.load_pipeline("./xxxx")
-
-        # 将DataSet作为参数运行 pipeline
-        pos_dataset = pipeline(pos_dataset)
-
-        # 加载模型
-        model = ModelLoader().load_pytorch("./xxx")
-
-        # 调 predictor
-        predictor = Predictor()
-        output = predictor.predict(model, pos_dataset)
-
-        # TODO: 转成最终输出
-        return None
-
-    @staticmethod
-    def load_pipeline(path):
-        with open(path, "r") as fp:
-            pipeline = pickle.load(fp)
-        return pipeline

fastNLP/api/processor.py

@@ -1,7 +1,7 @@
-
 from fastNLP.core.dataset import DataSet
 from fastNLP.core.vocabulary import Vocabulary
 
+
 import re
 
 class Processor:
@@ -12,15 +12,18 @@ class Processor:
         else:
             self.new_added_field_name = new_added_field_name
 
-    def process(self):
+    def process(self, *args, **kwargs):
         pass
 
     def __call__(self, *args, **kwargs):
         return self.process(*args, **kwargs)
 
 
-
 class FullSpaceToHalfSpaceProcessor(Processor):
+    """全角转半角，以字符为处理单元
+
+    """
+
     def __init__(self, field_name, change_alpha=True, change_digit=True, change_punctuation=True,
                  change_space=True):
         super(FullSpaceToHalfSpaceProcessor, self).__init__(field_name, None)
@@ -71,7 +74,7 @@ class FullSpaceToHalfSpaceProcessor(Processor):
         assert isinstance(dataset, DataSet), "Only Dataset class is allowed, not {}.".format(type(dataset))
         for ins in dataset:
             sentence = ins[self.field_name]
-            new_sentence = [None]*len(sentence)
+            new_sentence = [None] * len(sentence)
             for idx, char in enumerate(sentence):
                 if char in self.convert_map:
                     char = self.convert_map[char]
@@ -132,7 +135,7 @@ class IndexerProcessor(Processor):
             index = [self.vocab.to_index(token) for token in tokens]
             ins[self.new_added_field_name] = index
 
-        dataset.set_need_tensor(**{self.new_added_field_name:True})
+        dataset.set_need_tensor(**{self.new_added_field_name: True})
 
         if self.delete_old_field:
             dataset.delete_field(self.field_name)
@@ -156,3 +159,16 @@ class VocabProcessor(Processor):
     def get_vocab(self):
         self.vocab.build_vocab()
         return self.vocab
+
+
+class SeqLenProcessor(Processor):
+    def __init__(self, field_name, new_added_field_name='seq_lens'):
+        super(SeqLenProcessor, self).__init__(field_name, new_added_field_name)
+
+    def process(self, dataset):
+        assert isinstance(dataset, DataSet), "Only Dataset class is allowed, not {}.".format(type(dataset))
+        for ins in dataset:
+            length = len(ins[self.field_name])
+            ins[self.new_added_field_name] = length
+        dataset.set_need_tensor(**{self.new_added_field_name: True})
+        return dataset

fastNLP/core/batch.py

@@ -1,5 +1,3 @@
-from collections import defaultdict
-
 import torch
 
 
@@ -68,4 +66,3 @@ class Batch(object):
             self.curidx = endidx
 
             return batch_x, batch_y
-

fastNLP/core/dataset.py

@@ -1,23 +1,27 @@
-import random
-import sys, os
-sys.path.append('../..')
-sys.path = [os.path.join(os.path.dirname(__file__), '../..')] + sys.path
-
-from collections import defaultdict
-from copy import deepcopy
-import numpy as np
-
-from fastNLP.core.field import TextField, LabelField
-from fastNLP.core.instance import Instance
-from fastNLP.core.vocabulary import Vocabulary
 from fastNLP.core.fieldarray import FieldArray
 
 _READERS = {}
 
+
+def construct_dataset(sentences):
+    """Construct a data set from a list of sentences.
+
+    :param sentences: list of str
+    :return dataset: a DataSet object
+    """
+    dataset = DataSet()
+    for sentence in sentences:
+        instance = Instance()
+        instance['raw_sentence'] = sentence
+        dataset.append(instance)
+    return dataset
+
+
 class DataSet(object):
     """A DataSet object is a list of Instance objects.
 
     """
+
     class DataSetIter(object):
         def __init__(self, dataset):
             self.dataset = dataset
@@ -34,13 +38,12 @@ class DataSet(object):
 
         def __setitem__(self, name, val):
             if name not in self.dataset:
-                new_fields = [None]*len(self.dataset)
+                new_fields = [None] * len(self.dataset)
                 self.dataset.add_field(name, new_fields)
             self.dataset[name][self.idx] = val
 
         def __repr__(self):
-            # TODO
-            pass
+            return " ".join([repr(self.dataset[name][self.idx]) for name in self.dataset])
 
     def __init__(self, instance=None):
         self.field_arrays = {}
@@ -72,7 +75,7 @@ class DataSet(object):
                 self.field_arrays[name].append(field)
 
     def add_field(self, name, fields):
-        if len(self.field_arrays)!=0:
+        if len(self.field_arrays) != 0:
             assert len(self) == len(fields)
         self.field_arrays[name] = FieldArray(name, fields)
 
@@ -92,27 +95,10 @@ class DataSet(object):
         return len(field)
 
     def get_length(self):
-        """Fetch lengths of all fields in all instances in a dataset.
-
-        :return lengths: dict of (str: list). The str is the field name.
-                The list contains lengths of this field in all instances.
-
-        """
-        pass
-
-    def shuffle(self):
-        pass
-
-    def split(self, ratio, shuffle=True):
-        """Train/dev splitting
-
-        :param ratio: float, between 0 and 1. The ratio of development set in origin data set.
-        :param shuffle: bool, whether shuffle the data set before splitting. Default: True.
-        :return train_set: a DataSet object, representing the training set
-                dev_set: a DataSet object, representing the validation set
+        """The same as __len__
 
         """
-        pass
+        return len(self)
 
     def rename_field(self, old_name, new_name):
         """rename a field
@@ -120,7 +106,7 @@ class DataSet(object):
         if old_name in self.field_arrays:
             self.field_arrays[new_name] = self.field_arrays.pop(old_name)
         else:
-            raise KeyError
+            raise KeyError("{} is not a valid name. ".format(old_name))
         return self
 
     def set_is_target(self, **fields):
@@ -152,6 +138,7 @@ class DataSet(object):
                 data = _READERS[name]().load(*args, **kwargs)
                 self.extend(data)
                 return self
+
             return _read
         else:
             return object.__getattribute__(self, name)
@@ -161,18 +148,21 @@ class DataSet(object):
         """decorator to add dataloader support
         """
         assert isinstance(method_name, str)
+
         def wrapper(read_cls):
             _READERS[method_name] = read_cls
             return read_cls
+
         return wrapper
 
 
 if __name__ == '__main__':
     from fastNLP.core.instance import Instance
+
     ins = Instance(test='test0')
     dataset = DataSet([ins])
     for _iter in dataset:
         print(_iter['test'])
         _iter['test'] = 'abc'
         print(_iter['test'])
-    print(dataset.field_arrays)
+    print(dataset.field_arrays)

fastNLP/core/instance.py

@@ -1,4 +1,4 @@
-import torch
+
 
 class Instance(object):
     """An instance which consists of Fields is an example in the DataSet.
@@ -35,4 +35,4 @@ class Instance(object):
         return self.add_field(name, field)
 
     def __repr__(self):
-        return self.fields.__repr__()
+        return self.fields.__repr__()

fastNLP/loader/dataset_loader.py

@@ -1,9 +1,9 @@
 import os
 
-from fastNLP.loader.base_loader import BaseLoader
 from fastNLP.core.dataset import DataSet
-from fastNLP.core.instance import Instance
 from fastNLP.core.field import *
+from fastNLP.core.instance import Instance
+from fastNLP.loader.base_loader import BaseLoader
 
 
 def convert_seq_dataset(data):
@@ -393,6 +393,7 @@ class PeopleDailyCorpusLoader(DataSetLoader):
                 sent_words.append(token)
             pos_tag_examples.append([sent_words, sent_pos_tag])
             ner_examples.append([sent_words, sent_ner])
+        # List[List[List[str], List[str]]]
         return pos_tag_examples, ner_examples
 
     def convert(self, data):

fastNLP/models/sequence_modeling.py

@@ -44,6 +44,9 @@ class SeqLabeling(BaseModel):
         :return y: If truth is None, return list of [decode path(list)]. Used in testing and predicting.
                     If truth is not None, return loss, a scalar. Used in training.
         """
+        assert word_seq.shape[0] == word_seq_origin_len.shape[0]
+        if truth is not None:
+            assert truth.shape == word_seq.shape
         self.mask = self.make_mask(word_seq, word_seq_origin_len)
 
         x = self.Embedding(word_seq)
@@ -80,7 +83,7 @@ class SeqLabeling(BaseModel):
         batch_size, max_len = x.size(0), x.size(1)
         mask = seq_mask(seq_len, max_len)
         mask = mask.byte().view(batch_size, max_len)
-        mask = mask.to(x)
+        mask = mask.to(x).float()
         return mask
 
     def decode(self, x, pad=True):
@@ -130,6 +133,9 @@ class AdvSeqLabel(SeqLabeling):
         :return y: If truth is None, return list of [decode path(list)]. Used in testing and predicting.
                    If truth is not None, return loss, a scalar. Used in training.
         """
+        word_seq = word_seq.long()
+        word_seq_origin_len = word_seq_origin_len.long()
+        truth = truth.long()
         self.mask = self.make_mask(word_seq, word_seq_origin_len)
 
         batch_size = word_seq.size(0)

fastNLP/modules/decoder/CRF.py

@@ -89,8 +89,9 @@ class ConditionalRandomField(nn.Module):
         score = score.sum(0) + emit_score[-1]
         if self.include_start_end_trans:
             st_scores = self.start_scores.view(1, -1).repeat(batch_size, 1)[batch_idx, tags[0]]
-            last_idx = mask.long().sum(0)
+            last_idx = mask.long().sum(0) - 1
             ed_scores = self.end_scores.view(1, -1).repeat(batch_size, 1)[batch_idx, tags[last_idx, batch_idx]]
+            print(score.size(), st_scores.size(), ed_scores.size())
             score += st_scores + ed_scores
         # return [B,]
         return score
@@ -104,8 +105,8 @@ class ConditionalRandomField(nn.Module):
         :return:FloatTensor, batch_size
         """
         feats = feats.transpose(0, 1)
-        tags = tags.transpose(0, 1)
-        mask = mask.transpose(0, 1)
+        tags = tags.transpose(0, 1).long()
+        mask = mask.transpose(0, 1).float()
         all_path_score = self._normalizer_likelihood(feats, mask)
         gold_path_score = self._glod_score(feats, tags, mask)
 
@@ -156,4 +157,4 @@ class ConditionalRandomField(nn.Module):
 
         if get_score:
             return ans_score, ans.transpose(0, 1)
-        return ans.transpose(0, 1)
+        return ans.transpose(0, 1)

reproduction/pos_tag_model/pos_tag.cfg

@@ -1,10 +1,12 @@
 [train]
-epochs = 30
-batch_size = 64
+epochs = 5
+batch_size = 2
 pickle_path = "./save/"
-validate = true
+validate = false
 save_best_dev = true
 model_saved_path = "./save/"
+
+[model]
 rnn_hidden_units = 100
 word_emb_dim = 100
 use_crf = true

reproduction/pos_tag_model/train_pos_tag.py

@@ -1,130 +1,88 @@
 import os
-import sys
 
-sys.path.append(os.path.join(os.path.dirname(__file__), '../..'))
+import torch
 
+from fastNLP.api.pipeline import Pipeline
+from fastNLP.api.processor import VocabProcessor, IndexerProcessor, SeqLenProcessor
+from fastNLP.core.dataset import DataSet
+from fastNLP.core.instance import Instance
+from fastNLP.core.trainer import Trainer
 from fastNLP.loader.config_loader import ConfigLoader, ConfigSection
-from fastNLP.core.trainer import SeqLabelTrainer
-from fastNLP.loader.dataset_loader import PeopleDailyCorpusLoader, BaseLoader
-from fastNLP.core.preprocess import SeqLabelPreprocess, load_pickle
-from fastNLP.saver.model_saver import ModelSaver
-from fastNLP.loader.model_loader import ModelLoader
-from fastNLP.core.tester import SeqLabelTester
+from fastNLP.loader.dataset_loader import PeopleDailyCorpusLoader
 from fastNLP.models.sequence_modeling import AdvSeqLabel
-from fastNLP.core.predictor import SeqLabelInfer
 
-# not in the file's dir
-if len(os.path.dirname(__file__)) != 0:
-    os.chdir(os.path.dirname(__file__))
-datadir = "/home/zyfeng/data/"
 cfgfile = './pos_tag.cfg'
-data_name = "CWS_POS_TAG_NER_people_daily.txt"
+datadir = "/home/zyfeng/fastnlp_0.2.0/test/data_for_tests/"
+data_name = "people_daily_raw.txt"
 
 pos_tag_data_path = os.path.join(datadir, data_name)
 pickle_path = "save"
 data_infer_path = os.path.join(datadir, "infer.utf8")
 
 
-def infer():
-    # Config Loader
-    test_args = ConfigSection()
-    ConfigLoader("config").load_config(cfgfile, {"POS_test": test_args})
-
-    # fetch dictionary size and number of labels from pickle files
-    word2index = load_pickle(pickle_path, "word2id.pkl")
-    test_args["vocab_size"] = len(word2index)
-    index2label = load_pickle(pickle_path, "class2id.pkl")
-    test_args["num_classes"] = len(index2label)
-
-    # Define the same model
-    model = AdvSeqLabel(test_args)
-
-    try:
-        ModelLoader.load_pytorch(model, "./save/saved_model.pkl")
-        print('model loaded!')
-    except Exception as e:
-        print('cannot load model!')
-        raise
-
-    # Data Loader
-    raw_data_loader = BaseLoader(data_infer_path)
-    infer_data = raw_data_loader.load_lines()
-    print('data loaded')
-
-    # Inference interface
-    infer = SeqLabelInfer(pickle_path)
-    results = infer.predict(model, infer_data)
-
-    print(results)
-    print("Inference finished!")
-
-
-def train(): 
+def train():
     # load config
-    trainer_args = ConfigSection()
-    model_args = ConfigSection()
-    ConfigLoader().load_config(cfgfile, {"train": train_args, "test": test_args})
+    train_param = ConfigSection()
+    model_param = ConfigSection()
+    ConfigLoader().load_config(cfgfile, {"train": train_param, "model": model_param})
+    print("config loaded")
 
     # Data Loader
     loader = PeopleDailyCorpusLoader()
-    train_data, _ = loader.load()
-
-    # TODO: define processors
-    
-    # define pipeline
-    pp = Pipeline()
-    # TODO: pp.add_processor()
-
-    # run the pipeline, get data_set
-    train_data = pp(train_data)
+    train_data, _ = loader.load(os.path.join(datadir, data_name))
+    print("data loaded")
+
+    dataset = DataSet()
+    for data in train_data:
+        instance = Instance()
+        instance["words"] = data[0]
+        instance["tag"] = data[1]
+        dataset.append(instance)
+    print("dataset transformed")
+
+    # processor_1 = FullSpaceToHalfSpaceProcessor('words')
+    # processor_1(dataset)
+    word_vocab_proc = VocabProcessor('words')
+    tag_vocab_proc = VocabProcessor("tag")
+    word_vocab_proc(dataset)
+    tag_vocab_proc(dataset)
+    word_indexer = IndexerProcessor(word_vocab_proc.get_vocab(), 'words', 'word_seq', delete_old_field=True)
+    word_indexer(dataset)
+    tag_indexer = IndexerProcessor(tag_vocab_proc.get_vocab(), 'tag', 'truth', delete_old_field=True)
+    tag_indexer(dataset)
+    seq_len_proc = SeqLenProcessor("word_seq", "word_seq_origin_len")
+    seq_len_proc(dataset)
+
+    print("processors defined")
+    # dataset.set_is_target(tag_ids=True)
+    model_param["vocab_size"] = len(word_vocab_proc.get_vocab())
+    model_param["num_classes"] = len(tag_vocab_proc.get_vocab())
+    print("vocab_size={}  num_classes={}".format(len(word_vocab_proc.get_vocab()), len(tag_vocab_proc.get_vocab())))
 
     # define a model
-    model = AdvSeqLabel(train_args)
+    model = AdvSeqLabel(model_param)
 
     # call trainer to train
-    trainer = SeqLabelTrainer(train_args)
-    trainer.train(model, data_train, data_dev)
-
-    # save model
-    ModelSaver("./saved_model.pkl").save_pytorch(model, param_only=False)
-
-    # TODO:save pipeline
+    trainer = Trainer(**train_param.data)
+    trainer.train(model, dataset)
 
+    # save model & pipeline
+    pp = Pipeline([word_vocab_proc, word_indexer, seq_len_proc])
+    save_dict = {"pipeline": pp, "model": model}
+    torch.save(save_dict, "model_pp.pkl")
 
 
 def test():
-    # Config Loader
-    test_args = ConfigSection()
-    ConfigLoader("config").load_config(cfgfile, {"POS_test": test_args})
-
-    # fetch dictionary size and number of labels from pickle files
-    word2index = load_pickle(pickle_path, "word2id.pkl")
-    test_args["vocab_size"] = len(word2index)
-    index2label = load_pickle(pickle_path, "class2id.pkl")
-    test_args["num_classes"] = len(index2label)
-
-    # load dev data
-    dev_data = load_pickle(pickle_path, "data_dev.pkl")
-
-    # Define the same model
-    model = AdvSeqLabel(test_args)
+    pass
 
-    # Dump trained parameters into the model
-    ModelLoader.load_pytorch(model, "./save/saved_model.pkl")
-    print("model loaded!")
 
-    # Tester
-    tester = SeqLabelTester(**test_args.data)
-
-    # Start testing
-    tester.test(model, dev_data)
-
-    # print test results
-    print(tester.show_metrics())
-    print("model tested!")
+def infer():
+    pass
 
 
 if __name__ == "__main__":
+    train()
+    """
     import argparse
 
     parser = argparse.ArgumentParser(description='Run a chinese word segmentation model')
@@ -139,3 +97,5 @@ if __name__ == "__main__":
     else:
         print('no mode specified for model!')
         parser.print_help()
+
+"""