1. 优化trainer checkcode过程的报错信息

fastNLP/core/dataset.py

@@ -69,7 +69,7 @@ class DataSet(object):
                 self.idx = idx
 
             def __getitem__(self, item):
-                assert item in self.dataset.field_arrays, "no such field:{} in instance {}".format(item, self.dataset[self.idx])
+                assert item in self.dataset.field_arrays, "no such field:{} in Instance {}".format(item, self.dataset[self.idx])
                 assert self.idx < len(self.dataset.field_arrays[item]), "index:{} out of range".format(self.idx)
                 return self.dataset.field_arrays[item][self.idx]
 

fastNLP/core/fieldarray.py

@@ -83,7 +83,8 @@ class FieldArray(object):
         elif isinstance(content, list):
             # content is a 1-D list
             if len(content) == 0:
-                raise RuntimeError("Cannot create FieldArray with an empty list.")
+                # the old error is not informative enough.
+                raise RuntimeError("Cannot create FieldArray with an empty list. Or one element in the list is empty.")
             type_set = set([type(item) for item in content])
 
             if len(type_set) == 1 and tuple(type_set)[0] in self.BASIC_TYPES:
@@ -164,11 +165,13 @@ class FieldArray(object):
         # TODO 当这个fieldArray是seq_length这种只有一位的内容时，不需要padding，需要再讨论一下
         if not is_iterable(self.content[0]):
             array = np.array([self.content[i] for i in indices], dtype=self.dtype)
-        else:
+        elif self.dtype in (np.int64, np.float64):
             max_len = max([len(self.content[i]) for i in indices])
             array = np.full((batch_size, max_len), self.padding_val, dtype=self.dtype)
             for i, idx in enumerate(indices):
                 array[i][:len(self.content[idx])] = self.content[idx]
+        else: # should only be str
+            array = np.array([self.content[i] for i in indices])
         return array
 
     def __len__(self):

fastNLP/core/losses.py

@@ -80,7 +80,7 @@ class LossBase(object):
         fast_param = {}
         if len(self.param_map) == 2 and len(pred_dict) == 1 and len(target_dict) == 1:
             fast_param['pred'] = list(pred_dict.values())[0]
-            fast_param['target'] = list(pred_dict.values())[0]
+            fast_param['target'] = list(target_dict.values())[0]
             return fast_param
         return fast_param
 
@@ -134,10 +134,11 @@ class LossBase(object):
         # missing
         if not self._checked:
             check_res = _check_arg_dict_list(self.get_loss, [mapped_pred_dict, mapped_target_dict])
-            # only check missing.
+            # replace missing.
             missing = check_res.missing
             replaced_missing = list(missing)
             for idx, func_arg in enumerate(missing):
+                # Don't delete `` in this information, nor add ``
                 replaced_missing[idx] = f"{self.param_map[func_arg]}" + f"(assign to `{func_arg}` " \
                                                                         f"in `{self.__class__.__name__}`)"
 
@@ -188,7 +189,7 @@ class CrossEntropyLoss(LossBase):
 class L1Loss(LossBase):
     def __init__(self, pred=None, target=None):
         super(L1Loss, self).__init__()
-        self._init_param_map(input=pred, target=target)
+        self._init_param_map(pred=pred, target=target)
 
     def get_loss(self, pred, target):
         return F.l1_loss(input=pred, target=target)
@@ -197,7 +198,7 @@ class L1Loss(LossBase):
 class BCELoss(LossBase):
     def __init__(self, pred=None, target=None):
         super(BCELoss, self).__init__()
-        self._init_param_map(input=pred, target=target)
+        self._init_param_map(pred=pred, target=target)
 
     def get_loss(self, pred, target):
         return F.binary_cross_entropy(input=pred, target=target)
@@ -205,7 +206,7 @@ class BCELoss(LossBase):
 class NLLLoss(LossBase):
     def __init__(self, pred=None, target=None):
         super(NLLLoss, self).__init__()
-        self._init_param_map(input=pred, target=target)
+        self._init_param_map(pred=pred, target=target)
 
     def get_loss(self, pred, target):
         return F.nll_loss(input=pred, target=target)

fastNLP/core/metrics.py

@@ -151,9 +151,11 @@ class MetricBase(object):
         if not self._checked:
             check_res = _check_arg_dict_list(self.evaluate, [mapped_pred_dict, mapped_target_dict])
             # only check missing.
+            # replace missing.
             missing = check_res.missing
             replaced_missing = list(missing)
             for idx, func_arg in enumerate(missing):
+                # Don't delete `` in this information, nor add ``
                 replaced_missing[idx] = f"{self.param_map[func_arg]}" + f"(assign to `{func_arg}` " \
                                                                         f"in `{self.__class__.__name__}`)"
 

fastNLP/core/trainer.py

@@ -2,7 +2,7 @@ import os
 import time
 from datetime import datetime
 from datetime import timedelta
-from tqdm import tqdm
+from tqdm.autonotebook import tqdm
 
 import torch
 from tensorboardX import SummaryWriter
@@ -23,7 +23,6 @@ from fastNLP.core.utils import _check_forward_error
 from fastNLP.core.utils import _check_loss_evaluate
 from fastNLP.core.utils import _move_dict_value_to_device
 from fastNLP.core.utils import get_func_signature
-from fastNLP.core.utils import _relocate_pbar
 
 class Trainer(object):
     """Main Training Loop
@@ -45,7 +44,7 @@ class Trainer(object):
         :param int validate_every: step interval to do next validation. Default: -1(validate every epoch).
         :param DataSet dev_data: the validation data
         :param use_cuda:
-        :param str save_path: file path to save models
+        :param save_path: file path to save models
         :param Optimizer optimizer: an optimizer object
         :param int check_code_level: level of FastNLP code checker. -1: don't check, 0: ignore. 1: warning. 2: strict.
             `ignore` will not check unused field; `warning` when warn if some field are not used; `strict` means
@@ -149,7 +148,7 @@ class Trainer(object):
             self._mode(self.model, is_test=False)
 
             self.start_time = str(datetime.now().strftime('%Y-%m-%d %H:%M:%S'))
-            print("training epochs started " + self.start_time)
+            print("training epochs started " + self.start_time, flush=True)
             if self.save_path is None:
                 class psudoSW:
                     def __getattr__(self, item):
@@ -172,12 +171,12 @@ class Trainer(object):
             del self._summary_writer
 
     def _tqdm_train(self):
+        self.step = 0
         data_iterator = Batch(self.train_data, batch_size=self.batch_size, sampler=self.sampler,
                               as_numpy=False)
         total_steps = data_iterator.num_batches*self.n_epochs
         epoch = 1
-        with tqdm(total=total_steps, postfix='loss:{0:<6.5f}', desc="Epoch {}/{}"
+        with tqdm(total=total_steps, postfix='loss:{0:<6.5f}', leave=False, dynamic_ncols=True) as pbar:
-                .format(epoch, self.n_epochs), leave=False, dynamic_ncols=True) as pbar:
             ava_loss = 0
             for epoch in range(1, self.n_epochs+1):
                 pbar.set_description_str(desc="Epoch {}/{}".format(epoch, self.n_epochs))
@@ -195,28 +194,26 @@ class Trainer(object):
                             # self._summary_writer.add_scalar(name + "_std", param.std(), global_step=self.step)
                             # self._summary_writer.add_scalar(name + "_grad_sum", param.sum(), global_step=self.step)
                     if (self.step+1) % self.print_every == 0:
-                        pbar.update(self.print_every)
+                        pbar.set_postfix_str("loss:{0:<6.5f}".format(ava_loss / self.print_every))
-                        pbar.set_postfix_str("loss:{0:<6.5f}".format(ava_loss/self.print_every))
                         ava_loss = 0
-
+                    pbar.update(1)
                     self.step += 1
                     if self.validate_every > 0 and self.step % self.validate_every == 0 \
                             and self.dev_data is not None:
                         eval_res = self._do_validation()
                         eval_str = "Epoch {}/{}. Step:{}/{}. ".format(epoch, self.n_epochs, self.step, total_steps) + \
                                    self.tester._format_eval_results(eval_res)
-                        pbar = _relocate_pbar(pbar, print_str=eval_str)
+                        pbar.write(eval_str)
                 if self.validate_every < 0 and self.dev_data:
                     eval_res = self._do_validation()
                     eval_str = "Epoch {}/{}. Step:{}/{}. ".format(epoch, self.n_epochs, self.step, total_steps) + \
                                self.tester._format_eval_results(eval_res)
-                    pbar = _relocate_pbar(pbar, print_str=eval_str)
+                    pbar.write(eval_str)
                 if epoch!=self.n_epochs:
                     data_iterator = Batch(self.train_data, batch_size=self.batch_size, sampler=self.sampler,
                                           as_numpy=False)
             pbar.close()
 
-
     def _print_train(self):
         """
 
@@ -264,9 +261,6 @@ class Trainer(object):
                 self._do_validation()
             epoch += 1
 
-
-
-
     def _do_validation(self):
         res = self.tester.test()
         for name, num in res.items():

fastNLP/core/utils.py

@@ -258,29 +258,48 @@ def _check_loss_evaluate(prev_func_signature: str, func_signature: str, check_re
         if _unused_param:
             unuseds.append(f"\tunused param: {_unused_param}") # output from predict or forward
 
+    module_name = ''
     if check_res.missing:
         errs.append(f"\tmissing param: {check_res.missing}")
-        _miss_in_dataset = []
+        import re
-        _miss_out_dataset = []
+        mapped_missing = []
+        unmapped_missing = []
+        input_func_map = {}
         for _miss in check_res.missing:
+            fun_arg, module_name = re.findall("(?<=`)[a-zA-Z0-9]*?(?=`)", _miss)
             if '(' in _miss:
                 # if they are like 'SomeParam(assign to xxx)'
                 _miss = _miss.split('(')[0]
-            if _miss in dataset:
+            input_func_map[_miss] = fun_arg
-                _miss_in_dataset.append(_miss)
+            if fun_arg == _miss:
+                unmapped_missing.append(_miss)
             else:
-                _miss_out_dataset.append(_miss)
+                mapped_missing.append(_miss)
 
-        if _miss_in_dataset:
+        for _miss in mapped_missing:
-            suggestions.append(f"You might need to set {_miss_in_dataset} as target(Right now "
+            if _miss in dataset:
-                               f"target is {list(target_dict.keys())}).")
+                suggestions.append(f"Set {_miss} as target.")
-        if _miss_out_dataset:
+            else:
-            _tmp = (f"You might need to provide {_miss_out_dataset} in DataSet and set it as target(Right now "
+                _tmp = ''
-                    f"target has {list(target_dict.keys())}) or output it "
+                if check_res.unused:
-                    f"in {prev_func_signature}(Right now output has {list(pred_dict.keys())}).")
+                    _tmp = f"Check key assignment for `{input_func_map[_miss]}` when initialize {module_name}."
-            # if _unused_field:
+                if _tmp:
-            #     _tmp += f"You can use DataSet.rename_field() to rename the field in `unused field:`. "
+                    _tmp += f' Or provide {_miss} in DataSet or output of {prev_func_signature}.'
-            suggestions.append(_tmp)
+                else:
+                    _tmp = f'Provide {_miss} in DataSet or output of {prev_func_signature}.'
+                suggestions.append(_tmp)
+        for _miss in unmapped_missing:
+            if _miss in dataset:
+                suggestions.append(f"Set {_miss} as target.")
+            else:
+                _tmp = ''
+                if check_res.unused:
+                    _tmp = f"Specify your assignment for `{input_func_map[_miss]}` when initialize {module_name}."
+                if _tmp:
+                    _tmp += f' Or provide {_miss} in DataSet or output of {prev_func_signature}.'
+                else:
+                    _tmp = f'Provide {_miss} in DataSet or output of {prev_func_signature}.'
+                suggestions.append(_tmp)
 
     if check_res.duplicated:
         errs.append(f"\tduplicated param: {check_res.duplicated}.")
@@ -297,17 +316,23 @@ def _check_loss_evaluate(prev_func_signature: str, func_signature: str, check_re
         sugg_str = ""
         if len(suggestions) > 1:
             for idx, sugg in enumerate(suggestions):
-                sugg_str += f'({idx+1}). {sugg}'
+                if idx>0:
+                    sugg_str += '\t\t\t'
+                sugg_str += f'({idx+1}). {sugg}\n'
+            sugg_str = sugg_str[:-1]
         else:
             sugg_str += suggestions[0]
+        errs.append(f'\ttarget field: {list(target_dict.keys())}')
+        errs.append(f'\tparam from {prev_func_signature}: {list(pred_dict.keys())}')
         err_str = '\n' + '\n'.join(errs) + '\n\tSuggestion: ' + sugg_str
         raise NameError(err_str)
     if check_res.unused:
         if check_level == WARNING_CHECK_LEVEL:
-            _unused_warn = f'{check_res.unused} is not used by {func_signature}.'
+            if not module_name:
+                module_name = func_signature.split('.')[0]
+            _unused_warn = f'{check_res.unused} is not used by {module_name}.'
             warnings.warn(message=_unused_warn)
 
-
 def _check_forward_error(forward_func, batch_x, dataset, check_level):
     check_res = _check_arg_dict_list(forward_func, batch_x)
     func_signature = get_func_signature(forward_func)
@@ -402,40 +427,3 @@ def seq_mask(seq_len, max_len):
     seq_len = seq_len.view(-1, 1).long()   # [batch_size, 1]
     seq_range = torch.arange(start=0, end=max_len, dtype=torch.long, device=seq_len.device).view(1, -1) # [1, max_len]
     return torch.gt(seq_len, seq_range) # [batch_size, max_len]
-
-
-def _relocate_pbar(pbar:tqdm, print_str:str):
-    """
-
-    When using tqdm, you cannot print. If you print, the tqdm will duplicate. By using this function, print_str will
-        show above tqdm.
-    :param pbar: tqdm
-    :param print_str:
-    :return:
-    """
-
-    params = ['desc', 'total', 'leave', 'file', 'ncols', 'mininterval', 'maxinterval', 'miniters', 'ascii', 'disable',
-     'unit', 'unit_scale', 'dynamic_ncols', 'smoothing', 'bar_format', 'initial', 'position', 'postfix', 'unit_divisor',
-     'gui']
-
-    attr_map = {'file': 'fp', 'initial':'n', 'position':'pos'}
-
-    param_dict = {}
-    for param in params:
-        attr_name = param
-        if param in attr_map:
-            attr_name = attr_map[param]
-        value = getattr(pbar, attr_name)
-        if attr_name == 'pos':
-            value = abs(value)
-        param_dict[param] = value
-
-    pbar.close()
-    avg_time = pbar.avg_time
-    start_t = pbar.start_t
-    print(print_str)
-    pbar = tqdm(**param_dict)
-    pbar.start_t = start_t
-    pbar.avg_time = avg_time
-    pbar.sp(pbar.__repr__())
-    return pbar

requirements.txt

@@ -1,4 +1,4 @@
 numpy>=1.14.2
 torch>=0.4.0
 tensorboardX
-tqdm
+tqdm>=4.28.1

test/core/test_dataset.py

@@ -142,9 +142,16 @@ class TestDataSet(unittest.TestCase):
         def split_sent(ins):
             return ins['raw_sentence'].split()
         dataset = DataSet.read_csv('../../sentence.csv', headers=('raw_sentence', 'label'), sep='\t')
-        dataset.apply(split_sent, new_field_name='words')
+        dataset.drop(lambda x:len(x['raw_sentence'].split())==0)
+        dataset.apply(split_sent, new_field_name='words', is_input=True)
         # print(dataset)
 
+    def test_add_field(self):
+        ds = DataSet({"x": [3, 4]})
+        ds.add_field('y', [['hello', 'world'], ['this', 'is', 'a', 'test']], is_input=True, is_target=True)
+        # ds.apply(lambda x:[x['x']]*3, is_input=True, is_target=True, new_field_name='y')
+        print(ds)
+
     def test_save_load(self):
         ds = DataSet({"x": [[1, 2, 3, 4]] * 10, "y": [[5, 6]] * 10})
         ds.save("./my_ds.pkl")

test/core/test_tester.py

@@ -4,6 +4,64 @@ data_name = "pku_training.utf8"
 pickle_path = "data_for_tests"
 
 
+import numpy as np
+import torch.nn.functional as F
+from torch import nn
+import time
+from fastNLP.core.utils import CheckError
+from fastNLP.core.dataset import DataSet
+from fastNLP.core.instance import Instance
+from fastNLP.core.losses import BCELoss
+from fastNLP.core.losses import CrossEntropyLoss
+from fastNLP.core.metrics import AccuracyMetric
+from fastNLP.core.optimizer import SGD
+from fastNLP.core.tester import Tester
+from fastNLP.models.base_model import NaiveClassifier
+
+def prepare_fake_dataset():
+    mean = np.array([-3, -3])
+    cov = np.array([[1, 0], [0, 1]])
+    class_A = np.random.multivariate_normal(mean, cov, size=(1000,))
+
+    mean = np.array([3, 3])
+    cov = np.array([[1, 0], [0, 1]])
+    class_B = np.random.multivariate_normal(mean, cov, size=(1000,))
+
+    data_set = DataSet([Instance(x=[float(item[0]), float(item[1])], y=[0.0]) for item in class_A] +
+                       [Instance(x=[float(item[0]), float(item[1])], y=[1.0]) for item in class_B])
+    return data_set
+
+
+def prepare_fake_dataset2(*args, size=100):
+    ys = np.random.randint(4, size=100, dtype=np.int64)
+    data = {'y': ys}
+    for arg in args:
+        data[arg] = np.random.randn(size, 5)
+    return DataSet(data=data)
+
 class TestTester(unittest.TestCase):
     def test_case_1(self):
-        pass
+        # 检查报错提示能否正确提醒用户
+        # 这里传入多余参数，让其duplicate
+        dataset = prepare_fake_dataset2('x1', 'x_unused')
+        dataset.rename_field('x_unused', 'x2')
+        dataset.set_input('x1', 'x2')
+        dataset.set_target('y', 'x1')
+        class Model(nn.Module):
+            def __init__(self):
+                super().__init__()
+                self.fc = nn.Linear(5, 4)
+            def forward(self, x1, x2):
+                x1 = self.fc(x1)
+                x2 = self.fc(x2)
+                x = x1 + x2
+                time.sleep(0.1)
+                # loss = F.cross_entropy(x, y)
+                return {'preds': x}
+
+        model = Model()
+        tester = Tester(
+            data=dataset,
+            model=model,
+            metrics=AccuracyMetric())
+        tester.test()

test/core/test_trainer.py

@@ -3,7 +3,7 @@ import unittest
 import numpy as np
 import torch.nn.functional as F
 from torch import nn
-
+import time
 from fastNLP.core.utils import CheckError
 from fastNLP.core.dataset import DataSet
 from fastNLP.core.instance import Instance
@@ -212,8 +212,8 @@ class TrainerTestGround(unittest.TestCase):
         # 这里传入多余参数，让其duplicate
         dataset = prepare_fake_dataset2('x1', 'x_unused')
         dataset.rename_field('x_unused', 'x2')
-        dataset.set_input('x1', 'x2', 'y')
+        dataset.set_input('x1', 'x2')
-        dataset.set_target('x1', 'x2')
+        dataset.set_target('y', 'x1')
         class Model(nn.Module):
             def __init__(self):
                 super().__init__()
@@ -222,8 +222,9 @@ class TrainerTestGround(unittest.TestCase):
                 x1 = self.fc(x1)
                 x2 = self.fc(x2)
                 x = x1 + x2
+                time.sleep(0.1)
                 # loss = F.cross_entropy(x, y)
-                return {'pred': x}
+                return {'preds': x}
 
         model = Model()
         trainer = Trainer(

tutorials/fastnlp_tutorial_1204.ipynb

@@ -12,7 +12,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -34,17 +34,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
+   "outputs": [],
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "8529\n"
-     ]
-    }
-   ],
    "source": [
     "from fastNLP import DataSet\n",
     "from fastNLP import Instance\n",
@@ -56,20 +48,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
+   "outputs": [],
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'raw_sentence': A series of escapades demonstrating the adage that what is good for the goose is also good for the gander , some of which occasionally amuses but none of which amounts to much of a story .,\n",
-      "'label': 1}\n",
-      "{'raw_sentence': -LRB- Tries -RRB- to parody a genre that 's already a joke in the United States .,\n",
-      "'label': 1}\n"
-     ]
-    }
-   ],
    "source": [
     "# 使用数字索引[k]，获取第k个样本\n",
     "print(dataset[0])\n",
@@ -90,21 +71,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
+   "outputs": [],
-    {
-     "data": {
-      "text/plain": [
-       "{'raw_sentence': fake data,\n",
-       "'label': 0}"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
    "source": [
     "# DataSet.append(Instance)加入新数据\n",
     "dataset.append(Instance(raw_sentence='fake data', label='0'))\n",
@@ -121,18 +90,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
+   "outputs": [],
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'raw_sentence': a series of escapades demonstrating the adage that what is good for the goose is also good for the gander , some of which occasionally amuses but none of which amounts to much of a story .,\n",
-      "'label': 1}\n"
-     ]
-    }
-   ],
    "source": [
     "# 将所有数字转为小写\n",
     "dataset.apply(lambda x: x['raw_sentence'].lower(), new_field_name='raw_sentence')\n",
@@ -141,18 +101,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
+   "outputs": [],
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'raw_sentence': a series of escapades demonstrating the adage that what is good for the goose is also good for the gander , some of which occasionally amuses but none of which amounts to much of a story .,\n",
-      "'label': 1}\n"
-     ]
-    }
-   ],
    "source": [
     "# label转int\n",
     "dataset.apply(lambda x: int(x['label']), new_field_name='label')\n",
@@ -161,28 +112,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
+   "outputs": [],
-    {
-     "ename": "RuntimeError",
-     "evalue": "Cannot create FieldArray with an empty list.",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-9-d70cf5545af4>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0msplit_sent\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mins\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mins\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'raw_sentence'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mdataset\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msplit_sent\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnew_field_name\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'words'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/Desktop/fastNLP/fastNLP/fastNLP/core/dataset.py\u001b[0m in \u001b[0;36mapply\u001b[0;34m(self, func, new_field_name, **kwargs)\u001b[0m\n\u001b[1;32m    265\u001b[0m                                **extra_param)\n\u001b[1;32m    266\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 267\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_field\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnew_field_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfields\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mresults\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mextra_param\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    268\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    269\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mresults\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/Desktop/fastNLP/fastNLP/fastNLP/core/dataset.py\u001b[0m in \u001b[0;36madd_field\u001b[0;34m(self, name, fields, padding_val, is_input, is_target)\u001b[0m\n\u001b[1;32m    158\u001b[0m                                    f\"Dataset size {len(self)} != field size {len(fields)}\")\n\u001b[1;32m    159\u001b[0m         self.field_arrays[name] = FieldArray(name, fields, padding_val=padding_val, is_target=is_target,\n\u001b[0;32m--> 160\u001b[0;31m                                              is_input=is_input)\n\u001b[0m\u001b[1;32m    161\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    162\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mdelete_field\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/Desktop/fastNLP/fastNLP/fastNLP/core/fieldarray.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, name, content, padding_val, is_target, is_input)\u001b[0m\n\u001b[1;32m     36\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     37\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mis_input\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 38\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_input\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mis_input\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     39\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mis_target\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     40\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_target\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mis_target\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/Desktop/fastNLP/fastNLP/fastNLP/core/fieldarray.py\u001b[0m in \u001b[0;36mis_input\u001b[0;34m(self, value)\u001b[0m\n\u001b[1;32m     46\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mis_input\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msetter\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     47\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mis_input\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 48\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpytype\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_type_detection\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcontent\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     49\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_map_to_np_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpytype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     50\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_is_input\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/Desktop/fastNLP/fastNLP/fastNLP/core/fieldarray.py\u001b[0m in \u001b[0;36m_type_detection\u001b[0;34m(self, content)\u001b[0m\n\u001b[1;32m     71\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mall\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0m_\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mcontent\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# strict check 2-D list\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     72\u001b[0m                 \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Please provide 2-D list.\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 73\u001b[0;31m             \u001b[0mtype_set\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_type_detection\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mcontent\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     74\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtype_set\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m2\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mint\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtype_set\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mfloat\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtype_set\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     75\u001b[0m                 \u001b[0mtype_set\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0mfloat\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/Desktop/fastNLP/fastNLP/fastNLP/core/fieldarray.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m     71\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mall\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0m_\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mcontent\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# strict check 2-D list\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     72\u001b[0m                 \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Please provide 2-D list.\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 73\u001b[0;31m             \u001b[0mtype_set\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_type_detection\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mcontent\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     74\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtype_set\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m2\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mint\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtype_set\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mfloat\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtype_set\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     75\u001b[0m                 \u001b[0mtype_set\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0mfloat\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/Desktop/fastNLP/fastNLP/fastNLP/core/fieldarray.py\u001b[0m in \u001b[0;36m_type_detection\u001b[0;34m(self, content)\u001b[0m\n\u001b[1;32m     82\u001b[0m             \u001b[0;31m# content is a 1-D list\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     83\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcontent\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 84\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mRuntimeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Cannot create FieldArray with an empty list.\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     85\u001b[0m             \u001b[0mtype_set\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mitem\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mcontent\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     86\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mRuntimeError\u001b[0m: Cannot create FieldArray with an empty list."
-     ]
-    }
-   ],
    "source": [
     "# 使用空格分割句子\n",
     "def split_sent(ins):\n",
@@ -193,20 +125,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
+   "outputs": [],
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'raw_sentence': a series of escapades demonstrating the adage that what is good for the goose is also good for the gander , some of which occasionally amuses but none of which amounts to much of a story .,\n",
-      "'label': 1,\n",
-      "'words': ['a', 'series', 'of', 'escapades', 'demonstrating', 'the', 'adage', 'that', 'what', 'is', 'good', 'for', 'the', 'goose', 'is', 'also', 'good', 'for', 'the', 'gander', ',', 'some', 'of', 'which', 'occasionally', 'amuses', 'but', 'none', 'of', 'which', 'amounts', 'to', 'much', 'of', 'a', 'story', '.'],\n",
-      "'seq_len': 37}\n"
-     ]
-    }
-   ],
    "source": [
     "# 增加长度信息\n",
     "dataset.apply(lambda x: len(x['words']), new_field_name='seq_len')\n",
@@ -223,17 +144,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
+   "outputs": [],
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "38\n"
-     ]
-    }
-   ],
    "source": [
     "dataset.drop(lambda x: x['seq_len'] <= 3)\n",
     "print(len(dataset))"
@@ -250,7 +163,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -264,18 +177,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
+   "outputs": [],
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "27\n",
-      "11"
-     ]
-    }
-   ],
    "source": [
     "# 分出测试集、训练集\n",
     "\n",
@@ -296,20 +200,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
+   "outputs": [],
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'raw_sentence': that the chuck norris `` grenade gag '' occurs about 7 times during windtalkers is a good indication of how serious-minded the film is .,\n",
-      "'label': 2,\n",
-      "'words': [6, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 10, 8, 24, 1, 5, 1, 1, 2, 15, 10, 3],\n",
-      "'seq_len': 25}\n"
-     ]
-    }
-   ],
    "source": [
     "from fastNLP import Vocabulary\n",
     "\n",
@@ -336,36 +229,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
+   "outputs": [],
-    {
-     "data": {
-      "text/plain": [
-       "CNNText(\n",
-       "  (embed): Embedding(\n",
-       "    (embed): Embedding(32, 50, padding_idx=0)\n",
-       "    (dropout): Dropout(p=0.0)\n",
-       "  )\n",
-       "  (conv_pool): ConvMaxpool(\n",
-       "    (convs): ModuleList(\n",
-       "      (0): Conv1d(50, 3, kernel_size=(3,), stride=(1,), padding=(2,))\n",
-       "      (1): Conv1d(50, 4, kernel_size=(4,), stride=(1,), padding=(2,))\n",
-       "      (2): Conv1d(50, 5, kernel_size=(5,), stride=(1,), padding=(2,))\n",
-       "    )\n",
-       "  )\n",
-       "  (dropout): Dropout(p=0.1)\n",
-       "  (fc): Linear(\n",
-       "    (linear): Linear(in_features=12, out_features=5, bias=True)\n",
-       "  )\n",
-       ")"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
    "source": [
     "from fastNLP.models import CNNText\n",
     "model = CNNText(embed_num=len(vocab), embed_dim=50, num_classes=5, padding=2, dropout=0.1)\n",
@@ -432,7 +298,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -469,7 +335,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -492,7 +358,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -501,94 +367,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
+   "outputs": [],
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "training epochs started 2018-12-04 22:51:24\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "                                                                \r"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 1/5. Step:1/5. AccuracyMetric: acc=0.296296\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "                                                                \r"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 2/5. Step:2/5. AccuracyMetric: acc=0.407407\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "                                                                \r"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 3/5. Step:3/5. AccuracyMetric: acc=0.518519\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "                                                                \r"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 4/5. Step:4/5. AccuracyMetric: acc=0.481481\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "                                                                \r"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 5/5. Step:5/5. AccuracyMetric: acc=0.592593\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "                                                                \r"
-     ]
-    }
-   ],
    "source": [
     "# 实例化Trainer，传入模型和数据，进行训练\n",
     "# 先在test_data拟合\n",
@@ -604,101 +385,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
+   "outputs": [],
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "training epochs started 2018-12-04 22:52:01\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "                                                                \r"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 1/5. Step:1/5. AccuracyMetric: acc=0.296296\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "                                                                \r"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 2/5. Step:2/5. AccuracyMetric: acc=0.222222\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "                                                                \r"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 3/5. Step:3/5. AccuracyMetric: acc=0.259259\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "                                                                \r"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 4/5. Step:4/5. AccuracyMetric: acc=0.296296\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "                                                                \r"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 5/5. Step:5/5. AccuracyMetric: acc=0.259259\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "                                                                \r"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Train finished!\n"
-     ]
-    }
-   ],
    "source": [
     "# 用train_data训练，在test_data验证\n",
     "trainer = Trainer(model=model, train_data=train_data, dev_data=test_data,\n",
@@ -713,19 +402,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
+   "outputs": [],
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[tester] \n",
-      "AccuracyMetric: acc=0.259259\n",
-      "{'AccuracyMetric': {'acc': 0.259259}}\n"
-     ]
-    }
-   ],
    "source": [
     "# 调用Tester在test_data上评价效果\n",
     "from fastNLP import Tester\n",