diff --git a/.github/PULL_REQUEST_TEMPLATE.md b/.github/PULL_REQUEST_TEMPLATE.md
new file mode 100644
index 00000000..9f550edf
--- /dev/null
+++ b/.github/PULL_REQUEST_TEMPLATE.md
@@ -0,0 +1,24 @@
+Description：简要描述这次PR的内容
+
+Main reason: 做出这次修改的原因
+
+
+Checklist  检查下面各项是否完成
+
+Please feel free to remove inapplicable items for your PR.
+
+-	[ ] The PR title starts with [$CATEGORY] (例如[bugfix]修复bug，[new]添加新功能，[test]修改测试，[rm]删除旧代码)
+-	[ ] Changes are complete (i.e. I finished coding on this PR)  修改完成才提PR
+-	[ ] All changes have test coverage  修改的部分顺利通过测试。对于fastnlp/fastnlp/*的修改，测试代码**必须**提供在fastnlp/test/*。
+-	[ ] Code is well-documented  注释写好，API文档会从注释中抽取
+-	[ ] To the my best knowledge, examples are either not affected by this change, or have been fixed to be compatible with this change  修改导致例子或tutorial有变化，请找核心开发人员
+
+Changes: 逐项描述修改的内容
+- 添加了新模型；用于句子分类的CNN，来自Yoon Kim的Convolutional Neural Networks for Sentence Classification
+- 修改dataset.py中过时的和不合规则的注释 #286
+- 添加对var-LSTM的测试代码
+
+Mention: 找人review你的PR
+
+@修改过这个文件的人
+@核心开发人员
diff --git a/README.md b/README.md
index c9c934eb..65d713e6 100644
--- a/README.md
+++ b/README.md
@@ -30,6 +30,7 @@ A deep learning NLP model is the composition of three types of modules:
     <td> decode the representation into the output </td>
     <td> MLP, CRF </td>
 </tr>
+</table>
 
 For example:
 
@@ -37,9 +38,11 @@ For example:
 
 ## Requirements
 
+- Python>=3.6
 - numpy>=1.14.2
 - torch>=0.4.0
 - tensorboardX
+- tqdm>=4.28.1
 
 
 ## Resources
diff --git a/docs/requirements.txt b/docs/requirements.txt
index 294a44d0..c7d94486 100644
--- a/docs/requirements.txt
+++ b/docs/requirements.txt
@@ -1,5 +1,8 @@
 numpy>=1.14.2
-http://download.pytorch.org/whl/cpu/torch-0.4.1-cp35-cp35m-linux_x86_64.whl
+http://download.pytorch.org/whl/cpu/torch-0.4.1-cp36-cp36m-linux_x86_64.whl
 torchvision>=0.1.8
 sphinx-rtd-theme==0.4.1
-tensorboardX>=1.4
\ No newline at end of file
+tensorboardX>=1.4
+tqdm>=4.28.1
+ipython>=6.4.0
+ipython-genutils>=0.2.0
\ No newline at end of file
diff --git a/docs/source/fastNLP.api.rst b/docs/source/fastNLP.api.rst
new file mode 100644
index 00000000..eb9192da
--- /dev/null
+++ b/docs/source/fastNLP.api.rst
@@ -0,0 +1,36 @@
+fastNLP.api 
+============
+
+fastNLP.api.api 
+----------------
+
+.. automodule:: fastNLP.api.api
+    :members:
+
+fastNLP.api.converter 
+----------------------
+
+.. automodule:: fastNLP.api.converter
+    :members:
+
+fastNLP.api.model\_zoo 
+-----------------------
+
+.. automodule:: fastNLP.api.model_zoo
+    :members:
+
+fastNLP.api.pipeline 
+---------------------
+
+.. automodule:: fastNLP.api.pipeline
+    :members:
+
+fastNLP.api.processor 
+----------------------
+
+.. automodule:: fastNLP.api.processor
+    :members:
+
+
+.. automodule:: fastNLP.api
+    :members:
diff --git a/docs/source/fastNLP.core.rst b/docs/source/fastNLP.core.rst
index b70b6798..b9f6c89f 100644
--- a/docs/source/fastNLP.core.rst
+++ b/docs/source/fastNLP.core.rst
@@ -13,8 +13,8 @@ fastNLP.core.dataset
 .. automodule:: fastNLP.core.dataset
     :members:
 
-fastNLP.core.fieldarray
--------------------
+fastNLP.core.fieldarray 
+------------------------
 
 .. automodule:: fastNLP.core.fieldarray
     :members:
@@ -25,8 +25,8 @@ fastNLP.core.instance
 .. automodule:: fastNLP.core.instance
     :members:
 
-fastNLP.core.losses
-------------------
+fastNLP.core.losses 
+--------------------
 
 .. automodule:: fastNLP.core.losses
     :members:
@@ -67,6 +67,12 @@ fastNLP.core.trainer
 .. automodule:: fastNLP.core.trainer
     :members:
 
+fastNLP.core.utils 
+-------------------
+
+.. automodule:: fastNLP.core.utils
+    :members:
+
 fastNLP.core.vocabulary 
 ------------------------
 
diff --git a/docs/source/fastNLP.io.rst b/docs/source/fastNLP.io.rst
new file mode 100644
index 00000000..d91e0d1c
--- /dev/null
+++ b/docs/source/fastNLP.io.rst
@@ -0,0 +1,42 @@
+fastNLP.io 
+===========
+
+fastNLP.io.base\_loader 
+------------------------
+
+.. automodule:: fastNLP.io.base_loader
+    :members:
+
+fastNLP.io.config\_io 
+----------------------
+
+.. automodule:: fastNLP.io.config_io
+    :members:
+
+fastNLP.io.dataset\_loader 
+---------------------------
+
+.. automodule:: fastNLP.io.dataset_loader
+    :members:
+
+fastNLP.io.embed\_loader 
+-------------------------
+
+.. automodule:: fastNLP.io.embed_loader
+    :members:
+
+fastNLP.io.logger 
+------------------
+
+.. automodule:: fastNLP.io.logger
+    :members:
+
+fastNLP.io.model\_io 
+---------------------
+
+.. automodule:: fastNLP.io.model_io
+    :members:
+
+
+.. automodule:: fastNLP.io
+    :members:
diff --git a/docs/source/fastNLP.loader.rst b/docs/source/fastNLP.loader.rst
deleted file mode 100644
index 658e07ff..00000000
--- a/docs/source/fastNLP.loader.rst
+++ /dev/null
@@ -1,36 +0,0 @@
-fastNLP.loader 
-===============
-
-fastNLP.loader.base\_loader 
-----------------------------
-
-.. automodule:: fastNLP.loader.base_loader
-    :members:
-
-fastNLP.loader.config\_loader 
-------------------------------
-
-.. automodule:: fastNLP.loader.config_loader
-    :members:
-
-fastNLP.loader.dataset\_loader 
--------------------------------
-
-.. automodule:: fastNLP.loader.dataset_loader
-    :members:
-
-fastNLP.loader.embed\_loader 
------------------------------
-
-.. automodule:: fastNLP.loader.embed_loader
-    :members:
-
-fastNLP.loader.model\_loader 
------------------------------
-
-.. automodule:: fastNLP.loader.model_loader
-    :members:
-
-
-.. automodule:: fastNLP.loader
-    :members:
diff --git a/docs/source/fastNLP.models.rst b/docs/source/fastNLP.models.rst
index f17b1d49..7452fdf6 100644
--- a/docs/source/fastNLP.models.rst
+++ b/docs/source/fastNLP.models.rst
@@ -7,6 +7,12 @@ fastNLP.models.base\_model
 .. automodule:: fastNLP.models.base_model
     :members:
 
+fastNLP.models.biaffine\_parser 
+--------------------------------
+
+.. automodule:: fastNLP.models.biaffine_parser
+    :members:
+
 fastNLP.models.char\_language\_model 
 -------------------------------------
 
@@ -25,6 +31,12 @@ fastNLP.models.sequence\_modeling
 .. automodule:: fastNLP.models.sequence_modeling
     :members:
 
+fastNLP.models.snli 
+--------------------
+
+.. automodule:: fastNLP.models.snli
+    :members:
+
 
 .. automodule:: fastNLP.models
     :members:
diff --git a/docs/source/fastNLP.modules.encoder.rst b/docs/source/fastNLP.modules.encoder.rst
index 41b4ce13..ea8fc699 100644
--- a/docs/source/fastNLP.modules.encoder.rst
+++ b/docs/source/fastNLP.modules.encoder.rst
@@ -43,6 +43,12 @@ fastNLP.modules.encoder.masked\_rnn
 .. automodule:: fastNLP.modules.encoder.masked_rnn
     :members:
 
+fastNLP.modules.encoder.transformer 
+------------------------------------
+
+.. automodule:: fastNLP.modules.encoder.transformer
+    :members:
+
 fastNLP.modules.encoder.variational\_rnn 
 -----------------------------------------
 
diff --git a/docs/source/fastNLP.modules.interactor.rst b/docs/source/fastNLP.modules.interactor.rst
deleted file mode 100644
index 5eb3bdef..00000000
--- a/docs/source/fastNLP.modules.interactor.rst
+++ /dev/null
@@ -1,5 +0,0 @@
-fastNLP.modules.interactor 
-===========================
-
-.. automodule:: fastNLP.modules.interactor
-    :members:
diff --git a/docs/source/fastNLP.modules.rst b/docs/source/fastNLP.modules.rst
index eda85aa7..965fb27d 100644
--- a/docs/source/fastNLP.modules.rst
+++ b/docs/source/fastNLP.modules.rst
@@ -6,7 +6,12 @@ fastNLP.modules
     fastNLP.modules.aggregator
     fastNLP.modules.decoder
     fastNLP.modules.encoder
-    fastNLP.modules.interactor
+
+fastNLP.modules.dropout 
+------------------------
+
+.. automodule:: fastNLP.modules.dropout
+    :members:
 
 fastNLP.modules.other\_modules 
 -------------------------------
diff --git a/docs/source/fastNLP.rst b/docs/source/fastNLP.rst
index bb5037ce..61882359 100644
--- a/docs/source/fastNLP.rst
+++ b/docs/source/fastNLP.rst
@@ -3,18 +3,11 @@ fastNLP
 
 .. toctree::
 
+    fastNLP.api
     fastNLP.core
-    fastNLP.loader
+    fastNLP.io
     fastNLP.models
     fastNLP.modules
-    fastNLP.saver
-
-fastNLP.fastnlp 
-----------------
-
-.. automodule:: fastNLP.fastnlp
-    :members:
-
 
 .. automodule:: fastNLP
     :members:
diff --git a/docs/source/fastNLP.saver.rst b/docs/source/fastNLP.saver.rst
deleted file mode 100644
index 1a02572d..00000000
--- a/docs/source/fastNLP.saver.rst
+++ /dev/null
@@ -1,24 +0,0 @@
-fastNLP.saver 
-==============
-
-fastNLP.saver.config\_saver 
-----------------------------
-
-.. automodule:: fastNLP.saver.config_saver
-    :members:
-
-fastNLP.saver.logger 
----------------------
-
-.. automodule:: fastNLP.saver.logger
-    :members:
-
-fastNLP.saver.model\_saver 
----------------------------
-
-.. automodule:: fastNLP.saver.model_saver
-    :members:
-
-
-.. automodule:: fastNLP.saver
-    :members:
diff --git a/docs/source/index.rst b/docs/source/index.rst
index b58f712a..9f410f41 100644
--- a/docs/source/index.rst
+++ b/docs/source/index.rst
@@ -1,33 +1,35 @@
 fastNLP documentation
 =====================
-fastNLP，目前仍在孵化中。
+A Modularized and Extensible Toolkit for Natural Language Processing. Currently still in incubation. 
 
 
 Introduction
 ------------
 
-fastNLP是一个基于PyTorch的模块化自然语言处理系统，用于快速开发NLP工具。 
-它将基于深度学习的NLP模型划分为不同的模块。 
-这些模块分为4类：encoder（编码），interaction（交互）, aggregration（聚合） and decoder（解码），
-而每个类别包含不同的实现模块。 
+FastNLP is a modular Natural Language Processing system based on
+PyTorch, built for fast development of NLP models.
 
-大多数当前的NLP模型可以构建在这些模块上，这极大地简化了开发NLP模型的过程。 
-fastNLP的架构如图所示：
+A deep learning NLP model is the composition of three types of modules:
 
-.. image:: figures/procedures.PNG
++-----------------------+-----------------------+-----------------------+
+| module type           | functionality         | example               |
++=======================+=======================+=======================+
+| encoder               | encode the input into | embedding, RNN, CNN,  |
+|                       | some abstract         | transformer           |
+|                       | representation        |                       |
++-----------------------+-----------------------+-----------------------+
+| aggregator            | aggregate and reduce  | self-attention,       |
+|                       | information           | max-pooling           |
++-----------------------+-----------------------+-----------------------+
+| decoder               | decode the            | MLP, CRF              |
+|                       | representation into   |                       |
+|                       | the output            |                       |
++-----------------------+-----------------------+-----------------------+
 
-在constructing model部分，以序列标注和文本分类为例进行说明：
 
-.. image:: figures/text_classification.png
-.. image:: figures/sequence_labeling.PNG
-    :width: 400
-
-* encoder module：将输入编码为一些抽象表示，输入的是单词序列，输出向量序列。
-* interaction module：使表示中的信息相互交互，输入的是向量序列，输出的也是向量序列。
-* aggregation module：聚合和减少信息，输入向量序列，输出一个向量。
-* decoder module：将表示解码为输出，输出一个label（文本分类）或者输出label序列（序列标注）
+For example:
 
-其中interaction module和aggregation module在模型中不一定存在，例如上面的序列标注模型。
+.. image:: figures/text_classification.png
 
 
 
diff --git a/docs/source/tutorials/fastnlp_10tmin_tutorial.rst b/docs/source/tutorials/fastnlp_10tmin_tutorial.rst
new file mode 100644
index 00000000..30293796
--- /dev/null
+++ b/docs/source/tutorials/fastnlp_10tmin_tutorial.rst
@@ -0,0 +1,375 @@
+
+fastNLP上手教程
+===============
+
+fastNLP提供方便的数据预处理，训练和测试模型的功能
+
+DataSet & Instance
+------------------
+
+fastNLP用DataSet和Instance保存和处理数据。每个DataSet表示一个数据集，每个Instance表示一个数据样本。一个DataSet存有多个Instance，每个Instance可以自定义存哪些内容。
+
+有一些read\_\*方法，可以轻松从文件读取数据，存成DataSet。
+
+.. code:: ipython3
+
+    from fastNLP import DataSet
+    from fastNLP import Instance
+    
+    # 从csv读取数据到DataSet
+    win_path = "C:\\Users\zyfeng\Desktop\FudanNLP\\fastNLP\\test\\data_for_tests\\tutorial_sample_dataset.csv"
+    dataset = DataSet.read_csv(win_path, headers=('raw_sentence', 'label'), sep='\t')
+    print(dataset[0])
+
+
+.. parsed-literal::
+
+    {'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 .,
+    'label': 1}
+    
+
+.. code:: ipython3
+
+    # DataSet.append(Instance)加入新数据
+    
+    dataset.append(Instance(raw_sentence='fake data', label='0'))
+    dataset[-1]
+
+
+
+
+.. parsed-literal::
+
+    {'raw_sentence': fake data,
+    'label': 0}
+
+
+
+.. code:: ipython3
+
+    # DataSet.apply(func, new_field_name)对数据预处理
+    
+    # 将所有数字转为小写
+    dataset.apply(lambda x: x['raw_sentence'].lower(), new_field_name='raw_sentence')
+    # label转int
+    dataset.apply(lambda x: int(x['label']), new_field_name='label_seq', is_target=True)
+    # 使用空格分割句子
+    dataset.drop(lambda x: len(x['raw_sentence'].split()) == 0)
+    def split_sent(ins):
+        return ins['raw_sentence'].split()
+    dataset.apply(split_sent, new_field_name='words', is_input=True)
+
+.. code:: ipython3
+
+    # DataSet.drop(func)筛除数据
+    # 删除低于某个长度的词语
+    dataset.drop(lambda x: len(x['words']) <= 3)
+
+.. code:: ipython3
+
+    # 分出测试集、训练集
+    
+    test_data, train_data = dataset.split(0.3)
+    print("Train size: ", len(test_data))
+    print("Test size: ", len(train_data))
+
+
+.. parsed-literal::
+
+    Train size:  54
+    Test size: 
+
+Vocabulary
+----------
+
+fastNLP中的Vocabulary轻松构建词表，将词转成数字
+
+.. code:: ipython3
+
+    from fastNLP import Vocabulary
+    
+    # 构建词表, Vocabulary.add(word)
+    vocab = Vocabulary(min_freq=2)
+    train_data.apply(lambda x: [vocab.add(word) for word in x['words']])
+    vocab.build_vocab()
+    
+    # index句子, Vocabulary.to_index(word)
+    train_data.apply(lambda x: [vocab.to_index(word) for word in x['words']], new_field_name='word_seq', is_input=True)
+    test_data.apply(lambda x: [vocab.to_index(word) for word in x['words']], new_field_name='word_seq', is_input=True)
+    
+    
+    print(test_data[0])
+
+
+.. parsed-literal::
+
+    {'raw_sentence': the plot is romantic comedy boilerplate from start to finish .,
+    'label': 2,
+    'label_seq': 2,
+    'words': ['the', 'plot', 'is', 'romantic', 'comedy', 'boilerplate', 'from', 'start', 'to', 'finish', '.'],
+    'word_seq': [2, 13, 9, 24, 25, 26, 15, 27, 11, 28, 3]}
+    
+
+.. code:: ipython3
+
+    # 假设你们需要做强化学习或者gan之类的项目，也许你们可以使用这里的dataset
+    from fastNLP.core.batch import Batch
+    from fastNLP.core.sampler import RandomSampler
+    
+    batch_iterator = Batch(dataset=train_data, batch_size=2, sampler=RandomSampler())
+    for batch_x, batch_y in batch_iterator:
+        print("batch_x has: ", batch_x)
+        print("batch_y has: ", batch_y)
+        break
+
+
+.. parsed-literal::
+
+    batch_x has:  {'words': array([list(['this', 'kind', 'of', 'hands-on', 'storytelling', 'is', 'ultimately', 'what', 'makes', 'shanghai', 'ghetto', 'move', 'beyond', 'a', 'good', ',', 'dry', ',', 'reliable', 'textbook', 'and', 'what', 'allows', 'it', 'to', 'rank', 'with', 'its', 'worthy', 'predecessors', '.']),
+           list(['the', 'entire', 'movie', 'is', 'filled', 'with', 'deja', 'vu', 'moments', '.'])],
+          dtype=object), 'word_seq': tensor([[  19,  184,    6,    1,  481,    9,  206,   50,   91, 1210, 1609, 1330,
+              495,    5,   63,    4, 1269,    4,    1, 1184,    7,   50, 1050,   10,
+                8, 1611,   16,   21, 1039,    1,    2],
+            [   3,  711,   22,    9, 1282,   16, 2482, 2483,  200,    2,    0,    0,
+                0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,
+                0,    0,    0,    0,    0,    0,    0]])}
+    batch_y has:  {'label_seq': tensor([3, 2])}
+    
+
+Model
+-----
+
+.. code:: ipython3
+
+    # 定义一个简单的Pytorch模型
+    
+    from fastNLP.models import CNNText
+    model = CNNText(embed_num=len(vocab), embed_dim=50, num_classes=5, padding=2, dropout=0.1)
+    model
+
+
+
+
+.. parsed-literal::
+
+    CNNText(
+      (embed): Embedding(
+        (embed): Embedding(77, 50, padding_idx=0)
+        (dropout): Dropout(p=0.0)
+      )
+      (conv_pool): ConvMaxpool(
+        (convs): ModuleList(
+          (0): Conv1d(50, 3, kernel_size=(3,), stride=(1,), padding=(2,))
+          (1): Conv1d(50, 4, kernel_size=(4,), stride=(1,), padding=(2,))
+          (2): Conv1d(50, 5, kernel_size=(5,), stride=(1,), padding=(2,))
+        )
+      )
+      (dropout): Dropout(p=0.1)
+      (fc): Linear(
+        (linear): Linear(in_features=12, out_features=5, bias=True)
+      )
+    )
+
+
+
+Trainer & Tester
+----------------
+
+使用fastNLP的Trainer训练模型
+
+.. code:: ipython3
+
+    from fastNLP import Trainer
+    from copy import deepcopy
+    from fastNLP import CrossEntropyLoss
+    from fastNLP import AccuracyMetric
+
+.. code:: ipython3
+
+    # 进行overfitting测试
+    copy_model = deepcopy(model)
+    overfit_trainer = Trainer(model=copy_model, 
+                              train_data=test_data, 
+                              dev_data=test_data,
+                              loss=CrossEntropyLoss(pred="output", target="label_seq"),
+                              metrics=AccuracyMetric(),
+                              n_epochs=10,
+                              save_path=None)
+    overfit_trainer.train()
+
+
+.. parsed-literal::
+
+    training epochs started 2018-12-07 14:07:20
+    
+
+
+
+.. parsed-literal::
+
+    HBox(children=(IntProgress(value=0, layout=Layout(flex='2'), max=20), HTML(value='')), layout=Layout(display='…
+
+
+
+.. parsed-literal::
+
+    Epoch 1/10. Step:2/20. AccuracyMetric: acc=0.037037
+    Epoch 2/10. Step:4/20. AccuracyMetric: acc=0.296296
+    Epoch 3/10. Step:6/20. AccuracyMetric: acc=0.333333
+    Epoch 4/10. Step:8/20. AccuracyMetric: acc=0.555556
+    Epoch 5/10. Step:10/20. AccuracyMetric: acc=0.611111
+    Epoch 6/10. Step:12/20. AccuracyMetric: acc=0.481481
+    Epoch 7/10. Step:14/20. AccuracyMetric: acc=0.62963
+    Epoch 8/10. Step:16/20. AccuracyMetric: acc=0.685185
+    Epoch 9/10. Step:18/20. AccuracyMetric: acc=0.722222
+    Epoch 10/10. Step:20/20. AccuracyMetric: acc=0.777778
+    
+
+.. code:: ipython3
+
+    # 实例化Trainer，传入模型和数据，进行训练
+    trainer = Trainer(model=model, 
+                      train_data=train_data, 
+                      dev_data=test_data,
+                      loss=CrossEntropyLoss(pred="output", target="label_seq"),
+                      metrics=AccuracyMetric(),
+                      n_epochs=5)
+    trainer.train()
+    print('Train finished!')
+
+
+.. parsed-literal::
+
+    training epochs started 2018-12-07 14:08:10
+    
+
+
+
+.. parsed-literal::
+
+    HBox(children=(IntProgress(value=0, layout=Layout(flex='2'), max=5), HTML(value='')), layout=Layout(display='i…
+
+
+
+.. parsed-literal::
+
+    Epoch 1/5. Step:1/5. AccuracyMetric: acc=0.037037
+    Epoch 2/5. Step:2/5. AccuracyMetric: acc=0.037037
+    Epoch 3/5. Step:3/5. AccuracyMetric: acc=0.037037
+    Epoch 4/5. Step:4/5. AccuracyMetric: acc=0.185185
+    Epoch 5/5. Step:5/5. AccuracyMetric: acc=0.240741
+    Train finished!
+    
+
+.. code:: ipython3
+
+    from fastNLP import Tester
+    
+    tester = Tester(data=test_data, model=model, metrics=AccuracyMetric())
+    acc = tester.test()
+
+
+.. parsed-literal::
+
+    [tester] 
+    AccuracyMetric: acc=0.240741
+    
+
+In summary
+----------
+
+fastNLP Trainer的伪代码逻辑
+---------------------------
+
+1. 准备DataSet，假设DataSet中共有如下的fields
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+::
+
+    ['raw_sentence', 'word_seq1', 'word_seq2', 'raw_label','label']
+    通过
+        DataSet.set_input('word_seq1', word_seq2', flag=True)将'word_seq1', 'word_seq2'设置为input
+    通过
+        DataSet.set_target('label', flag=True)将'label'设置为target
+
+2. 初始化模型
+~~~~~~~~~~~~~
+
+::
+
+    class Model(nn.Module):
+        def __init__(self):
+            xxx
+        def forward(self, word_seq1, word_seq2):
+            # (1) 这里使用的形参名必须和DataSet中的input field的名称对应。因为我们是通过形参名, 进行赋值的
+            # (2) input field的数量可以多于这里的形参数量。但是不能少于。
+            xxxx
+            # 输出必须是一个dict
+
+3. Trainer的训练过程
+~~~~~~~~~~~~~~~~~~~~
+
+::
+
+    (1) 从DataSet中按照batch_size取出一个batch，调用Model.forward
+    (2) 将 Model.forward的结果 与 标记为target的field 传入Losser当中。
+           由于每个人写的Model.forward的output的dict可能key并不一样，比如有人是{'pred':xxx}, {'output': xxx}; 
+           另外每个人将target可能也会设置为不同的名称, 比如有人是label, 有人设置为target；
+        为了解决以上的问题，我们的loss提供映射机制
+           比如CrossEntropyLosser的需要的输入是(prediction, target)。但是forward的output是{'output': xxx}; 'label'是target
+           那么初始化losser的时候写为CrossEntropyLosser(prediction='output', target='label')即可
+     (3) 对于Metric是同理的
+         Metric计算也是从 forward的结果中取值 与 设置target的field中取值。 也是可以通过映射找到对应的值        
+
+一些问题.
+---------
+
+1. DataSet中为什么需要设置input和target
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+::
+
+    只有被设置为input或者target的数据才会在train的过程中被取出来
+    (1.1) 我们只会在设置为input的field中寻找传递给Model.forward的参数。
+    (1.2) 我们在传递值给losser或者metric的时候会使用来自: 
+            (a)Model.forward的output
+            (b)被设置为target的field
+          
+
+2. 我们是通过forwad中的形参名将DataSet中的field赋值给对应的参数
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+::
+
+     (1.1) 构建模型过程中，
+      例如:
+          DataSet中x，seq_lens是input，那么forward就应该是
+          def forward(self, x, seq_lens):
+              pass
+          我们是通过形参名称进行匹配的field的
+       
+
+1. 加载数据到DataSet
+~~~~~~~~~~~~~~~~~~~~
+
+2. 使用apply操作对DataSet进行预处理
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+::
+
+      (2.1) 处理过程中将某些field设置为input，某些field设置为target
+
+3. 构建模型
+~~~~~~~~~~~
+
+::
+
+      (3.1) 构建模型过程中，需要注意forward函数的形参名需要和DataSet中设置为input的field名称是一致的。
+      例如:
+          DataSet中x，seq_lens是input，那么forward就应该是
+          def forward(self, x, seq_lens):
+              pass
+          我们是通过形参名称进行匹配的field的
+      (3.2) 模型的forward的output需要是dict类型的。
+          建议将输出设置为{"pred": xx}.
+          
diff --git a/docs/source/tutorials/fastnlp_1_minute_tutorial.rst b/docs/source/tutorials/fastnlp_1_minute_tutorial.rst
new file mode 100644
index 00000000..b4471e00
--- /dev/null
+++ b/docs/source/tutorials/fastnlp_1_minute_tutorial.rst
@@ -0,0 +1,111 @@
+
+FastNLP 1分钟上手教程
+=====================
+
+step 1
+------
+
+读取数据集
+
+.. code:: ipython3
+
+    from fastNLP import DataSet
+    # linux_path = "../test/data_for_tests/tutorial_sample_dataset.csv"
+    win_path = "C:\\Users\zyfeng\Desktop\FudanNLP\\fastNLP\\test\\data_for_tests\\tutorial_sample_dataset.csv"
+    ds = DataSet.read_csv(win_path, headers=('raw_sentence', 'label'), sep='\t')
+
+step 2
+------
+
+数据预处理 1. 类型转换 2. 切分验证集 3. 构建词典
+
+.. code:: ipython3
+
+    # 将所有数字转为小写
+    ds.apply(lambda x: x['raw_sentence'].lower(), new_field_name='raw_sentence')
+    # label转int
+    ds.apply(lambda x: int(x['label']), new_field_name='label_seq', is_target=True)
+    
+    def split_sent(ins):
+        return ins['raw_sentence'].split()
+    ds.apply(split_sent, new_field_name='words', is_input=True)
+    
+
+.. code:: ipython3
+
+    # 分割训练集/验证集
+    train_data, dev_data = ds.split(0.3)
+    print("Train size: ", len(train_data))
+    print("Test size: ", len(dev_data))
+
+
+.. parsed-literal::
+
+    Train size:  54
+    Test size:  23
+    
+
+.. code:: ipython3
+
+    from fastNLP import Vocabulary
+    vocab = Vocabulary(min_freq=2)
+    train_data.apply(lambda x: [vocab.add(word) for word in x['words']])
+    
+    # index句子, Vocabulary.to_index(word)
+    train_data.apply(lambda x: [vocab.to_index(word) for word in x['words']], new_field_name='word_seq', is_input=True)
+    dev_data.apply(lambda x: [vocab.to_index(word) for word in x['words']], new_field_name='word_seq', is_input=True)
+    
+
+step 3
+------
+
+定义模型
+
+.. code:: ipython3
+
+    from fastNLP.models import CNNText
+    model = CNNText(embed_num=len(vocab), embed_dim=50, num_classes=5, padding=2, dropout=0.1)
+    
+
+step 4
+------
+
+开始训练
+
+.. code:: ipython3
+
+    from fastNLP import Trainer, CrossEntropyLoss, AccuracyMetric
+    trainer = Trainer(model=model, 
+                      train_data=train_data, 
+                      dev_data=dev_data,
+                      loss=CrossEntropyLoss(),
+                      metrics=AccuracyMetric()
+                      )
+    trainer.train()
+    print('Train finished!')
+    
+
+
+.. parsed-literal::
+
+    training epochs started 2018-12-07 14:03:41
+    
+
+
+
+.. parsed-literal::
+
+    HBox(children=(IntProgress(value=0, layout=Layout(flex='2'), max=6), HTML(value='')), layout=Layout(display='i…
+
+
+
+.. parsed-literal::
+
+    Epoch 1/3. Step:2/6. AccuracyMetric: acc=0.26087
+    Epoch 2/3. Step:4/6. AccuracyMetric: acc=0.347826
+    Epoch 3/3. Step:6/6. AccuracyMetric: acc=0.608696
+    Train finished!
+    
+
+本教程结束。更多操作请参考进阶教程。
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
diff --git a/docs/source/user/installation.rst b/docs/source/user/installation.rst
index 0655041b..7dc39b3b 100644
--- a/docs/source/user/installation.rst
+++ b/docs/source/user/installation.rst
@@ -6,26 +6,11 @@ Installation
    :local:
 
 
-Cloning From GitHub
-~~~~~~~~~~~~~~~~~~~
-
-If you just want to use fastNLP, use:
+Run the following commands to install fastNLP package:
 
 .. code:: shell
 
-   git clone https://github.com/fastnlp/fastNLP
-   cd fastNLP
+   pip install fastNLP
    
 
-PyTorch Installation
-~~~~~~~~~~~~~~~~~~~~
-
-Visit the [PyTorch official website] for installation instructions based
-on your system. In general, you could use:
-
-.. code:: shell
 
-   # using conda
-   conda install pytorch torchvision -c pytorch
-   # or using pip
-   pip3 install torch torchvision
diff --git a/docs/source/user/quickstart.rst b/docs/source/user/quickstart.rst
index 24c7363c..baa49eef 100644
--- a/docs/source/user/quickstart.rst
+++ b/docs/source/user/quickstart.rst
@@ -1,84 +1,9 @@
-==========
 Quickstart
 ==========
 
-Example
--------
-
-Basic Usage
-~~~~~~~~~~~
-
-A typical fastNLP routine is composed of four phases: loading dataset,
-pre-processing data, constructing model and training model.
-
-.. code:: python
-
-   from fastNLP.models.base_model import BaseModel
-   from fastNLP.modules import encoder
-   from fastNLP.modules import aggregation
-   from fastNLP.modules import decoder
-
-   from fastNLP.loader.dataset_loader import ClassDataSetLoader
-   from fastNLP.loader.preprocess import ClassPreprocess
-   from fastNLP.core.trainer import ClassificationTrainer
-   from fastNLP.core.inference import ClassificationInfer
-
-
-   class ClassificationModel(BaseModel):
-       """
-       Simple text classification model based on CNN.
-       """
-
-       def __init__(self, num_classes, vocab_size):
-           super(ClassificationModel, self).__init__()
-
-           self.emb = encoder.Embedding(nums=vocab_size, dims=300)
-           self.enc = encoder.Conv(
-               in_channels=300, out_channels=100, kernel_size=3)
-           self.agg = aggregation.MaxPool()
-           self.dec = decoder.MLP([100, num_classes])
-
-       def forward(self, x):
-           x = self.emb(x)  # [N,L] -> [N,L,C]
-           x = self.enc(x)  # [N,L,C_in] -> [N,L,C_out]
-           x = self.agg(x)  # [N,L,C] -> [N,C]
-           x = self.dec(x)  # [N,C] -> [N, N_class]
-           return x
-
-
-   data_dir = 'data'  # directory to save data and model
-   train_path = 'test/data_for_tests/text_classify.txt'  # training set file
-
-   # load dataset
-   ds_loader = ClassDataSetLoader("train", train_path)
-   data = ds_loader.load()
-
-   # pre-process dataset
-   pre = ClassPreprocess(data_dir)
-   vocab_size, n_classes = pre.process(data, "data_train.pkl")
-
-   # construct model
-   model_args = {
-       'num_classes': n_classes,
-       'vocab_size': vocab_size
-   }
-   model = ClassificationModel(num_classes=n_classes, vocab_size=vocab_size)
+.. toctree::
+   :maxdepth: 1
 
-   # train model
-   train_args = {
-       "epochs": 20,
-       "batch_size": 50,
-       "pickle_path": data_dir,
-       "validate": False,
-       "save_best_dev": False,
-       "model_saved_path": None,
-       "use_cuda": True,
-       "learn_rate": 1e-3,
-       "momentum": 0.9}
-   trainer = ClassificationTrainer(train_args)
-   trainer.train(model)
+   ../tutorials/fastnlp_1_minute_tutorial
+   ../tutorials/fastnlp_10tmin_tutorial
 
-   # predict using model
-   seqs = [x[0] for x in data]
-   infer = ClassificationInfer(data_dir)
-   labels_pred = infer.predict(model, seqs)
\ No newline at end of file
diff --git a/fastNLP/io/dataset_loader.py b/fastNLP/io/dataset_loader.py
index 641a631e..76b9584d 100644
--- a/fastNLP/io/dataset_loader.py
+++ b/fastNLP/io/dataset_loader.py
@@ -417,6 +417,55 @@ class PeopleDailyCorpusLoader(DataSetLoader):
         data_set.set_input("seq_len")
         return data_set
 
+    
+class Conll2003Loader(DataSetLoader):
+    """Self-defined loader of conll2003 dataset
+    
+        More information about the given dataset cound be found on 
+        https://sites.google.com/site/ermasoftware/getting-started/ne-tagging-conll2003-data 
+    
+    """
+    
+    def __init__(self):
+        super(Conll2003Loader, self).__init__()
+        
+    def load(self, dataset_path):
+        with open(dataset_path, "r", encoding="utf-8") as f:
+            lines = f.readlines()
+        
+        ##Parse the dataset line by line
+        parsed_data = []
+        sentence = []
+        tokens = []
+        for line in lines:
+            if '-DOCSTART- -X- -X- O' in line or line == '\n':
+                if sentence != []:
+                    parsed_data.append((sentence, tokens))
+                    sentence = []
+                    tokens = []
+                continue
+                
+            temp = line.strip().split(" ")
+            sentence.append(temp[0]) 
+            tokens.append(temp[1:4])
+                           
+        return self.convert(parsed_data)
+            
+    def convert(self, parsed_data):
+        dataset = DataSet()
+        for sample in parsed_data:
+            label0_list = list(map(
+                lambda labels: labels[0], sample[1]))
+            label1_list = list(map(
+                lambda labels: labels[1], sample[1]))
+            label2_list = list(map(
+                lambda labels: labels[2], sample[1]))
+            dataset.append(Instance(token_list=sample[0], 
+                                    label0_list=label0_list, 
+                                    label1_list=label1_list,
+                                    label2_list=label2_list))
+            
+        return dataset
 
 class SNLIDataSetLoader(DataSetLoader):
     """A data set loader for SNLI data set.
diff --git a/readthedocs.yml b/readthedocs.yml
new file mode 100644
index 00000000..9b172987
--- /dev/null
+++ b/readthedocs.yml
@@ -0,0 +1,6 @@
+build:
+  image: latest
+
+python:
+  version: 3.6
+  setup_py_install: true
\ No newline at end of file
diff --git a/test/data_for_tests/conll_2003_example.txt b/test/data_for_tests/conll_2003_example.txt
new file mode 100644
index 00000000..d11a8264
--- /dev/null
+++ b/test/data_for_tests/conll_2003_example.txt
@@ -0,0 +1,442 @@
+-DOCSTART- -X- -X- O
+
+SOCCER NN B-NP O
+- : O O
+JAPAN NNP B-NP B-LOC
+GET VB B-VP O
+LUCKY NNP B-NP O
+WIN NNP I-NP O
+, , O O
+CHINA NNP B-NP B-PER
+IN IN B-PP O
+SURPRISE DT B-NP O
+DEFEAT NN I-NP O
+. . O O
+
+Nadim NNP B-NP B-PER
+Ladki NNP I-NP I-PER
+
+AL-AIN NNP B-NP B-LOC
+, , O O
+United NNP B-NP B-LOC
+Arab NNP I-NP I-LOC
+Emirates NNPS I-NP I-LOC
+1996-12-06 CD I-NP O
+
+Japan NNP B-NP B-LOC
+began VBD B-VP O
+the DT B-NP O
+defence NN I-NP O
+of IN B-PP O
+their PRP$ B-NP O
+Asian JJ I-NP B-MISC
+Cup NNP I-NP I-MISC
+title NN I-NP O
+with IN B-PP O
+a DT B-NP O
+lucky JJ I-NP O
+2-1 CD I-NP O
+win VBP B-VP O
+against IN B-PP O
+Syria NNP B-NP B-LOC
+in IN B-PP O
+a DT B-NP O
+Group NNP I-NP O
+C NNP I-NP O
+championship NN I-NP O
+match NN I-NP O
+on IN B-PP O
+Friday NNP B-NP O
+. . O O
+
+But CC O O
+China NNP B-NP B-LOC
+saw VBD B-VP O
+their PRP$ B-NP O
+luck NN I-NP O
+desert VB B-VP O
+them PRP B-NP O
+in IN B-PP O
+the DT B-NP O
+second NN I-NP O
+match NN I-NP O
+of IN B-PP O
+the DT B-NP O
+group NN I-NP O
+, , O O
+crashing VBG B-VP O
+to TO B-PP O
+a DT B-NP O
+surprise NN I-NP O
+2-0 CD I-NP O
+defeat NN I-NP O
+to TO B-PP O
+newcomers NNS B-NP O
+Uzbekistan NNP I-NP B-LOC
+. . O O
+
+China NNP B-NP B-LOC
+controlled VBD B-VP O
+most JJS B-NP O
+of IN B-PP O
+the DT B-NP O
+match NN I-NP O
+and CC O O
+saw VBD B-VP O
+several JJ B-NP O
+chances NNS I-NP O
+missed VBD B-VP O
+until IN B-SBAR O
+the DT B-NP O
+78th JJ I-NP O
+minute NN I-NP O
+when WRB B-ADVP O
+Uzbek NNP B-NP B-MISC
+striker NN I-NP O
+Igor JJ B-NP B-PER
+Shkvyrin NNP I-NP I-PER
+took VBD B-VP O
+advantage NN B-NP O
+of IN B-PP O
+a DT B-NP O
+misdirected JJ I-NP O
+defensive JJ I-NP O
+header NN I-NP O
+to TO B-VP O
+lob VB I-VP O
+the DT B-NP O
+ball NN I-NP O
+over IN B-PP O
+the DT B-NP O
+advancing VBG I-NP O
+Chinese JJ I-NP B-MISC
+keeper NN I-NP O
+and CC O O
+into IN B-PP O
+an DT B-NP O
+empty JJ I-NP O
+net NN I-NP O
+. . O O
+
+Oleg NNP B-NP B-PER
+Shatskiku NNP I-NP I-PER
+made VBD B-VP O
+sure JJ B-ADJP O
+of IN B-PP O
+the DT B-NP O
+win VBP B-VP O
+in IN B-PP O
+injury NN B-NP O
+time NN I-NP O
+, , O O
+hitting VBG B-VP O
+an DT B-NP O
+unstoppable JJ I-NP O
+left VBD B-VP O
+foot NN B-NP O
+shot NN I-NP O
+from IN B-PP O
+just RB B-NP O
+outside IN B-PP O
+the DT B-NP O
+area NN I-NP O
+. . O O
+
+The DT B-NP O
+former JJ I-NP O
+Soviet JJ I-NP B-MISC
+republic NN I-NP O
+was VBD B-VP O
+playing VBG I-VP O
+in IN B-PP O
+an DT B-NP O
+Asian NNP I-NP B-MISC
+Cup NNP I-NP I-MISC
+finals NNS I-NP O
+tie NN I-NP O
+for IN B-PP O
+the DT B-NP O
+first JJ I-NP O
+time NN I-NP O
+. . O O
+
+Despite IN B-PP O
+winning VBG B-VP O
+the DT B-NP O
+Asian JJ I-NP B-MISC
+Games NNPS I-NP I-MISC
+title NN I-NP O
+two CD B-NP O
+years NNS I-NP O
+ago RB B-ADVP O
+, , O O
+Uzbekistan NNP B-NP B-LOC
+are VBP B-VP O
+in IN B-PP O
+the DT B-NP O
+finals NNS I-NP O
+as IN B-SBAR O
+outsiders NNS B-NP O
+. . O O
+
+Two CD B-NP O
+goals NNS I-NP O
+from IN B-PP O
+defensive JJ B-NP O
+errors NNS I-NP O
+in IN B-PP O
+the DT B-NP O
+last JJ I-NP O
+six CD I-NP O
+minutes NNS I-NP O
+allowed VBD B-VP O
+Japan NNP B-NP B-LOC
+to TO B-VP O
+come VB I-VP O
+from IN B-PP O
+behind NN B-NP O
+and CC O O
+collect VB B-VP O
+all DT B-NP O
+three CD I-NP O
+points NNS I-NP O
+from IN B-PP O
+their PRP$ B-NP O
+opening NN I-NP O
+meeting NN I-NP O
+against IN B-PP O
+Syria NNP B-NP B-LOC
+. . O O
+
+Takuya NNP B-NP B-PER
+Takagi NNP I-NP I-PER
+scored VBD B-VP O
+the DT B-NP O
+winner NN I-NP O
+in IN B-PP O
+the DT B-NP O
+88th JJ I-NP O
+minute NN I-NP O
+, , O O
+rising VBG B-VP O
+to TO I-VP O
+head VB I-VP O
+a DT B-NP O
+Hiroshige NNP I-NP B-PER
+Yanagimoto NNP I-NP I-PER
+cross VB B-VP O
+towards IN B-PP O
+the DT B-NP O
+Syrian JJ I-NP B-MISC
+goal NN I-NP O
+which WDT B-NP O
+goalkeeper VBD B-VP O
+Salem NNP B-NP B-PER
+Bitar NNP I-NP I-PER
+appeared VBD B-VP O
+to TO I-VP O
+have VB I-VP O
+covered VBN I-VP O
+but CC O O
+then RB B-VP O
+allowed VBN I-VP O
+to TO I-VP O
+slip VB I-VP O
+into IN B-PP O
+the DT B-NP O
+net NN I-NP O
+. . O O
+
+It PRP B-NP O
+was VBD B-VP O
+the DT B-NP O
+second JJ I-NP O
+costly JJ I-NP O
+blunder NN I-NP O
+by IN B-PP O
+Syria NNP B-NP B-LOC
+in IN B-PP O
+four CD B-NP O
+minutes NNS I-NP O
+. . O O
+
+Defender NNP B-NP O
+Hassan NNP I-NP B-PER
+Abbas NNP I-NP I-PER
+rose VBD B-VP O
+to TO I-VP O
+intercept VB I-VP O
+a DT B-NP O
+long JJ I-NP O
+ball NN I-NP O
+into IN B-PP O
+the DT B-NP O
+area NN I-NP O
+in IN B-PP O
+the DT B-NP O
+84th JJ I-NP O
+minute NN I-NP O
+but CC O O
+only RB B-ADVP O
+managed VBD B-VP O
+to TO I-VP O
+divert VB I-VP O
+it PRP B-NP O
+into IN B-PP O
+the DT B-NP O
+top JJ I-NP O
+corner NN I-NP O
+of IN B-PP O
+Bitar NN B-NP B-PER
+'s POS B-NP O
+goal NN I-NP O
+. . O O
+
+Nader NNP B-NP B-PER
+Jokhadar NNP I-NP I-PER
+had VBD B-VP O
+given VBN I-VP O
+Syria NNP B-NP B-LOC
+the DT B-NP O
+lead NN I-NP O
+with IN B-PP O
+a DT B-NP O
+well-struck NN I-NP O
+header NN I-NP O
+in IN B-PP O
+the DT B-NP O
+seventh JJ I-NP O
+minute NN I-NP O
+. . O O
+
+Japan NNP B-NP B-LOC
+then RB B-ADVP O
+laid VBD B-VP O
+siege NN B-NP O
+to TO B-PP O
+the DT B-NP O
+Syrian JJ I-NP B-MISC
+penalty NN I-NP O
+area NN I-NP O
+for IN B-PP O
+most JJS B-NP O
+of IN B-PP O
+the DT B-NP O
+game NN I-NP O
+but CC O O
+rarely RB B-VP O
+breached VBD I-VP O
+the DT B-NP O
+Syrian JJ I-NP B-MISC
+defence NN I-NP O
+. . O O
+
+Bitar NN B-NP B-PER
+pulled VBD B-VP O
+off RP B-PRT O
+fine JJ B-NP O
+saves VBZ B-VP O
+whenever WRB B-ADVP O
+they PRP B-NP O
+did VBD B-VP O
+. . O O
+
+Japan NNP B-NP B-LOC
+coach NN I-NP O
+Shu NNP I-NP B-PER
+Kamo NNP I-NP I-PER
+said VBD B-VP O
+: : O O
+' '' O O
+' POS B-NP O
+The DT I-NP O
+Syrian JJ I-NP B-MISC
+own JJ I-NP O
+goal NN I-NP O
+proved VBD B-VP O
+lucky JJ B-ADJP O
+for IN B-PP O
+us PRP B-NP O
+. . O O
+
+The DT B-NP O
+Syrians NNPS I-NP B-MISC
+scored VBD B-VP O
+early JJ B-NP O
+and CC O O
+then RB B-VP O
+played VBN I-VP O
+defensively RB B-ADVP O
+and CC O O
+adopted VBD B-VP O
+long RB I-VP O
+balls VBZ I-VP O
+which WDT B-NP O
+made VBD B-VP O
+it PRP B-NP O
+hard JJ B-ADJP O
+for IN B-PP O
+us PRP B-NP O
+. . O O
+' '' O O
+
+' '' O O
+
+Japan NNP B-NP B-LOC
+, , O O
+co-hosts VBZ B-VP O
+of IN B-PP O
+the DT B-NP O
+World NNP I-NP B-MISC
+Cup NNP I-NP I-MISC
+in IN B-PP O
+2002 CD B-NP O
+and CC O O
+ranked VBD B-VP O
+20th JJ B-NP O
+in IN B-PP O
+the DT B-NP O
+world NN I-NP O
+by IN B-PP O
+FIFA NNP B-NP B-ORG
+, , O O
+are VBP B-VP O
+favourites JJ B-ADJP O
+to TO B-VP O
+regain VB I-VP O
+their PRP$ B-NP O
+title NN I-NP O
+here RB B-ADVP O
+. . O O
+
+Hosts NNPS B-NP O
+UAE NNP I-NP B-LOC
+play NN I-NP O
+Kuwait NNP I-NP B-LOC
+and CC O O
+South NNP B-NP B-LOC
+Korea NNP I-NP I-LOC
+take VBP B-VP O
+on IN B-PP O
+Indonesia NNP B-NP B-LOC
+on IN B-PP O
+Saturday NNP B-NP O
+in IN B-PP O
+Group NNP B-NP O
+A NNP I-NP O
+matches VBZ B-VP O
+. . O O
+
+All DT B-NP O
+four CD I-NP O
+teams NNS I-NP O
+are VBP B-VP O
+level NN B-NP O
+with IN B-PP O
+one CD B-NP O
+point NN I-NP O
+each DT B-NP O
+from IN B-PP O
+one CD B-NP O
+game NN I-NP O
+. . O O
\ No newline at end of file
diff --git a/test/io/test_dataset_loader.py b/test/io/test_dataset_loader.py
new file mode 100644
index 00000000..9bee175b
--- /dev/null
+++ b/test/io/test_dataset_loader.py
@@ -0,0 +1,23 @@
+import os
+import unittest
+
+from fastNLP.io.dataset_loader import Conll2003Loader
+class TestDatasetLoader(unittest.TestCase):
+    
+    def test_case_1(self):
+        '''
+            Test the the loader of Conll2003 dataset
+        '''
+
+        dataset_path = "test/data_for_tests/conll_2003_example.txt"
+        loader = Conll2003Loader()
+        dataset_2003 = loader.load(dataset_path)
+        
+        for item in dataset_2003:
+            len0 = len(item["label0_list"])
+            len1 = len(item["label1_list"])
+            len2 = len(item["label2_list"])
+            lentoken = len(item["token_list"])
+            self.assertNotEqual(len0, 0)
+            self.assertEqual(len0, len1)
+            self.assertEqual(len1, len2)
\ No newline at end of file