Merge pull request #111 from lyhuang18/docs_new

[Doc] update
7 years ago · eee52672b4
--- 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
 tensorboardX>=1.4
 tqdm>=4.28.1
 ipython>=6.4.0
 ipython-genutils>=0.2.0
--- a/docs/source/fastNLP.api.rst
+++ 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:
--- a/docs/source/fastNLP.core.rst
+++ b/docs/source/fastNLP.core.rst
@@ -13,10 +13,10 @@ fastNLP.core.dataset
 .. automodule:: fastNLP.core.dataset
    :members:

 fastNLP.core.field 
 -------------------
 fastNLP.core.fieldarray 
 ------------------------

 .. automodule:: fastNLP.core.field
 .. automodule:: fastNLP.core.fieldarray
    :members:

 fastNLP.core.instance 
@@ -25,10 +25,10 @@ fastNLP.core.instance
 .. automodule:: fastNLP.core.instance
    :members:

 fastNLP.core.loss 
 ------------------
 fastNLP.core.losses 
 --------------------

 .. automodule:: fastNLP.core.loss
 .. automodule:: fastNLP.core.losses
    :members:

 fastNLP.core.metrics 
@@ -49,12 +49,6 @@ fastNLP.core.predictor
 .. automodule:: fastNLP.core.predictor
    :members:

 fastNLP.core.preprocess 
 ------------------------

 .. automodule:: fastNLP.core.preprocess
    :members:

 fastNLP.core.sampler 
 ---------------------

@@ -73,6 +67,12 @@ fastNLP.core.trainer
 .. automodule:: fastNLP.core.trainer
    :members:

 fastNLP.core.utils 
 -------------------

 .. automodule:: fastNLP.core.utils
    :members:

 fastNLP.core.vocabulary 
 ------------------------

--- a/docs/source/fastNLP.io.rst
+++ 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:
--- a/docs/source/fastNLP.loader.rst
+++ b/docs/source/fastNLP.loader.rst
@@ -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:
--- 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:
--- 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 
 -----------------------------------------

--- a/docs/source/fastNLP.modules.interactor.rst
+++ b/docs/source/fastNLP.modules.interactor.rst
@@ -1,5 +0,0 @@
 fastNLP.modules.interactor 
 ===========================

 .. automodule:: fastNLP.modules.interactor
    :members:
--- 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 
 -------------------------------
--- 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:
--- a/docs/source/fastNLP.saver.rst
+++ b/docs/source/fastNLP.saver.rst
@@ -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:
--- 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



--- a/docs/source/tutorials/fastnlp_10tmin_tutorial.rst
+++ 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}.
          
--- a/docs/source/tutorials/fastnlp_1_minute_tutorial.rst
+++ 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。
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
--- 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
--- 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)
--- a/readthedocs.yml
+++ b/readthedocs.yml
@@ -0,0 +1,6 @@
 build:
  image: latest

 python:
  version: 3.6
  setup_py_install: true