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tags/v0.3.0
lyhuang 5 years ago
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      docs/source/tutorials/fastnlp_10tmin_tutorial.rst
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docs/requirements.txt View File

@@ -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

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docs/source/fastNLP.api.rst View File

@@ -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:

+ 12
- 12
docs/source/fastNLP.core.rst View File

@@ -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
------------------------



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docs/source/fastNLP.io.rst View File

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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:

+ 0
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docs/source/fastNLP.loader.rst View File

@@ -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:

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docs/source/fastNLP.models.rst View File

@@ -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:

+ 6
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docs/source/fastNLP.modules.encoder.rst View File

@@ -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
-----------------------------------------



+ 0
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docs/source/fastNLP.modules.interactor.rst View File

@@ -1,5 +0,0 @@
fastNLP.modules.interactor
===========================

.. automodule:: fastNLP.modules.interactor
:members:

+ 6
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docs/source/fastNLP.modules.rst View File

@@ -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
-------------------------------


+ 2
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docs/source/fastNLP.rst View File

@@ -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:

+ 0
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docs/source/fastNLP.saver.rst View File

@@ -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:

+ 20
- 18
docs/source/index.rst View File

@@ -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





+ 375
- 0
docs/source/tutorials/fastnlp_10tmin_tutorial.rst View File

@@ -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}.

+ 111
- 0
docs/source/tutorials/fastnlp_1_minute_tutorial.rst View File

@@ -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。
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

+ 2
- 17
docs/source/user/installation.rst View File

@@ -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

+ 4
- 79
docs/source/user/quickstart.rst View File

@@ -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)

+ 6
- 0
readthedocs.yml View File

@@ -0,0 +1,6 @@
build:
image: latest

python:
version: 3.6
setup_py_install: true

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