@@ -1,5 +1,8 @@ | |||||
numpy>=1.14.2 | 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 | torchvision>=0.1.8 | ||||
sphinx-rtd-theme==0.4.1 | 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 |
@@ -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: |
@@ -13,10 +13,10 @@ fastNLP.core.dataset | |||||
.. automodule:: fastNLP.core.dataset | .. automodule:: fastNLP.core.dataset | ||||
:members: | :members: | ||||
fastNLP.core.field | |||||
------------------- | |||||
fastNLP.core.fieldarray | |||||
------------------------ | |||||
.. automodule:: fastNLP.core.field | |||||
.. automodule:: fastNLP.core.fieldarray | |||||
:members: | :members: | ||||
fastNLP.core.instance | fastNLP.core.instance | ||||
@@ -25,10 +25,10 @@ fastNLP.core.instance | |||||
.. automodule:: fastNLP.core.instance | .. automodule:: fastNLP.core.instance | ||||
:members: | :members: | ||||
fastNLP.core.loss | |||||
------------------ | |||||
fastNLP.core.losses | |||||
-------------------- | |||||
.. automodule:: fastNLP.core.loss | |||||
.. automodule:: fastNLP.core.losses | |||||
:members: | :members: | ||||
fastNLP.core.metrics | fastNLP.core.metrics | ||||
@@ -49,12 +49,6 @@ fastNLP.core.predictor | |||||
.. automodule:: fastNLP.core.predictor | .. automodule:: fastNLP.core.predictor | ||||
:members: | :members: | ||||
fastNLP.core.preprocess | |||||
------------------------ | |||||
.. automodule:: fastNLP.core.preprocess | |||||
:members: | |||||
fastNLP.core.sampler | fastNLP.core.sampler | ||||
--------------------- | --------------------- | ||||
@@ -73,6 +67,12 @@ fastNLP.core.trainer | |||||
.. automodule:: fastNLP.core.trainer | .. automodule:: fastNLP.core.trainer | ||||
:members: | :members: | ||||
fastNLP.core.utils | |||||
------------------- | |||||
.. automodule:: fastNLP.core.utils | |||||
:members: | |||||
fastNLP.core.vocabulary | fastNLP.core.vocabulary | ||||
------------------------ | ------------------------ | ||||
@@ -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: |
@@ -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: |
@@ -7,6 +7,12 @@ fastNLP.models.base\_model | |||||
.. automodule:: fastNLP.models.base_model | .. automodule:: fastNLP.models.base_model | ||||
:members: | :members: | ||||
fastNLP.models.biaffine\_parser | |||||
-------------------------------- | |||||
.. automodule:: fastNLP.models.biaffine_parser | |||||
:members: | |||||
fastNLP.models.char\_language\_model | fastNLP.models.char\_language\_model | ||||
------------------------------------- | ------------------------------------- | ||||
@@ -25,6 +31,12 @@ fastNLP.models.sequence\_modeling | |||||
.. automodule:: fastNLP.models.sequence_modeling | .. automodule:: fastNLP.models.sequence_modeling | ||||
:members: | :members: | ||||
fastNLP.models.snli | |||||
-------------------- | |||||
.. automodule:: fastNLP.models.snli | |||||
:members: | |||||
.. automodule:: fastNLP.models | .. automodule:: fastNLP.models | ||||
:members: | :members: |
@@ -43,6 +43,12 @@ fastNLP.modules.encoder.masked\_rnn | |||||
.. automodule:: fastNLP.modules.encoder.masked_rnn | .. automodule:: fastNLP.modules.encoder.masked_rnn | ||||
:members: | :members: | ||||
fastNLP.modules.encoder.transformer | |||||
------------------------------------ | |||||
.. automodule:: fastNLP.modules.encoder.transformer | |||||
:members: | |||||
fastNLP.modules.encoder.variational\_rnn | fastNLP.modules.encoder.variational\_rnn | ||||
----------------------------------------- | ----------------------------------------- | ||||
@@ -1,5 +0,0 @@ | |||||
fastNLP.modules.interactor | |||||
=========================== | |||||
.. automodule:: fastNLP.modules.interactor | |||||
:members: |
@@ -6,7 +6,12 @@ fastNLP.modules | |||||
fastNLP.modules.aggregator | fastNLP.modules.aggregator | ||||
fastNLP.modules.decoder | fastNLP.modules.decoder | ||||
fastNLP.modules.encoder | fastNLP.modules.encoder | ||||
fastNLP.modules.interactor | |||||
fastNLP.modules.dropout | |||||
------------------------ | |||||
.. automodule:: fastNLP.modules.dropout | |||||
:members: | |||||
fastNLP.modules.other\_modules | fastNLP.modules.other\_modules | ||||
------------------------------- | ------------------------------- | ||||
@@ -3,18 +3,11 @@ fastNLP | |||||
.. toctree:: | .. toctree:: | ||||
fastNLP.api | |||||
fastNLP.core | fastNLP.core | ||||
fastNLP.loader | |||||
fastNLP.io | |||||
fastNLP.models | fastNLP.models | ||||
fastNLP.modules | fastNLP.modules | ||||
fastNLP.saver | |||||
fastNLP.fastnlp | |||||
---------------- | |||||
.. automodule:: fastNLP.fastnlp | |||||
:members: | |||||
.. automodule:: fastNLP | .. automodule:: fastNLP | ||||
:members: | :members: |
@@ -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: |
@@ -1,33 +1,35 @@ | |||||
fastNLP documentation | fastNLP documentation | ||||
===================== | ===================== | ||||
fastNLP,目前仍在孵化中。 | |||||
A Modularized and Extensible Toolkit for Natural Language Processing. Currently still in incubation. | |||||
Introduction | 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 | |||||
@@ -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}. | |||||
@@ -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。 | |||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ |
@@ -6,26 +6,11 @@ Installation | |||||
:local: | :local: | ||||
Cloning From GitHub | |||||
~~~~~~~~~~~~~~~~~~~ | |||||
If you just want to use fastNLP, use: | |||||
Run the following commands to install fastNLP package: | |||||
.. code:: shell | .. 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 |
@@ -1,84 +1,9 @@ | |||||
========== | |||||
Quickstart | 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) |
@@ -0,0 +1,6 @@ | |||||
build: | |||||
image: latest | |||||
python: | |||||
version: 3.6 | |||||
setup_py_install: true |