@@ -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 |
@@ -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 | |||
: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 | |||
------------------------ | |||
@@ -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 | |||
: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: |
@@ -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 | |||
----------------------------------------- | |||
@@ -1,5 +0,0 @@ | |||
fastNLP.modules.interactor | |||
=========================== | |||
.. automodule:: fastNLP.modules.interactor | |||
:members: |
@@ -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 | |||
------------------------------- | |||
@@ -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: |
@@ -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,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 | |||
@@ -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: | |||
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 |
@@ -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) |
@@ -0,0 +1,6 @@ | |||
build: | |||
image: latest | |||
python: | |||
version: 3.6 | |||
setup_py_install: true |