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merge dev branch with master
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FengZiYjun
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d43d738536
24
.github/PULL_REQUEST_TEMPLATE.md
vendored
Normal file
24
.github/PULL_REQUEST_TEMPLATE.md
vendored
Normal file
@ -0,0 +1,24 @@
|
||||
Description：简要描述这次PR的内容
|
||||
|
||||
Main reason: 做出这次修改的原因
|
||||
|
||||
|
||||
Checklist 检查下面各项是否完成
|
||||
|
||||
Please feel free to remove inapplicable items for your PR.
|
||||
|
||||
- [ ] The PR title starts with [$CATEGORY] (例如[bugfix]修复bug，[new]添加新功能，[test]修改测试，[rm]删除旧代码)
|
||||
- [ ] Changes are complete (i.e. I finished coding on this PR) 修改完成才提PR
|
||||
- [ ] All changes have test coverage 修改的部分顺利通过测试。对于fastnlp/fastnlp/*的修改，测试代码**必须**提供在fastnlp/test/*。
|
||||
- [ ] Code is well-documented 注释写好，API文档会从注释中抽取
|
||||
- [ ] To the my best knowledge, examples are either not affected by this change, or have been fixed to be compatible with this change 修改导致例子或tutorial有变化，请找核心开发人员
|
||||
|
||||
Changes: 逐项描述修改的内容
|
||||
- 添加了新模型；用于句子分类的CNN，来自Yoon Kim的Convolutional Neural Networks for Sentence Classification
|
||||
- 修改dataset.py中过时的和不合规则的注释 #286
|
||||
- 添加对var-LSTM的测试代码
|
||||
|
||||
Mention: 找人review你的PR
|
||||
|
||||
@修改过这个文件的人
|
||||
@核心开发人员
|
||||
@ -30,6 +30,7 @@ A deep learning NLP model is the composition of three types of modules:
|
||||
<td> decode the representation into the output </td>
|
||||
<td> MLP, CRF </td>
|
||||
</tr>
|
||||
</table>
|
||||
|
||||
For example:
|
||||
|
||||
@ -37,9 +38,11 @@ For example:
|
||||
|
||||
## Requirements
|
||||
|
||||
- Python>=3.6
|
||||
- numpy>=1.14.2
|
||||
- torch>=0.4.0
|
||||
- tensorboardX
|
||||
- tqdm>=4.28.1
|
||||
|
||||
|
||||
## Resources
|
||||
|
||||
@ -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
|
||||
36
docs/source/fastNLP.api.rst
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36
docs/source/fastNLP.api.rst
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@ -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:
|
||||
@ -13,8 +13,8 @@ fastNLP.core.dataset
|
||||
.. automodule:: fastNLP.core.dataset
|
||||
:members:
|
||||
|
||||
fastNLP.core.fieldarray
|
||||
-------------------
|
||||
fastNLP.core.fieldarray
|
||||
------------------------
|
||||
|
||||
.. automodule:: fastNLP.core.fieldarray
|
||||
:members:
|
||||
@ -25,8 +25,8 @@ fastNLP.core.instance
|
||||
.. automodule:: fastNLP.core.instance
|
||||
:members:
|
||||
|
||||
fastNLP.core.losses
|
||||
------------------
|
||||
fastNLP.core.losses
|
||||
--------------------
|
||||
|
||||
.. automodule:: fastNLP.core.losses
|
||||
:members:
|
||||
@ -67,6 +67,12 @@ fastNLP.core.trainer
|
||||
.. automodule:: fastNLP.core.trainer
|
||||
:members:
|
||||
|
||||
fastNLP.core.utils
|
||||
-------------------
|
||||
|
||||
.. automodule:: fastNLP.core.utils
|
||||
:members:
|
||||
|
||||
fastNLP.core.vocabulary
|
||||
------------------------
|
||||
|
||||
|
||||
42
docs/source/fastNLP.io.rst
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42
docs/source/fastNLP.io.rst
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@ -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：
|
||||
|
||||
For example:
|
||||
|
||||
.. 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）
|
||||
|
||||
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。
|
||||
|
||||
|
||||
|
||||
|
||||
375
docs/source/tutorials/fastnlp_10tmin_tutorial.rst
Normal file
375
docs/source/tutorials/fastnlp_10tmin_tutorial.rst
Normal 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
docs/source/tutorials/fastnlp_1_minute_tutorial.rst
Normal file
111
docs/source/tutorials/fastnlp_1_minute_tutorial.rst
Normal 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。
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
@ -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
|
||||
-------
|
||||
.. toctree::
|
||||
:maxdepth: 1
|
||||
|
||||
Basic Usage
|
||||
~~~~~~~~~~~
|
||||
../tutorials/fastnlp_1_minute_tutorial
|
||||
../tutorials/fastnlp_10tmin_tutorial
|
||||
|
||||
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)
|
||||
|
||||
# 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)
|
||||
|
||||
# predict using model
|
||||
seqs = [x[0] for x in data]
|
||||
infer = ClassificationInfer(data_dir)
|
||||
labels_pred = infer.predict(model, seqs)
|
||||
@ -417,6 +417,55 @@ class PeopleDailyCorpusLoader(DataSetLoader):
|
||||
data_set.set_input("seq_len")
|
||||
return data_set
|
||||
|
||||
|
||||
class Conll2003Loader(DataSetLoader):
|
||||
"""Self-defined loader of conll2003 dataset
|
||||
|
||||
More information about the given dataset cound be found on
|
||||
https://sites.google.com/site/ermasoftware/getting-started/ne-tagging-conll2003-data
|
||||
|
||||
"""
|
||||
|
||||
def __init__(self):
|
||||
super(Conll2003Loader, self).__init__()
|
||||
|
||||
def load(self, dataset_path):
|
||||
with open(dataset_path, "r", encoding="utf-8") as f:
|
||||
lines = f.readlines()
|
||||
|
||||
##Parse the dataset line by line
|
||||
parsed_data = []
|
||||
sentence = []
|
||||
tokens = []
|
||||
for line in lines:
|
||||
if '-DOCSTART- -X- -X- O' in line or line == '\n':
|
||||
if sentence != []:
|
||||
parsed_data.append((sentence, tokens))
|
||||
sentence = []
|
||||
tokens = []
|
||||
continue
|
||||
|
||||
temp = line.strip().split(" ")
|
||||
sentence.append(temp[0])
|
||||
tokens.append(temp[1:4])
|
||||
|
||||
return self.convert(parsed_data)
|
||||
|
||||
def convert(self, parsed_data):
|
||||
dataset = DataSet()
|
||||
for sample in parsed_data:
|
||||
label0_list = list(map(
|
||||
lambda labels: labels[0], sample[1]))
|
||||
label1_list = list(map(
|
||||
lambda labels: labels[1], sample[1]))
|
||||
label2_list = list(map(
|
||||
lambda labels: labels[2], sample[1]))
|
||||
dataset.append(Instance(token_list=sample[0],
|
||||
label0_list=label0_list,
|
||||
label1_list=label1_list,
|
||||
label2_list=label2_list))
|
||||
|
||||
return dataset
|
||||
|
||||
class SNLIDataSetLoader(DataSetLoader):
|
||||
"""A data set loader for SNLI data set.
|
||||
|
||||
6
readthedocs.yml
Normal file
6
readthedocs.yml
Normal file
@ -0,0 +1,6 @@
|
||||
build:
|
||||
image: latest
|
||||
|
||||
python:
|
||||
version: 3.6
|
||||
setup_py_install: true
|
||||
442
test/data_for_tests/conll_2003_example.txt
Normal file
442
test/data_for_tests/conll_2003_example.txt
Normal file
@ -0,0 +1,442 @@
|
||||
-DOCSTART- -X- -X- O
|
||||
|
||||
SOCCER NN B-NP O
|
||||
- : O O
|
||||
JAPAN NNP B-NP B-LOC
|
||||
GET VB B-VP O
|
||||
LUCKY NNP B-NP O
|
||||
WIN NNP I-NP O
|
||||
, , O O
|
||||
CHINA NNP B-NP B-PER
|
||||
IN IN B-PP O
|
||||
SURPRISE DT B-NP O
|
||||
DEFEAT NN I-NP O
|
||||
. . O O
|
||||
|
||||
Nadim NNP B-NP B-PER
|
||||
Ladki NNP I-NP I-PER
|
||||
|
||||
AL-AIN NNP B-NP B-LOC
|
||||
, , O O
|
||||
United NNP B-NP B-LOC
|
||||
Arab NNP I-NP I-LOC
|
||||
Emirates NNPS I-NP I-LOC
|
||||
1996-12-06 CD I-NP O
|
||||
|
||||
Japan NNP B-NP B-LOC
|
||||
began VBD B-VP O
|
||||
the DT B-NP O
|
||||
defence NN I-NP O
|
||||
of IN B-PP O
|
||||
their PRP$ B-NP O
|
||||
Asian JJ I-NP B-MISC
|
||||
Cup NNP I-NP I-MISC
|
||||
title NN I-NP O
|
||||
with IN B-PP O
|
||||
a DT B-NP O
|
||||
lucky JJ I-NP O
|
||||
2-1 CD I-NP O
|
||||
win VBP B-VP O
|
||||
against IN B-PP O
|
||||
Syria NNP B-NP B-LOC
|
||||
in IN B-PP O
|
||||
a DT B-NP O
|
||||
Group NNP I-NP O
|
||||
C NNP I-NP O
|
||||
championship NN I-NP O
|
||||
match NN I-NP O
|
||||
on IN B-PP O
|
||||
Friday NNP B-NP O
|
||||
. . O O
|
||||
|
||||
But CC O O
|
||||
China NNP B-NP B-LOC
|
||||
saw VBD B-VP O
|
||||
their PRP$ B-NP O
|
||||
luck NN I-NP O
|
||||
desert VB B-VP O
|
||||
them PRP B-NP O
|
||||
in IN B-PP O
|
||||
the DT B-NP O
|
||||
second NN I-NP O
|
||||
match NN I-NP O
|
||||
of IN B-PP O
|
||||
the DT B-NP O
|
||||
group NN I-NP O
|
||||
, , O O
|
||||
crashing VBG B-VP O
|
||||
to TO B-PP O
|
||||
a DT B-NP O
|
||||
surprise NN I-NP O
|
||||
2-0 CD I-NP O
|
||||
defeat NN I-NP O
|
||||
to TO B-PP O
|
||||
newcomers NNS B-NP O
|
||||
Uzbekistan NNP I-NP B-LOC
|
||||
. . O O
|
||||
|
||||
China NNP B-NP B-LOC
|
||||
controlled VBD B-VP O
|
||||
most JJS B-NP O
|
||||
of IN B-PP O
|
||||
the DT B-NP O
|
||||
match NN I-NP O
|
||||
and CC O O
|
||||
saw VBD B-VP O
|
||||
several JJ B-NP O
|
||||
chances NNS I-NP O
|
||||
missed VBD B-VP O
|
||||
until IN B-SBAR O
|
||||
the DT B-NP O
|
||||
78th JJ I-NP O
|
||||
minute NN I-NP O
|
||||
when WRB B-ADVP O
|
||||
Uzbek NNP B-NP B-MISC
|
||||
striker NN I-NP O
|
||||
Igor JJ B-NP B-PER
|
||||
Shkvyrin NNP I-NP I-PER
|
||||
took VBD B-VP O
|
||||
advantage NN B-NP O
|
||||
of IN B-PP O
|
||||
a DT B-NP O
|
||||
misdirected JJ I-NP O
|
||||
defensive JJ I-NP O
|
||||
header NN I-NP O
|
||||
to TO B-VP O
|
||||
lob VB I-VP O
|
||||
the DT B-NP O
|
||||
ball NN I-NP O
|
||||
over IN B-PP O
|
||||
the DT B-NP O
|
||||
advancing VBG I-NP O
|
||||
Chinese JJ I-NP B-MISC
|
||||
keeper NN I-NP O
|
||||
and CC O O
|
||||
into IN B-PP O
|
||||
an DT B-NP O
|
||||
empty JJ I-NP O
|
||||
net NN I-NP O
|
||||
. . O O
|
||||
|
||||
Oleg NNP B-NP B-PER
|
||||
Shatskiku NNP I-NP I-PER
|
||||
made VBD B-VP O
|
||||
sure JJ B-ADJP O
|
||||
of IN B-PP O
|
||||
the DT B-NP O
|
||||
win VBP B-VP O
|
||||
in IN B-PP O
|
||||
injury NN B-NP O
|
||||
time NN I-NP O
|
||||
, , O O
|
||||
hitting VBG B-VP O
|
||||
an DT B-NP O
|
||||
unstoppable JJ I-NP O
|
||||
left VBD B-VP O
|
||||
foot NN B-NP O
|
||||
shot NN I-NP O
|
||||
from IN B-PP O
|
||||
just RB B-NP O
|
||||
outside IN B-PP O
|
||||
the DT B-NP O
|
||||
area NN I-NP O
|
||||
. . O O
|
||||
|
||||
The DT B-NP O
|
||||
former JJ I-NP O
|
||||
Soviet JJ I-NP B-MISC
|
||||
republic NN I-NP O
|
||||
was VBD B-VP O
|
||||
playing VBG I-VP O
|
||||
in IN B-PP O
|
||||
an DT B-NP O
|
||||
Asian NNP I-NP B-MISC
|
||||
Cup NNP I-NP I-MISC
|
||||
finals NNS I-NP O
|
||||
tie NN I-NP O
|
||||
for IN B-PP O
|
||||
the DT B-NP O
|
||||
first JJ I-NP O
|
||||
time NN I-NP O
|
||||
. . O O
|
||||
|
||||
Despite IN B-PP O
|
||||
winning VBG B-VP O
|
||||
the DT B-NP O
|
||||
Asian JJ I-NP B-MISC
|
||||
Games NNPS I-NP I-MISC
|
||||
title NN I-NP O
|
||||
two CD B-NP O
|
||||
years NNS I-NP O
|
||||
ago RB B-ADVP O
|
||||
, , O O
|
||||
Uzbekistan NNP B-NP B-LOC
|
||||
are VBP B-VP O
|
||||
in IN B-PP O
|
||||
the DT B-NP O
|
||||
finals NNS I-NP O
|
||||
as IN B-SBAR O
|
||||
outsiders NNS B-NP O
|
||||
. . O O
|
||||
|
||||
Two CD B-NP O
|
||||
goals NNS I-NP O
|
||||
from IN B-PP O
|
||||
defensive JJ B-NP O
|
||||
errors NNS I-NP O
|
||||
in IN B-PP O
|
||||
the DT B-NP O
|
||||
last JJ I-NP O
|
||||
six CD I-NP O
|
||||
minutes NNS I-NP O
|
||||
allowed VBD B-VP O
|
||||
Japan NNP B-NP B-LOC
|
||||
to TO B-VP O
|
||||
come VB I-VP O
|
||||
from IN B-PP O
|
||||
behind NN B-NP O
|
||||
and CC O O
|
||||
collect VB B-VP O
|
||||
all DT B-NP O
|
||||
three CD I-NP O
|
||||
points NNS I-NP O
|
||||
from IN B-PP O
|
||||
their PRP$ B-NP O
|
||||
opening NN I-NP O
|
||||
meeting NN I-NP O
|
||||
against IN B-PP O
|
||||
Syria NNP B-NP B-LOC
|
||||
. . O O
|
||||
|
||||
Takuya NNP B-NP B-PER
|
||||
Takagi NNP I-NP I-PER
|
||||
scored VBD B-VP O
|
||||
the DT B-NP O
|
||||
winner NN I-NP O
|
||||
in IN B-PP O
|
||||
the DT B-NP O
|
||||
88th JJ I-NP O
|
||||
minute NN I-NP O
|
||||
, , O O
|
||||
rising VBG B-VP O
|
||||
to TO I-VP O
|
||||
head VB I-VP O
|
||||
a DT B-NP O
|
||||
Hiroshige NNP I-NP B-PER
|
||||
Yanagimoto NNP I-NP I-PER
|
||||
cross VB B-VP O
|
||||
towards IN B-PP O
|
||||
the DT B-NP O
|
||||
Syrian JJ I-NP B-MISC
|
||||
goal NN I-NP O
|
||||
which WDT B-NP O
|
||||
goalkeeper VBD B-VP O
|
||||
Salem NNP B-NP B-PER
|
||||
Bitar NNP I-NP I-PER
|
||||
appeared VBD B-VP O
|
||||
to TO I-VP O
|
||||
have VB I-VP O
|
||||
covered VBN I-VP O
|
||||
but CC O O
|
||||
then RB B-VP O
|
||||
allowed VBN I-VP O
|
||||
to TO I-VP O
|
||||
slip VB I-VP O
|
||||
into IN B-PP O
|
||||
the DT B-NP O
|
||||
net NN I-NP O
|
||||
. . O O
|
||||
|
||||
It PRP B-NP O
|
||||
was VBD B-VP O
|
||||
the DT B-NP O
|
||||
second JJ I-NP O
|
||||
costly JJ I-NP O
|
||||
blunder NN I-NP O
|
||||
by IN B-PP O
|
||||
Syria NNP B-NP B-LOC
|
||||
in IN B-PP O
|
||||
four CD B-NP O
|
||||
minutes NNS I-NP O
|
||||
. . O O
|
||||
|
||||
Defender NNP B-NP O
|
||||
Hassan NNP I-NP B-PER
|
||||
Abbas NNP I-NP I-PER
|
||||
rose VBD B-VP O
|
||||
to TO I-VP O
|
||||
intercept VB I-VP O
|
||||
a DT B-NP O
|
||||
long JJ I-NP O
|
||||
ball NN I-NP O
|
||||
into IN B-PP O
|
||||
the DT B-NP O
|
||||
area NN I-NP O
|
||||
in IN B-PP O
|
||||
the DT B-NP O
|
||||
84th JJ I-NP O
|
||||
minute NN I-NP O
|
||||
but CC O O
|
||||
only RB B-ADVP O
|
||||
managed VBD B-VP O
|
||||
to TO I-VP O
|
||||
divert VB I-VP O
|
||||
it PRP B-NP O
|
||||
into IN B-PP O
|
||||
the DT B-NP O
|
||||
top JJ I-NP O
|
||||
corner NN I-NP O
|
||||
of IN B-PP O
|
||||
Bitar NN B-NP B-PER
|
||||
's POS B-NP O
|
||||
goal NN I-NP O
|
||||
. . O O
|
||||
|
||||
Nader NNP B-NP B-PER
|
||||
Jokhadar NNP I-NP I-PER
|
||||
had VBD B-VP O
|
||||
given VBN I-VP O
|
||||
Syria NNP B-NP B-LOC
|
||||
the DT B-NP O
|
||||
lead NN I-NP O
|
||||
with IN B-PP O
|
||||
a DT B-NP O
|
||||
well-struck NN I-NP O
|
||||
header NN I-NP O
|
||||
in IN B-PP O
|
||||
the DT B-NP O
|
||||
seventh JJ I-NP O
|
||||
minute NN I-NP O
|
||||
. . O O
|
||||
|
||||
Japan NNP B-NP B-LOC
|
||||
then RB B-ADVP O
|
||||
laid VBD B-VP O
|
||||
siege NN B-NP O
|
||||
to TO B-PP O
|
||||
the DT B-NP O
|
||||
Syrian JJ I-NP B-MISC
|
||||
penalty NN I-NP O
|
||||
area NN I-NP O
|
||||
for IN B-PP O
|
||||
most JJS B-NP O
|
||||
of IN B-PP O
|
||||
the DT B-NP O
|
||||
game NN I-NP O
|
||||
but CC O O
|
||||
rarely RB B-VP O
|
||||
breached VBD I-VP O
|
||||
the DT B-NP O
|
||||
Syrian JJ I-NP B-MISC
|
||||
defence NN I-NP O
|
||||
. . O O
|
||||
|
||||
Bitar NN B-NP B-PER
|
||||
pulled VBD B-VP O
|
||||
off RP B-PRT O
|
||||
fine JJ B-NP O
|
||||
saves VBZ B-VP O
|
||||
whenever WRB B-ADVP O
|
||||
they PRP B-NP O
|
||||
did VBD B-VP O
|
||||
. . O O
|
||||
|
||||
Japan NNP B-NP B-LOC
|
||||
coach NN I-NP O
|
||||
Shu NNP I-NP B-PER
|
||||
Kamo NNP I-NP I-PER
|
||||
said VBD B-VP O
|
||||
: : O O
|
||||
' '' O O
|
||||
' POS B-NP O
|
||||
The DT I-NP O
|
||||
Syrian JJ I-NP B-MISC
|
||||
own JJ I-NP O
|
||||
goal NN I-NP O
|
||||
proved VBD B-VP O
|
||||
lucky JJ B-ADJP O
|
||||
for IN B-PP O
|
||||
us PRP B-NP O
|
||||
. . O O
|
||||
|
||||
The DT B-NP O
|
||||
Syrians NNPS I-NP B-MISC
|
||||
scored VBD B-VP O
|
||||
early JJ B-NP O
|
||||
and CC O O
|
||||
then RB B-VP O
|
||||
played VBN I-VP O
|
||||
defensively RB B-ADVP O
|
||||
and CC O O
|
||||
adopted VBD B-VP O
|
||||
long RB I-VP O
|
||||
balls VBZ I-VP O
|
||||
which WDT B-NP O
|
||||
made VBD B-VP O
|
||||
it PRP B-NP O
|
||||
hard JJ B-ADJP O
|
||||
for IN B-PP O
|
||||
us PRP B-NP O
|
||||
. . O O
|
||||
' '' O O
|
||||
|
||||
' '' O O
|
||||
|
||||
Japan NNP B-NP B-LOC
|
||||
, , O O
|
||||
co-hosts VBZ B-VP O
|
||||
of IN B-PP O
|
||||
the DT B-NP O
|
||||
World NNP I-NP B-MISC
|
||||
Cup NNP I-NP I-MISC
|
||||
in IN B-PP O
|
||||
2002 CD B-NP O
|
||||
and CC O O
|
||||
ranked VBD B-VP O
|
||||
20th JJ B-NP O
|
||||
in IN B-PP O
|
||||
the DT B-NP O
|
||||
world NN I-NP O
|
||||
by IN B-PP O
|
||||
FIFA NNP B-NP B-ORG
|
||||
, , O O
|
||||
are VBP B-VP O
|
||||
favourites JJ B-ADJP O
|
||||
to TO B-VP O
|
||||
regain VB I-VP O
|
||||
their PRP$ B-NP O
|
||||
title NN I-NP O
|
||||
here RB B-ADVP O
|
||||
. . O O
|
||||
|
||||
Hosts NNPS B-NP O
|
||||
UAE NNP I-NP B-LOC
|
||||
play NN I-NP O
|
||||
Kuwait NNP I-NP B-LOC
|
||||
and CC O O
|
||||
South NNP B-NP B-LOC
|
||||
Korea NNP I-NP I-LOC
|
||||
take VBP B-VP O
|
||||
on IN B-PP O
|
||||
Indonesia NNP B-NP B-LOC
|
||||
on IN B-PP O
|
||||
Saturday NNP B-NP O
|
||||
in IN B-PP O
|
||||
Group NNP B-NP O
|
||||
A NNP I-NP O
|
||||
matches VBZ B-VP O
|
||||
. . O O
|
||||
|
||||
All DT B-NP O
|
||||
four CD I-NP O
|
||||
teams NNS I-NP O
|
||||
are VBP B-VP O
|
||||
level NN B-NP O
|
||||
with IN B-PP O
|
||||
one CD B-NP O
|
||||
point NN I-NP O
|
||||
each DT B-NP O
|
||||
from IN B-PP O
|
||||
one CD B-NP O
|
||||
game NN I-NP O
|
||||
. . O O
|
||||
23
test/io/test_dataset_loader.py
Normal file
23
test/io/test_dataset_loader.py
Normal file
@ -0,0 +1,23 @@
|
||||
import os
|
||||
import unittest
|
||||
|
||||
from fastNLP.io.dataset_loader import Conll2003Loader
|
||||
class TestDatasetLoader(unittest.TestCase):
|
||||
|
||||
def test_case_1(self):
|
||||
'''
|
||||
Test the the loader of Conll2003 dataset
|
||||
'''
|
||||
|
||||
dataset_path = "test/data_for_tests/conll_2003_example.txt"
|
||||
loader = Conll2003Loader()
|
||||
dataset_2003 = loader.load(dataset_path)
|
||||
|
||||
for item in dataset_2003:
|
||||
len0 = len(item["label0_list"])
|
||||
len1 = len(item["label1_list"])
|
||||
len2 = len(item["label2_list"])
|
||||
lentoken = len(item["token_list"])
|
||||
self.assertNotEqual(len0, 0)
|
||||
self.assertEqual(len0, len1)
|
||||
self.assertEqual(len1, len2)
|
||||
Loading…
Reference in New Issue
Block a user