diff --git a/docs/requirements.txt b/docs/requirements.txt index 294a44d0..c7d94486 100644 --- a/docs/requirements.txt +++ b/docs/requirements.txt @@ -1,5 +1,8 @@ numpy>=1.14.2 -http://download.pytorch.org/whl/cpu/torch-0.4.1-cp35-cp35m-linux_x86_64.whl +http://download.pytorch.org/whl/cpu/torch-0.4.1-cp36-cp36m-linux_x86_64.whl torchvision>=0.1.8 sphinx-rtd-theme==0.4.1 -tensorboardX>=1.4 \ No newline at end of file +tensorboardX>=1.4 +tqdm>=4.28.1 +ipython>=6.4.0 +ipython-genutils>=0.2.0 \ No newline at end of file diff --git a/docs/source/fastNLP.api.rst b/docs/source/fastNLP.api.rst new file mode 100644 index 00000000..eb9192da --- /dev/null +++ b/docs/source/fastNLP.api.rst @@ -0,0 +1,36 @@ +fastNLP.api +============ + +fastNLP.api.api +---------------- + +.. automodule:: fastNLP.api.api + :members: + +fastNLP.api.converter +---------------------- + +.. automodule:: fastNLP.api.converter + :members: + +fastNLP.api.model\_zoo +----------------------- + +.. automodule:: fastNLP.api.model_zoo + :members: + +fastNLP.api.pipeline +--------------------- + +.. automodule:: fastNLP.api.pipeline + :members: + +fastNLP.api.processor +---------------------- + +.. automodule:: fastNLP.api.processor + :members: + + +.. automodule:: fastNLP.api + :members: diff --git a/docs/source/fastNLP.core.rst b/docs/source/fastNLP.core.rst index 5c941e55..b9f6c89f 100644 --- a/docs/source/fastNLP.core.rst +++ b/docs/source/fastNLP.core.rst @@ -13,10 +13,10 @@ fastNLP.core.dataset .. automodule:: fastNLP.core.dataset :members: -fastNLP.core.field -------------------- +fastNLP.core.fieldarray +------------------------ -.. automodule:: fastNLP.core.field +.. automodule:: fastNLP.core.fieldarray :members: fastNLP.core.instance @@ -25,10 +25,10 @@ fastNLP.core.instance .. automodule:: fastNLP.core.instance :members: -fastNLP.core.loss ------------------- +fastNLP.core.losses +-------------------- -.. automodule:: fastNLP.core.loss +.. automodule:: fastNLP.core.losses :members: fastNLP.core.metrics @@ -49,12 +49,6 @@ fastNLP.core.predictor .. automodule:: fastNLP.core.predictor :members: -fastNLP.core.preprocess ------------------------- - -.. automodule:: fastNLP.core.preprocess - :members: - fastNLP.core.sampler --------------------- @@ -73,6 +67,12 @@ fastNLP.core.trainer .. automodule:: fastNLP.core.trainer :members: +fastNLP.core.utils +------------------- + +.. automodule:: fastNLP.core.utils + :members: + fastNLP.core.vocabulary ------------------------ diff --git a/docs/source/fastNLP.io.rst b/docs/source/fastNLP.io.rst new file mode 100644 index 00000000..d91e0d1c --- /dev/null +++ b/docs/source/fastNLP.io.rst @@ -0,0 +1,42 @@ +fastNLP.io +=========== + +fastNLP.io.base\_loader +------------------------ + +.. automodule:: fastNLP.io.base_loader + :members: + +fastNLP.io.config\_io +---------------------- + +.. automodule:: fastNLP.io.config_io + :members: + +fastNLP.io.dataset\_loader +--------------------------- + +.. automodule:: fastNLP.io.dataset_loader + :members: + +fastNLP.io.embed\_loader +------------------------- + +.. automodule:: fastNLP.io.embed_loader + :members: + +fastNLP.io.logger +------------------ + +.. automodule:: fastNLP.io.logger + :members: + +fastNLP.io.model\_io +--------------------- + +.. automodule:: fastNLP.io.model_io + :members: + + +.. automodule:: fastNLP.io + :members: diff --git a/docs/source/fastNLP.loader.rst b/docs/source/fastNLP.loader.rst deleted file mode 100644 index 658e07ff..00000000 --- a/docs/source/fastNLP.loader.rst +++ /dev/null @@ -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: diff --git a/docs/source/fastNLP.models.rst b/docs/source/fastNLP.models.rst index f17b1d49..7452fdf6 100644 --- a/docs/source/fastNLP.models.rst +++ b/docs/source/fastNLP.models.rst @@ -7,6 +7,12 @@ fastNLP.models.base\_model .. automodule:: fastNLP.models.base_model :members: +fastNLP.models.biaffine\_parser +-------------------------------- + +.. automodule:: fastNLP.models.biaffine_parser + :members: + fastNLP.models.char\_language\_model ------------------------------------- @@ -25,6 +31,12 @@ fastNLP.models.sequence\_modeling .. automodule:: fastNLP.models.sequence_modeling :members: +fastNLP.models.snli +-------------------- + +.. automodule:: fastNLP.models.snli + :members: + .. automodule:: fastNLP.models :members: diff --git a/docs/source/fastNLP.modules.encoder.rst b/docs/source/fastNLP.modules.encoder.rst index 41b4ce13..ea8fc699 100644 --- a/docs/source/fastNLP.modules.encoder.rst +++ b/docs/source/fastNLP.modules.encoder.rst @@ -43,6 +43,12 @@ fastNLP.modules.encoder.masked\_rnn .. automodule:: fastNLP.modules.encoder.masked_rnn :members: +fastNLP.modules.encoder.transformer +------------------------------------ + +.. automodule:: fastNLP.modules.encoder.transformer + :members: + fastNLP.modules.encoder.variational\_rnn ----------------------------------------- diff --git a/docs/source/fastNLP.modules.interactor.rst b/docs/source/fastNLP.modules.interactor.rst deleted file mode 100644 index 5eb3bdef..00000000 --- a/docs/source/fastNLP.modules.interactor.rst +++ /dev/null @@ -1,5 +0,0 @@ -fastNLP.modules.interactor -=========================== - -.. automodule:: fastNLP.modules.interactor - :members: diff --git a/docs/source/fastNLP.modules.rst b/docs/source/fastNLP.modules.rst index eda85aa7..965fb27d 100644 --- a/docs/source/fastNLP.modules.rst +++ b/docs/source/fastNLP.modules.rst @@ -6,7 +6,12 @@ fastNLP.modules fastNLP.modules.aggregator fastNLP.modules.decoder fastNLP.modules.encoder - fastNLP.modules.interactor + +fastNLP.modules.dropout +------------------------ + +.. automodule:: fastNLP.modules.dropout + :members: fastNLP.modules.other\_modules ------------------------------- diff --git a/docs/source/fastNLP.rst b/docs/source/fastNLP.rst index bb5037ce..61882359 100644 --- a/docs/source/fastNLP.rst +++ b/docs/source/fastNLP.rst @@ -3,18 +3,11 @@ fastNLP .. toctree:: + fastNLP.api fastNLP.core - fastNLP.loader + fastNLP.io fastNLP.models fastNLP.modules - fastNLP.saver - -fastNLP.fastnlp ----------------- - -.. automodule:: fastNLP.fastnlp - :members: - .. automodule:: fastNLP :members: diff --git a/docs/source/fastNLP.saver.rst b/docs/source/fastNLP.saver.rst deleted file mode 100644 index 1a02572d..00000000 --- a/docs/source/fastNLP.saver.rst +++ /dev/null @@ -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: diff --git a/docs/source/index.rst b/docs/source/index.rst index b58f712a..9f410f41 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -1,33 +1,35 @@ fastNLP documentation ===================== -fastNLP，目前仍在孵化中。 +A Modularized and Extensible Toolkit for Natural Language Processing. Currently still in incubation. Introduction ------------ -fastNLP是一个基于PyTorch的模块化自然语言处理系统，用于快速开发NLP工具。 -它将基于深度学习的NLP模型划分为不同的模块。 -这些模块分为4类：encoder（编码），interaction（交互）, aggregration（聚合） and decoder（解码）， -而每个类别包含不同的实现模块。 +FastNLP is a modular Natural Language Processing system based on +PyTorch, built for fast development of NLP models. -大多数当前的NLP模型可以构建在这些模块上，这极大地简化了开发NLP模型的过程。 -fastNLP的架构如图所示： +A deep learning NLP model is the composition of three types of modules: -.. image:: figures/procedures.PNG ++-----------------------+-----------------------+-----------------------+ +| module type | functionality | example | ++=======================+=======================+=======================+ +| encoder | encode the input into | embedding, RNN, CNN, | +| | some abstract | transformer | +| | representation | | ++-----------------------+-----------------------+-----------------------+ +| aggregator | aggregate and reduce | self-attention, | +| | information | max-pooling | ++-----------------------+-----------------------+-----------------------+ +| decoder | decode the | MLP, CRF | +| | representation into | | +| | the output | | ++-----------------------+-----------------------+-----------------------+ -在constructing model部分，以序列标注和文本分类为例进行说明： -.. image:: figures/text_classification.png -.. image:: figures/sequence_labeling.PNG - :width: 400 - -* encoder module：将输入编码为一些抽象表示，输入的是单词序列，输出向量序列。 -* interaction module：使表示中的信息相互交互，输入的是向量序列，输出的也是向量序列。 -* aggregation module：聚合和减少信息，输入向量序列，输出一个向量。 -* decoder module：将表示解码为输出，输出一个label（文本分类）或者输出label序列（序列标注） +For example: -其中interaction module和aggregation module在模型中不一定存在，例如上面的序列标注模型。 +.. image:: figures/text_classification.png diff --git a/docs/source/tutorials/fastnlp_10tmin_tutorial.rst b/docs/source/tutorials/fastnlp_10tmin_tutorial.rst new file mode 100644 index 00000000..30293796 --- /dev/null +++ b/docs/source/tutorials/fastnlp_10tmin_tutorial.rst @@ -0,0 +1,375 @@ + +fastNLP上手教程 +=============== + +fastNLP提供方便的数据预处理，训练和测试模型的功能 + +DataSet & Instance +------------------ + +fastNLP用DataSet和Instance保存和处理数据。每个DataSet表示一个数据集，每个Instance表示一个数据样本。一个DataSet存有多个Instance，每个Instance可以自定义存哪些内容。 + +有一些read\_\*方法，可以轻松从文件读取数据，存成DataSet。 + +.. code:: ipython3 + + from fastNLP import DataSet + from fastNLP import Instance + + # 从csv读取数据到DataSet + win_path = "C:\\Users\zyfeng\Desktop\FudanNLP\\fastNLP\\test\\data_for_tests\\tutorial_sample_dataset.csv" + dataset = DataSet.read_csv(win_path, headers=('raw_sentence', 'label'), sep='\t') + print(dataset[0]) + + +.. parsed-literal:: + + {'raw_sentence': A series of escapades demonstrating the adage that what is good for the goose is also good for the gander , some of which occasionally amuses but none of which amounts to much of a story ., + 'label': 1} + + +.. code:: ipython3 + + # DataSet.append(Instance)加入新数据 + + dataset.append(Instance(raw_sentence='fake data', label='0')) + dataset[-1] + + + + +.. parsed-literal:: + + {'raw_sentence': fake data, + 'label': 0} + + + +.. code:: ipython3 + + # DataSet.apply(func, new_field_name)对数据预处理 + + # 将所有数字转为小写 + dataset.apply(lambda x: x['raw_sentence'].lower(), new_field_name='raw_sentence') + # label转int + dataset.apply(lambda x: int(x['label']), new_field_name='label_seq', is_target=True) + # 使用空格分割句子 + dataset.drop(lambda x: len(x['raw_sentence'].split()) == 0) + def split_sent(ins): + return ins['raw_sentence'].split() + dataset.apply(split_sent, new_field_name='words', is_input=True) + +.. code:: ipython3 + + # DataSet.drop(func)筛除数据 + # 删除低于某个长度的词语 + dataset.drop(lambda x: len(x['words']) <= 3) + +.. code:: ipython3 + + # 分出测试集、训练集 + + test_data, train_data = dataset.split(0.3) + print("Train size: ", len(test_data)) + print("Test size: ", len(train_data)) + + +.. parsed-literal:: + + Train size: 54 + Test size: + +Vocabulary +---------- + +fastNLP中的Vocabulary轻松构建词表，将词转成数字 + +.. code:: ipython3 + + from fastNLP import Vocabulary + + # 构建词表, Vocabulary.add(word) + vocab = Vocabulary(min_freq=2) + train_data.apply(lambda x: [vocab.add(word) for word in x['words']]) + vocab.build_vocab() + + # index句子, Vocabulary.to_index(word) + train_data.apply(lambda x: [vocab.to_index(word) for word in x['words']], new_field_name='word_seq', is_input=True) + test_data.apply(lambda x: [vocab.to_index(word) for word in x['words']], new_field_name='word_seq', is_input=True) + + + print(test_data[0]) + + +.. parsed-literal:: + + {'raw_sentence': the plot is romantic comedy boilerplate from start to finish ., + 'label': 2, + 'label_seq': 2, + 'words': ['the', 'plot', 'is', 'romantic', 'comedy', 'boilerplate', 'from', 'start', 'to', 'finish', '.'], + 'word_seq': [2, 13, 9, 24, 25, 26, 15, 27, 11, 28, 3]} + + +.. code:: ipython3 + + # 假设你们需要做强化学习或者gan之类的项目，也许你们可以使用这里的dataset + from fastNLP.core.batch import Batch + from fastNLP.core.sampler import RandomSampler + + batch_iterator = Batch(dataset=train_data, batch_size=2, sampler=RandomSampler()) + for batch_x, batch_y in batch_iterator: + print("batch_x has: ", batch_x) + print("batch_y has: ", batch_y) + break + + +.. parsed-literal:: + + batch_x has: {'words': array([list(['this', 'kind', 'of', 'hands-on', 'storytelling', 'is', 'ultimately', 'what', 'makes', 'shanghai', 'ghetto', 'move', 'beyond', 'a', 'good', ',', 'dry', ',', 'reliable', 'textbook', 'and', 'what', 'allows', 'it', 'to', 'rank', 'with', 'its', 'worthy', 'predecessors', '.']), + list(['the', 'entire', 'movie', 'is', 'filled', 'with', 'deja', 'vu', 'moments', '.'])], + dtype=object), 'word_seq': tensor([[ 19, 184, 6, 1, 481, 9, 206, 50, 91, 1210, 1609, 1330, + 495, 5, 63, 4, 1269, 4, 1, 1184, 7, 50, 1050, 10, + 8, 1611, 16, 21, 1039, 1, 2], + [ 3, 711, 22, 9, 1282, 16, 2482, 2483, 200, 2, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0]])} + batch_y has: {'label_seq': tensor([3, 2])} + + +Model +----- + +.. code:: ipython3 + + # 定义一个简单的Pytorch模型 + + from fastNLP.models import CNNText + model = CNNText(embed_num=len(vocab), embed_dim=50, num_classes=5, padding=2, dropout=0.1) + model + + + + +.. parsed-literal:: + + CNNText( + (embed): Embedding( + (embed): Embedding(77, 50, padding_idx=0) + (dropout): Dropout(p=0.0) + ) + (conv_pool): ConvMaxpool( + (convs): ModuleList( + (0): Conv1d(50, 3, kernel_size=(3,), stride=(1,), padding=(2,)) + (1): Conv1d(50, 4, kernel_size=(4,), stride=(1,), padding=(2,)) + (2): Conv1d(50, 5, kernel_size=(5,), stride=(1,), padding=(2,)) + ) + ) + (dropout): Dropout(p=0.1) + (fc): Linear( + (linear): Linear(in_features=12, out_features=5, bias=True) + ) + ) + + + +Trainer & Tester +---------------- + +使用fastNLP的Trainer训练模型 + +.. code:: ipython3 + + from fastNLP import Trainer + from copy import deepcopy + from fastNLP import CrossEntropyLoss + from fastNLP import AccuracyMetric + +.. code:: ipython3 + + # 进行overfitting测试 + copy_model = deepcopy(model) + overfit_trainer = Trainer(model=copy_model, + train_data=test_data, + dev_data=test_data, + loss=CrossEntropyLoss(pred="output", target="label_seq"), + metrics=AccuracyMetric(), + n_epochs=10, + save_path=None) + overfit_trainer.train() + + +.. parsed-literal:: + + training epochs started 2018-12-07 14:07:20 + + + + +.. parsed-literal:: + + HBox(children=(IntProgress(value=0, layout=Layout(flex='2'), max=20), HTML(value='')), layout=Layout(display='… + + + +.. parsed-literal:: + + Epoch 1/10. Step:2/20. AccuracyMetric: acc=0.037037 + Epoch 2/10. Step:4/20. AccuracyMetric: acc=0.296296 + Epoch 3/10. Step:6/20. AccuracyMetric: acc=0.333333 + Epoch 4/10. Step:8/20. AccuracyMetric: acc=0.555556 + Epoch 5/10. Step:10/20. AccuracyMetric: acc=0.611111 + Epoch 6/10. Step:12/20. AccuracyMetric: acc=0.481481 + Epoch 7/10. Step:14/20. AccuracyMetric: acc=0.62963 + Epoch 8/10. Step:16/20. AccuracyMetric: acc=0.685185 + Epoch 9/10. Step:18/20. AccuracyMetric: acc=0.722222 + Epoch 10/10. Step:20/20. AccuracyMetric: acc=0.777778 + + +.. code:: ipython3 + + # 实例化Trainer，传入模型和数据，进行训练 + trainer = Trainer(model=model, + train_data=train_data, + dev_data=test_data, + loss=CrossEntropyLoss(pred="output", target="label_seq"), + metrics=AccuracyMetric(), + n_epochs=5) + trainer.train() + print('Train finished!') + + +.. parsed-literal:: + + training epochs started 2018-12-07 14:08:10 + + + + +.. parsed-literal:: + + HBox(children=(IntProgress(value=0, layout=Layout(flex='2'), max=5), HTML(value='')), layout=Layout(display='i… + + + +.. parsed-literal:: + + Epoch 1/5. Step:1/5. AccuracyMetric: acc=0.037037 + Epoch 2/5. Step:2/5. AccuracyMetric: acc=0.037037 + Epoch 3/5. Step:3/5. AccuracyMetric: acc=0.037037 + Epoch 4/5. Step:4/5. AccuracyMetric: acc=0.185185 + Epoch 5/5. Step:5/5. AccuracyMetric: acc=0.240741 + Train finished! + + +.. code:: ipython3 + + from fastNLP import Tester + + tester = Tester(data=test_data, model=model, metrics=AccuracyMetric()) + acc = tester.test() + + +.. parsed-literal:: + + [tester] + AccuracyMetric: acc=0.240741 + + +In summary +---------- + +fastNLP Trainer的伪代码逻辑 +--------------------------- + +1. 准备DataSet，假设DataSet中共有如下的fields +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +:: + + ['raw_sentence', 'word_seq1', 'word_seq2', 'raw_label','label'] + 通过 + DataSet.set_input('word_seq1', word_seq2', flag=True)将'word_seq1', 'word_seq2'设置为input + 通过 + DataSet.set_target('label', flag=True)将'label'设置为target + +2. 初始化模型 +~~~~~~~~~~~~~ + +:: + + class Model(nn.Module): + def __init__(self): + xxx + def forward(self, word_seq1, word_seq2): + # (1) 这里使用的形参名必须和DataSet中的input field的名称对应。因为我们是通过形参名, 进行赋值的 + # (2) input field的数量可以多于这里的形参数量。但是不能少于。 + xxxx + # 输出必须是一个dict + +3. Trainer的训练过程 +~~~~~~~~~~~~~~~~~~~~ + +:: + + (1) 从DataSet中按照batch_size取出一个batch，调用Model.forward + (2) 将 Model.forward的结果 与 标记为target的field 传入Losser当中。 + 由于每个人写的Model.forward的output的dict可能key并不一样，比如有人是{'pred':xxx}, {'output': xxx}; + 另外每个人将target可能也会设置为不同的名称, 比如有人是label, 有人设置为target； + 为了解决以上的问题，我们的loss提供映射机制 + 比如CrossEntropyLosser的需要的输入是(prediction, target)。但是forward的output是{'output': xxx}; 'label'是target + 那么初始化losser的时候写为CrossEntropyLosser(prediction='output', target='label')即可 + (3) 对于Metric是同理的 + Metric计算也是从 forward的结果中取值 与 设置target的field中取值。 也是可以通过映射找到对应的值 + +一些问题. +--------- + +1. DataSet中为什么需要设置input和target +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +:: + + 只有被设置为input或者target的数据才会在train的过程中被取出来 + (1.1) 我们只会在设置为input的field中寻找传递给Model.forward的参数。 + (1.2) 我们在传递值给losser或者metric的时候会使用来自: + (a)Model.forward的output + (b)被设置为target的field + + +2. 我们是通过forwad中的形参名将DataSet中的field赋值给对应的参数 +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +:: + + (1.1) 构建模型过程中， + 例如: + DataSet中x，seq_lens是input，那么forward就应该是 + def forward(self, x, seq_lens): + pass + 我们是通过形参名称进行匹配的field的 + + +1. 加载数据到DataSet +~~~~~~~~~~~~~~~~~~~~ + +2. 使用apply操作对DataSet进行预处理 +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +:: + + (2.1) 处理过程中将某些field设置为input，某些field设置为target + +3. 构建模型 +~~~~~~~~~~~ + +:: + + (3.1) 构建模型过程中，需要注意forward函数的形参名需要和DataSet中设置为input的field名称是一致的。 + 例如: + DataSet中x，seq_lens是input，那么forward就应该是 + def forward(self, x, seq_lens): + pass + 我们是通过形参名称进行匹配的field的 + (3.2) 模型的forward的output需要是dict类型的。 + 建议将输出设置为{"pred": xx}. + diff --git a/docs/source/tutorials/fastnlp_1_minute_tutorial.rst b/docs/source/tutorials/fastnlp_1_minute_tutorial.rst new file mode 100644 index 00000000..b4471e00 --- /dev/null +++ b/docs/source/tutorials/fastnlp_1_minute_tutorial.rst @@ -0,0 +1,111 @@ + +FastNLP 1分钟上手教程 +===================== + +step 1 +------ + +读取数据集 + +.. code:: ipython3 + + from fastNLP import DataSet + # linux_path = "../test/data_for_tests/tutorial_sample_dataset.csv" + win_path = "C:\\Users\zyfeng\Desktop\FudanNLP\\fastNLP\\test\\data_for_tests\\tutorial_sample_dataset.csv" + ds = DataSet.read_csv(win_path, headers=('raw_sentence', 'label'), sep='\t') + +step 2 +------ + +数据预处理 1. 类型转换 2. 切分验证集 3. 构建词典 + +.. code:: ipython3 + + # 将所有数字转为小写 + ds.apply(lambda x: x['raw_sentence'].lower(), new_field_name='raw_sentence') + # label转int + ds.apply(lambda x: int(x['label']), new_field_name='label_seq', is_target=True) + + def split_sent(ins): + return ins['raw_sentence'].split() + ds.apply(split_sent, new_field_name='words', is_input=True) + + +.. code:: ipython3 + + # 分割训练集/验证集 + train_data, dev_data = ds.split(0.3) + print("Train size: ", len(train_data)) + print("Test size: ", len(dev_data)) + + +.. parsed-literal:: + + Train size: 54 + Test size: 23 + + +.. code:: ipython3 + + from fastNLP import Vocabulary + vocab = Vocabulary(min_freq=2) + train_data.apply(lambda x: [vocab.add(word) for word in x['words']]) + + # index句子, Vocabulary.to_index(word) + train_data.apply(lambda x: [vocab.to_index(word) for word in x['words']], new_field_name='word_seq', is_input=True) + dev_data.apply(lambda x: [vocab.to_index(word) for word in x['words']], new_field_name='word_seq', is_input=True) + + +step 3 +------ + +定义模型 + +.. code:: ipython3 + + from fastNLP.models import CNNText + model = CNNText(embed_num=len(vocab), embed_dim=50, num_classes=5, padding=2, dropout=0.1) + + +step 4 +------ + +开始训练 + +.. code:: ipython3 + + from fastNLP import Trainer, CrossEntropyLoss, AccuracyMetric + trainer = Trainer(model=model, + train_data=train_data, + dev_data=dev_data, + loss=CrossEntropyLoss(), + metrics=AccuracyMetric() + ) + trainer.train() + print('Train finished!') + + + +.. parsed-literal:: + + training epochs started 2018-12-07 14:03:41 + + + + +.. parsed-literal:: + + HBox(children=(IntProgress(value=0, layout=Layout(flex='2'), max=6), HTML(value='')), layout=Layout(display='i… + + + +.. parsed-literal:: + + Epoch 1/3. Step:2/6. AccuracyMetric: acc=0.26087 + Epoch 2/3. Step:4/6. AccuracyMetric: acc=0.347826 + Epoch 3/3. Step:6/6. AccuracyMetric: acc=0.608696 + Train finished! + + +本教程结束。更多操作请参考进阶教程。 +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ diff --git a/docs/source/user/installation.rst b/docs/source/user/installation.rst index 0655041b..7dc39b3b 100644 --- a/docs/source/user/installation.rst +++ b/docs/source/user/installation.rst @@ -6,26 +6,11 @@ Installation :local: -Cloning From GitHub -~~~~~~~~~~~~~~~~~~~ - -If you just want to use fastNLP, use: +Run the following commands to install fastNLP package: .. code:: shell - git clone https://github.com/fastnlp/fastNLP - cd fastNLP + pip install fastNLP -PyTorch Installation -~~~~~~~~~~~~~~~~~~~~ - -Visit the [PyTorch official website] for installation instructions based -on your system. In general, you could use: - -.. code:: shell - # using conda - conda install pytorch torchvision -c pytorch - # or using pip - pip3 install torch torchvision diff --git a/docs/source/user/quickstart.rst b/docs/source/user/quickstart.rst index 24c7363c..baa49eef 100644 --- a/docs/source/user/quickstart.rst +++ b/docs/source/user/quickstart.rst @@ -1,84 +1,9 @@ -========== Quickstart ========== -Example -------- - -Basic Usage -~~~~~~~~~~~ - -A typical fastNLP routine is composed of four phases: loading dataset, -pre-processing data, constructing model and training model. - -.. code:: python - - from fastNLP.models.base_model import BaseModel - from fastNLP.modules import encoder - from fastNLP.modules import aggregation - from fastNLP.modules import decoder - - from fastNLP.loader.dataset_loader import ClassDataSetLoader - from fastNLP.loader.preprocess import ClassPreprocess - from fastNLP.core.trainer import ClassificationTrainer - from fastNLP.core.inference import ClassificationInfer - - - class ClassificationModel(BaseModel): - """ - Simple text classification model based on CNN. - """ - - def __init__(self, num_classes, vocab_size): - super(ClassificationModel, self).__init__() - - self.emb = encoder.Embedding(nums=vocab_size, dims=300) - self.enc = encoder.Conv( - in_channels=300, out_channels=100, kernel_size=3) - self.agg = aggregation.MaxPool() - self.dec = decoder.MLP([100, num_classes]) - - def forward(self, x): - x = self.emb(x) # [N,L] -> [N,L,C] - x = self.enc(x) # [N,L,C_in] -> [N,L,C_out] - x = self.agg(x) # [N,L,C] -> [N,C] - x = self.dec(x) # [N,C] -> [N, N_class] - return x - - - data_dir = 'data' # directory to save data and model - train_path = 'test/data_for_tests/text_classify.txt' # training set file - - # load dataset - ds_loader = ClassDataSetLoader("train", train_path) - data = ds_loader.load() - - # pre-process dataset - pre = ClassPreprocess(data_dir) - vocab_size, n_classes = pre.process(data, "data_train.pkl") - - # construct model - model_args = { - 'num_classes': n_classes, - 'vocab_size': vocab_size - } - model = ClassificationModel(num_classes=n_classes, vocab_size=vocab_size) +.. toctree:: + :maxdepth: 1 - # train model - train_args = { - "epochs": 20, - "batch_size": 50, - "pickle_path": data_dir, - "validate": False, - "save_best_dev": False, - "model_saved_path": None, - "use_cuda": True, - "learn_rate": 1e-3, - "momentum": 0.9} - trainer = ClassificationTrainer(train_args) - trainer.train(model) + ../tutorials/fastnlp_1_minute_tutorial + ../tutorials/fastnlp_10tmin_tutorial - # predict using model - seqs = [x[0] for x in data] - infer = ClassificationInfer(data_dir) - labels_pred = infer.predict(model, seqs) \ No newline at end of file diff --git a/readthedocs.yml b/readthedocs.yml new file mode 100644 index 00000000..9b172987 --- /dev/null +++ b/readthedocs.yml @@ -0,0 +1,6 @@ +build: + image: latest + +python: + version: 3.6 + setup_py_install: true \ No newline at end of file