@@ -39,30 +39,30 @@ python -m spacy download en | |||
## fastNLP教程 | |||
中文[文档](https://fastnlp.readthedocs.io/)、[教程](https://fastnlp.readthedocs.io/zh/latest/user/tutorials.html) | |||
中文[文档](http://www.fastnlp.top/docs/fastNLP/)、 [教程](http://www.fastnlp.top/docs/fastNLP/user/quickstart.html) | |||
### 快速入门 | |||
- [0. 快速入门](https://fastnlp.readthedocs.io/zh/latest/user/quickstart.html) | |||
- [Quick-1. 文本分类](http://www.fastnlp.top/docs/fastNLP/tutorials/%E6%96%87%E6%9C%AC%E5%88%86%E7%B1%BB.html) | |||
- [Quick-2. 序列标注](http://www.fastnlp.top/docs/fastNLP/tutorials/%E5%BA%8F%E5%88%97%E6%A0%87%E6%B3%A8.html) | |||
### 详细使用教程 | |||
- [1. 使用DataSet预处理文本](https://fastnlp.readthedocs.io/zh/latest/tutorials/tutorial_1_data_preprocess.html) | |||
- [2. 使用Vocabulary转换文本与index](https://fastnlp.readthedocs.io/zh/latest/tutorials/tutorial_2_vocabulary.html) | |||
- [3. 使用Embedding模块将文本转成向量](https://fastnlp.readthedocs.io/zh/latest/tutorials/tutorial_3_embedding.html) | |||
- [4. 使用Loader和Pipe加载并处理数据集](https://fastnlp.readthedocs.io/zh/latest/tutorials/tutorial_4_load_dataset.html) | |||
- [5. 动手实现一个文本分类器I-使用Trainer和Tester快速训练和测试](https://fastnlp.readthedocs.io/zh/latest/tutorials/tutorial_5_loss_optimizer.html) | |||
- [6. 动手实现一个文本分类器II-使用DataSetIter实现自定义训练过程](https://fastnlp.readthedocs.io/zh/latest/tutorials/tutorial_6_datasetiter.html) | |||
- [7. 使用Metric快速评测你的模型](https://fastnlp.readthedocs.io/zh/latest/tutorials/tutorial_7_metrics.html) | |||
- [8. 使用Modules和Models快速搭建自定义模型](https://fastnlp.readthedocs.io/zh/latest/tutorials/tutorial_8_modules_models.html) | |||
- [9. 快速实现序列标注模型](https://fastnlp.readthedocs.io/zh/latest/tutorials/tutorial_9_seq_labeling.html) | |||
- [10. 使用Callback自定义你的训练过程](https://fastnlp.readthedocs.io/zh/latest/tutorials/tutorial_10_callback.html) | |||
- [1. 使用DataSet预处理文本](http://www.fastnlp.top/docs/fastNLP/tutorials/tutorial_1_data_preprocess.html) | |||
- [2. 使用Vocabulary转换文本与index](http://www.fastnlp.top/docs/fastNLP/tutorials/tutorial_2_vocabulary.html) | |||
- [3. 使用Embedding模块将文本转成向量](http://www.fastnlp.top/docs/fastNLP/tutorials/tutorial_3_embedding.html) | |||
- [4. 使用Loader和Pipe加载并处理数据集](http://www.fastnlp.top/docs/fastNLP/tutorials/tutorial_4_load_dataset.html) | |||
- [5. 动手实现一个文本分类器I-使用Trainer和Tester快速训练和测试](http://www.fastnlp.top/docs/fastNLP/tutorials/tutorial_5_loss_optimizer.html) | |||
- [6. 动手实现一个文本分类器II-使用DataSetIter实现自定义训练过程](http://www.fastnlp.top/docs/fastNLP/tutorials/tutorial_6_datasetiter.html) | |||
- [7. 使用Metric快速评测你的模型](http://www.fastnlp.top/docs/fastNLP/tutorials/tutorial_7_metrics.html) | |||
- [8. 使用Modules和Models快速搭建自定义模型](http://www.fastnlp.top/docs/fastNLP/tutorials/tutorial_8_modules_models.html) | |||
- [9. 使用Callback自定义你的训练过程](http://www.fastnlp.top/docs/fastNLP/tutorials/tutorial_9_callback.html) | |||
### 扩展教程 | |||
- [Extend-1. BertEmbedding的各种用法](https://fastnlp.readthedocs.io/zh/latest/tutorials/extend_1_bert_embedding.html) | |||
- [Extend-2. 分布式训练简介](https://fastnlp.readthedocs.io/zh/latest/tutorials/extend_2_dist.html) | |||
- [Extend-3. 使用fitlog 辅助 fastNLP 进行科研](https://fastnlp.readthedocs.io/zh/latest/tutorials/extend_3_fitlog.html) | |||
- [Extend-1. BertEmbedding的各种用法](http://www.fastnlp.top/docs/fastNLP/tutorials/extend_1_bert_embedding.html) | |||
- [Extend-2. 分布式训练简介](http://www.fastnlp.top/docs/fastNLP/tutorials/extend_2_dist.html) | |||
- [Extend-3. 使用fitlog 辅助 fastNLP 进行科研](http://www.fastnlp.top/docs/fastNLP/tutorials/extend_3_fitlog.html) | |||
## 内置组件 | |||
@@ -1,8 +1,4 @@ | |||
numpy>=1.14.2 | |||
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 | |||
tqdm>=4.28.1 | |||
ipython>=6.4.0 | |||
ipython-genutils>=0.2.0 | |||
sphinx==3.2.1 | |||
docutils==0.16 | |||
sphinx-rtd-theme==0.5.0 | |||
readthedocs-sphinx-search==0.1.0rc3 |
@@ -4,6 +4,10 @@ fastNLP 中文文档 | |||
`fastNLP <https://github.com/fastnlp/fastNLP/>`_ 是一款轻量级的自然语言处理(NLP)工具包。你既可以用它来快速地完成一个NLP任务, | |||
也可以用它在研究中快速构建更复杂的模型。 | |||
.. hint:: | |||
如果你是从 readthedocs 访问的该文档,请跳转到我们的 `最新网站 <http://www.fastnlp.top/docs/fastNLP/>`_ | |||
fastNLP具有如下的特性: | |||
- 统一的Tabular式数据容器,简化数据预处理过程; | |||
@@ -41,7 +45,7 @@ API 文档 | |||
fitlog文档 | |||
---------- | |||
您可以 `点此 <https://fitlog.readthedocs.io/zh/latest/>`_ 查看fitlog的文档。 | |||
您可以 `点此 <http://www.fastnlp.top/docs/fitlog/>`_ 查看fitlog的文档。 | |||
fitlog 是由我们团队开发的日志记录+代码管理的工具。 | |||
索引与搜索 | |||
@@ -4,7 +4,7 @@ | |||
本文介绍结合使用 fastNLP 和 fitlog 进行科研的方法。 | |||
首先,我们需要安装 `fitlog <https://fitlog.readthedocs.io/>`_ 。你需要确认你的电脑中没有其它名为 `fitlog` 的命令。 | |||
首先,我们需要安装 `fitlog <http://www.fastnlp.top/docs/fitlog/>`_ 。你需要确认你的电脑中没有其它名为 `fitlog` 的命令。 | |||
我们从命令行中进入到一个文件夹,现在我们要在文件夹中创建我们的 fastNLP 项目。你可以在命令行输入 `fitlog init test1` , | |||
然后你会看到如下提示:: | |||
@@ -15,7 +15,7 @@ | |||
Fitlog project test1 is initialized. | |||
这表明你已经创建成功了项目文件夹,并且在项目文件夹中已经初始化了 Git。如果你不想初始化 Git, | |||
可以参考文档 `命令行工具 <https://fitlog.readthedocs.io/zh/latest/user/command_line.html>`_ | |||
可以参考文档 `命令行工具 <http://www.fastnlp.top/docs/fitlog/user/command_line.html>`_ | |||
现在我们进入你创建的项目文件夹 test1 中,可以看到有一个名为 logs 的文件夹,后面我们将会在里面存放你的实验记录。 | |||
同时也有一个名为 main.py 的文件,这是我们推荐你使用的训练入口文件。文件的内容如下:: | |||
@@ -37,7 +37,7 @@ | |||
fitlog.finish() # finish the logging | |||
我们推荐你保留除注释外的四行代码,它们有助于你的实验, | |||
他们的具体用处参见文档 `用户 API <https://fitlog.readthedocs.io/zh/latest/fitlog.html>`_ | |||
他们的具体用处参见文档 `用户 API <http://www.fastnlp.top/docs/fitlog/>`_ | |||
我们假定你要进行前两个教程中的实验,并已经把数据复制到了项目根目录下的 tutorial_sample_dataset.csv 文件中。 | |||
现在我们编写如下的训练代码,使用 :class:`~fastNLP.core.callback.FitlogCallback` 进行实验记录保存:: | |||
@@ -291,7 +291,7 @@ fastNLP提供了Trainer对象来组织训练过程,包括完成loss计算(所 | |||
PS: 使用Bert进行文本分类 | |||
~~~~~~~~~~~~~~~~~~~~ | |||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ | |||
.. code-block:: python | |||
@@ -368,7 +368,7 @@ PS: 使用Bert进行文本分类 | |||
PS: 基于词进行文本分类 | |||
~~~~~~~~~~~~~~~~~~~~ | |||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ | |||
由于汉字中没有显示的字与字的边界,一般需要通过分词器先将句子进行分词操作。 | |||
下面的例子演示了如何不基于fastNLP已有的数据读取、预处理代码进行文本分类。 | |||
@@ -53,7 +53,7 @@ r""" | |||
from fastNLP import DataSet | |||
from fastNLP import Instance | |||
instances = [] | |||
winstances.append(Instance(sentence="This is the first instance", | |||
instances.append(Instance(sentence="This is the first instance", | |||
ords=['this', 'is', 'the', 'first', 'instance', '.'], | |||
seq_len=6)) | |||
instances.append(Instance(sentence="Second instance .", | |||
@@ -148,7 +148,7 @@ class Tester(object): | |||
self._predict_func = self._model.predict | |||
self._predict_func_wrapper = self._model.predict | |||
else: | |||
if _model_contains_inner_module(model): | |||
if _model_contains_inner_module(self._model): | |||
self._predict_func_wrapper = self._model.forward | |||
self._predict_func = self._model.module.forward | |||
else: | |||
@@ -103,6 +103,11 @@ DATASET_DIR = { | |||
"yelp-review-polarity": "yelp_review_polarity.tar.gz", | |||
"sst-2": "SST-2.zip", | |||
"sst": "SST.zip", | |||
'mr': 'mr.zip', | |||
"R8": "R8.zip", | |||
"R52": "R52.zip", | |||
"20ng": "20ng.zip", | |||
"ohsumed": "ohsumed.zip", | |||
# Classification, Chinese | |||
"chn-senti-corp": "chn_senti_corp.zip", | |||
@@ -23,15 +23,15 @@ __all__ = [ | |||
"ChnSentiCorpPipe", | |||
"THUCNewsPipe", | |||
"WeiboSenti100kPipe", | |||
"MRPipe", "R52Pipe", "R8Pipe", "OhsumedPipe", "NG20Loader", | |||
"MRPipe", "R52Pipe", "R8Pipe", "OhsumedPipe", "NG20Pipe", | |||
"Conll2003NERPipe", | |||
"OntoNotesNERPipe", | |||
"MsraNERPipe", | |||
"WeiboNERPipe", | |||
"PeopleDailyPipe", | |||
"Conll2003Pipe", | |||
"MatchingBertPipe", | |||
"RTEBertPipe", | |||
"SNLIBertPipe", | |||
@@ -53,14 +53,20 @@ __all__ = [ | |||
"RenamePipe", | |||
"GranularizePipe", | |||
"MachingTruncatePipe", | |||
"CoReferencePipe", | |||
"CMRC2018BertPipe" | |||
"CMRC2018BertPipe", | |||
"R52PmiGraphPipe", | |||
"R8PmiGraphPipe", | |||
"OhsumedPmiGraphPipe", | |||
"NG20PmiGraphPipe", | |||
"MRPmiGraphPipe" | |||
] | |||
from .classification import CLSBasePipe, YelpFullPipe, YelpPolarityPipe, SSTPipe, SST2Pipe, IMDBPipe, ChnSentiCorpPipe, THUCNewsPipe, \ | |||
WeiboSenti100kPipe, AGsNewsPipe, DBPediaPipe, MRPipe, R8Pipe, R52Pipe, OhsumedPipe, NG20Loader | |||
WeiboSenti100kPipe, AGsNewsPipe, DBPediaPipe, MRPipe, R8Pipe, R52Pipe, OhsumedPipe, NG20Pipe | |||
from .conll import Conll2003NERPipe, OntoNotesNERPipe, MsraNERPipe, WeiboNERPipe, PeopleDailyPipe | |||
from .conll import Conll2003Pipe | |||
from .coreference import CoReferencePipe | |||
@@ -70,3 +76,5 @@ from .matching import MatchingBertPipe, RTEBertPipe, SNLIBertPipe, QuoraBertPipe | |||
LCQMCPipe, BQCorpusPipe, LCQMCBertPipe, RenamePipe, GranularizePipe, MachingTruncatePipe | |||
from .pipe import Pipe | |||
from .qa import CMRC2018BertPipe | |||
from .construct_graph import MRPmiGraphPipe, R8PmiGraphPipe, R52PmiGraphPipe, NG20PmiGraphPipe, OhsumedPmiGraphPipe |
@@ -0,0 +1,268 @@ | |||
__all__ =[ | |||
'MRPmiGraphPipe', | |||
'R8PmiGraphPipe', | |||
'R52PmiGraphPipe', | |||
'OhsumedPmiGraphPipe', | |||
'NG20PmiGraphPipe' | |||
] | |||
try: | |||
import networkx as nx | |||
from sklearn.feature_extraction.text import CountVectorizer | |||
from sklearn.feature_extraction.text import TfidfTransformer | |||
from sklearn.pipeline import Pipeline | |||
except: | |||
pass | |||
from collections import defaultdict | |||
import itertools | |||
import math | |||
from tqdm import tqdm | |||
import numpy as np | |||
from ..data_bundle import DataBundle | |||
from ...core.const import Const | |||
from ..loader.classification import MRLoader, OhsumedLoader, R52Loader, R8Loader, NG20Loader | |||
def _get_windows(content_lst: list, window_size:int): | |||
r""" | |||
滑动窗口处理文本,获取词频和共现词语的词频 | |||
:param content_lst: | |||
:param window_size: | |||
:return: 词频,共现词频,窗口化后文本段的数量 | |||
""" | |||
word_window_freq = defaultdict(int) # w(i) 单词在窗口单位内出现的次数 | |||
word_pair_count = defaultdict(int) # w(i, j) | |||
windows_len = 0 | |||
for words in tqdm(content_lst, desc="Split by window"): | |||
windows = list() | |||
if isinstance(words, str): | |||
words = words.split() | |||
length = len(words) | |||
if length <= window_size: | |||
windows.append(words) | |||
else: | |||
for j in range(length - window_size + 1): | |||
window = words[j: j + window_size] | |||
windows.append(list(set(window))) | |||
for window in windows: | |||
for word in window: | |||
word_window_freq[word] += 1 | |||
for word_pair in itertools.combinations(window, 2): | |||
word_pair_count[word_pair] += 1 | |||
windows_len += len(windows) | |||
return word_window_freq, word_pair_count, windows_len | |||
def _cal_pmi(W_ij, W, word_freq_i, word_freq_j): | |||
r""" | |||
params: w_ij:为词语i,j的共现词频 | |||
w:文本数量 | |||
word_freq_i: 词语i的词频 | |||
word_freq_j: 词语j的词频 | |||
return: 词语i,j的tfidf值 | |||
""" | |||
p_i = word_freq_i / W | |||
p_j = word_freq_j / W | |||
p_i_j = W_ij / W | |||
pmi = math.log(p_i_j / (p_i * p_j)) | |||
return pmi | |||
def _count_pmi(windows_len, word_pair_count, word_window_freq, threshold): | |||
r""" | |||
params: windows_len: 文本段数量 | |||
word_pair_count: 词共现频率字典 | |||
word_window_freq: 词频率字典 | |||
threshold: 阈值 | |||
return 词语pmi的list列表,其中元素为[word1, word2, pmi] | |||
""" | |||
word_pmi_lst = list() | |||
for word_pair, W_i_j in tqdm(word_pair_count.items(), desc="Calculate pmi between words"): | |||
word_freq_1 = word_window_freq[word_pair[0]] | |||
word_freq_2 = word_window_freq[word_pair[1]] | |||
pmi = _cal_pmi(W_i_j, windows_len, word_freq_1, word_freq_2) | |||
if pmi <= threshold: | |||
continue | |||
word_pmi_lst.append([word_pair[0], word_pair[1], pmi]) | |||
return word_pmi_lst | |||
class GraphBuilderBase: | |||
def __init__(self, graph_type='pmi', widow_size=10, threshold=0.): | |||
self.graph = nx.Graph() | |||
self.word2id = dict() | |||
self.graph_type = graph_type | |||
self.window_size = widow_size | |||
self.doc_node_num = 0 | |||
self.tr_doc_index = None | |||
self.te_doc_index = None | |||
self.dev_doc_index = None | |||
self.doc = None | |||
self.threshold = threshold | |||
def _get_doc_edge(self, data_bundle: DataBundle): | |||
r''' | |||
对输入的DataBundle进行处理,然后生成文档-单词的tfidf值 | |||
:param: data_bundle中的文本若为英文,形式为[ 'This is the first document.'],若为中文则为['他 喜欢 吃 苹果'] | |||
: return 返回带有具有tfidf边文档-单词稀疏矩阵 | |||
''' | |||
tr_doc = list(data_bundle.get_dataset("train").get_field(Const.RAW_WORD)) | |||
val_doc = list(data_bundle.get_dataset("dev").get_field(Const.RAW_WORD)) | |||
te_doc = list(data_bundle.get_dataset("test").get_field(Const.RAW_WORD)) | |||
doc = tr_doc + val_doc + te_doc | |||
self.doc = doc | |||
self.tr_doc_index = [ind for ind in range(len(tr_doc))] | |||
self.dev_doc_index = [ind+len(tr_doc) for ind in range(len(val_doc))] | |||
self.te_doc_index = [ind+len(tr_doc)+len(val_doc) for ind in range(len(te_doc))] | |||
text_tfidf = Pipeline([('count', CountVectorizer(token_pattern=r'\S+', min_df=1, max_df=1.0)), | |||
('tfidf', TfidfTransformer(norm=None, use_idf=True, smooth_idf=False, sublinear_tf=False))]) | |||
tfidf_vec = text_tfidf.fit_transform(doc) | |||
self.doc_node_num = tfidf_vec.shape[0] | |||
vocab_lst = text_tfidf['count'].get_feature_names() | |||
for ind, word in enumerate(vocab_lst): | |||
self.word2id[word] = ind | |||
for ind, row in enumerate(tfidf_vec): | |||
for col_index, value in zip(row.indices, row.data): | |||
self.graph.add_edge(ind, self.doc_node_num+col_index, weight=value) | |||
return nx.to_scipy_sparse_matrix(self.graph) | |||
def _get_word_edge(self): | |||
word_window_freq, word_pair_count, windows_len = _get_windows(self.doc, self.window_size) | |||
pmi_edge_lst = _count_pmi(windows_len, word_pair_count, word_window_freq, self.threshold) | |||
for edge_item in pmi_edge_lst: | |||
word_indx1 = self.doc_node_num + self.word2id[edge_item[0]] | |||
word_indx2 = self.doc_node_num + self.word2id[edge_item[1]] | |||
if word_indx1 == word_indx2: | |||
continue | |||
self.graph.add_edge(word_indx1, word_indx2, weight=edge_item[2]) | |||
def build_graph(self, data_bundle: DataBundle): | |||
r""" | |||
对输入的DataBundle进行处理,然后返回该scipy_sparse_matrix类型的邻接矩阵。 | |||
:param ~fastNLP.DataBundle data_bundle: 需要处理的DataBundle对象 | |||
:return: | |||
""" | |||
raise NotImplementedError | |||
def build_graph_from_file(self, path: str): | |||
r""" | |||
传入文件路径,生成处理好的scipy_sparse_matrix对象。paths支持的路径形式可以参考 ::meth:`fastNLP.io.Loader.load()` | |||
:param paths: | |||
:return: scipy_sparse_matrix | |||
""" | |||
raise NotImplementedError | |||
class MRPmiGraphPipe(GraphBuilderBase): | |||
def __init__(self, graph_type='pmi', widow_size=10, threshold=0.): | |||
super().__init__(graph_type=graph_type, widow_size=widow_size, threshold=threshold) | |||
def build_graph(self, data_bundle: DataBundle): | |||
r''' | |||
params: ~fastNLP.DataBundle data_bundle: 需要处理的DataBundle对象. | |||
return 返回csr类型的稀疏矩阵图;训练集,验证集,测试集,在图中的index. | |||
''' | |||
self._get_doc_edge(data_bundle) | |||
self._get_word_edge() | |||
return nx.to_scipy_sparse_matrix(self.graph, | |||
nodelist=list(range(self.graph.number_of_nodes())), | |||
weight='weight', dtype=np.float32, format='csr'), (self.tr_doc_index, self.dev_doc_index, self.te_doc_index) | |||
def build_graph_from_file(self, path: str): | |||
data_bundle = MRLoader().load(path) | |||
return self.build_graph(data_bundle) | |||
class R8PmiGraphPipe(GraphBuilderBase): | |||
def __init__(self, graph_type='pmi', widow_size=10, threshold=0.): | |||
super().__init__(graph_type=graph_type, widow_size=widow_size, threshold=threshold) | |||
def build_graph(self, data_bundle: DataBundle): | |||
r''' | |||
params: ~fastNLP.DataBundle data_bundle: 需要处理的DataBundle对象. | |||
return 返回csr类型的稀疏矩阵图;训练集,验证集,测试集,在图中的index. | |||
''' | |||
self._get_doc_edge(data_bundle) | |||
self._get_word_edge() | |||
return nx.to_scipy_sparse_matrix(self.graph, | |||
nodelist=list(range(self.graph.number_of_nodes())), | |||
weight='weight', dtype=np.float32, format='csr'), (self.tr_doc_index, self.dev_doc_index, self.te_doc_index) | |||
def build_graph_from_file(self, path: str): | |||
data_bundle = R8Loader().load(path) | |||
return self.build_graph(data_bundle) | |||
class R52PmiGraphPipe(GraphBuilderBase): | |||
def __init__(self, graph_type='pmi', widow_size=10, threshold=0.): | |||
super().__init__(graph_type=graph_type, widow_size=widow_size, threshold=threshold) | |||
def build_graph(self, data_bundle: DataBundle): | |||
r''' | |||
params: ~fastNLP.DataBundle data_bundle: 需要处理的DataBundle对象. | |||
return 返回csr类型的稀疏矩阵;训练集,验证集,测试集,在图中的index. | |||
''' | |||
self._get_doc_edge(data_bundle) | |||
self._get_word_edge() | |||
return nx.to_scipy_sparse_matrix(self.graph, | |||
nodelist=list(range(self.graph.number_of_nodes())), | |||
weight='weight', dtype=np.float32, format='csr'), (self.tr_doc_index, self.dev_doc_index, self.te_doc_index) | |||
def build_graph_from_file(self, path: str): | |||
data_bundle = R52Loader().load(path) | |||
return self.build_graph(data_bundle) | |||
class OhsumedPmiGraphPipe(GraphBuilderBase): | |||
def __init__(self, graph_type='pmi', widow_size=10, threshold=0.): | |||
super().__init__(graph_type=graph_type, widow_size=widow_size, threshold=threshold) | |||
def build_graph(self, data_bundle: DataBundle): | |||
r''' | |||
params: ~fastNLP.DataBundle data_bundle: 需要处理的DataBundle对象. | |||
return 返回csr类型的稀疏矩阵图;训练集,验证集,测试集,在图中的index. | |||
''' | |||
self._get_doc_edge(data_bundle) | |||
self._get_word_edge() | |||
return nx.to_scipy_sparse_matrix(self.graph, | |||
nodelist=list(range(self.graph.number_of_nodes())), | |||
weight='weight', dtype=np.float32, format='csr'), (self.tr_doc_index, self.dev_doc_index, self.te_doc_index) | |||
def build_graph_from_file(self, path: str): | |||
data_bundle = OhsumedLoader().load(path) | |||
return self.build_graph(data_bundle) | |||
class NG20PmiGraphPipe(GraphBuilderBase): | |||
def __init__(self, graph_type='pmi', widow_size=10, threshold=0.): | |||
super().__init__(graph_type=graph_type, widow_size=widow_size, threshold=threshold) | |||
def build_graph(self, data_bundle: DataBundle): | |||
r''' | |||
params: ~fastNLP.DataBundle data_bundle: 需要处理的DataBundle对象. | |||
return 返回csr类型的稀疏矩阵图;训练集,验证集,测试集,在图中的index. | |||
''' | |||
self._get_doc_edge(data_bundle) | |||
self._get_word_edge() | |||
return nx.to_scipy_sparse_matrix(self.graph, | |||
nodelist=list(range(self.graph.number_of_nodes())), | |||
weight='weight', dtype=np.float32, format='csr'), ( | |||
self.tr_doc_index, self.dev_doc_index, self.te_doc_index) | |||
def build_graph_from_file(self, path: str): | |||
r''' | |||
param: path->数据集的路径. | |||
return: 返回csr类型的稀疏矩阵图;训练集,验证集,测试集,在图中的index. | |||
''' | |||
data_bundle = NG20Loader().load(path) | |||
return self.build_graph(data_bundle) |
@@ -376,7 +376,7 @@ class BiaffineParser(GraphParser): | |||
if self.encoder_name.endswith('lstm'): | |||
sort_lens, sort_idx = torch.sort(seq_len, dim=0, descending=True) | |||
x = x[sort_idx] | |||
x = nn.utils.rnn.pack_padded_sequence(x, sort_lens, batch_first=True) | |||
x = nn.utils.rnn.pack_padded_sequence(x, sort_lens.cpu(), batch_first=True) | |||
feat, _ = self.encoder(x) # -> [N,L,C] | |||
feat, _ = nn.utils.rnn.pad_packed_sequence(feat, batch_first=True) | |||
_, unsort_idx = torch.sort(sort_idx, dim=0, descending=False) | |||
@@ -251,7 +251,7 @@ class LstmbiLm(nn.Module): | |||
def forward(self, inputs, seq_len): | |||
sort_lens, sort_idx = torch.sort(seq_len, dim=0, descending=True) | |||
inputs = inputs[sort_idx] | |||
inputs = nn.utils.rnn.pack_padded_sequence(inputs, sort_lens, batch_first=self.batch_first) | |||
inputs = nn.utils.rnn.pack_padded_sequence(inputs, sort_lens.cpu(), batch_first=self.batch_first) | |||
output, hx = self.encoder(inputs, None) # -> [N,L,C] | |||
output, _ = nn.utils.rnn.pad_packed_sequence(output, batch_first=self.batch_first) | |||
_, unsort_idx = torch.sort(sort_idx, dim=0, descending=False) | |||
@@ -316,7 +316,7 @@ class ElmobiLm(torch.nn.Module): | |||
max_len = inputs.size(1) | |||
sort_lens, sort_idx = torch.sort(seq_len, dim=0, descending=True) | |||
inputs = inputs[sort_idx] | |||
inputs = nn.utils.rnn.pack_padded_sequence(inputs, sort_lens, batch_first=True) | |||
inputs = nn.utils.rnn.pack_padded_sequence(inputs, sort_lens.cpu(), batch_first=True) | |||
output, _ = self._lstm_forward(inputs, None) | |||
_, unsort_idx = torch.sort(sort_idx, dim=0, descending=False) | |||
output = output[:, unsort_idx] | |||
@@ -7,10 +7,11 @@ build: | |||
image: latest | |||
python: | |||
version: 3.6 | |||
version: 3.8 | |||
install: | |||
- requirements: docs/requirements.txt | |||
- method: setuptools | |||
path: . | |||
formats: | |||
- htmlzip | |||
- htmlzip |
@@ -23,7 +23,7 @@ setup( | |||
long_description_content_type='text/markdown', | |||
license='Apache License', | |||
author='Fudan FastNLP Team', | |||
python_requires='>=3.6', | |||
python_requires='>=3.7', | |||
packages=pkgs, | |||
install_requires=reqs.strip().split('\n'), | |||
) |