test

README.md

@@ -84,7 +84,17 @@ DeepKE 提供了多种知识抽取模型。
 
 2. NER
 
-   数据为txt文件，样式范例为：
+   数据为txt文件
+
+   中文样式范例为：
+
+   |                           Sentence                           |           Person           |   Location   |          Organization          |
+   | :----------------------------------------------------------: | :------------------------: | :----------: | :----------------------------: |
+   | 本报北京9月4日讯记者杨涌报道：部分省区人民日报宣传发行工作座谈会9月3日在4日在京举行。 |            杨涌            |   北京，京   |            人民日报            |
+   | 《红楼梦》是中央电视台和中国电视剧制作中心根据中国古典文学名著《红楼梦》摄制于1987年的一部古装连续剧，由王扶林导演，周汝昌、王蒙、周岭等多位红学家参与制作。 | 王扶林，周汝昌，王蒙，周岭 |      /       | 中央电视台，中国电视剧制作中心 |
+   | 秦始皇兵马俑位于陕西省西安市，1961年被国务院公布为第一批全国重点文物保护单位，是世界八大奇迹之一。 |           秦始皇           | 陕西省西安市 |             国务院             |
+
+   英文样式范例为：
 
    |                           Sentence                           |                Person                |     Location      |        Organization         |     Miscellaneous     |
    | :----------------------------------------------------------: | :----------------------------------: | :---------------: | :-------------------------: | :-------------------: |

example/ae/standard/predict.py

@@ -4,12 +4,14 @@ import torch
 import logging
 import hydra
 from hydra import utils
-from deepke.ae_st_tools import Serializer
+from deepke.attribution_extraction.standard.tools import Serializer
-from deepke.ae_st_tools import _serialize_sentence, _convert_tokens_into_index, _add_pos_seq, _handle_attribute_data
+from deepke.attribution_extraction.standard.tools import _serialize_sentence, _convert_tokens_into_index, _add_pos_seq, _handle_attribute_data
 import matplotlib.pyplot as plt
 sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), "../")))
-from deepke.ae_st_utils import load_pkl, load_csv
+from deepke.attribution_extraction.standard.utils import load_pkl, load_csv
-import deepke.ae_st_models as models
+import deepke.attribution_extraction.standard.models as models
+
+
 
 
 logger = logging.getLogger(__name__)

example/ae/standard/run.py

@@ -11,9 +11,9 @@ from torch.utils.tensorboard import SummaryWriter
 # self
 import sys
 sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), "../")))
-import deepke.ae_st_models as models
+import deepke.attribution_extraction.standard.models as models
-from deepke.ae_st_tools import preprocess , CustomDataset, collate_fn ,train, validate
+from deepke.attribution_extraction.standard.tools import preprocess , CustomDataset, collate_fn ,train, validate
-from deepke.ae_st_utils import manual_seed, load_pkl
+from deepke.attribution_extraction.standard.utils import manual_seed, load_pkl
 
 
 logger = logging.getLogger(__name__)

setup.py

@@ -0,0 +1,20 @@
+from setuptools import setup, find_packages
+setup(
+    name='deepke',  # 打包后的包文件名
+    version='0.2.24',    #版本号
+    keywords=["pip", "RE","NER","AE"],    # 关键字
+    description='DeepKE 是基于 Pytorch 的深度学习中文关系抽取处理套件。',  # 说明
+    long_description="client",  #详细说明
+    license="Apache-2.0 License",  # 许可
+    url='https://github.com/zjunlp/deepke',
+    author='ZJUNLP',
+    author_email='xx2020@zju.edu.cn',
+    include_package_data=True,
+    platforms="any",
+    package_dir={"": "src"},
+    packages=find_packages("src"),
+    classifiers=[
+        "Programming Language :: Python :: 3",
+        "Operating System :: OS Independent",
+    ]
+)

src/deepke/__init__.py

@@ -0,0 +1,2 @@
+from .attribution_extraction import *
+from .relation_extraction import *

src/deepke/attribution_extraction/__init__.py

@@ -0,0 +1 @@
+from .standard import *

src/deepke/attribution_extraction/standard/__init__.py

@@ -0,0 +1,4 @@
+from .models import *
+from .module import *
+from .tools import *
+from .utils import *

src/deepke/attribution_extraction/standard/models/BiLSTM.py

@@ -1,6 +1,6 @@
 import torch.nn as nn
 from . import BasicModule
-from module import Embedding, RNN
+from ..module import Embedding, RNN
 
 
 class BiLSTM(BasicModule):

src/deepke/attribution_extraction/standard/models/Capsule.py

@@ -1,9 +1,9 @@
 import torch
 from . import BasicModule
-from module import Embedding, CNN
+from ..module import Embedding, CNN
-from module import Capsule as CapsuleLayer
+from ..module import Capsule as CapsuleLayer
 
-from utils import seq_len_to_mask, to_one_hot
+from ..utils import seq_len_to_mask, to_one_hot
 
 
 class Capsule(BasicModule):

src/deepke/attribution_extraction/standard/models/GCN.py

@@ -1,10 +1,10 @@
 import torch
 import torch.nn as nn
 from . import BasicModule
-from module import Embedding
+from ..module import Embedding
-from module import GCN as GCNBlock
+from ..module import GCN as GCNBlock
 
-from utils import seq_len_to_mask
+from ..utils import seq_len_to_mask
 
 
 class GCN(BasicModule):

src/deepke/attribution_extraction/standard/models/LM.py

@@ -1,9 +1,9 @@
 from torch import nn
 from . import BasicModule
-from module import RNN
+from ..module import RNN
 from transformers import BertModel
 
-from utils import seq_len_to_mask
+from ..utils import seq_len_to_mask
 
 
 class LM(BasicModule):

src/deepke/attribution_extraction/standard/models/PCNN.py

@@ -2,9 +2,9 @@ import torch
 import torch.nn as nn
 import torch.nn.functional as F
 from . import BasicModule
-from module import Embedding, CNN
+from ..module import Embedding, CNN
 
-from utils import seq_len_to_mask
+from ..utils import seq_len_to_mask
 
 
 class PCNN(BasicModule):

src/deepke/attribution_extraction/standard/models/Transformer.py

@@ -1,9 +1,9 @@
 import torch.nn as nn
 from . import BasicModule
-from module import Embedding
+from ..module import Embedding
-from module import Transformer as TransformerBlock
+from ..module import Transformer as TransformerBlock
 
-from utils import seq_len_to_mask
+from ..utils import seq_len_to_mask
 
 
 class Transformer(BasicModule):

src/deepke/attribution_extraction/standard/tools/dataset.py

@@ -3,7 +3,7 @@ from torch.utils.data import Dataset
 import os
 import sys
 sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), "../")))
-from deepke.utils import load_pkl
+from utils import load_pkl
 
 def collate_fn(cfg):
     

src/deepke/attribution_extraction/standard/tools/preprocess.py

@@ -3,8 +3,8 @@ import logging
 from collections import OrderedDict
 from typing import List, Dict
 from transformers import BertTokenizer
-from serializer import Serializer
+from .serializer import Serializer
-from vocab import Vocab
+from .vocab import Vocab
 import sys
 sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), "../")))
 from utils import save_pkl, load_csv

src/deepke/attribution_extraction/standard/tools/trainer.py

@@ -1,7 +1,7 @@
 import torch
 import logging
 import matplotlib.pyplot as plt
-from metrics import PRMetric
+from .metrics import PRMetric
 
 logger = logging.getLogger(__name__)
 

src/deepke/relation_extraction/__init__.py

@@ -0,0 +1 @@
+from .standard import *

src/deepke/relation_extraction/standard/__init__.py

@@ -0,0 +1,4 @@
+from .models import *
+from .module import *
+from .tools import *
+from .utils import *

src/deepke/relation_extraction/standard/models/BiLSTM.py

@@ -1,6 +1,6 @@
 import torch.nn as nn
 from . import BasicModule
-from module import Embedding, RNN
+from ..module import Embedding, RNN
 
 
 class BiLSTM(BasicModule):

src/deepke/relation_extraction/standard/models/Capsule.py

@@ -1,9 +1,9 @@
 import torch
 from . import BasicModule
-from module import Embedding, CNN
+from ..module import Embedding, CNN
-from module import Capsule as CapsuleLayer
+from ..module import Capsule as CapsuleLayer
 
-from utils import seq_len_to_mask, to_one_hot
+from ..utils import seq_len_to_mask, to_one_hot
 
 
 class Capsule(BasicModule):

src/deepke/relation_extraction/standard/models/GCN.py

@@ -1,9 +1,9 @@
 import torch
 import torch.nn as nn
 from . import BasicModule
-from module import Embedding
+from ..module import Embedding
-from module import GCN as GCNBlock
+from ..module import GCN as GCNBlock
-from utils import seq_len_to_mask
+from ..utils import seq_len_to_mask
 
 
 class GCN(BasicModule):

src/deepke/relation_extraction/standard/models/LM.py

@@ -1,8 +1,8 @@
 from torch import nn
 from . import BasicModule
-from module import RNN
+from ..module import RNN
 from transformers import BertModel
-from utils import seq_len_to_mask
+from ..utils import seq_len_to_mask
 
 
 class LM(BasicModule):

src/deepke/relation_extraction/standard/models/PCNN.py

@@ -2,8 +2,8 @@ import torch
 import torch.nn as nn
 import torch.nn.functional as F
 from . import BasicModule
-from module import Embedding, CNN
+from ..module import Embedding, CNN
-from utils import seq_len_to_mask
+from ..utils import seq_len_to_mask
 
 
 class PCNN(BasicModule):

src/deepke/relation_extraction/standard/models/Transformer.py

@@ -1,8 +1,8 @@
 import torch.nn as nn
 from . import BasicModule
-from module import Embedding
+from ..module import Embedding
-from module import Transformer as TransformerBlock
+from ..module import Transformer as TransformerBlock
-from utils import seq_len_to_mask
+from ..utils import seq_len_to_mask
 
 
 class Transformer(BasicModule):