Merge pull request #18 from BrikerMan/develop

fix several minimal details

kashgari/embeddings/embeddings.py

@@ -46,7 +46,7 @@ class BaseEmbedding(object):
 
     def __init__(self,
                  name_or_path: str,
-                 sequence_length: int,
+                 sequence_length: int = None,
                  embedding_size: int = None,
                  **kwargs):
         """
@@ -322,6 +322,20 @@ def prepare_model_input(self, input_x: np.array, **kwargs) -> np.array:
 
 
 class CustomEmbedding(BaseEmbedding):
+    def __init__(self,
+                 name_or_path: str = 'custom-embedding',
+                 sequence_length: int = None,
+                 embedding_size: int = None,
+                 **kwargs):
+        """
+        :param name_or_path: just a name for custom embedding
+        :param sequence_length: length of max sequence, all embedding is shaped as (sequence_length, embedding_size)
+        :param embedding_size: embedding vector size, only need to set when using a CustomEmbedding
+        :param kwargs: kwargs to pass to the method, func: `BaseEmbedding.build`
+        """
+        if sequence_length is None or embedding_size is None:
+            raise ValueError('Must set sequence_length and sequence_length when using the CustomEmbedding layer')
+        super(CustomEmbedding, self).__init__(name_or_path, sequence_length, embedding_size, **kwargs)
 
     def build(self, **kwargs):
         if self._token2idx is None:

kashgari/tasks/classification/base_model.py

@@ -187,11 +187,11 @@ def _format_output_dic(self, words: List[str], res: np.ndarray):
         results = sorted(list(enumerate(res)), key=lambda x: -x[1])
         candidates = []
         for result in results:
-            if float(result[1]) > 0.01:
-                candidates.append({
-                    'name': self.convert_idx_to_label([result[0]])[0],
-                    'confidence': float(result[1]),
-                })
+            candidates.append({
+                'name': self.convert_idx_to_label([result[0]])[0],
+                'confidence': float(result[1]),
+            })
+
         data = {
             'words': words,
             'class': candidates[0],

setup.py

@@ -12,7 +12,7 @@
 
 """
 import pathlib
-
+from version import __version__
 from setuptools import find_packages, setup
 
 # Package meta-data.
@@ -28,7 +28,7 @@
 
 required = [
     'Keras>=2.2.0',
-    'keras_bert',
+    'keras-bert==0.25.0',
     'h5py>=2.7.1',
     'keras-bert==0.25.0',
     'scikit-learn>=0.19.1',
@@ -46,7 +46,7 @@
 
 setup(
     name=NAME,
-    version='0.1.6',
+    version=__version__,
     description=DESCRIPTION,
     long_description=README,
     long_description_content_type="text/markdown",

version.py

@@ -0,0 +1,14 @@
+# encoding: utf-8
+"""
+@author: BrikerMan
+@contact: eliyar917@gmail.com
+@blog: https://eliyar.biz
+
+@version: 1.0
+@license: Apache Licence
+@file: __version__.py
+@time: 2019-02-21 15:22
+
+"""
+
+__version__ = '0.1.7'