🐛 Fixing vocab_path typo.

BrikerMan · Mar 29, 2020 · 878237d · 878237d
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diff --git a/docs/about/release-notes.md b/docs/about/release-notes.md
@@ -17,6 +17,10 @@ pip show kashgari
 
 ## Current Release
 
+### [1.1.3] - 2020.03.28
+
+- 🐛 Fixing vocab_path typo.
+
 ### [1.1.2] - 2020.03.27
 
 - ✨ Add save best model callback `KashgariModelCheckpoint`.

diff --git a/docs/embeddings/bert-embedding_v2.md b/docs/embeddings/bert-embedding_v2.md
@@ -62,10 +62,10 @@ from kashgari.tokenizer import BertTokenizer
 model_folder = '/Users/brikerman/Desktop/nlp/language_models/albert_base'
 checkpoint_path = os.path.join(model_folder, 'model.ckpt-best')
 config_path = os.path.join(model_folder, 'albert_config.json')
-vacab_path = os.path.join(model_folder, 'vocab_chinese.txt')
+vocab_path = os.path.join(model_folder, 'vocab_chinese.txt')
 
-tokenizer = BertTokenizer.load_from_vacab_file(vacab_path)
-embed = BERTEmbeddingV2(vacab_path, config_path, checkpoint_path,
+tokenizer = BertTokenizer.load_from_vacob_file(vocab_path)
+embed = BERTEmbeddingV2(vocab_path, config_path, checkpoint_path,
                         bert_type='albert',
                         task=kashgari.CLASSIFICATION,
                         sequence_length=100)

diff --git a/kashgari/__version__.py b/kashgari/__version__.py
@@ -7,4 +7,4 @@
 # file: __version__.py.py
 # time: 2019-05-20 16:32
 
-__version__ = '1.1.2'
+__version__ = '1.1.3'
diff --git a/tests/embedding/test_bert_embedding_v2.py b/tests/embedding/test_bert_embedding_v2.py
@@ -22,10 +22,11 @@ def test_basic_use(self):
 
         checkpoint_path = os.path.join(model_folder, 'model.ckpt-best')
         config_path = os.path.join(model_folder, 'albert_config.json')
-        vacab_path = os.path.join(model_folder, 'vocab_chinese.txt')
+        vocab_path = os.path.join(model_folder, 'vocab_chinese.txt')
 
-        tokenizer = BertTokenizer.load_from_vacab_file(vacab_path)
-        embed = BERTEmbeddingV2(vacab_path, config_path, checkpoint_path,
+        tokenizer = BertTokenizer.load_from_vacab_file(vocab_path)
+        tokenizer = BertTokenizer.load_from_vocab_file(vocab_path)
+        embed = BERTEmbeddingV2(vocab_path, config_path, checkpoint_path,
                                 bert_type='albert',
                                 task=kashgari.CLASSIFICATION,
                                 sequence_length=100)