Merge pull request #1902 from linjieccc/fix_ernie

Update pretained model name for ernie_v2_eng_base

modules/text/language_model/ernie_v2_eng_base/README.md

 # ernie_v2_eng_base
 |模型名称|ernie_v2_eng_base|
-| :--- | :---: | 
+| :--- | :---: |
 |类别|文本-语义模型|
 |网络|ernie_v2_eng_base|
 |数据集|百度自建数据集|
 |是否支持Fine-tuning|是|
 |模型大小|1.3G|
-|最新更新日期|2021-03-16|
+|最新更新日期|2021-06-28|
 |数据指标|-|
 
 ## 一、模型基本信息
@@ -36,7 +36,7 @@ Ernie是百度提出的基于知识增强的持续学习语义理解模型，该
 - ### 2、安装
 
   - ```shell
-    $ hub install ernie_tiny
+    $ hub install ernie_v2_eng_base
     ```
   - 如您安装时遇到问题，可参考：[零基础windows安装](../../../../docs/docs_ch/get_start/windows_quickstart.md)
  | [零基础Linux安装](../../../../docs/docs_ch/get_start/linux_quickstart.md) | [零基础MacOS安装](../../../../docs/docs_ch/get_start/mac_quickstart.md)
@@ -57,7 +57,7 @@ label_map = {0: 'negative', 1: 'positive'}
 
 model = hub.Module(
     name='ernie_v2_eng_base',
-    version='2.0.2',
+    version='2.0.3',
     task='seq-cls',
     load_checkpoint='/path/to/parameters',
     label_map=label_map)
@@ -194,7 +194,11 @@ for idx, text in enumerate(data):
 
 * 2.0.2
 
-  增加文本匹配任务`text-matching`  
+  增加文本匹配任务`text-matching`
+
+* 2.0.3
+
+  模型底座名称调整
   ```shell
-  $ hub install ernie_v2_eng_base==2.0.2
+  $ hub install ernie_v2_eng_base==2.0.3
   ```

modules/text/language_model/ernie_v2_eng_base/module.py

-# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -11,17 +11,19 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-from typing import Dict
-import os
 import math
+import os
+from typing import Dict
 
 import paddle
 import paddle.nn as nn
 import paddle.nn.functional as F
-
-from paddlenlp.transformers.ernie.modeling import ErnieModel, ErnieForSequenceClassification, ErnieForTokenClassification
-from paddlenlp.transformers.ernie.tokenizer import ErnieTokenizer
 from paddlenlp.metrics import ChunkEvaluator
+from paddlenlp.transformers.ernie.modeling import ErnieForSequenceClassification
+from paddlenlp.transformers.ernie.modeling import ErnieForTokenClassification
+from paddlenlp.transformers.ernie.modeling import ErnieModel
+from paddlenlp.transformers.ernie.tokenizer import ErnieTokenizer
+
 from paddlehub.module.module import moduleinfo
 from paddlehub.module.nlp_module import TransformerModule
 from paddlehub.utils.log import logger
@@ -29,7 +31,7 @@ from paddlehub.utils.log import logger
 
 @moduleinfo(
     name="ernie_v2_eng_base",
-    version="2.0.2",
+    version="2.0.3",
     summary=
     "Baidu's ERNIE 2.0, Enhanced Representation through kNowledge IntEgration, max_seq_len=512 when predtrained. The module is executed as paddle.dygraph.",
     author="paddlepaddle",
@@ -42,13 +44,13 @@ class ErnieV2(nn.Layer):
     """
 
     def __init__(
-            self,
-            task: str = None,
-            load_checkpoint: str = None,
-            label_map: Dict = None,
-            num_classes: int = 2,
-            suffix: bool = False,
-            **kwargs,
+        self,
+        task: str = None,
+        load_checkpoint: str = None,
+        label_map: Dict = None,
+        num_classes: int = 2,
+        suffix: bool = False,
+        **kwargs,
     ):
         super(ErnieV2, self).__init__()
         if label_map:
@@ -64,22 +66,24 @@ class ErnieV2(nn.Layer):
                 "'sequence_classification' has been deprecated and will be removed in the future.", )
         if task == 'seq-cls':
             self.model = ErnieForSequenceClassification.from_pretrained(
-                pretrained_model_name_or_path='ernie-2.0-en', num_classes=self.num_classes, **kwargs)
+                pretrained_model_name_or_path='ernie-2.0-base-en', num_classes=self.num_classes, **kwargs)
             self.criterion = paddle.nn.loss.CrossEntropyLoss()
             self.metric = paddle.metric.Accuracy()
         elif task == 'token-cls':
-            self.model = ErnieForTokenClassification.from_pretrained(
-                pretrained_model_name_or_path='ernie-2.0-en', num_classes=self.num_classes, **kwargs)
+            self.model = ErnieForTokenClassification.from_pretrained(pretrained_model_name_or_path='ernie-2.0-base-en',
+                                                                     num_classes=self.num_classes,
+                                                                     **kwargs)
             self.criterion = paddle.nn.loss.CrossEntropyLoss()
-            self.metric = ChunkEvaluator(label_list=[self.label_map[i] for i in sorted(self.label_map.keys())], suffix=suffix)
+            self.metric = ChunkEvaluator(label_list=[self.label_map[i] for i in sorted(self.label_map.keys())],
+                                         suffix=suffix)
         elif task == 'text-matching':
-            self.model = ErnieModel.from_pretrained(pretrained_model_name_or_path='ernie-2.0-en', **kwargs)
+            self.model = ErnieModel.from_pretrained(pretrained_model_name_or_path='ernie-2.0-base-en', **kwargs)
             self.dropout = paddle.nn.Dropout(0.1)
             self.classifier = paddle.nn.Linear(self.model.config['hidden_size'] * 3, 2)
             self.criterion = paddle.nn.loss.CrossEntropyLoss()
             self.metric = paddle.metric.Accuracy()
         elif task is None:
-            self.model = ErnieModel.from_pretrained(pretrained_model_name_or_path='ernie-2.0-en', **kwargs)
+            self.model = ErnieModel.from_pretrained(pretrained_model_name_or_path='ernie-2.0-base-en', **kwargs)
         else:
             raise RuntimeError("Unknown task {}, task should be one in {}".format(task, self._tasks_supported))
 
@@ -171,4 +175,4 @@ class ErnieV2(nn.Layer):
         """
         Gets the tokenizer that is customized for this module.
         """
-        return ErnieTokenizer.from_pretrained(pretrained_model_name_or_path='ernie-2.0-en', *args, **kwargs)
+        return ErnieTokenizer.from_pretrained(pretrained_model_name_or_path='ernie-2.0-base-en', *args, **kwargs)