Upgrade embedding modules (#1230)

* do finetune text cls & ner via using embedding module

modules/text/embedding/fasttext_crawl_target_word-word_dim300_en/README.md

@@ -56,6 +56,25 @@ def dot(
 * `word_a`： 需要计算内积的单词a。
 * `word_b`： 需要计算内积的单词b。
 
+
+```python
+def get_vocab_path()
+```
+获取本地词表文件的路径信息。
+
+
+```python
+def get_tokenizer(*args, **kwargs)
+```
+获取当前模型的tokenizer，返回一个JiebaTokenizer的实例，当前只支持中文embedding模型。
+
+**参数**
+* `*args`： 额外传递的列表形式的参数。
+* `**kwargs`： 额外传递的字典形式的参数。
+
+关于额外参数的详情，可查看[paddlenlp.data.tokenizer.JiebaTokenizer](https://github.com/PaddlePaddle/models/blob/develop/PaddleNLP/paddlenlp/data/tokenizer.py)
+
+
 更多api详情和用法可参考[paddlenlp.embeddings](https://github.com/PaddlePaddle/models/tree/release/2.0-beta/PaddleNLP/paddlenlp/embeddings)
 
 ## 代码示例
@@ -125,3 +144,6 @@ paddlehub >= 2.0.0
 
   初始发布
 
+* 1.0.1
+
+  支持基于embedding的文本分类和序列标注finetune任务

modules/text/embedding/fasttext_crawl_target_word-word_dim300_en/module.py

@@ -12,44 +12,24 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from typing import List
 from paddlenlp.embeddings import TokenEmbedding
-from paddlehub.module.module import moduleinfo, serving
+from paddlehub.module.module import moduleinfo
+from paddlehub.module.nlp_module import EmbeddingModule
 
 
 @moduleinfo(
     name="fasttext_crawl_target_word-word_dim300_en",
-    version="1.0.0",
+    version="1.0.1",
     summary="",
     author="paddlepaddle",
     author_email="",
-    type="nlp/semantic_model")
+    type="nlp/semantic_model",
+    meta=EmbeddingModule)
 class Embedding(TokenEmbedding):
     """
     Embedding model
     """
-    def __init__(self, *args, **kwargs):
-        super(Embedding, self).__init__(embedding_name="fasttext.crawl.target.word-word.dim300.en", *args, **kwargs)
-
-    @serving
-    def calc_similarity(self, data: List[List[str]]):
-        """
-        Calculate similarities of giving word pairs.
-        """
-        results = []
-        for word_pair in data:
-            if len(word_pair) != 2:
-                raise RuntimeError(
-                    f'The input must have two words, but got {len(word_pair)}. Please check your inputs.')
-            if not isinstance(word_pair[0], str) or not isinstance(word_pair[1], str):
-                raise RuntimeError(
-                    f'The types of text pair must be (str, str), but got'
-                    f' ({type(word_pair[0]).__name__}, {type(word_pair[1]).__name__}). Please check your inputs.')
+    embedding_name = "fasttext.crawl.target.word-word.dim300.en"
 
-            for word in word_pair:
-                if self.get_idx_from_word(word) == \
-                        self.get_idx_from_word(self.vocab.unk_token):
-                    raise RuntimeError(
-                        f'Word "{word}" is not in vocab. Please check your inputs.')
-            results.append(str(self.cosine_sim(*word_pair)))
-        return results
+    def __init__(self, *args, **kwargs):
+        super(Embedding, self).__init__(embedding_name=self.embedding_name, *args, **kwargs)

modules/text/embedding/fasttext_wiki-news_target_word-word_dim300_en/README.md

@@ -56,6 +56,25 @@ def dot(
 * `word_a`： 需要计算内积的单词a。
 * `word_b`： 需要计算内积的单词b。
 
+
+```python
+def get_vocab_path()
+```
+获取本地词表文件的路径信息。
+
+
+```python
+def get_tokenizer(*args, **kwargs)
+```
+获取当前模型的tokenizer，返回一个JiebaTokenizer的实例，当前只支持中文embedding模型。
+
+**参数**
+* `*args`： 额外传递的列表形式的参数。
+* `**kwargs`： 额外传递的字典形式的参数。
+
+关于额外参数的详情，可查看[paddlenlp.data.tokenizer.JiebaTokenizer](https://github.com/PaddlePaddle/models/blob/develop/PaddleNLP/paddlenlp/data/tokenizer.py)
+
+
 更多api详情和用法可参考[paddlenlp.embeddings](https://github.com/PaddlePaddle/models/tree/release/2.0-beta/PaddleNLP/paddlenlp/embeddings)
 
 ## 代码示例
@@ -125,3 +144,6 @@ paddlehub >= 2.0.0
 
   初始发布
 
+* 1.0.1
+
+  支持基于embedding的文本分类和序列标注finetune任务

modules/text/embedding/fasttext_wiki-news_target_word-word_dim300_en/module.py

@@ -12,44 +12,24 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from typing import List
 from paddlenlp.embeddings import TokenEmbedding
-from paddlehub.module.module import moduleinfo, serving
+from paddlehub.module.module import moduleinfo
+from paddlehub.module.nlp_module import EmbeddingModule
 
 
 @moduleinfo(
     name="fasttext_wiki-news_target_word-word_dim300_en",
-    version="1.0.0",
+    version="1.0.1",
     summary="",
     author="paddlepaddle",
     author_email="",
-    type="nlp/semantic_model")
+    type="nlp/semantic_model",
+    meta=EmbeddingModule)
 class Embedding(TokenEmbedding):
     """
     Embedding model
     """
-    def __init__(self, *args, **kwargs):
-        super(Embedding, self).__init__(embedding_name="fasttext.wiki-news.target.word-word.dim300.en", *args, **kwargs)
-
-    @serving
-    def calc_similarity(self, data: List[List[str]]):
-        """
-        Calculate similarities of giving word pairs.
-        """
-        results = []
-        for word_pair in data:
-            if len(word_pair) != 2:
-                raise RuntimeError(
-                    f'The input must have two words, but got {len(word_pair)}. Please check your inputs.')
-            if not isinstance(word_pair[0], str) or not isinstance(word_pair[1], str):
-                raise RuntimeError(
-                    f'The types of text pair must be (str, str), but got'
-                    f' ({type(word_pair[0]).__name__}, {type(word_pair[1]).__name__}). Please check your inputs.')
+    embedding_name = "fasttext.wiki-news.target.word-word.dim300.en"
 
-            for word in word_pair:
-                if self.get_idx_from_word(word) == \
-                        self.get_idx_from_word(self.vocab.unk_token):
-                    raise RuntimeError(
-                        f'Word "{word}" is not in vocab. Please check your inputs.')
-            results.append(str(self.cosine_sim(*word_pair)))
-        return results
+    def __init__(self, *args, **kwargs):
+        super(Embedding, self).__init__(embedding_name=self.embedding_name, *args, **kwargs)

modules/text/embedding/glove_twitter_target_word-word_dim100_en/README.md

@@ -56,6 +56,25 @@ def dot(
 * `word_a`： 需要计算内积的单词a。
 * `word_b`： 需要计算内积的单词b。
 
+
+```python
+def get_vocab_path()
+```
+获取本地词表文件的路径信息。
+
+
+```python
+def get_tokenizer(*args, **kwargs)
+```
+获取当前模型的tokenizer，返回一个JiebaTokenizer的实例，当前只支持中文embedding模型。
+
+**参数**
+* `*args`： 额外传递的列表形式的参数。
+* `**kwargs`： 额外传递的字典形式的参数。
+
+关于额外参数的详情，可查看[paddlenlp.data.tokenizer.JiebaTokenizer](https://github.com/PaddlePaddle/models/blob/develop/PaddleNLP/paddlenlp/data/tokenizer.py)
+
+
 更多api详情和用法可参考[paddlenlp.embeddings](https://github.com/PaddlePaddle/models/tree/release/2.0-beta/PaddleNLP/paddlenlp/embeddings)
 
 ## 代码示例
@@ -125,3 +144,6 @@ paddlehub >= 2.0.0
 
   初始发布
 
+* 1.0.1
+
+  支持基于embedding的文本分类和序列标注finetune任务

modules/text/embedding/glove_twitter_target_word-word_dim100_en/module.py

@@ -12,44 +12,24 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from typing import List
 from paddlenlp.embeddings import TokenEmbedding
-from paddlehub.module.module import moduleinfo, serving
+from paddlehub.module.module import moduleinfo
+from paddlehub.module.nlp_module import EmbeddingModule
 
 
 @moduleinfo(
     name="glove_twitter_target_word-word_dim100_en",
-    version="1.0.0",
+    version="1.0.1",
     summary="",
     author="paddlepaddle",
     author_email="",
-    type="nlp/semantic_model")
+    type="nlp/semantic_model",
+    meta=EmbeddingModule)
 class Embedding(TokenEmbedding):
     """
     Embedding model
     """
-    def __init__(self, *args, **kwargs):
-        super(Embedding, self).__init__(embedding_name="glove.twitter.target.word-word.dim100.en", *args, **kwargs)
-
-    @serving
-    def calc_similarity(self, data: List[List[str]]):
-        """
-        Calculate similarities of giving word pairs.
-        """
-        results = []
-        for word_pair in data:
-            if len(word_pair) != 2:
-                raise RuntimeError(
-                    f'The input must have two words, but got {len(word_pair)}. Please check your inputs.')
-            if not isinstance(word_pair[0], str) or not isinstance(word_pair[1], str):
-                raise RuntimeError(
-                    f'The types of text pair must be (str, str), but got'
-                    f' ({type(word_pair[0]).__name__}, {type(word_pair[1]).__name__}). Please check your inputs.')
+    embedding_name = "glove.twitter.target.word-word.dim100.en"
 
-            for word in word_pair:
-                if self.get_idx_from_word(word) == \
-                        self.get_idx_from_word(self.vocab.unk_token):
-                    raise RuntimeError(
-                        f'Word "{word}" is not in vocab. Please check your inputs.')
-            results.append(str(self.cosine_sim(*word_pair)))
-        return results
+    def __init__(self, *args, **kwargs):
+        super(Embedding, self).__init__(embedding_name=self.embedding_name, *args, **kwargs)

modules/text/embedding/glove_twitter_target_word-word_dim200_en/README.md

@@ -56,6 +56,25 @@ def dot(
 * `word_a`： 需要计算内积的单词a。
 * `word_b`： 需要计算内积的单词b。
 
+
+```python
+def get_vocab_path()
+```
+获取本地词表文件的路径信息。
+
+
+```python
+def get_tokenizer(*args, **kwargs)
+```
+获取当前模型的tokenizer，返回一个JiebaTokenizer的实例，当前只支持中文embedding模型。
+
+**参数**
+* `*args`： 额外传递的列表形式的参数。
+* `**kwargs`： 额外传递的字典形式的参数。
+
+关于额外参数的详情，可查看[paddlenlp.data.tokenizer.JiebaTokenizer](https://github.com/PaddlePaddle/models/blob/develop/PaddleNLP/paddlenlp/data/tokenizer.py)
+
+
 更多api详情和用法可参考[paddlenlp.embeddings](https://github.com/PaddlePaddle/models/tree/release/2.0-beta/PaddleNLP/paddlenlp/embeddings)
 
 ## 代码示例
@@ -125,3 +144,6 @@ paddlehub >= 2.0.0
 
   初始发布
 
+* 1.0.1
+
+  支持基于embedding的文本分类和序列标注finetune任务

modules/text/embedding/glove_twitter_target_word-word_dim200_en/module.py

@@ -12,44 +12,24 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from typing import List
 from paddlenlp.embeddings import TokenEmbedding
-from paddlehub.module.module import moduleinfo, serving
+from paddlehub.module.module import moduleinfo
+from paddlehub.module.nlp_module import EmbeddingModule
 
 
 @moduleinfo(
     name="glove_twitter_target_word-word_dim200_en",
-    version="1.0.0",
+    version="1.0.1",
     summary="",
     author="paddlepaddle",
     author_email="",
-    type="nlp/semantic_model")
+    type="nlp/semantic_model",
+    meta=EmbeddingModule)
 class Embedding(TokenEmbedding):
     """
     Embedding model
     """
-    def __init__(self, *args, **kwargs):
-        super(Embedding, self).__init__(embedding_name="glove.twitter.target.word-word.dim200.en", *args, **kwargs)
-
-    @serving
-    def calc_similarity(self, data: List[List[str]]):
-        """
-        Calculate similarities of giving word pairs.
-        """
-        results = []
-        for word_pair in data:
-            if len(word_pair) != 2:
-                raise RuntimeError(
-                    f'The input must have two words, but got {len(word_pair)}. Please check your inputs.')
-            if not isinstance(word_pair[0], str) or not isinstance(word_pair[1], str):
-                raise RuntimeError(
-                    f'The types of text pair must be (str, str), but got'
-                    f' ({type(word_pair[0]).__name__}, {type(word_pair[1]).__name__}). Please check your inputs.')
+    embedding_name = "glove.twitter.target.word-word.dim200.en"
 
-            for word in word_pair:
-                if self.get_idx_from_word(word) == \
-                        self.get_idx_from_word(self.vocab.unk_token):
-                    raise RuntimeError(
-                        f'Word "{word}" is not in vocab. Please check your inputs.')
-            results.append(str(self.cosine_sim(*word_pair)))
-        return results
+    def __init__(self, *args, **kwargs):
+        super(Embedding, self).__init__(embedding_name=self.embedding_name, *args, **kwargs)

modules/text/embedding/glove_twitter_target_word-word_dim25_en/README.md

@@ -56,6 +56,25 @@ def dot(
 * `word_a`： 需要计算内积的单词a。
 * `word_b`： 需要计算内积的单词b。
 
+
+```python
+def get_vocab_path()
+```
+获取本地词表文件的路径信息。
+
+
+```python
+def get_tokenizer(*args, **kwargs)
+```
+获取当前模型的tokenizer，返回一个JiebaTokenizer的实例，当前只支持中文embedding模型。
+
+**参数**
+* `*args`： 额外传递的列表形式的参数。
+* `**kwargs`： 额外传递的字典形式的参数。
+
+关于额外参数的详情，可查看[paddlenlp.data.tokenizer.JiebaTokenizer](https://github.com/PaddlePaddle/models/blob/develop/PaddleNLP/paddlenlp/data/tokenizer.py)
+
+
 更多api详情和用法可参考[paddlenlp.embeddings](https://github.com/PaddlePaddle/models/tree/release/2.0-beta/PaddleNLP/paddlenlp/embeddings)
 
 ## 代码示例
@@ -125,3 +144,6 @@ paddlehub >= 2.0.0
 
   初始发布
 
+* 1.0.1
+
+  支持基于embedding的文本分类和序列标注finetune任务

modules/text/embedding/glove_twitter_target_word-word_dim25_en/module.py

@@ -12,44 +12,24 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from typing import List
 from paddlenlp.embeddings import TokenEmbedding
-from paddlehub.module.module import moduleinfo, serving
+from paddlehub.module.module import moduleinfo
+from paddlehub.module.nlp_module import EmbeddingModule
 
 
 @moduleinfo(
     name="glove_twitter_target_word-word_dim25_en",
-    version="1.0.0",
+    version="1.0.1",
     summary="",
     author="paddlepaddle",
     author_email="",
-    type="nlp/semantic_model")
+    type="nlp/semantic_model",
+    meta=EmbeddingModule)
 class Embedding(TokenEmbedding):
     """
     Embedding model
     """
-    def __init__(self, *args, **kwargs):
-        super(Embedding, self).__init__(embedding_name="glove.twitter.target.word-word.dim25.en", *args, **kwargs)
-
-    @serving
-    def calc_similarity(self, data: List[List[str]]):
-        """
-        Calculate similarities of giving word pairs.
-        """
-        results = []
-        for word_pair in data:
-            if len(word_pair) != 2:
-                raise RuntimeError(
-                    f'The input must have two words, but got {len(word_pair)}. Please check your inputs.')
-            if not isinstance(word_pair[0], str) or not isinstance(word_pair[1], str):
-                raise RuntimeError(
-                    f'The types of text pair must be (str, str), but got'
-                    f' ({type(word_pair[0]).__name__}, {type(word_pair[1]).__name__}). Please check your inputs.')
+    embedding_name = "glove.twitter.target.word-word.dim25.en"
 
-            for word in word_pair:
-                if self.get_idx_from_word(word) == \
-                        self.get_idx_from_word(self.vocab.unk_token):
-                    raise RuntimeError(
-                        f'Word "{word}" is not in vocab. Please check your inputs.')
-            results.append(str(self.cosine_sim(*word_pair)))
-        return results
+    def __init__(self, *args, **kwargs):
+        super(Embedding, self).__init__(embedding_name=self.embedding_name, *args, **kwargs)

modules/text/embedding/glove_twitter_target_word-word_dim50_en/README.md

@@ -56,6 +56,25 @@ def dot(
 * `word_a`： 需要计算内积的单词a。
 * `word_b`： 需要计算内积的单词b。
 
+
+```python
+def get_vocab_path()
+```
+获取本地词表文件的路径信息。
+
+
+```python
+def get_tokenizer(*args, **kwargs)
+```
+获取当前模型的tokenizer，返回一个JiebaTokenizer的实例，当前只支持中文embedding模型。
+
+**参数**
+* `*args`： 额外传递的列表形式的参数。
+* `**kwargs`： 额外传递的字典形式的参数。
+
+关于额外参数的详情，可查看[paddlenlp.data.tokenizer.JiebaTokenizer](https://github.com/PaddlePaddle/models/blob/develop/PaddleNLP/paddlenlp/data/tokenizer.py)
+
+
 更多api详情和用法可参考[paddlenlp.embeddings](https://github.com/PaddlePaddle/models/tree/release/2.0-beta/PaddleNLP/paddlenlp/embeddings)
 
 ## 代码示例
@@ -125,3 +144,6 @@ paddlehub >= 2.0.0
 
   初始发布
 
+* 1.0.1
+
+  支持基于embedding的文本分类和序列标注finetune任务

modules/text/embedding/glove_twitter_target_word-word_dim50_en/module.py

@@ -12,44 +12,24 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from typing import List
 from paddlenlp.embeddings import TokenEmbedding
-from paddlehub.module.module import moduleinfo, serving
+from paddlehub.module.module import moduleinfo
+from paddlehub.module.nlp_module import EmbeddingModule
 
 
 @moduleinfo(
     name="glove_twitter_target_word-word_dim50_en",
-    version="1.0.0",
+    version="1.0.1",
     summary="",
     author="paddlepaddle",
     author_email="",
-    type="nlp/semantic_model")
+    type="nlp/semantic_model",
+    meta=EmbeddingModule)
 class Embedding(TokenEmbedding):
     """
     Embedding model
     """
-    def __init__(self, *args, **kwargs):
-        super(Embedding, self).__init__(embedding_name="glove.twitter.target.word-word.dim50.en", *args, **kwargs)
-
-    @serving
-    def calc_similarity(self, data: List[List[str]]):
-        """
-        Calculate similarities of giving word pairs.
-        """
-        results = []
-        for word_pair in data:
-            if len(word_pair) != 2:
-                raise RuntimeError(
-                    f'The input must have two words, but got {len(word_pair)}. Please check your inputs.')
-            if not isinstance(word_pair[0], str) or not isinstance(word_pair[1], str):
-                raise RuntimeError(
-                    f'The types of text pair must be (str, str), but got'
-                    f' ({type(word_pair[0]).__name__}, {type(word_pair[1]).__name__}). Please check your inputs.')
+    embedding_name = "glove.twitter.target.word-word.dim50.en"
 
-            for word in word_pair:
-                if self.get_idx_from_word(word) == \
-                        self.get_idx_from_word(self.vocab.unk_token):
-                    raise RuntimeError(
-                        f'Word "{word}" is not in vocab. Please check your inputs.')
-            results.append(str(self.cosine_sim(*word_pair)))
-        return results
+    def __init__(self, *args, **kwargs):
+        super(Embedding, self).__init__(embedding_name=self.embedding_name, *args, **kwargs)

modules/text/embedding/glove_wiki2014-gigaword_target_word-word_dim100_en/README.md

@@ -56,6 +56,25 @@ def dot(
 * `word_a`： 需要计算内积的单词a。
 * `word_b`： 需要计算内积的单词b。
 
+
+```python
+def get_vocab_path()
+```
+获取本地词表文件的路径信息。
+
+
+```python
+def get_tokenizer(*args, **kwargs)
+```
+获取当前模型的tokenizer，返回一个JiebaTokenizer的实例，当前只支持中文embedding模型。
+
+**参数**
+* `*args`： 额外传递的列表形式的参数。
+* `**kwargs`： 额外传递的字典形式的参数。
+
+关于额外参数的详情，可查看[paddlenlp.data.tokenizer.JiebaTokenizer](https://github.com/PaddlePaddle/models/blob/develop/PaddleNLP/paddlenlp/data/tokenizer.py)
+
+
 更多api详情和用法可参考[paddlenlp.embeddings](https://github.com/PaddlePaddle/models/tree/release/2.0-beta/PaddleNLP/paddlenlp/embeddings)
 
 ## 代码示例
@@ -125,3 +144,6 @@ paddlehub >= 2.0.0
 
   初始发布
 
+* 1.0.1
+
+  支持基于embedding的文本分类和序列标注finetune任务

modules/text/embedding/glove_wiki2014-gigaword_target_word-word_dim100_en/module.py

@@ -12,44 +12,24 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from typing import List
 from paddlenlp.embeddings import TokenEmbedding
-from paddlehub.module.module import moduleinfo, serving
+from paddlehub.module.module import moduleinfo
+from paddlehub.module.nlp_module import EmbeddingModule
 
 
 @moduleinfo(
     name="glove_wiki2014-gigaword_target_word-word_dim100_en",
-    version="1.0.0",
+    version="1.0.1",
     summary="",
     author="paddlepaddle",
     author_email="",
-    type="nlp/semantic_model")
+    type="nlp/semantic_model",
+    meta=EmbeddingModule)
 class Embedding(TokenEmbedding):
     """
     Embedding model
     """
-    def __init__(self, *args, **kwargs):
-        super(Embedding, self).__init__(embedding_name="glove.wiki2014-gigaword.target.word-word.dim100.en", *args, **kwargs)
-
-    @serving
-    def calc_similarity(self, data: List[List[str]]):
-        """
-        Calculate similarities of giving word pairs.
-        """
-        results = []
-        for word_pair in data:
-            if len(word_pair) != 2:
-                raise RuntimeError(
-                    f'The input must have two words, but got {len(word_pair)}. Please check your inputs.')
-            if not isinstance(word_pair[0], str) or not isinstance(word_pair[1], str):
-                raise RuntimeError(
-                    f'The types of text pair must be (str, str), but got'
-                    f' ({type(word_pair[0]).__name__}, {type(word_pair[1]).__name__}). Please check your inputs.')
+    embedding_name = "glove.wiki2014-gigaword.target.word-word.dim100.en"
 
-            for word in word_pair:
-                if self.get_idx_from_word(word) == \
-                        self.get_idx_from_word(self.vocab.unk_token):
-                    raise RuntimeError(
-                        f'Word "{word}" is not in vocab. Please check your inputs.')
-            results.append(str(self.cosine_sim(*word_pair)))
-        return results
+    def __init__(self, *args, **kwargs):
+        super(Embedding, self).__init__(embedding_name=self.embedding_name, *args, **kwargs)

modules/text/embedding/glove_wiki2014-gigaword_target_word-word_dim200_en/README.md

@@ -56,6 +56,25 @@ def dot(
 * `word_a`： 需要计算内积的单词a。
 * `word_b`： 需要计算内积的单词b。
 
+
+```python
+def get_vocab_path()
+```
+获取本地词表文件的路径信息。
+
+
+```python
+def get_tokenizer(*args, **kwargs)
+```
+获取当前模型的tokenizer，返回一个JiebaTokenizer的实例，当前只支持中文embedding模型。
+
+**参数**
+* `*args`： 额外传递的列表形式的参数。
+* `**kwargs`： 额外传递的字典形式的参数。
+
+关于额外参数的详情，可查看[paddlenlp.data.tokenizer.JiebaTokenizer](https://github.com/PaddlePaddle/models/blob/develop/PaddleNLP/paddlenlp/data/tokenizer.py)
+
+
 更多api详情和用法可参考[paddlenlp.embeddings](https://github.com/PaddlePaddle/models/tree/release/2.0-beta/PaddleNLP/paddlenlp/embeddings)
 
 ## 代码示例
@@ -125,3 +144,6 @@ paddlehub >= 2.0.0
 
   初始发布
 
+* 1.0.1
+
+  支持基于embedding的文本分类和序列标注finetune任务

modules/text/embedding/glove_wiki2014-gigaword_target_word-word_dim200_en/module.py

@@ -12,44 +12,24 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from typing import List
 from paddlenlp.embeddings import TokenEmbedding
-from paddlehub.module.module import moduleinfo, serving
+from paddlehub.module.module import moduleinfo
+from paddlehub.module.nlp_module import EmbeddingModule
 
 
 @moduleinfo(
     name="glove_wiki2014-gigaword_target_word-word_dim200_en",
-    version="1.0.0",
+    version="1.0.1",
     summary="",
     author="paddlepaddle",
     author_email="",
-    type="nlp/semantic_model")
+    type="nlp/semantic_model",
+    meta=EmbeddingModule)
 class Embedding(TokenEmbedding):
     """
     Embedding model
     """
-    def __init__(self, *args, **kwargs):
-        super(Embedding, self).__init__(embedding_name="glove.wiki2014-gigaword.target.word-word.dim200.en", *args, **kwargs)
-
-    @serving
-    def calc_similarity(self, data: List[List[str]]):
-        """
-        Calculate similarities of giving word pairs.
-        """
-        results = []
-        for word_pair in data:
-            if len(word_pair) != 2:
-                raise RuntimeError(
-                    f'The input must have two words, but got {len(word_pair)}. Please check your inputs.')
-            if not isinstance(word_pair[0], str) or not isinstance(word_pair[1], str):
-                raise RuntimeError(
-                    f'The types of text pair must be (str, str), but got'
-                    f' ({type(word_pair[0]).__name__}, {type(word_pair[1]).__name__}). Please check your inputs.')
+    embedding_name = "glove.wiki2014-gigaword.target.word-word.dim200.en"
 
-            for word in word_pair:
-                if self.get_idx_from_word(word) == \
-                        self.get_idx_from_word(self.vocab.unk_token):
-                    raise RuntimeError(
-                        f'Word "{word}" is not in vocab. Please check your inputs.')
-            results.append(str(self.cosine_sim(*word_pair)))
-        return results
+    def __init__(self, *args, **kwargs):
+        super(Embedding, self).__init__(embedding_name=self.embedding_name, *args, **kwargs)

modules/text/embedding/glove_wiki2014-gigaword_target_word-word_dim300_en/README.md

@@ -56,6 +56,25 @@ def dot(
 * `word_a`： 需要计算内积的单词a。
 * `word_b`： 需要计算内积的单词b。
 
+
+```python
+def get_vocab_path()
+```
+获取本地词表文件的路径信息。
+
+
+```python
+def get_tokenizer(*args, **kwargs)
+```
+获取当前模型的tokenizer，返回一个JiebaTokenizer的实例，当前只支持中文embedding模型。
+
+**参数**
+* `*args`： 额外传递的列表形式的参数。
+* `**kwargs`： 额外传递的字典形式的参数。
+
+关于额外参数的详情，可查看[paddlenlp.data.tokenizer.JiebaTokenizer](https://github.com/PaddlePaddle/models/blob/develop/PaddleNLP/paddlenlp/data/tokenizer.py)
+
+
 更多api详情和用法可参考[paddlenlp.embeddings](https://github.com/PaddlePaddle/models/tree/release/2.0-beta/PaddleNLP/paddlenlp/embeddings)
 
 ## 代码示例
@@ -125,3 +144,6 @@ paddlehub >= 2.0.0
 
   初始发布
 
+* 1.0.1
+
+  支持基于embedding的文本分类和序列标注finetune任务

modules/text/embedding/glove_wiki2014-gigaword_target_word-word_dim300_en/module.py

@@ -12,44 +12,24 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from typing import List
 from paddlenlp.embeddings import TokenEmbedding
-from paddlehub.module.module import moduleinfo, serving
+from paddlehub.module.module import moduleinfo
+from paddlehub.module.nlp_module import EmbeddingModule
 
 
 @moduleinfo(
     name="glove_wiki2014-gigaword_target_word-word_dim300_en",
-    version="1.0.0",
+    version="1.0.1",
     summary="",
     author="paddlepaddle",
     author_email="",
-    type="nlp/semantic_model")
+    type="nlp/semantic_model",
+    meta=EmbeddingModule)
 class Embedding(TokenEmbedding):
     """
     Embedding model
     """
-    def __init__(self, *args, **kwargs):
-        super(Embedding, self).__init__(embedding_name="glove.wiki2014-gigaword.target.word-word.dim300.en", *args, **kwargs)
-
-    @serving
-    def calc_similarity(self, data: List[List[str]]):
-        """
-        Calculate similarities of giving word pairs.
-        """
-        results = []
-        for word_pair in data:
-            if len(word_pair) != 2:
-                raise RuntimeError(
-                    f'The input must have two words, but got {len(word_pair)}. Please check your inputs.')
-            if not isinstance(word_pair[0], str) or not isinstance(word_pair[1], str):
-                raise RuntimeError(
-                    f'The types of text pair must be (str, str), but got'
-                    f' ({type(word_pair[0]).__name__}, {type(word_pair[1]).__name__}). Please check your inputs.')
+    embedding_name = "glove.wiki2014-gigaword.target.word-word.dim300.en"
 
-            for word in word_pair:
-                if self.get_idx_from_word(word) == \
-                        self.get_idx_from_word(self.vocab.unk_token):
-                    raise RuntimeError(
-                        f'Word "{word}" is not in vocab. Please check your inputs.')
-            results.append(str(self.cosine_sim(*word_pair)))
-        return results
+    def __init__(self, *args, **kwargs):
+        super(Embedding, self).__init__(embedding_name=self.embedding_name, *args, **kwargs)

modules/text/embedding/glove_wiki2014-gigaword_target_word-word_dim50_en/README.md

@@ -56,6 +56,25 @@ def dot(
 * `word_a`： 需要计算内积的单词a。
 * `word_b`： 需要计算内积的单词b。
 
+
+```python
+def get_vocab_path()
+```
+获取本地词表文件的路径信息。
+
+
+```python
+def get_tokenizer(*args, **kwargs)
+```
+获取当前模型的tokenizer，返回一个JiebaTokenizer的实例，当前只支持中文embedding模型。
+
+**参数**
+* `*args`： 额外传递的列表形式的参数。
+* `**kwargs`： 额外传递的字典形式的参数。
+
+关于额外参数的详情，可查看[paddlenlp.data.tokenizer.JiebaTokenizer](https://github.com/PaddlePaddle/models/blob/develop/PaddleNLP/paddlenlp/data/tokenizer.py)
+
+
 更多api详情和用法可参考[paddlenlp.embeddings](https://github.com/PaddlePaddle/models/tree/release/2.0-beta/PaddleNLP/paddlenlp/embeddings)
 
 ## 代码示例
@@ -125,3 +144,6 @@ paddlehub >= 2.0.0
 
   初始发布
 
+* 1.0.1
+
+  支持基于embedding的文本分类和序列标注finetune任务

modules/text/embedding/glove_wiki2014-gigaword_target_word-word_dim50_en/module.py

@@ -12,44 +12,24 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from typing import List
 from paddlenlp.embeddings import TokenEmbedding
-from paddlehub.module.module import moduleinfo, serving
+from paddlehub.module.module import moduleinfo
+from paddlehub.module.nlp_module import EmbeddingModule
 
 
 @moduleinfo(
     name="glove_wiki2014-gigaword_target_word-word_dim50_en",
-    version="1.0.0",
+    version="1.0.1",
     summary="",
     author="paddlepaddle",
     author_email="",
-    type="nlp/semantic_model")
+    type="nlp/semantic_model",
+    meta=EmbeddingModule)
 class Embedding(TokenEmbedding):
     """
     Embedding model
     """
-    def __init__(self, *args, **kwargs):
-        super(Embedding, self).__init__(embedding_name="glove.wiki2014-gigaword.target.word-word.dim50.en", *args, **kwargs)
-
-    @serving
-    def calc_similarity(self, data: List[List[str]]):
-        """
-        Calculate similarities of giving word pairs.
-        """
-        results = []
-        for word_pair in data:
-            if len(word_pair) != 2:
-                raise RuntimeError(
-                    f'The input must have two words, but got {len(word_pair)}. Please check your inputs.')
-            if not isinstance(word_pair[0], str) or not isinstance(word_pair[1], str):
-                raise RuntimeError(
-                    f'The types of text pair must be (str, str), but got'
-                    f' ({type(word_pair[0]).__name__}, {type(word_pair[1]).__name__}). Please check your inputs.')
+    embedding_name = "glove.wiki2014-gigaword.target.word-word.dim50.en"
 
-            for word in word_pair:
-                if self.get_idx_from_word(word) == \
-                        self.get_idx_from_word(self.vocab.unk_token):
-                    raise RuntimeError(
-                        f'Word "{word}" is not in vocab. Please check your inputs.')
-            results.append(str(self.cosine_sim(*word_pair)))
-        return results
+    def __init__(self, *args, **kwargs):
+        super(Embedding, self).__init__(embedding_name=self.embedding_name, *args, **kwargs)

modules/text/embedding/w2v_baidu_encyclopedia_context_word-character_char1-1_dim300/README.md

@@ -56,6 +56,25 @@ def dot(
 * `word_a`： 需要计算内积的单词a。
 * `word_b`： 需要计算内积的单词b。
 
+
+```python
+def get_vocab_path()
+```
+获取本地词表文件的路径信息。
+
+
+```python
+def get_tokenizer(*args, **kwargs)
+```
+获取当前模型的tokenizer，返回一个JiebaTokenizer的实例，当前只支持中文embedding模型。
+
+**参数**
+* `*args`： 额外传递的列表形式的参数。
+* `**kwargs`： 额外传递的字典形式的参数。
+
+关于额外参数的详情，可查看[paddlenlp.data.tokenizer.JiebaTokenizer](https://github.com/PaddlePaddle/models/blob/develop/PaddleNLP/paddlenlp/data/tokenizer.py)
+
+
 更多api详情和用法可参考[paddlenlp.embeddings](https://github.com/PaddlePaddle/models/tree/release/2.0-beta/PaddleNLP/paddlenlp/embeddings)
 
 ## 代码示例
@@ -125,3 +144,6 @@ paddlehub >= 2.0.0
 
   初始发布
 
+* 1.0.1
+
+  支持基于embedding的文本分类和序列标注finetune任务

modules/text/embedding/w2v_baidu_encyclopedia_context_word-character_char1-1_dim300/module.py

@@ -12,44 +12,24 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from typing import List
 from paddlenlp.embeddings import TokenEmbedding
-from paddlehub.module.module import moduleinfo, serving
+from paddlehub.module.module import moduleinfo
+from paddlehub.module.nlp_module import EmbeddingModule
 
 
 @moduleinfo(
     name="w2v_baidu_encyclopedia_context_word-character_char1-1_dim300",
-    version="1.0.0",
+    version="1.0.1",
     summary="",
     author="paddlepaddle",
     author_email="",
-    type="nlp/semantic_model")
+    type="nlp/semantic_model",
+    meta=EmbeddingModule)
 class Embedding(TokenEmbedding):
     """
     Embedding model
     """
-    def __init__(self, *args, **kwargs):
-        super(Embedding, self).__init__(embedding_name="w2v.baidu_encyclopedia.context.word-character.char1-1.dim300", *args, **kwargs)
-
-    @serving
-    def calc_similarity(self, data: List[List[str]]):
-        """
-        Calculate similarities of giving word pairs.
-        """
-        results = []
-        for word_pair in data:
-            if len(word_pair) != 2:
-                raise RuntimeError(
-                    f'The input must have two words, but got {len(word_pair)}. Please check your inputs.')
-            if not isinstance(word_pair[0], str) or not isinstance(word_pair[1], str):
-                raise RuntimeError(
-                    f'The types of text pair must be (str, str), but got'
-                    f' ({type(word_pair[0]).__name__}, {type(word_pair[1]).__name__}). Please check your inputs.')
+    embedding_name = "w2v.baidu_encyclopedia.context.word-character.char1-1.dim300"
 
-            for word in word_pair:
-                if self.get_idx_from_word(word) == \
-                        self.get_idx_from_word(self.vocab.unk_token):
-                    raise RuntimeError(
-                        f'Word "{word}" is not in vocab. Please check your inputs.')
-            results.append(str(self.cosine_sim(*word_pair)))
-        return results
+    def __init__(self, *args, **kwargs):
+        super(Embedding, self).__init__(embedding_name=self.embedding_name, *args, **kwargs)

modules/text/embedding/w2v_baidu_encyclopedia_context_word-character_char1-2_dim300/README.md

@@ -56,6 +56,25 @@ def dot(
 * `word_a`： 需要计算内积的单词a。
 * `word_b`： 需要计算内积的单词b。
 
+
+```python
+def get_vocab_path()
+```
+获取本地词表文件的路径信息。
+
+
+```python
+def get_tokenizer(*args, **kwargs)
+```
+获取当前模型的tokenizer，返回一个JiebaTokenizer的实例，当前只支持中文embedding模型。
+
+**参数**
+* `*args`： 额外传递的列表形式的参数。
+* `**kwargs`： 额外传递的字典形式的参数。
+
+关于额外参数的详情，可查看[paddlenlp.data.tokenizer.JiebaTokenizer](https://github.com/PaddlePaddle/models/blob/develop/PaddleNLP/paddlenlp/data/tokenizer.py)
+
+
 更多api详情和用法可参考[paddlenlp.embeddings](https://github.com/PaddlePaddle/models/tree/release/2.0-beta/PaddleNLP/paddlenlp/embeddings)
 
 ## 代码示例
@@ -125,3 +144,6 @@ paddlehub >= 2.0.0
 
   初始发布
 
+* 1.0.1
+
+  支持基于embedding的文本分类和序列标注finetune任务

modules/text/embedding/w2v_baidu_encyclopedia_context_word-character_char1-2_dim300/module.py

@@ -12,44 +12,24 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from typing import List
 from paddlenlp.embeddings import TokenEmbedding
-from paddlehub.module.module import moduleinfo, serving
+from paddlehub.module.module import moduleinfo
+from paddlehub.module.nlp_module import EmbeddingModule
 
 
 @moduleinfo(
     name="w2v_baidu_encyclopedia_context_word-character_char1-2_dim300",
-    version="1.0.0",
+    version="1.0.1",
     summary="",
     author="paddlepaddle",
     author_email="",
-    type="nlp/semantic_model")
+    type="nlp/semantic_model",
+    meta=EmbeddingModule)
 class Embedding(TokenEmbedding):
     """
     Embedding model
     """
-    def __init__(self, *args, **kwargs):
-        super(Embedding, self).__init__(embedding_name="w2v.baidu_encyclopedia.context.word-character.char1-2.dim300", *args, **kwargs)
-
-    @serving
-    def calc_similarity(self, data: List[List[str]]):
-        """
-        Calculate similarities of giving word pairs.
-        """
-        results = []
-        for word_pair in data:
-            if len(word_pair) != 2:
-                raise RuntimeError(
-                    f'The input must have two words, but got {len(word_pair)}. Please check your inputs.')
-            if not isinstance(word_pair[0], str) or not isinstance(word_pair[1], str):
-                raise RuntimeError(
-                    f'The types of text pair must be (str, str), but got'
-                    f' ({type(word_pair[0]).__name__}, {type(word_pair[1]).__name__}). Please check your inputs.')
+    embedding_name = "w2v.baidu_encyclopedia.context.word-character.char1-2.dim300"
 
-            for word in word_pair:
-                if self.get_idx_from_word(word) == \
-                        self.get_idx_from_word(self.vocab.unk_token):
-                    raise RuntimeError(
-                        f'Word "{word}" is not in vocab. Please check your inputs.')
-            results.append(str(self.cosine_sim(*word_pair)))
-        return results
+    def __init__(self, *args, **kwargs):
+        super(Embedding, self).__init__(embedding_name=self.embedding_name, *args, **kwargs)

modules/text/embedding/w2v_baidu_encyclopedia_context_word-character_char1-4_dim300/README.md

@@ -56,6 +56,25 @@ def dot(
 * `word_a`： 需要计算内积的单词a。
 * `word_b`： 需要计算内积的单词b。
 
+
+```python
+def get_vocab_path()
+```
+获取本地词表文件的路径信息。
+
+
+```python
+def get_tokenizer(*args, **kwargs)
+```
+获取当前模型的tokenizer，返回一个JiebaTokenizer的实例，当前只支持中文embedding模型。
+
+**参数**
+* `*args`： 额外传递的列表形式的参数。
+* `**kwargs`： 额外传递的字典形式的参数。
+
+关于额外参数的详情，可查看[paddlenlp.data.tokenizer.JiebaTokenizer](https://github.com/PaddlePaddle/models/blob/develop/PaddleNLP/paddlenlp/data/tokenizer.py)
+
+
 更多api详情和用法可参考[paddlenlp.embeddings](https://github.com/PaddlePaddle/models/tree/release/2.0-beta/PaddleNLP/paddlenlp/embeddings)
 
 ## 代码示例
@@ -125,3 +144,6 @@ paddlehub >= 2.0.0
 
   初始发布
 
+* 1.0.1
+
+  支持基于embedding的文本分类和序列标注finetune任务

modules/text/embedding/w2v_baidu_encyclopedia_context_word-character_char1-4_dim300/module.py

@@ -12,44 +12,24 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from typing import List
 from paddlenlp.embeddings import TokenEmbedding
-from paddlehub.module.module import moduleinfo, serving
+from paddlehub.module.module import moduleinfo
+from paddlehub.module.nlp_module import EmbeddingModule
 
 
 @moduleinfo(
     name="w2v_baidu_encyclopedia_context_word-character_char1-4_dim300",
-    version="1.0.0",
+    version="1.0.1",
     summary="",
     author="paddlepaddle",
     author_email="",
-    type="nlp/semantic_model")
+    type="nlp/semantic_model",
+    meta=EmbeddingModule)
 class Embedding(TokenEmbedding):
     """
     Embedding model
     """
-    def __init__(self, *args, **kwargs):
-        super(Embedding, self).__init__(embedding_name="w2v.baidu_encyclopedia.context.word-character.char1-4.dim300", *args, **kwargs)
-
-    @serving
-    def calc_similarity(self, data: List[List[str]]):
-        """
-        Calculate similarities of giving word pairs.
-        """
-        results = []
-        for word_pair in data:
-            if len(word_pair) != 2:
-                raise RuntimeError(
-                    f'The input must have two words, but got {len(word_pair)}. Please check your inputs.')
-            if not isinstance(word_pair[0], str) or not isinstance(word_pair[1], str):
-                raise RuntimeError(
-                    f'The types of text pair must be (str, str), but got'
-                    f' ({type(word_pair[0]).__name__}, {type(word_pair[1]).__name__}). Please check your inputs.')
+    embedding_name = "w2v.baidu_encyclopedia.context.word-character.char1-4.dim300"
 
-            for word in word_pair:
-                if self.get_idx_from_word(word) == \
-                        self.get_idx_from_word(self.vocab.unk_token):
-                    raise RuntimeError(
-                        f'Word "{word}" is not in vocab. Please check your inputs.')
-            results.append(str(self.cosine_sim(*word_pair)))
-        return results
+    def __init__(self, *args, **kwargs):
+        super(Embedding, self).__init__(embedding_name=self.embedding_name, *args, **kwargs)

modules/text/embedding/w2v_baidu_encyclopedia_context_word-ngram_1-2_dim300/README.md

@@ -56,6 +56,25 @@ def dot(
 * `word_a`： 需要计算内积的单词a。
 * `word_b`： 需要计算内积的单词b。
 
+
+```python
+def get_vocab_path()
+```
+获取本地词表文件的路径信息。
+
+
+```python
+def get_tokenizer(*args, **kwargs)
+```
+获取当前模型的tokenizer，返回一个JiebaTokenizer的实例，当前只支持中文embedding模型。
+
+**参数**
+* `*args`： 额外传递的列表形式的参数。
+* `**kwargs`： 额外传递的字典形式的参数。
+
+关于额外参数的详情，可查看[paddlenlp.data.tokenizer.JiebaTokenizer](https://github.com/PaddlePaddle/models/blob/develop/PaddleNLP/paddlenlp/data/tokenizer.py)
+
+
 更多api详情和用法可参考[paddlenlp.embeddings](https://github.com/PaddlePaddle/models/tree/release/2.0-beta/PaddleNLP/paddlenlp/embeddings)
 
 ## 代码示例
@@ -125,3 +144,6 @@ paddlehub >= 2.0.0
 
   初始发布
 
+* 1.0.1
+
+  支持基于embedding的文本分类和序列标注finetune任务

modules/text/embedding/w2v_baidu_encyclopedia_context_word-ngram_1-2_dim300/module.py

@@ -12,44 +12,24 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from typing import List
 from paddlenlp.embeddings import TokenEmbedding
-from paddlehub.module.module import moduleinfo, serving
+from paddlehub.module.module import moduleinfo
+from paddlehub.module.nlp_module import EmbeddingModule
 
 
 @moduleinfo(
     name="w2v_baidu_encyclopedia_context_word-ngram_1-2_dim300",
-    version="1.0.0",
+    version="1.0.1",
     summary="",
     author="paddlepaddle",
     author_email="",
-    type="nlp/semantic_model")
+    type="nlp/semantic_model",
+    meta=EmbeddingModule)
 class Embedding(TokenEmbedding):
     """
     Embedding model
     """
-    def __init__(self, *args, **kwargs):
-        super(Embedding, self).__init__(embedding_name="w2v.baidu_encyclopedia.context.word-ngram.1-2.dim300", *args, **kwargs)
-
-    @serving
-    def calc_similarity(self, data: List[List[str]]):
-        """
-        Calculate similarities of giving word pairs.
-        """
-        results = []
-        for word_pair in data:
-            if len(word_pair) != 2:
-                raise RuntimeError(
-                    f'The input must have two words, but got {len(word_pair)}. Please check your inputs.')
-            if not isinstance(word_pair[0], str) or not isinstance(word_pair[1], str):
-                raise RuntimeError(
-                    f'The types of text pair must be (str, str), but got'
-                    f' ({type(word_pair[0]).__name__}, {type(word_pair[1]).__name__}). Please check your inputs.')
+    embedding_name = "w2v.baidu_encyclopedia.context.word-ngram.1-2.dim300"
 
-            for word in word_pair:
-                if self.get_idx_from_word(word) == \
-                        self.get_idx_from_word(self.vocab.unk_token):
-                    raise RuntimeError(
-                        f'Word "{word}" is not in vocab. Please check your inputs.')
-            results.append(str(self.cosine_sim(*word_pair)))
-        return results
+    def __init__(self, *args, **kwargs):
+        super(Embedding, self).__init__(embedding_name=self.embedding_name, *args, **kwargs)

modules/text/embedding/w2v_baidu_encyclopedia_context_word-ngram_1-3_dim300/README.md

@@ -56,6 +56,25 @@ def dot(
 * `word_a`： 需要计算内积的单词a。
 * `word_b`： 需要计算内积的单词b。
 
+
+```python
+def get_vocab_path()
+```
+获取本地词表文件的路径信息。
+
+
+```python
+def get_tokenizer(*args, **kwargs)
+```
+获取当前模型的tokenizer，返回一个JiebaTokenizer的实例，当前只支持中文embedding模型。
+
+**参数**
+* `*args`： 额外传递的列表形式的参数。
+* `**kwargs`： 额外传递的字典形式的参数。
+
+关于额外参数的详情，可查看[paddlenlp.data.tokenizer.JiebaTokenizer](https://github.com/PaddlePaddle/models/blob/develop/PaddleNLP/paddlenlp/data/tokenizer.py)
+
+
 更多api详情和用法可参考[paddlenlp.embeddings](https://github.com/PaddlePaddle/models/tree/release/2.0-beta/PaddleNLP/paddlenlp/embeddings)
 
 ## 代码示例
@@ -125,3 +144,6 @@ paddlehub >= 2.0.0
 
   初始发布
 
+* 1.0.1
+
+  支持基于embedding的文本分类和序列标注finetune任务

modules/text/embedding/w2v_baidu_encyclopedia_context_word-ngram_1-3_dim300/module.py

@@ -12,44 +12,24 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from typing import List
 from paddlenlp.embeddings import TokenEmbedding
-from paddlehub.module.module import moduleinfo, serving
+from paddlehub.module.module import moduleinfo
+from paddlehub.module.nlp_module import EmbeddingModule
 
 
 @moduleinfo(
     name="w2v_baidu_encyclopedia_context_word-ngram_1-3_dim300",
-    version="1.0.0",
+    version="1.0.1",
     summary="",
     author="paddlepaddle",
     author_email="",
-    type="nlp/semantic_model")
+    type="nlp/semantic_model",
+    meta=EmbeddingModule)
 class Embedding(TokenEmbedding):
     """
     Embedding model
     """
-    def __init__(self, *args, **kwargs):
-        super(Embedding, self).__init__(embedding_name="w2v.baidu_encyclopedia.context.word-ngram.1-3.dim300", *args, **kwargs)
-
-    @serving
-    def calc_similarity(self, data: List[List[str]]):
-        """
-        Calculate similarities of giving word pairs.
-        """
-        results = []
-        for word_pair in data:
-            if len(word_pair) != 2:
-                raise RuntimeError(
-                    f'The input must have two words, but got {len(word_pair)}. Please check your inputs.')
-            if not isinstance(word_pair[0], str) or not isinstance(word_pair[1], str):
-                raise RuntimeError(
-                    f'The types of text pair must be (str, str), but got'
-                    f' ({type(word_pair[0]).__name__}, {type(word_pair[1]).__name__}). Please check your inputs.')
+    embedding_name = "w2v.baidu_encyclopedia.context.word-ngram.1-3.dim300"
 
-            for word in word_pair:
-                if self.get_idx_from_word(word) == \
-                        self.get_idx_from_word(self.vocab.unk_token):
-                    raise RuntimeError(
-                        f'Word "{word}" is not in vocab. Please check your inputs.')
-            results.append(str(self.cosine_sim(*word_pair)))
-        return results
+    def __init__(self, *args, **kwargs):
+        super(Embedding, self).__init__(embedding_name=self.embedding_name, *args, **kwargs)

modules/text/embedding/w2v_baidu_encyclopedia_context_word-ngram_2-2_dim300/README.md

@@ -56,6 +56,25 @@ def dot(
 * `word_a`： 需要计算内积的单词a。
 * `word_b`： 需要计算内积的单词b。
 
+
+```python
+def get_vocab_path()
+```
+获取本地词表文件的路径信息。
+
+
+```python
+def get_tokenizer(*args, **kwargs)
+```
+获取当前模型的tokenizer，返回一个JiebaTokenizer的实例，当前只支持中文embedding模型。
+
+**参数**
+* `*args`： 额外传递的列表形式的参数。
+* `**kwargs`： 额外传递的字典形式的参数。
+
+关于额外参数的详情，可查看[paddlenlp.data.tokenizer.JiebaTokenizer](https://github.com/PaddlePaddle/models/blob/develop/PaddleNLP/paddlenlp/data/tokenizer.py)
+
+
 更多api详情和用法可参考[paddlenlp.embeddings](https://github.com/PaddlePaddle/models/tree/release/2.0-beta/PaddleNLP/paddlenlp/embeddings)
 
 ## 代码示例
@@ -125,3 +144,6 @@ paddlehub >= 2.0.0
 
   初始发布
 
+* 1.0.1
+
+  支持基于embedding的文本分类和序列标注finetune任务

modules/text/embedding/w2v_baidu_encyclopedia_context_word-ngram_2-2_dim300/module.py

@@ -12,44 +12,24 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from typing import List
 from paddlenlp.embeddings import TokenEmbedding
-from paddlehub.module.module import moduleinfo, serving
+from paddlehub.module.module import moduleinfo
+from paddlehub.module.nlp_module import EmbeddingModule
 
 
 @moduleinfo(
     name="w2v_baidu_encyclopedia_context_word-ngram_2-2_dim300",
-    version="1.0.0",
+    version="1.0.1",
     summary="",
     author="paddlepaddle",
     author_email="",
-    type="nlp/semantic_model")
+    type="nlp/semantic_model",
+    meta=EmbeddingModule)
 class Embedding(TokenEmbedding):
     """
     Embedding model
     """
-    def __init__(self, *args, **kwargs):
-        super(Embedding, self).__init__(embedding_name="w2v.baidu_encyclopedia.context.word-ngram.2-2.dim300", *args, **kwargs)
-
-    @serving
-    def calc_similarity(self, data: List[List[str]]):
-        """
-        Calculate similarities of giving word pairs.
-        """
-        results = []
-        for word_pair in data:
-            if len(word_pair) != 2:
-                raise RuntimeError(
-                    f'The input must have two words, but got {len(word_pair)}. Please check your inputs.')
-            if not isinstance(word_pair[0], str) or not isinstance(word_pair[1], str):
-                raise RuntimeError(
-                    f'The types of text pair must be (str, str), but got'
-                    f' ({type(word_pair[0]).__name__}, {type(word_pair[1]).__name__}). Please check your inputs.')
+    embedding_name = "w2v.baidu_encyclopedia.context.word-ngram.2-2.dim300"
 
-            for word in word_pair:
-                if self.get_idx_from_word(word) == \
-                        self.get_idx_from_word(self.vocab.unk_token):
-                    raise RuntimeError(
-                        f'Word "{word}" is not in vocab. Please check your inputs.')
-            results.append(str(self.cosine_sim(*word_pair)))
-        return results
+    def __init__(self, *args, **kwargs):
+        super(Embedding, self).__init__(embedding_name=self.embedding_name, *args, **kwargs)

modules/text/embedding/w2v_baidu_encyclopedia_context_word-wordLR_dim300/README.md

@@ -56,6 +56,25 @@ def dot(
 * `word_a`： 需要计算内积的单词a。
 * `word_b`： 需要计算内积的单词b。
 
+
+```python
+def get_vocab_path()
+```
+获取本地词表文件的路径信息。
+
+
+```python
+def get_tokenizer(*args, **kwargs)
+```
+获取当前模型的tokenizer，返回一个JiebaTokenizer的实例，当前只支持中文embedding模型。
+
+**参数**
+* `*args`： 额外传递的列表形式的参数。
+* `**kwargs`： 额外传递的字典形式的参数。
+
+关于额外参数的详情，可查看[paddlenlp.data.tokenizer.JiebaTokenizer](https://github.com/PaddlePaddle/models/blob/develop/PaddleNLP/paddlenlp/data/tokenizer.py)
+
+
 更多api详情和用法可参考[paddlenlp.embeddings](https://github.com/PaddlePaddle/models/tree/release/2.0-beta/PaddleNLP/paddlenlp/embeddings)
 
 ## 代码示例
@@ -125,3 +144,6 @@ paddlehub >= 2.0.0
 
   初始发布
 
+* 1.0.1
+
+  支持基于embedding的文本分类和序列标注finetune任务

modules/text/embedding/w2v_baidu_encyclopedia_context_word-wordLR_dim300/module.py

@@ -12,44 +12,24 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from typing import List
 from paddlenlp.embeddings import TokenEmbedding
-from paddlehub.module.module import moduleinfo, serving
+from paddlehub.module.module import moduleinfo
+from paddlehub.module.nlp_module import EmbeddingModule
 
 
 @moduleinfo(
     name="w2v_baidu_encyclopedia_context_word-wordLR_dim300",
-    version="1.0.0",
+    version="1.0.1",
     summary="",
     author="paddlepaddle",
     author_email="",
-    type="nlp/semantic_model")
+    type="nlp/semantic_model",
+    meta=EmbeddingModule)
 class Embedding(TokenEmbedding):
     """
     Embedding model
     """
-    def __init__(self, *args, **kwargs):
-        super(Embedding, self).__init__(embedding_name="w2v.baidu_encyclopedia.context.word-wordLR.dim300", *args, **kwargs)
-
-    @serving
-    def calc_similarity(self, data: List[List[str]]):
-        """
-        Calculate similarities of giving word pairs.
-        """
-        results = []
-        for word_pair in data:
-            if len(word_pair) != 2:
-                raise RuntimeError(
-                    f'The input must have two words, but got {len(word_pair)}. Please check your inputs.')
-            if not isinstance(word_pair[0], str) or not isinstance(word_pair[1], str):
-                raise RuntimeError(
-                    f'The types of text pair must be (str, str), but got'
-                    f' ({type(word_pair[0]).__name__}, {type(word_pair[1]).__name__}). Please check your inputs.')
+    embedding_name = "w2v.baidu_encyclopedia.context.word-wordLR.dim300"
 
-            for word in word_pair:
-                if self.get_idx_from_word(word) == \
-                        self.get_idx_from_word(self.vocab.unk_token):
-                    raise RuntimeError(
-                        f'Word "{word}" is not in vocab. Please check your inputs.')
-            results.append(str(self.cosine_sim(*word_pair)))
-        return results
+    def __init__(self, *args, **kwargs):
+        super(Embedding, self).__init__(embedding_name=self.embedding_name, *args, **kwargs)

modules/text/embedding/w2v_baidu_encyclopedia_context_word-wordPosition_dim300/README.md

@@ -56,6 +56,25 @@ def dot(
 * `word_a`： 需要计算内积的单词a。
 * `word_b`： 需要计算内积的单词b。
 
+
+```python
+def get_vocab_path()
+```
+获取本地词表文件的路径信息。
+
+
+```python
+def get_tokenizer(*args, **kwargs)
+```
+获取当前模型的tokenizer，返回一个JiebaTokenizer的实例，当前只支持中文embedding模型。
+
+**参数**
+* `*args`： 额外传递的列表形式的参数。
+* `**kwargs`： 额外传递的字典形式的参数。
+
+关于额外参数的详情，可查看[paddlenlp.data.tokenizer.JiebaTokenizer](https://github.com/PaddlePaddle/models/blob/develop/PaddleNLP/paddlenlp/data/tokenizer.py)
+
+
 更多api详情和用法可参考[paddlenlp.embeddings](https://github.com/PaddlePaddle/models/tree/release/2.0-beta/PaddleNLP/paddlenlp/embeddings)
 
 ## 代码示例
@@ -125,3 +144,6 @@ paddlehub >= 2.0.0
 
   初始发布
 
+* 1.0.1
+
+  支持基于embedding的文本分类和序列标注finetune任务

modules/text/embedding/w2v_baidu_encyclopedia_context_word-wordPosition_dim300/module.py

@@ -12,44 +12,24 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from typing import List
 from paddlenlp.embeddings import TokenEmbedding
-from paddlehub.module.module import moduleinfo, serving
+from paddlehub.module.module import moduleinfo
+from paddlehub.module.nlp_module import EmbeddingModule
 
 
 @moduleinfo(
     name="w2v_baidu_encyclopedia_context_word-wordPosition_dim300",
-    version="1.0.0",
+    version="1.0.1",
     summary="",
     author="paddlepaddle",
     author_email="",
-    type="nlp/semantic_model")
+    type="nlp/semantic_model",
+    meta=EmbeddingModule)
 class Embedding(TokenEmbedding):
     """
     Embedding model
     """
-    def __init__(self, *args, **kwargs):
-        super(Embedding, self).__init__(embedding_name="w2v.baidu_encyclopedia.context.word-wordPosition.dim300", *args, **kwargs)
-
-    @serving
-    def calc_similarity(self, data: List[List[str]]):
-        """
-        Calculate similarities of giving word pairs.
-        """
-        results = []
-        for word_pair in data:
-            if len(word_pair) != 2:
-                raise RuntimeError(
-                    f'The input must have two words, but got {len(word_pair)}. Please check your inputs.')
-            if not isinstance(word_pair[0], str) or not isinstance(word_pair[1], str):
-                raise RuntimeError(
-                    f'The types of text pair must be (str, str), but got'
-                    f' ({type(word_pair[0]).__name__}, {type(word_pair[1]).__name__}). Please check your inputs.')
+    embedding_name = "w2v.baidu_encyclopedia.context.word-wordPosition.dim300"
 
-            for word in word_pair:
-                if self.get_idx_from_word(word) == \
-                        self.get_idx_from_word(self.vocab.unk_token):
-                    raise RuntimeError(
-                        f'Word "{word}" is not in vocab. Please check your inputs.')
-            results.append(str(self.cosine_sim(*word_pair)))
-        return results
+    def __init__(self, *args, **kwargs):
+        super(Embedding, self).__init__(embedding_name=self.embedding_name, *args, **kwargs)

modules/text/embedding/w2v_baidu_encyclopedia_context_word-word_dim300/README.md

@@ -56,6 +56,25 @@ def dot(
 * `word_a`： 需要计算内积的单词a。
 * `word_b`： 需要计算内积的单词b。
 
+
+```python
+def get_vocab_path()
+```
+获取本地词表文件的路径信息。
+
+
+```python
+def get_tokenizer(*args, **kwargs)
+```
+获取当前模型的tokenizer，返回一个JiebaTokenizer的实例，当前只支持中文embedding模型。
+
+**参数**
+* `*args`： 额外传递的列表形式的参数。
+* `**kwargs`： 额外传递的字典形式的参数。
+
+关于额外参数的详情，可查看[paddlenlp.data.tokenizer.JiebaTokenizer](https://github.com/PaddlePaddle/models/blob/develop/PaddleNLP/paddlenlp/data/tokenizer.py)
+
+
 更多api详情和用法可参考[paddlenlp.embeddings](https://github.com/PaddlePaddle/models/tree/release/2.0-beta/PaddleNLP/paddlenlp/embeddings)
 
 ## 代码示例
@@ -125,3 +144,6 @@ paddlehub >= 2.0.0
 
   初始发布
 
+* 1.0.1
+
+  支持基于embedding的文本分类和序列标注finetune任务

modules/text/embedding/w2v_baidu_encyclopedia_context_word-word_dim300/module.py

@@ -12,44 +12,24 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from typing import List
 from paddlenlp.embeddings import TokenEmbedding
-from paddlehub.module.module import moduleinfo, serving
+from paddlehub.module.module import moduleinfo
+from paddlehub.module.nlp_module import EmbeddingModule
 
 
 @moduleinfo(
     name="w2v_baidu_encyclopedia_context_word-word_dim300",
-    version="1.0.0",
+    version="1.0.1",
     summary="",
     author="paddlepaddle",
     author_email="",
-    type="nlp/semantic_model")
+    type="nlp/semantic_model",
+    meta=EmbeddingModule)
 class Embedding(TokenEmbedding):
     """
     Embedding model
     """
-    def __init__(self, *args, **kwargs):
-        super(Embedding, self).__init__(embedding_name="w2v.baidu_encyclopedia.context.word-word.dim300", *args, **kwargs)
-
-    @serving
-    def calc_similarity(self, data: List[List[str]]):
-        """
-        Calculate similarities of giving word pairs.
-        """
-        results = []
-        for word_pair in data:
-            if len(word_pair) != 2:
-                raise RuntimeError(
-                    f'The input must have two words, but got {len(word_pair)}. Please check your inputs.')
-            if not isinstance(word_pair[0], str) or not isinstance(word_pair[1], str):
-                raise RuntimeError(
-                    f'The types of text pair must be (str, str), but got'
-                    f' ({type(word_pair[0]).__name__}, {type(word_pair[1]).__name__}). Please check your inputs.')
+    embedding_name = "w2v.baidu_encyclopedia.context.word-word.dim300"
 
-            for word in word_pair:
-                if self.get_idx_from_word(word) == \
-                        self.get_idx_from_word(self.vocab.unk_token):
-                    raise RuntimeError(
-                        f'Word "{word}" is not in vocab. Please check your inputs.')
-            results.append(str(self.cosine_sim(*word_pair)))
-        return results
+    def __init__(self, *args, **kwargs):
+        super(Embedding, self).__init__(embedding_name=self.embedding_name, *args, **kwargs)

modules/text/embedding/w2v_baidu_encyclopedia_target_bigram-char_dim300/README.md

@@ -56,6 +56,25 @@ def dot(
 * `word_a`： 需要计算内积的单词a。
 * `word_b`： 需要计算内积的单词b。
 
+
+```python
+def get_vocab_path()
+```
+获取本地词表文件的路径信息。
+
+
+```python
+def get_tokenizer(*args, **kwargs)
+```
+获取当前模型的tokenizer，返回一个JiebaTokenizer的实例，当前只支持中文embedding模型。
+
+**参数**
+* `*args`： 额外传递的列表形式的参数。
+* `**kwargs`： 额外传递的字典形式的参数。
+
+关于额外参数的详情，可查看[paddlenlp.data.tokenizer.JiebaTokenizer](https://github.com/PaddlePaddle/models/blob/develop/PaddleNLP/paddlenlp/data/tokenizer.py)
+
+
 更多api详情和用法可参考[paddlenlp.embeddings](https://github.com/PaddlePaddle/models/tree/release/2.0-beta/PaddleNLP/paddlenlp/embeddings)
 
 ## 代码示例
@@ -125,3 +144,6 @@ paddlehub >= 2.0.0
 
   初始发布
 
+* 1.0.1
+
+  支持基于embedding的文本分类和序列标注finetune任务

modules/text/embedding/w2v_baidu_encyclopedia_target_bigram-char_dim300/module.py

@@ -12,44 +12,24 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from typing import List
 from paddlenlp.embeddings import TokenEmbedding
-from paddlehub.module.module import moduleinfo, serving
+from paddlehub.module.module import moduleinfo
+from paddlehub.module.nlp_module import EmbeddingModule
 
 
 @moduleinfo(
     name="w2v_baidu_encyclopedia_target_bigram-char_dim300",
-    version="1.0.0",
+    version="1.0.1",
     summary="",
     author="paddlepaddle",
     author_email="",
-    type="nlp/semantic_model")
+    type="nlp/semantic_model",
+    meta=EmbeddingModule)
 class Embedding(TokenEmbedding):
     """
     Embedding model
     """
-    def __init__(self, *args, **kwargs):
-        super(Embedding, self).__init__(embedding_name="w2v.baidu_encyclopedia.target.bigram-char.dim300", *args, **kwargs)
-
-    @serving
-    def calc_similarity(self, data: List[List[str]]):
-        """
-        Calculate similarities of giving word pairs.
-        """
-        results = []
-        for word_pair in data:
-            if len(word_pair) != 2:
-                raise RuntimeError(
-                    f'The input must have two words, but got {len(word_pair)}. Please check your inputs.')
-            if not isinstance(word_pair[0], str) or not isinstance(word_pair[1], str):
-                raise RuntimeError(
-                    f'The types of text pair must be (str, str), but got'
-                    f' ({type(word_pair[0]).__name__}, {type(word_pair[1]).__name__}). Please check your inputs.')
+    embedding_name = "w2v.baidu_encyclopedia.target.bigram-char.dim300"
 
-            for word in word_pair:
-                if self.get_idx_from_word(word) == \
-                        self.get_idx_from_word(self.vocab.unk_token):
-                    raise RuntimeError(
-                        f'Word "{word}" is not in vocab. Please check your inputs.')
-            results.append(str(self.cosine_sim(*word_pair)))
-        return results
+    def __init__(self, *args, **kwargs):
+        super(Embedding, self).__init__(embedding_name=self.embedding_name, *args, **kwargs)

modules/text/embedding/w2v_baidu_encyclopedia_target_word-character_char1-1_dim300/README.md

@@ -56,6 +56,25 @@ def dot(
 * `word_a`： 需要计算内积的单词a。
 * `word_b`： 需要计算内积的单词b。
 
+
+```python
+def get_vocab_path()
+```
+获取本地词表文件的路径信息。
+
+
+```python
+def get_tokenizer(*args, **kwargs)
+```
+获取当前模型的tokenizer，返回一个JiebaTokenizer的实例，当前只支持中文embedding模型。
+
+**参数**
+* `*args`： 额外传递的列表形式的参数。
+* `**kwargs`： 额外传递的字典形式的参数。
+
+关于额外参数的详情，可查看[paddlenlp.data.tokenizer.JiebaTokenizer](https://github.com/PaddlePaddle/models/blob/develop/PaddleNLP/paddlenlp/data/tokenizer.py)
+
+
 更多api详情和用法可参考[paddlenlp.embeddings](https://github.com/PaddlePaddle/models/tree/release/2.0-beta/PaddleNLP/paddlenlp/embeddings)
 
 ## 代码示例
@@ -125,3 +144,6 @@ paddlehub >= 2.0.0
 
   初始发布
 
+* 1.0.1
+
+  支持基于embedding的文本分类和序列标注finetune任务

modules/text/embedding/w2v_baidu_encyclopedia_target_word-character_char1-1_dim300/module.py

@@ -12,44 +12,24 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from typing import List
 from paddlenlp.embeddings import TokenEmbedding
-from paddlehub.module.module import moduleinfo, serving
+from paddlehub.module.module import moduleinfo
+from paddlehub.module.nlp_module import EmbeddingModule
 
 
 @moduleinfo(
     name="w2v_baidu_encyclopedia_target_word-character_char1-1_dim300",
-    version="1.0.0",
+    version="1.0.1",
     summary="",
     author="paddlepaddle",
     author_email="",
-    type="nlp/semantic_model")
+    type="nlp/semantic_model",
+    meta=EmbeddingModule)
 class Embedding(TokenEmbedding):
     """
     Embedding model
     """
-    def __init__(self, *args, **kwargs):
-        super(Embedding, self).__init__(embedding_name="w2v.baidu_encyclopedia.target.word-character.char1-1.dim300", *args, **kwargs)
-
-    @serving
-    def calc_similarity(self, data: List[List[str]]):
-        """
-        Calculate similarities of giving word pairs.
-        """
-        results = []
-        for word_pair in data:
-            if len(word_pair) != 2:
-                raise RuntimeError(
-                    f'The input must have two words, but got {len(word_pair)}. Please check your inputs.')
-            if not isinstance(word_pair[0], str) or not isinstance(word_pair[1], str):
-                raise RuntimeError(
-                    f'The types of text pair must be (str, str), but got'
-                    f' ({type(word_pair[0]).__name__}, {type(word_pair[1]).__name__}). Please check your inputs.')
+    embedding_name = "w2v.baidu_encyclopedia.target.word-character.char1-1.dim300"
 
-            for word in word_pair:
-                if self.get_idx_from_word(word) == \
-                        self.get_idx_from_word(self.vocab.unk_token):
-                    raise RuntimeError(
-                        f'Word "{word}" is not in vocab. Please check your inputs.')
-            results.append(str(self.cosine_sim(*word_pair)))
-        return results
+    def __init__(self, *args, **kwargs):
+        super(Embedding, self).__init__(embedding_name=self.embedding_name, *args, **kwargs)

modules/text/embedding/w2v_baidu_encyclopedia_target_word-character_char1-2_dim300/README.md

@@ -56,6 +56,25 @@ def dot(
 * `word_a`： 需要计算内积的单词a。
 * `word_b`： 需要计算内积的单词b。
 
+
+```python
+def get_vocab_path()
+```
+获取本地词表文件的路径信息。
+
+
+```python
+def get_tokenizer(*args, **kwargs)
+```
+获取当前模型的tokenizer，返回一个JiebaTokenizer的实例，当前只支持中文embedding模型。
+
+**参数**
+* `*args`： 额外传递的列表形式的参数。
+* `**kwargs`： 额外传递的字典形式的参数。
+
+关于额外参数的详情，可查看[paddlenlp.data.tokenizer.JiebaTokenizer](https://github.com/PaddlePaddle/models/blob/develop/PaddleNLP/paddlenlp/data/tokenizer.py)
+
+
 更多api详情和用法可参考[paddlenlp.embeddings](https://github.com/PaddlePaddle/models/tree/release/2.0-beta/PaddleNLP/paddlenlp/embeddings)
 
 ## 代码示例
@@ -125,3 +144,6 @@ paddlehub >= 2.0.0
 
   初始发布
 
+* 1.0.1
+
+  支持基于embedding的文本分类和序列标注finetune任务

modules/text/embedding/w2v_baidu_encyclopedia_target_word-character_char1-2_dim300/module.py

@@ -12,44 +12,24 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from typing import List
 from paddlenlp.embeddings import TokenEmbedding
-from paddlehub.module.module import moduleinfo, serving
+from paddlehub.module.module import moduleinfo
+from paddlehub.module.nlp_module import EmbeddingModule
 
 
 @moduleinfo(
     name="w2v_baidu_encyclopedia_target_word-character_char1-2_dim300",
-    version="1.0.0",
+    version="1.0.1",
     summary="",
     author="paddlepaddle",
     author_email="",
-    type="nlp/semantic_model")
+    type="nlp/semantic_model",
+    meta=EmbeddingModule)
 class Embedding(TokenEmbedding):
     """
     Embedding model
     """
-    def __init__(self, *args, **kwargs):
-        super(Embedding, self).__init__(embedding_name="w2v.baidu_encyclopedia.target.word-character.char1-2.dim300", *args, **kwargs)
-
-    @serving
-    def calc_similarity(self, data: List[List[str]]):
-        """
-        Calculate similarities of giving word pairs.
-        """
-        results = []
-        for word_pair in data:
-            if len(word_pair) != 2:
-                raise RuntimeError(
-                    f'The input must have two words, but got {len(word_pair)}. Please check your inputs.')
-            if not isinstance(word_pair[0], str) or not isinstance(word_pair[1], str):
-                raise RuntimeError(
-                    f'The types of text pair must be (str, str), but got'
-                    f' ({type(word_pair[0]).__name__}, {type(word_pair[1]).__name__}). Please check your inputs.')
+    embedding_name = "w2v.baidu_encyclopedia.target.word-character.char1-2.dim300"
 
-            for word in word_pair:
-                if self.get_idx_from_word(word) == \
-                        self.get_idx_from_word(self.vocab.unk_token):
-                    raise RuntimeError(
-                        f'Word "{word}" is not in vocab. Please check your inputs.')
-            results.append(str(self.cosine_sim(*word_pair)))
-        return results
+    def __init__(self, *args, **kwargs):
+        super(Embedding, self).__init__(embedding_name=self.embedding_name, *args, **kwargs)

modules/text/embedding/w2v_baidu_encyclopedia_target_word-character_char1-4_dim300/README.md

@@ -56,6 +56,25 @@ def dot(
 * `word_a`： 需要计算内积的单词a。
 * `word_b`： 需要计算内积的单词b。
 
+
+```python
+def get_vocab_path()
+```
+获取本地词表文件的路径信息。
+
+
+```python
+def get_tokenizer(*args, **kwargs)
+```
+获取当前模型的tokenizer，返回一个JiebaTokenizer的实例，当前只支持中文embedding模型。
+
+**参数**
+* `*args`： 额外传递的列表形式的参数。
+* `**kwargs`： 额外传递的字典形式的参数。
+
+关于额外参数的详情，可查看[paddlenlp.data.tokenizer.JiebaTokenizer](https://github.com/PaddlePaddle/models/blob/develop/PaddleNLP/paddlenlp/data/tokenizer.py)
+
+
 更多api详情和用法可参考[paddlenlp.embeddings](https://github.com/PaddlePaddle/models/tree/release/2.0-beta/PaddleNLP/paddlenlp/embeddings)
 
 ## 代码示例
@@ -125,3 +144,6 @@ paddlehub >= 2.0.0
 
   初始发布
 
+* 1.0.1
+
+  支持基于embedding的文本分类和序列标注finetune任务

modules/text/embedding/w2v_baidu_encyclopedia_target_word-character_char1-4_dim300/module.py

@@ -12,44 +12,24 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from typing import List
 from paddlenlp.embeddings import TokenEmbedding
-from paddlehub.module.module import moduleinfo, serving
+from paddlehub.module.module import moduleinfo
+from paddlehub.module.nlp_module import EmbeddingModule
 
 
 @moduleinfo(
     name="w2v_baidu_encyclopedia_target_word-character_char1-4_dim300",
-    version="1.0.0",
+    version="1.0.1",
     summary="",
     author="paddlepaddle",
     author_email="",
-    type="nlp/semantic_model")
+    type="nlp/semantic_model",
+    meta=EmbeddingModule)
 class Embedding(TokenEmbedding):
     """
     Embedding model
     """
-    def __init__(self, *args, **kwargs):
-        super(Embedding, self).__init__(embedding_name="w2v.baidu_encyclopedia.target.word-character.char1-4.dim300", *args, **kwargs)
-
-    @serving
-    def calc_similarity(self, data: List[List[str]]):
-        """
-        Calculate similarities of giving word pairs.
-        """
-        results = []
-        for word_pair in data:
-            if len(word_pair) != 2:
-                raise RuntimeError(
-                    f'The input must have two words, but got {len(word_pair)}. Please check your inputs.')
-            if not isinstance(word_pair[0], str) or not isinstance(word_pair[1], str):
-                raise RuntimeError(
-                    f'The types of text pair must be (str, str), but got'
-                    f' ({type(word_pair[0]).__name__}, {type(word_pair[1]).__name__}). Please check your inputs.')
+    embedding_name = "w2v.baidu_encyclopedia.target.word-character.char1-4.dim300"
 
-            for word in word_pair:
-                if self.get_idx_from_word(word) == \
-                        self.get_idx_from_word(self.vocab.unk_token):
-                    raise RuntimeError(
-                        f'Word "{word}" is not in vocab. Please check your inputs.')
-            results.append(str(self.cosine_sim(*word_pair)))
-        return results
+    def __init__(self, *args, **kwargs):
+        super(Embedding, self).__init__(embedding_name=self.embedding_name, *args, **kwargs)

modules/text/embedding/w2v_baidu_encyclopedia_target_word-ngram_1-2_dim300/README.md

@@ -56,6 +56,25 @@ def dot(
 * `word_a`： 需要计算内积的单词a。
 * `word_b`： 需要计算内积的单词b。
 
+
+```python
+def get_vocab_path()
+```
+获取本地词表文件的路径信息。
+
+
+```python
+def get_tokenizer(*args, **kwargs)
+```
+获取当前模型的tokenizer，返回一个JiebaTokenizer的实例，当前只支持中文embedding模型。
+
+**参数**
+* `*args`： 额外传递的列表形式的参数。
+* `**kwargs`： 额外传递的字典形式的参数。
+
+关于额外参数的详情，可查看[paddlenlp.data.tokenizer.JiebaTokenizer](https://github.com/PaddlePaddle/models/blob/develop/PaddleNLP/paddlenlp/data/tokenizer.py)
+
+
 更多api详情和用法可参考[paddlenlp.embeddings](https://github.com/PaddlePaddle/models/tree/release/2.0-beta/PaddleNLP/paddlenlp/embeddings)
 
 ## 代码示例
@@ -125,3 +144,6 @@ paddlehub >= 2.0.0
 
   初始发布
 
+* 1.0.1
+
+  支持基于embedding的文本分类和序列标注finetune任务

modules/text/embedding/w2v_baidu_encyclopedia_target_word-ngram_1-2_dim300/module.py

@@ -12,44 +12,24 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from typing import List
 from paddlenlp.embeddings import TokenEmbedding
-from paddlehub.module.module import moduleinfo, serving
+from paddlehub.module.module import moduleinfo
+from paddlehub.module.nlp_module import EmbeddingModule
 
 
 @moduleinfo(
     name="w2v_baidu_encyclopedia_target_word-ngram_1-2_dim300",
-    version="1.0.0",
+    version="1.0.1",
     summary="",
     author="paddlepaddle",
     author_email="",
-    type="nlp/semantic_model")
+    type="nlp/semantic_model",
+    meta=EmbeddingModule)
 class Embedding(TokenEmbedding):
     """
     Embedding model
     """
-    def __init__(self, *args, **kwargs):
-        super(Embedding, self).__init__(embedding_name="w2v.baidu_encyclopedia.target.word-ngram.1-2.dim300", *args, **kwargs)
-
-    @serving
-    def calc_similarity(self, data: List[List[str]]):
-        """
-        Calculate similarities of giving word pairs.
-        """
-        results = []
-        for word_pair in data:
-            if len(word_pair) != 2:
-                raise RuntimeError(
-                    f'The input must have two words, but got {len(word_pair)}. Please check your inputs.')
-            if not isinstance(word_pair[0], str) or not isinstance(word_pair[1], str):
-                raise RuntimeError(
-                    f'The types of text pair must be (str, str), but got'
-                    f' ({type(word_pair[0]).__name__}, {type(word_pair[1]).__name__}). Please check your inputs.')
+    embedding_name = "w2v.baidu_encyclopedia.target.word-ngram.1-2.dim300"
 
-            for word in word_pair:
-                if self.get_idx_from_word(word) == \
-                        self.get_idx_from_word(self.vocab.unk_token):
-                    raise RuntimeError(
-                        f'Word "{word}" is not in vocab. Please check your inputs.')
-            results.append(str(self.cosine_sim(*word_pair)))
-        return results
+    def __init__(self, *args, **kwargs):
+        super(Embedding, self).__init__(embedding_name=self.embedding_name, *args, **kwargs)

modules/text/embedding/w2v_baidu_encyclopedia_target_word-ngram_1-3_dim300/README.md

@@ -56,6 +56,25 @@ def dot(
 * `word_a`： 需要计算内积的单词a。
 * `word_b`： 需要计算内积的单词b。
 
+
+```python
+def get_vocab_path()
+```
+获取本地词表文件的路径信息。
+
+
+```python
+def get_tokenizer(*args, **kwargs)
+```
+获取当前模型的tokenizer，返回一个JiebaTokenizer的实例，当前只支持中文embedding模型。
+
+**参数**
+* `*args`： 额外传递的列表形式的参数。
+* `**kwargs`： 额外传递的字典形式的参数。
+
+关于额外参数的详情，可查看[paddlenlp.data.tokenizer.JiebaTokenizer](https://github.com/PaddlePaddle/models/blob/develop/PaddleNLP/paddlenlp/data/tokenizer.py)
+
+
 更多api详情和用法可参考[paddlenlp.embeddings](https://github.com/PaddlePaddle/models/tree/release/2.0-beta/PaddleNLP/paddlenlp/embeddings)
 
 ## 代码示例
@@ -125,3 +144,6 @@ paddlehub >= 2.0.0
 
   初始发布
 
+* 1.0.1
+
+  支持基于embedding的文本分类和序列标注finetune任务

modules/text/embedding/w2v_baidu_encyclopedia_target_word-ngram_1-3_dim300/module.py

@@ -12,44 +12,24 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from typing import List
 from paddlenlp.embeddings import TokenEmbedding
-from paddlehub.module.module import moduleinfo, serving
+from paddlehub.module.module import moduleinfo
+from paddlehub.module.nlp_module import EmbeddingModule
 
 
 @moduleinfo(
     name="w2v_baidu_encyclopedia_target_word-ngram_1-3_dim300",
-    version="1.0.0",
+    version="1.0.1",
     summary="",
     author="paddlepaddle",
     author_email="",
-    type="nlp/semantic_model")
+    type="nlp/semantic_model",
+    meta=EmbeddingModule)
 class Embedding(TokenEmbedding):
     """
     Embedding model
     """
-    def __init__(self, *args, **kwargs):
-        super(Embedding, self).__init__(embedding_name="w2v.baidu_encyclopedia.target.word-ngram.1-3.dim300", *args, **kwargs)
-
-    @serving
-    def calc_similarity(self, data: List[List[str]]):
-        """
-        Calculate similarities of giving word pairs.
-        """
-        results = []
-        for word_pair in data:
-            if len(word_pair) != 2:
-                raise RuntimeError(
-                    f'The input must have two words, but got {len(word_pair)}. Please check your inputs.')
-            if not isinstance(word_pair[0], str) or not isinstance(word_pair[1], str):
-                raise RuntimeError(
-                    f'The types of text pair must be (str, str), but got'
-                    f' ({type(word_pair[0]).__name__}, {type(word_pair[1]).__name__}). Please check your inputs.')
+    embedding_name = "w2v.baidu_encyclopedia.target.word-ngram.1-3.dim300"
 
-            for word in word_pair:
-                if self.get_idx_from_word(word) == \
-                        self.get_idx_from_word(self.vocab.unk_token):
-                    raise RuntimeError(
-                        f'Word "{word}" is not in vocab. Please check your inputs.')
-            results.append(str(self.cosine_sim(*word_pair)))
-        return results
+    def __init__(self, *args, **kwargs):
+        super(Embedding, self).__init__(embedding_name=self.embedding_name, *args, **kwargs)

modules/text/embedding/w2v_baidu_encyclopedia_target_word-ngram_2-2_dim300/README.md

@@ -56,6 +56,25 @@ def dot(
 * `word_a`： 需要计算内积的单词a。
 * `word_b`： 需要计算内积的单词b。
 
+
+```python
+def get_vocab_path()
+```
+获取本地词表文件的路径信息。
+
+
+```python
+def get_tokenizer(*args, **kwargs)
+```
+获取当前模型的tokenizer，返回一个JiebaTokenizer的实例，当前只支持中文embedding模型。
+
+**参数**
+* `*args`： 额外传递的列表形式的参数。
+* `**kwargs`： 额外传递的字典形式的参数。
+
+关于额外参数的详情，可查看[paddlenlp.data.tokenizer.JiebaTokenizer](https://github.com/PaddlePaddle/models/blob/develop/PaddleNLP/paddlenlp/data/tokenizer.py)
+
+
 更多api详情和用法可参考[paddlenlp.embeddings](https://github.com/PaddlePaddle/models/tree/release/2.0-beta/PaddleNLP/paddlenlp/embeddings)
 
 ## 代码示例
@@ -125,3 +144,6 @@ paddlehub >= 2.0.0
 
   初始发布
 
+* 1.0.1
+
+  支持基于embedding的文本分类和序列标注finetune任务

modules/text/embedding/w2v_baidu_encyclopedia_target_word-ngram_2-2_dim300/module.py

@@ -12,44 +12,24 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from typing import List
 from paddlenlp.embeddings import TokenEmbedding
-from paddlehub.module.module import moduleinfo, serving
+from paddlehub.module.module import moduleinfo
+from paddlehub.module.nlp_module import EmbeddingModule
 
 
 @moduleinfo(
     name="w2v_baidu_encyclopedia_target_word-ngram_2-2_dim300",
-    version="1.0.0",
+    version="1.0.1",
     summary="",
     author="paddlepaddle",
     author_email="",
-    type="nlp/semantic_model")
+    type="nlp/semantic_model",
+    meta=EmbeddingModule)
 class Embedding(TokenEmbedding):
     """
     Embedding model
     """
-    def __init__(self, *args, **kwargs):
-        super(Embedding, self).__init__(embedding_name="w2v.baidu_encyclopedia.target.word-ngram.2-2.dim300", *args, **kwargs)
-
-    @serving
-    def calc_similarity(self, data: List[List[str]]):
-        """
-        Calculate similarities of giving word pairs.
-        """
-        results = []
-        for word_pair in data:
-            if len(word_pair) != 2:
-                raise RuntimeError(
-                    f'The input must have two words, but got {len(word_pair)}. Please check your inputs.')
-            if not isinstance(word_pair[0], str) or not isinstance(word_pair[1], str):
-                raise RuntimeError(
-                    f'The types of text pair must be (str, str), but got'
-                    f' ({type(word_pair[0]).__name__}, {type(word_pair[1]).__name__}). Please check your inputs.')
+    embedding_name = "w2v.baidu_encyclopedia.target.word-ngram.2-2.dim300"
 
-            for word in word_pair:
-                if self.get_idx_from_word(word) == \
-                        self.get_idx_from_word(self.vocab.unk_token):
-                    raise RuntimeError(
-                        f'Word "{word}" is not in vocab. Please check your inputs.')
-            results.append(str(self.cosine_sim(*word_pair)))
-        return results
+    def __init__(self, *args, **kwargs):
+        super(Embedding, self).__init__(embedding_name=self.embedding_name, *args, **kwargs)

modules/text/embedding/w2v_baidu_encyclopedia_target_word-wordLR_dim300/README.md

@@ -56,6 +56,25 @@ def dot(
 * `word_a`： 需要计算内积的单词a。
 * `word_b`： 需要计算内积的单词b。
 
+
+```python
+def get_vocab_path()
+```
+获取本地词表文件的路径信息。
+
+
+```python
+def get_tokenizer(*args, **kwargs)
+```
+获取当前模型的tokenizer，返回一个JiebaTokenizer的实例，当前只支持中文embedding模型。
+
+**参数**
+* `*args`： 额外传递的列表形式的参数。
+* `**kwargs`： 额外传递的字典形式的参数。
+
+关于额外参数的详情，可查看[paddlenlp.data.tokenizer.JiebaTokenizer](https://github.com/PaddlePaddle/models/blob/develop/PaddleNLP/paddlenlp/data/tokenizer.py)
+
+
 更多api详情和用法可参考[paddlenlp.embeddings](https://github.com/PaddlePaddle/models/tree/release/2.0-beta/PaddleNLP/paddlenlp/embeddings)
 
 ## 代码示例
@@ -125,3 +144,6 @@ paddlehub >= 2.0.0
 
   初始发布
 
+* 1.0.1
+
+  支持基于embedding的文本分类和序列标注finetune任务

modules/text/embedding/w2v_baidu_encyclopedia_target_word-wordLR_dim300/module.py

@@ -12,44 +12,24 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from typing import List
 from paddlenlp.embeddings import TokenEmbedding
-from paddlehub.module.module import moduleinfo, serving
+from paddlehub.module.module import moduleinfo
+from paddlehub.module.nlp_module import EmbeddingModule
 
 
 @moduleinfo(
     name="w2v_baidu_encyclopedia_target_word-wordLR_dim300",
-    version="1.0.0",
+    version="1.0.1",
     summary="",
     author="paddlepaddle",
     author_email="",
-    type="nlp/semantic_model")
+    type="nlp/semantic_model",
+    meta=EmbeddingModule)
 class Embedding(TokenEmbedding):
     """
     Embedding model
     """
-    def __init__(self, *args, **kwargs):
-        super(Embedding, self).__init__(embedding_name="w2v.baidu_encyclopedia.target.word-wordLR.dim300", *args, **kwargs)
-
-    @serving
-    def calc_similarity(self, data: List[List[str]]):
-        """
-        Calculate similarities of giving word pairs.
-        """
-        results = []
-        for word_pair in data:
-            if len(word_pair) != 2:
-                raise RuntimeError(
-                    f'The input must have two words, but got {len(word_pair)}. Please check your inputs.')
-            if not isinstance(word_pair[0], str) or not isinstance(word_pair[1], str):
-                raise RuntimeError(
-                    f'The types of text pair must be (str, str), but got'
-                    f' ({type(word_pair[0]).__name__}, {type(word_pair[1]).__name__}). Please check your inputs.')
+    embedding_name = "w2v.baidu_encyclopedia.target.word-wordLR.dim300"
 
-            for word in word_pair:
-                if self.get_idx_from_word(word) == \
-                        self.get_idx_from_word(self.vocab.unk_token):
-                    raise RuntimeError(
-                        f'Word "{word}" is not in vocab. Please check your inputs.')
-            results.append(str(self.cosine_sim(*word_pair)))
-        return results
+    def __init__(self, *args, **kwargs):
+        super(Embedding, self).__init__(embedding_name=self.embedding_name, *args, **kwargs)

modules/text/embedding/w2v_baidu_encyclopedia_target_word-wordPosition_dim300/README.md

@@ -56,6 +56,25 @@ def dot(
 * `word_a`： 需要计算内积的单词a。
 * `word_b`： 需要计算内积的单词b。
 
+
+```python
+def get_vocab_path()
+```
+获取本地词表文件的路径信息。
+
+
+```python
+def get_tokenizer(*args, **kwargs)
+```
+获取当前模型的tokenizer，返回一个JiebaTokenizer的实例，当前只支持中文embedding模型。
+
+**参数**
+* `*args`： 额外传递的列表形式的参数。
+* `**kwargs`： 额外传递的字典形式的参数。
+
+关于额外参数的详情，可查看[paddlenlp.data.tokenizer.JiebaTokenizer](https://github.com/PaddlePaddle/models/blob/develop/PaddleNLP/paddlenlp/data/tokenizer.py)
+
+
 更多api详情和用法可参考[paddlenlp.embeddings](https://github.com/PaddlePaddle/models/tree/release/2.0-beta/PaddleNLP/paddlenlp/embeddings)
 
 ## 代码示例
@@ -125,3 +144,6 @@ paddlehub >= 2.0.0
 
   初始发布
 
+* 1.0.1
+
+  支持基于embedding的文本分类和序列标注finetune任务

modules/text/embedding/w2v_baidu_encyclopedia_target_word-wordPosition_dim300/module.py

@@ -12,44 +12,24 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from typing import List
 from paddlenlp.embeddings import TokenEmbedding
-from paddlehub.module.module import moduleinfo, serving
+from paddlehub.module.module import moduleinfo
+from paddlehub.module.nlp_module import EmbeddingModule
 
 
 @moduleinfo(
     name="w2v_baidu_encyclopedia_target_word-wordPosition_dim300",
-    version="1.0.0",
+    version="1.0.1",
     summary="",
     author="paddlepaddle",
     author_email="",
-    type="nlp/semantic_model")
+    type="nlp/semantic_model",
+    meta=EmbeddingModule)
 class Embedding(TokenEmbedding):
     """
     Embedding model
     """
-    def __init__(self, *args, **kwargs):
-        super(Embedding, self).__init__(embedding_name="w2v.baidu_encyclopedia.target.word-wordPosition.dim300", *args, **kwargs)
-
-    @serving
-    def calc_similarity(self, data: List[List[str]]):
-        """
-        Calculate similarities of giving word pairs.
-        """
-        results = []
-        for word_pair in data:
-            if len(word_pair) != 2:
-                raise RuntimeError(
-                    f'The input must have two words, but got {len(word_pair)}. Please check your inputs.')
-            if not isinstance(word_pair[0], str) or not isinstance(word_pair[1], str):
-                raise RuntimeError(
-                    f'The types of text pair must be (str, str), but got'
-                    f' ({type(word_pair[0]).__name__}, {type(word_pair[1]).__name__}). Please check your inputs.')
+    embedding_name = "w2v.baidu_encyclopedia.target.word-wordPosition.dim300"
 
-            for word in word_pair:
-                if self.get_idx_from_word(word) == \
-                        self.get_idx_from_word(self.vocab.unk_token):
-                    raise RuntimeError(
-                        f'Word "{word}" is not in vocab. Please check your inputs.')
-            results.append(str(self.cosine_sim(*word_pair)))
-        return results
+    def __init__(self, *args, **kwargs):
+        super(Embedding, self).__init__(embedding_name=self.embedding_name, *args, **kwargs)

modules/text/embedding/w2v_baidu_encyclopedia_target_word-word_dim300/README.md

@@ -56,6 +56,25 @@ def dot(
 * `word_a`： 需要计算内积的单词a。
 * `word_b`： 需要计算内积的单词b。
 
+
+```python
+def get_vocab_path()
+```
+获取本地词表文件的路径信息。
+
+
+```python
+def get_tokenizer(*args, **kwargs)
+```
+获取当前模型的tokenizer，返回一个JiebaTokenizer的实例，当前只支持中文embedding模型。
+
+**参数**
+* `*args`： 额外传递的列表形式的参数。
+* `**kwargs`： 额外传递的字典形式的参数。
+
+关于额外参数的详情，可查看[paddlenlp.data.tokenizer.JiebaTokenizer](https://github.com/PaddlePaddle/models/blob/develop/PaddleNLP/paddlenlp/data/tokenizer.py)
+
+
 更多api详情和用法可参考[paddlenlp.embeddings](https://github.com/PaddlePaddle/models/tree/release/2.0-beta/PaddleNLP/paddlenlp/embeddings)
 
 ## 代码示例
@@ -125,3 +144,6 @@ paddlehub >= 2.0.0
 
   初始发布
 
+* 1.0.1
+
+  支持基于embedding的文本分类和序列标注finetune任务

modules/text/embedding/w2v_baidu_encyclopedia_target_word-word_dim300/module.py

@@ -12,15 +12,14 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from typing import List
 from paddlenlp.embeddings import TokenEmbedding
-from paddlehub.module.module import moduleinfo, serving
+from paddlehub.module.module import moduleinfo
 from paddlehub.module.nlp_module import EmbeddingModule
 
 
 @moduleinfo(
     name="w2v_baidu_encyclopedia_target_word-word_dim300",
-    version="1.0.0",
+    version="1.0.1",
     summary="",
     author="paddlepaddle",
     author_email="",
@@ -30,7 +29,7 @@ class Embedding(TokenEmbedding):
     """
     Embedding model
     """
-    embedding_name = 'w2v.baidu_encyclopedia.target.word-word.dim300'
+    embedding_name = "w2v.baidu_encyclopedia.target.word-word.dim300"
 
     def __init__(self, *args, **kwargs):
-        super(Embedding, self).__init__(embedding_name=self.embedding_name, *args, **kwargs)
\ No newline at end of file
+        super(Embedding, self).__init__(embedding_name=self.embedding_name, *args, **kwargs)

modules/text/embedding/w2v_financial_target_bigram-char_dim300/README.md

@@ -56,6 +56,25 @@ def dot(
 * `word_a`： 需要计算内积的单词a。
 * `word_b`： 需要计算内积的单词b。
 
+
+```python
+def get_vocab_path()
+```
+获取本地词表文件的路径信息。
+
+
+```python
+def get_tokenizer(*args, **kwargs)
+```
+获取当前模型的tokenizer，返回一个JiebaTokenizer的实例，当前只支持中文embedding模型。
+
+**参数**
+* `*args`： 额外传递的列表形式的参数。
+* `**kwargs`： 额外传递的字典形式的参数。
+
+关于额外参数的详情，可查看[paddlenlp.data.tokenizer.JiebaTokenizer](https://github.com/PaddlePaddle/models/blob/develop/PaddleNLP/paddlenlp/data/tokenizer.py)
+
+
 更多api详情和用法可参考[paddlenlp.embeddings](https://github.com/PaddlePaddle/models/tree/release/2.0-beta/PaddleNLP/paddlenlp/embeddings)
 
 ## 代码示例
@@ -125,3 +144,6 @@ paddlehub >= 2.0.0
 
   初始发布
 
+* 1.0.1
+
+  支持基于embedding的文本分类和序列标注finetune任务

modules/text/embedding/w2v_financial_target_bigram-char_dim300/module.py

@@ -12,44 +12,24 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from typing import List
 from paddlenlp.embeddings import TokenEmbedding
-from paddlehub.module.module import moduleinfo, serving
+from paddlehub.module.module import moduleinfo
+from paddlehub.module.nlp_module import EmbeddingModule
 
 
 @moduleinfo(
     name="w2v_financial_target_bigram-char_dim300",
-    version="1.0.0",
+    version="1.0.1",
     summary="",
     author="paddlepaddle",
     author_email="",
-    type="nlp/semantic_model")
+    type="nlp/semantic_model",
+    meta=EmbeddingModule)
 class Embedding(TokenEmbedding):
     """
     Embedding model
     """
-    def __init__(self, *args, **kwargs):
-        super(Embedding, self).__init__(embedding_name="w2v.financial.target.bigram-char.dim300", *args, **kwargs)
-
-    @serving
-    def calc_similarity(self, data: List[List[str]]):
-        """
-        Calculate similarities of giving word pairs.
-        """
-        results = []
-        for word_pair in data:
-            if len(word_pair) != 2:
-                raise RuntimeError(
-                    f'The input must have two words, but got {len(word_pair)}. Please check your inputs.')
-            if not isinstance(word_pair[0], str) or not isinstance(word_pair[1], str):
-                raise RuntimeError(
-                    f'The types of text pair must be (str, str), but got'
-                    f' ({type(word_pair[0]).__name__}, {type(word_pair[1]).__name__}). Please check your inputs.')
+    embedding_name = "w2v.financial.target.bigram-char.dim300"
 
-            for word in word_pair:
-                if self.get_idx_from_word(word) == \
-                        self.get_idx_from_word(self.vocab.unk_token):
-                    raise RuntimeError(
-                        f'Word "{word}" is not in vocab. Please check your inputs.')
-            results.append(str(self.cosine_sim(*word_pair)))
-        return results
+    def __init__(self, *args, **kwargs):
+        super(Embedding, self).__init__(embedding_name=self.embedding_name, *args, **kwargs)

modules/text/embedding/w2v_financial_target_word-bigram_dim300/README.md

@@ -56,6 +56,25 @@ def dot(
 * `word_a`： 需要计算内积的单词a。
 * `word_b`： 需要计算内积的单词b。
 
+
+```python
+def get_vocab_path()
+```
+获取本地词表文件的路径信息。
+
+
+```python
+def get_tokenizer(*args, **kwargs)
+```
+获取当前模型的tokenizer，返回一个JiebaTokenizer的实例，当前只支持中文embedding模型。
+
+**参数**
+* `*args`： 额外传递的列表形式的参数。
+* `**kwargs`： 额外传递的字典形式的参数。
+
+关于额外参数的详情，可查看[paddlenlp.data.tokenizer.JiebaTokenizer](https://github.com/PaddlePaddle/models/blob/develop/PaddleNLP/paddlenlp/data/tokenizer.py)
+
+
 更多api详情和用法可参考[paddlenlp.embeddings](https://github.com/PaddlePaddle/models/tree/release/2.0-beta/PaddleNLP/paddlenlp/embeddings)
 
 ## 代码示例
@@ -125,3 +144,6 @@ paddlehub >= 2.0.0
 
   初始发布
 
+* 1.0.1
+
+  支持基于embedding的文本分类和序列标注finetune任务

modules/text/embedding/w2v_financial_target_word-bigram_dim300/module.py

@@ -12,44 +12,24 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from typing import List
 from paddlenlp.embeddings import TokenEmbedding
-from paddlehub.module.module import moduleinfo, serving
+from paddlehub.module.module import moduleinfo
+from paddlehub.module.nlp_module import EmbeddingModule
 
 
 @moduleinfo(
     name="w2v_financial_target_word-bigram_dim300",
-    version="1.0.0",
+    version="1.0.1",
     summary="",
     author="paddlepaddle",
     author_email="",
-    type="nlp/semantic_model")
+    type="nlp/semantic_model",
+    meta=EmbeddingModule)
 class Embedding(TokenEmbedding):
     """
     Embedding model
     """
-    def __init__(self, *args, **kwargs):
-        super(Embedding, self).__init__(embedding_name="w2v.financial.target.word-bigram.dim300", *args, **kwargs)
-
-    @serving
-    def calc_similarity(self, data: List[List[str]]):
-        """
-        Calculate similarities of giving word pairs.
-        """
-        results = []
-        for word_pair in data:
-            if len(word_pair) != 2:
-                raise RuntimeError(
-                    f'The input must have two words, but got {len(word_pair)}. Please check your inputs.')
-            if not isinstance(word_pair[0], str) or not isinstance(word_pair[1], str):
-                raise RuntimeError(
-                    f'The types of text pair must be (str, str), but got'
-                    f' ({type(word_pair[0]).__name__}, {type(word_pair[1]).__name__}). Please check your inputs.')
+    embedding_name = "w2v.financial.target.word-bigram.dim300"
 
-            for word in word_pair:
-                if self.get_idx_from_word(word) == \
-                        self.get_idx_from_word(self.vocab.unk_token):
-                    raise RuntimeError(
-                        f'Word "{word}" is not in vocab. Please check your inputs.')
-            results.append(str(self.cosine_sim(*word_pair)))
-        return results
+    def __init__(self, *args, **kwargs):
+        super(Embedding, self).__init__(embedding_name=self.embedding_name, *args, **kwargs)

modules/text/embedding/w2v_financial_target_word-char_dim300/README.md

@@ -56,6 +56,25 @@ def dot(
 * `word_a`： 需要计算内积的单词a。
 * `word_b`： 需要计算内积的单词b。
 
+
+```python
+def get_vocab_path()
+```
+获取本地词表文件的路径信息。
+
+
+```python
+def get_tokenizer(*args, **kwargs)
+```
+获取当前模型的tokenizer，返回一个JiebaTokenizer的实例，当前只支持中文embedding模型。
+
+**参数**
+* `*args`： 额外传递的列表形式的参数。
+* `**kwargs`： 额外传递的字典形式的参数。
+
+关于额外参数的详情，可查看[paddlenlp.data.tokenizer.JiebaTokenizer](https://github.com/PaddlePaddle/models/blob/develop/PaddleNLP/paddlenlp/data/tokenizer.py)
+
+
 更多api详情和用法可参考[paddlenlp.embeddings](https://github.com/PaddlePaddle/models/tree/release/2.0-beta/PaddleNLP/paddlenlp/embeddings)
 
 ## 代码示例
@@ -125,3 +144,6 @@ paddlehub >= 2.0.0
 
   初始发布
 
+* 1.0.1
+
+  支持基于embedding的文本分类和序列标注finetune任务

modules/text/embedding/w2v_financial_target_word-char_dim300/module.py

@@ -12,44 +12,24 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from typing import List
 from paddlenlp.embeddings import TokenEmbedding
-from paddlehub.module.module import moduleinfo, serving
+from paddlehub.module.module import moduleinfo
+from paddlehub.module.nlp_module import EmbeddingModule
 
 
 @moduleinfo(
     name="w2v_financial_target_word-char_dim300",
-    version="1.0.0",
+    version="1.0.1",
     summary="",
     author="paddlepaddle",
     author_email="",
-    type="nlp/semantic_model")
+    type="nlp/semantic_model",
+    meta=EmbeddingModule)
 class Embedding(TokenEmbedding):
     """
     Embedding model
     """
-    def __init__(self, *args, **kwargs):
-        super(Embedding, self).__init__(embedding_name="w2v.financial.target.word-char.dim300", *args, **kwargs)
-
-    @serving
-    def calc_similarity(self, data: List[List[str]]):
-        """
-        Calculate similarities of giving word pairs.
-        """
-        results = []
-        for word_pair in data:
-            if len(word_pair) != 2:
-                raise RuntimeError(
-                    f'The input must have two words, but got {len(word_pair)}. Please check your inputs.')
-            if not isinstance(word_pair[0], str) or not isinstance(word_pair[1], str):
-                raise RuntimeError(
-                    f'The types of text pair must be (str, str), but got'
-                    f' ({type(word_pair[0]).__name__}, {type(word_pair[1]).__name__}). Please check your inputs.')
+    embedding_name = "w2v.financial.target.word-char.dim300"
 
-            for word in word_pair:
-                if self.get_idx_from_word(word) == \
-                        self.get_idx_from_word(self.vocab.unk_token):
-                    raise RuntimeError(
-                        f'Word "{word}" is not in vocab. Please check your inputs.')
-            results.append(str(self.cosine_sim(*word_pair)))
-        return results
+    def __init__(self, *args, **kwargs):
+        super(Embedding, self).__init__(embedding_name=self.embedding_name, *args, **kwargs)

modules/text/embedding/w2v_financial_target_word-word_dim300/README.md

@@ -56,6 +56,25 @@ def dot(
 * `word_a`： 需要计算内积的单词a。
 * `word_b`： 需要计算内积的单词b。
 
+
+```python
+def get_vocab_path()
+```
+获取本地词表文件的路径信息。
+
+
+```python
+def get_tokenizer(*args, **kwargs)
+```
+获取当前模型的tokenizer，返回一个JiebaTokenizer的实例，当前只支持中文embedding模型。
+
+**参数**
+* `*args`： 额外传递的列表形式的参数。
+* `**kwargs`： 额外传递的字典形式的参数。
+
+关于额外参数的详情，可查看[paddlenlp.data.tokenizer.JiebaTokenizer](https://github.com/PaddlePaddle/models/blob/develop/PaddleNLP/paddlenlp/data/tokenizer.py)
+
+
 更多api详情和用法可参考[paddlenlp.embeddings](https://github.com/PaddlePaddle/models/tree/release/2.0-beta/PaddleNLP/paddlenlp/embeddings)
 
 ## 代码示例
@@ -125,3 +144,6 @@ paddlehub >= 2.0.0
 
   初始发布
 
+* 1.0.1
+
+  支持基于embedding的文本分类和序列标注finetune任务

modules/text/embedding/w2v_financial_target_word-word_dim300/module.py

@@ -12,44 +12,24 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from typing import List
 from paddlenlp.embeddings import TokenEmbedding
-from paddlehub.module.module import moduleinfo, serving
+from paddlehub.module.module import moduleinfo
+from paddlehub.module.nlp_module import EmbeddingModule
 
 
 @moduleinfo(
     name="w2v_financial_target_word-word_dim300",
-    version="1.0.0",
+    version="1.0.1",
     summary="",
     author="paddlepaddle",
     author_email="",
-    type="nlp/semantic_model")
+    type="nlp/semantic_model",
+    meta=EmbeddingModule)
 class Embedding(TokenEmbedding):
     """
     Embedding model
     """
-    def __init__(self, *args, **kwargs):
-        super(Embedding, self).__init__(embedding_name="w2v.financial.target.word-word.dim300", *args, **kwargs)
-
-    @serving
-    def calc_similarity(self, data: List[List[str]]):
-        """
-        Calculate similarities of giving word pairs.
-        """
-        results = []
-        for word_pair in data:
-            if len(word_pair) != 2:
-                raise RuntimeError(
-                    f'The input must have two words, but got {len(word_pair)}. Please check your inputs.')
-            if not isinstance(word_pair[0], str) or not isinstance(word_pair[1], str):
-                raise RuntimeError(
-                    f'The types of text pair must be (str, str), but got'
-                    f' ({type(word_pair[0]).__name__}, {type(word_pair[1]).__name__}). Please check your inputs.')
+    embedding_name = "w2v.financial.target.word-word.dim300"
 
-            for word in word_pair:
-                if self.get_idx_from_word(word) == \
-                        self.get_idx_from_word(self.vocab.unk_token):
-                    raise RuntimeError(
-                        f'Word "{word}" is not in vocab. Please check your inputs.')
-            results.append(str(self.cosine_sim(*word_pair)))
-        return results
+    def __init__(self, *args, **kwargs):
+        super(Embedding, self).__init__(embedding_name=self.embedding_name, *args, **kwargs)

modules/text/embedding/w2v_literature_target_bigram-char_dim300/README.md

@@ -56,6 +56,25 @@ def dot(
 * `word_a`： 需要计算内积的单词a。
 * `word_b`： 需要计算内积的单词b。
 
+
+```python
+def get_vocab_path()
+```
+获取本地词表文件的路径信息。
+
+
+```python
+def get_tokenizer(*args, **kwargs)
+```
+获取当前模型的tokenizer，返回一个JiebaTokenizer的实例，当前只支持中文embedding模型。
+
+**参数**
+* `*args`： 额外传递的列表形式的参数。
+* `**kwargs`： 额外传递的字典形式的参数。
+
+关于额外参数的详情，可查看[paddlenlp.data.tokenizer.JiebaTokenizer](https://github.com/PaddlePaddle/models/blob/develop/PaddleNLP/paddlenlp/data/tokenizer.py)
+
+
 更多api详情和用法可参考[paddlenlp.embeddings](https://github.com/PaddlePaddle/models/tree/release/2.0-beta/PaddleNLP/paddlenlp/embeddings)
 
 ## 代码示例
@@ -125,3 +144,6 @@ paddlehub >= 2.0.0
 
   初始发布
 
+* 1.0.1
+
+  支持基于embedding的文本分类和序列标注finetune任务

modules/text/embedding/w2v_literature_target_bigram-char_dim300/module.py

@@ -12,44 +12,24 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from typing import List
 from paddlenlp.embeddings import TokenEmbedding
-from paddlehub.module.module import moduleinfo, serving
+from paddlehub.module.module import moduleinfo
+from paddlehub.module.nlp_module import EmbeddingModule
 
 
 @moduleinfo(
     name="w2v_literature_target_bigram-char_dim300",
-    version="1.0.0",
+    version="1.0.1",
     summary="",
     author="paddlepaddle",
     author_email="",
-    type="nlp/semantic_model")
+    type="nlp/semantic_model",
+    meta=EmbeddingModule)
 class Embedding(TokenEmbedding):
     """
     Embedding model
     """
-    def __init__(self, *args, **kwargs):
-        super(Embedding, self).__init__(embedding_name="w2v.literature.target.bigram-char.dim300", *args, **kwargs)
-
-    @serving
-    def calc_similarity(self, data: List[List[str]]):
-        """
-        Calculate similarities of giving word pairs.
-        """
-        results = []
-        for word_pair in data:
-            if len(word_pair) != 2:
-                raise RuntimeError(
-                    f'The input must have two words, but got {len(word_pair)}. Please check your inputs.')
-            if not isinstance(word_pair[0], str) or not isinstance(word_pair[1], str):
-                raise RuntimeError(
-                    f'The types of text pair must be (str, str), but got'
-                    f' ({type(word_pair[0]).__name__}, {type(word_pair[1]).__name__}). Please check your inputs.')
+    embedding_name = "w2v.literature.target.bigram-char.dim300"
 
-            for word in word_pair:
-                if self.get_idx_from_word(word) == \
-                        self.get_idx_from_word(self.vocab.unk_token):
-                    raise RuntimeError(
-                        f'Word "{word}" is not in vocab. Please check your inputs.')
-            results.append(str(self.cosine_sim(*word_pair)))
-        return results
+    def __init__(self, *args, **kwargs):
+        super(Embedding, self).__init__(embedding_name=self.embedding_name, *args, **kwargs)

modules/text/embedding/w2v_literature_target_word-bigram_dim300/README.md

@@ -56,6 +56,25 @@ def dot(
 * `word_a`： 需要计算内积的单词a。
 * `word_b`： 需要计算内积的单词b。
 
+
+```python
+def get_vocab_path()
+```
+获取本地词表文件的路径信息。
+
+
+```python
+def get_tokenizer(*args, **kwargs)
+```
+获取当前模型的tokenizer，返回一个JiebaTokenizer的实例，当前只支持中文embedding模型。
+
+**参数**
+* `*args`： 额外传递的列表形式的参数。
+* `**kwargs`： 额外传递的字典形式的参数。
+
+关于额外参数的详情，可查看[paddlenlp.data.tokenizer.JiebaTokenizer](https://github.com/PaddlePaddle/models/blob/develop/PaddleNLP/paddlenlp/data/tokenizer.py)
+
+
 更多api详情和用法可参考[paddlenlp.embeddings](https://github.com/PaddlePaddle/models/tree/release/2.0-beta/PaddleNLP/paddlenlp/embeddings)
 
 ## 代码示例
@@ -125,3 +144,6 @@ paddlehub >= 2.0.0
 
   初始发布
 
+* 1.0.1
+
+  支持基于embedding的文本分类和序列标注finetune任务

modules/text/embedding/w2v_literature_target_word-bigram_dim300/module.py

@@ -12,44 +12,24 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from typing import List
 from paddlenlp.embeddings import TokenEmbedding
-from paddlehub.module.module import moduleinfo, serving
+from paddlehub.module.module import moduleinfo
+from paddlehub.module.nlp_module import EmbeddingModule
 
 
 @moduleinfo(
     name="w2v_literature_target_word-bigram_dim300",
-    version="1.0.0",
+    version="1.0.1",
     summary="",
     author="paddlepaddle",
     author_email="",
-    type="nlp/semantic_model")
+    type="nlp/semantic_model",
+    meta=EmbeddingModule)
 class Embedding(TokenEmbedding):
     """
     Embedding model
     """
-    def __init__(self, *args, **kwargs):
-        super(Embedding, self).__init__(embedding_name="w2v.literature.target.word-bigram.dim300", *args, **kwargs)
-
-    @serving
-    def calc_similarity(self, data: List[List[str]]):
-        """
-        Calculate similarities of giving word pairs.
-        """
-        results = []
-        for word_pair in data:
-            if len(word_pair) != 2:
-                raise RuntimeError(
-                    f'The input must have two words, but got {len(word_pair)}. Please check your inputs.')
-            if not isinstance(word_pair[0], str) or not isinstance(word_pair[1], str):
-                raise RuntimeError(
-                    f'The types of text pair must be (str, str), but got'
-                    f' ({type(word_pair[0]).__name__}, {type(word_pair[1]).__name__}). Please check your inputs.')
+    embedding_name = "w2v.literature.target.word-bigram.dim300"
 
-            for word in word_pair:
-                if self.get_idx_from_word(word) == \
-                        self.get_idx_from_word(self.vocab.unk_token):
-                    raise RuntimeError(
-                        f'Word "{word}" is not in vocab. Please check your inputs.')
-            results.append(str(self.cosine_sim(*word_pair)))
-        return results
+    def __init__(self, *args, **kwargs):
+        super(Embedding, self).__init__(embedding_name=self.embedding_name, *args, **kwargs)

modules/text/embedding/w2v_literature_target_word-char_dim300/README.md

@@ -56,6 +56,25 @@ def dot(
 * `word_a`： 需要计算内积的单词a。
 * `word_b`： 需要计算内积的单词b。
 
+
+```python
+def get_vocab_path()
+```
+获取本地词表文件的路径信息。
+
+
+```python
+def get_tokenizer(*args, **kwargs)
+```
+获取当前模型的tokenizer，返回一个JiebaTokenizer的实例，当前只支持中文embedding模型。
+
+**参数**
+* `*args`： 额外传递的列表形式的参数。
+* `**kwargs`： 额外传递的字典形式的参数。
+
+关于额外参数的详情，可查看[paddlenlp.data.tokenizer.JiebaTokenizer](https://github.com/PaddlePaddle/models/blob/develop/PaddleNLP/paddlenlp/data/tokenizer.py)
+
+
 更多api详情和用法可参考[paddlenlp.embeddings](https://github.com/PaddlePaddle/models/tree/release/2.0-beta/PaddleNLP/paddlenlp/embeddings)
 
 ## 代码示例
@@ -125,3 +144,6 @@ paddlehub >= 2.0.0
 
   初始发布
 
+* 1.0.1
+
+  支持基于embedding的文本分类和序列标注finetune任务

modules/text/embedding/w2v_literature_target_word-char_dim300/module.py

@@ -12,44 +12,24 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from typing import List
 from paddlenlp.embeddings import TokenEmbedding
-from paddlehub.module.module import moduleinfo, serving
+from paddlehub.module.module import moduleinfo
+from paddlehub.module.nlp_module import EmbeddingModule
 
 
 @moduleinfo(
     name="w2v_literature_target_word-char_dim300",
-    version="1.0.0",
+    version="1.0.1",
     summary="",
     author="paddlepaddle",
     author_email="",
-    type="nlp/semantic_model")
+    type="nlp/semantic_model",
+    meta=EmbeddingModule)
 class Embedding(TokenEmbedding):
     """
     Embedding model
     """
-    def __init__(self, *args, **kwargs):
-        super(Embedding, self).__init__(embedding_name="w2v.literature.target.word-char.dim300", *args, **kwargs)
-
-    @serving
-    def calc_similarity(self, data: List[List[str]]):
-        """
-        Calculate similarities of giving word pairs.
-        """
-        results = []
-        for word_pair in data:
-            if len(word_pair) != 2:
-                raise RuntimeError(
-                    f'The input must have two words, but got {len(word_pair)}. Please check your inputs.')
-            if not isinstance(word_pair[0], str) or not isinstance(word_pair[1], str):
-                raise RuntimeError(
-                    f'The types of text pair must be (str, str), but got'
-                    f' ({type(word_pair[0]).__name__}, {type(word_pair[1]).__name__}). Please check your inputs.')
+    embedding_name = "w2v.literature.target.word-char.dim300"
 
-            for word in word_pair:
-                if self.get_idx_from_word(word) == \
-                        self.get_idx_from_word(self.vocab.unk_token):
-                    raise RuntimeError(
-                        f'Word "{word}" is not in vocab. Please check your inputs.')
-            results.append(str(self.cosine_sim(*word_pair)))
-        return results
+    def __init__(self, *args, **kwargs):
+        super(Embedding, self).__init__(embedding_name=self.embedding_name, *args, **kwargs)

modules/text/embedding/w2v_literature_target_word-word_dim300/README.md

@@ -56,6 +56,25 @@ def dot(
 * `word_a`： 需要计算内积的单词a。
 * `word_b`： 需要计算内积的单词b。
 
+
+```python
+def get_vocab_path()
+```
+获取本地词表文件的路径信息。
+
+
+```python
+def get_tokenizer(*args, **kwargs)
+```
+获取当前模型的tokenizer，返回一个JiebaTokenizer的实例，当前只支持中文embedding模型。
+
+**参数**
+* `*args`： 额外传递的列表形式的参数。
+* `**kwargs`： 额外传递的字典形式的参数。
+
+关于额外参数的详情，可查看[paddlenlp.data.tokenizer.JiebaTokenizer](https://github.com/PaddlePaddle/models/blob/develop/PaddleNLP/paddlenlp/data/tokenizer.py)
+
+
 更多api详情和用法可参考[paddlenlp.embeddings](https://github.com/PaddlePaddle/models/tree/release/2.0-beta/PaddleNLP/paddlenlp/embeddings)
 
 ## 代码示例
@@ -125,3 +144,6 @@ paddlehub >= 2.0.0
 
   初始发布
 
+* 1.0.1
+
+  支持基于embedding的文本分类和序列标注finetune任务

modules/text/embedding/w2v_literature_target_word-word_dim300/module.py

@@ -12,44 +12,24 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from typing import List
 from paddlenlp.embeddings import TokenEmbedding
-from paddlehub.module.module import moduleinfo, serving
+from paddlehub.module.module import moduleinfo
+from paddlehub.module.nlp_module import EmbeddingModule
 
 
 @moduleinfo(
     name="w2v_literature_target_word-word_dim300",
-    version="1.0.0",
+    version="1.0.1",
     summary="",
     author="paddlepaddle",
     author_email="",
-    type="nlp/semantic_model")
+    type="nlp/semantic_model",
+    meta=EmbeddingModule)
 class Embedding(TokenEmbedding):
     """
     Embedding model
     """
-    def __init__(self, *args, **kwargs):
-        super(Embedding, self).__init__(embedding_name="w2v.literature.target.word-word.dim300", *args, **kwargs)
-
-    @serving
-    def calc_similarity(self, data: List[List[str]]):
-        """
-        Calculate similarities of giving word pairs.
-        """
-        results = []
-        for word_pair in data:
-            if len(word_pair) != 2:
-                raise RuntimeError(
-                    f'The input must have two words, but got {len(word_pair)}. Please check your inputs.')
-            if not isinstance(word_pair[0], str) or not isinstance(word_pair[1], str):
-                raise RuntimeError(
-                    f'The types of text pair must be (str, str), but got'
-                    f' ({type(word_pair[0]).__name__}, {type(word_pair[1]).__name__}). Please check your inputs.')
+    embedding_name = "w2v.literature.target.word-word.dim300"
 
-            for word in word_pair:
-                if self.get_idx_from_word(word) == \
-                        self.get_idx_from_word(self.vocab.unk_token):
-                    raise RuntimeError(
-                        f'Word "{word}" is not in vocab. Please check your inputs.')
-            results.append(str(self.cosine_sim(*word_pair)))
-        return results
+    def __init__(self, *args, **kwargs):
+        super(Embedding, self).__init__(embedding_name=self.embedding_name, *args, **kwargs)

modules/text/embedding/w2v_mixed-large_target_word-char_dim300/README.md

@@ -56,6 +56,25 @@ def dot(
 * `word_a`： 需要计算内积的单词a。
 * `word_b`： 需要计算内积的单词b。
 
+
+```python
+def get_vocab_path()
+```
+获取本地词表文件的路径信息。
+
+
+```python
+def get_tokenizer(*args, **kwargs)
+```
+获取当前模型的tokenizer，返回一个JiebaTokenizer的实例，当前只支持中文embedding模型。
+
+**参数**
+* `*args`： 额外传递的列表形式的参数。
+* `**kwargs`： 额外传递的字典形式的参数。
+
+关于额外参数的详情，可查看[paddlenlp.data.tokenizer.JiebaTokenizer](https://github.com/PaddlePaddle/models/blob/develop/PaddleNLP/paddlenlp/data/tokenizer.py)
+
+
 更多api详情和用法可参考[paddlenlp.embeddings](https://github.com/PaddlePaddle/models/tree/release/2.0-beta/PaddleNLP/paddlenlp/embeddings)
 
 ## 代码示例
@@ -125,3 +144,6 @@ paddlehub >= 2.0.0
 
   初始发布
 
+* 1.0.1
+
+  支持基于embedding的文本分类和序列标注finetune任务

modules/text/embedding/w2v_mixed-large_target_word-char_dim300/module.py

@@ -12,44 +12,24 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from typing import List
 from paddlenlp.embeddings import TokenEmbedding
-from paddlehub.module.module import moduleinfo, serving
+from paddlehub.module.module import moduleinfo
+from paddlehub.module.nlp_module import EmbeddingModule
 
 
 @moduleinfo(
     name="w2v_mixed-large_target_word-char_dim300",
-    version="1.0.0",
+    version="1.0.1",
     summary="",
     author="paddlepaddle",
     author_email="",
-    type="nlp/semantic_model")
+    type="nlp/semantic_model",
+    meta=EmbeddingModule)
 class Embedding(TokenEmbedding):
     """
     Embedding model
     """
-    def __init__(self, *args, **kwargs):
-        super(Embedding, self).__init__(embedding_name="w2v.mixed-large.target.word-char.dim300", *args, **kwargs)
-
-    @serving
-    def calc_similarity(self, data: List[List[str]]):
-        """
-        Calculate similarities of giving word pairs.
-        """
-        results = []
-        for word_pair in data:
-            if len(word_pair) != 2:
-                raise RuntimeError(
-                    f'The input must have two words, but got {len(word_pair)}. Please check your inputs.')
-            if not isinstance(word_pair[0], str) or not isinstance(word_pair[1], str):
-                raise RuntimeError(
-                    f'The types of text pair must be (str, str), but got'
-                    f' ({type(word_pair[0]).__name__}, {type(word_pair[1]).__name__}). Please check your inputs.')
+    embedding_name = "w2v.mixed-large.target.word-char.dim300"
 
-            for word in word_pair:
-                if self.get_idx_from_word(word) == \
-                        self.get_idx_from_word(self.vocab.unk_token):
-                    raise RuntimeError(
-                        f'Word "{word}" is not in vocab. Please check your inputs.')
-            results.append(str(self.cosine_sim(*word_pair)))
-        return results
+    def __init__(self, *args, **kwargs):
+        super(Embedding, self).__init__(embedding_name=self.embedding_name, *args, **kwargs)

modules/text/embedding/w2v_mixed-large_target_word-word_dim300/README.md

@@ -56,6 +56,25 @@ def dot(
 * `word_a`： 需要计算内积的单词a。
 * `word_b`： 需要计算内积的单词b。
 
+
+```python
+def get_vocab_path()
+```
+获取本地词表文件的路径信息。
+
+
+```python
+def get_tokenizer(*args, **kwargs)
+```
+获取当前模型的tokenizer，返回一个JiebaTokenizer的实例，当前只支持中文embedding模型。
+
+**参数**
+* `*args`： 额外传递的列表形式的参数。
+* `**kwargs`： 额外传递的字典形式的参数。
+
+关于额外参数的详情，可查看[paddlenlp.data.tokenizer.JiebaTokenizer](https://github.com/PaddlePaddle/models/blob/develop/PaddleNLP/paddlenlp/data/tokenizer.py)
+
+
 更多api详情和用法可参考[paddlenlp.embeddings](https://github.com/PaddlePaddle/models/tree/release/2.0-beta/PaddleNLP/paddlenlp/embeddings)
 
 ## 代码示例
@@ -125,3 +144,6 @@ paddlehub >= 2.0.0
 
   初始发布
 
+* 1.0.1
+
+  支持基于embedding的文本分类和序列标注finetune任务

modules/text/embedding/w2v_mixed-large_target_word-word_dim300/module.py

@@ -12,44 +12,24 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from typing import List
 from paddlenlp.embeddings import TokenEmbedding
-from paddlehub.module.module import moduleinfo, serving
+from paddlehub.module.module import moduleinfo
+from paddlehub.module.nlp_module import EmbeddingModule
 
 
 @moduleinfo(
     name="w2v_mixed-large_target_word-word_dim300",
-    version="1.0.0",
+    version="1.0.1",
     summary="",
     author="paddlepaddle",
     author_email="",
-    type="nlp/semantic_model")
+    type="nlp/semantic_model",
+    meta=EmbeddingModule)
 class Embedding(TokenEmbedding):
     """
     Embedding model
     """
-    def __init__(self, *args, **kwargs):
-        super(Embedding, self).__init__(embedding_name="w2v.mixed-large.target.word-word.dim300", *args, **kwargs)
-
-    @serving
-    def calc_similarity(self, data: List[List[str]]):
-        """
-        Calculate similarities of giving word pairs.
-        """
-        results = []
-        for word_pair in data:
-            if len(word_pair) != 2:
-                raise RuntimeError(
-                    f'The input must have two words, but got {len(word_pair)}. Please check your inputs.')
-            if not isinstance(word_pair[0], str) or not isinstance(word_pair[1], str):
-                raise RuntimeError(
-                    f'The types of text pair must be (str, str), but got'
-                    f' ({type(word_pair[0]).__name__}, {type(word_pair[1]).__name__}). Please check your inputs.')
+    embedding_name = "w2v.mixed-large.target.word-word.dim300"
 
-            for word in word_pair:
-                if self.get_idx_from_word(word) == \
-                        self.get_idx_from_word(self.vocab.unk_token):
-                    raise RuntimeError(
-                        f'Word "{word}" is not in vocab. Please check your inputs.')
-            results.append(str(self.cosine_sim(*word_pair)))
-        return results
+    def __init__(self, *args, **kwargs):
+        super(Embedding, self).__init__(embedding_name=self.embedding_name, *args, **kwargs)

modules/text/embedding/w2v_people_daily_target_bigram-char_dim300/README.md

@@ -56,6 +56,25 @@ def dot(
 * `word_a`： 需要计算内积的单词a。
 * `word_b`： 需要计算内积的单词b。
 
+
+```python
+def get_vocab_path()
+```
+获取本地词表文件的路径信息。
+
+
+```python
+def get_tokenizer(*args, **kwargs)
+```
+获取当前模型的tokenizer，返回一个JiebaTokenizer的实例，当前只支持中文embedding模型。
+
+**参数**
+* `*args`： 额外传递的列表形式的参数。
+* `**kwargs`： 额外传递的字典形式的参数。
+
+关于额外参数的详情，可查看[paddlenlp.data.tokenizer.JiebaTokenizer](https://github.com/PaddlePaddle/models/blob/develop/PaddleNLP/paddlenlp/data/tokenizer.py)
+
+
 更多api详情和用法可参考[paddlenlp.embeddings](https://github.com/PaddlePaddle/models/tree/release/2.0-beta/PaddleNLP/paddlenlp/embeddings)
 
 ## 代码示例
@@ -125,3 +144,6 @@ paddlehub >= 2.0.0
 
   初始发布
 
+* 1.0.1
+
+  支持基于embedding的文本分类和序列标注finetune任务

modules/text/embedding/w2v_people_daily_target_bigram-char_dim300/module.py

@@ -12,44 +12,24 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from typing import List
 from paddlenlp.embeddings import TokenEmbedding
-from paddlehub.module.module import moduleinfo, serving
+from paddlehub.module.module import moduleinfo
+from paddlehub.module.nlp_module import EmbeddingModule
 
 
 @moduleinfo(
     name="w2v_people_daily_target_bigram-char_dim300",
-    version="1.0.0",
+    version="1.0.1",
     summary="",
     author="paddlepaddle",
     author_email="",
-    type="nlp/semantic_model")
+    type="nlp/semantic_model",
+    meta=EmbeddingModule)
 class Embedding(TokenEmbedding):
     """
     Embedding model
     """
-    def __init__(self, *args, **kwargs):
-        super(Embedding, self).__init__(embedding_name="w2v.people_daily.target.bigram-char.dim300", *args, **kwargs)
-
-    @serving
-    def calc_similarity(self, data: List[List[str]]):
-        """
-        Calculate similarities of giving word pairs.
-        """
-        results = []
-        for word_pair in data:
-            if len(word_pair) != 2:
-                raise RuntimeError(
-                    f'The input must have two words, but got {len(word_pair)}. Please check your inputs.')
-            if not isinstance(word_pair[0], str) or not isinstance(word_pair[1], str):
-                raise RuntimeError(
-                    f'The types of text pair must be (str, str), but got'
-                    f' ({type(word_pair[0]).__name__}, {type(word_pair[1]).__name__}). Please check your inputs.')
+    embedding_name = "w2v.people_daily.target.bigram-char.dim300"
 
-            for word in word_pair:
-                if self.get_idx_from_word(word) == \
-                        self.get_idx_from_word(self.vocab.unk_token):
-                    raise RuntimeError(
-                        f'Word "{word}" is not in vocab. Please check your inputs.')
-            results.append(str(self.cosine_sim(*word_pair)))
-        return results
+    def __init__(self, *args, **kwargs):
+        super(Embedding, self).__init__(embedding_name=self.embedding_name, *args, **kwargs)

modules/text/embedding/w2v_people_daily_target_word-bigram_dim300/README.md

@@ -56,6 +56,25 @@ def dot(
 * `word_a`： 需要计算内积的单词a。
 * `word_b`： 需要计算内积的单词b。
 
+
+```python
+def get_vocab_path()
+```
+获取本地词表文件的路径信息。
+
+
+```python
+def get_tokenizer(*args, **kwargs)
+```
+获取当前模型的tokenizer，返回一个JiebaTokenizer的实例，当前只支持中文embedding模型。
+
+**参数**
+* `*args`： 额外传递的列表形式的参数。
+* `**kwargs`： 额外传递的字典形式的参数。
+
+关于额外参数的详情，可查看[paddlenlp.data.tokenizer.JiebaTokenizer](https://github.com/PaddlePaddle/models/blob/develop/PaddleNLP/paddlenlp/data/tokenizer.py)
+
+
 更多api详情和用法可参考[paddlenlp.embeddings](https://github.com/PaddlePaddle/models/tree/release/2.0-beta/PaddleNLP/paddlenlp/embeddings)
 
 ## 代码示例
@@ -125,3 +144,6 @@ paddlehub >= 2.0.0
 
   初始发布
 
+* 1.0.1
+
+  支持基于embedding的文本分类和序列标注finetune任务

modules/text/embedding/w2v_people_daily_target_word-bigram_dim300/module.py

@@ -12,44 +12,24 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from typing import List
 from paddlenlp.embeddings import TokenEmbedding
-from paddlehub.module.module import moduleinfo, serving
+from paddlehub.module.module import moduleinfo
+from paddlehub.module.nlp_module import EmbeddingModule
 
 
 @moduleinfo(
     name="w2v_people_daily_target_word-bigram_dim300",
-    version="1.0.0",
+    version="1.0.1",
     summary="",
     author="paddlepaddle",
     author_email="",
-    type="nlp/semantic_model")
+    type="nlp/semantic_model",
+    meta=EmbeddingModule)
 class Embedding(TokenEmbedding):
     """
     Embedding model
     """
-    def __init__(self, *args, **kwargs):
-        super(Embedding, self).__init__(embedding_name="w2v.people_daily.target.word-bigram.dim300", *args, **kwargs)
-
-    @serving
-    def calc_similarity(self, data: List[List[str]]):
-        """
-        Calculate similarities of giving word pairs.
-        """
-        results = []
-        for word_pair in data:
-            if len(word_pair) != 2:
-                raise RuntimeError(
-                    f'The input must have two words, but got {len(word_pair)}. Please check your inputs.')
-            if not isinstance(word_pair[0], str) or not isinstance(word_pair[1], str):
-                raise RuntimeError(
-                    f'The types of text pair must be (str, str), but got'
-                    f' ({type(word_pair[0]).__name__}, {type(word_pair[1]).__name__}). Please check your inputs.')
+    embedding_name = "w2v.people_daily.target.word-bigram.dim300"
 
-            for word in word_pair:
-                if self.get_idx_from_word(word) == \
-                        self.get_idx_from_word(self.vocab.unk_token):
-                    raise RuntimeError(
-                        f'Word "{word}" is not in vocab. Please check your inputs.')
-            results.append(str(self.cosine_sim(*word_pair)))
-        return results
+    def __init__(self, *args, **kwargs):
+        super(Embedding, self).__init__(embedding_name=self.embedding_name, *args, **kwargs)

modules/text/embedding/w2v_people_daily_target_word-char_dim300/README.md

@@ -56,6 +56,25 @@ def dot(
 * `word_a`： 需要计算内积的单词a。
 * `word_b`： 需要计算内积的单词b。
 
+
+```python
+def get_vocab_path()
+```
+获取本地词表文件的路径信息。
+
+
+```python
+def get_tokenizer(*args, **kwargs)
+```
+获取当前模型的tokenizer，返回一个JiebaTokenizer的实例，当前只支持中文embedding模型。
+
+**参数**
+* `*args`： 额外传递的列表形式的参数。
+* `**kwargs`： 额外传递的字典形式的参数。
+
+关于额外参数的详情，可查看[paddlenlp.data.tokenizer.JiebaTokenizer](https://github.com/PaddlePaddle/models/blob/develop/PaddleNLP/paddlenlp/data/tokenizer.py)
+
+
 更多api详情和用法可参考[paddlenlp.embeddings](https://github.com/PaddlePaddle/models/tree/release/2.0-beta/PaddleNLP/paddlenlp/embeddings)
 
 ## 代码示例
@@ -125,3 +144,6 @@ paddlehub >= 2.0.0
 
   初始发布
 
+* 1.0.1
+
+  支持基于embedding的文本分类和序列标注finetune任务

modules/text/embedding/w2v_people_daily_target_word-char_dim300/module.py

@@ -12,44 +12,24 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from typing import List
 from paddlenlp.embeddings import TokenEmbedding
-from paddlehub.module.module import moduleinfo, serving
+from paddlehub.module.module import moduleinfo
+from paddlehub.module.nlp_module import EmbeddingModule
 
 
 @moduleinfo(
     name="w2v_people_daily_target_word-char_dim300",
-    version="1.0.0",
+    version="1.0.1",
     summary="",
     author="paddlepaddle",
     author_email="",
-    type="nlp/semantic_model")
+    type="nlp/semantic_model",
+    meta=EmbeddingModule)
 class Embedding(TokenEmbedding):
     """
     Embedding model
     """
-    def __init__(self, *args, **kwargs):
-        super(Embedding, self).__init__(embedding_name="w2v.people_daily.target.word-char.dim300", *args, **kwargs)
-
-    @serving
-    def calc_similarity(self, data: List[List[str]]):
-        """
-        Calculate similarities of giving word pairs.
-        """
-        results = []
-        for word_pair in data:
-            if len(word_pair) != 2:
-                raise RuntimeError(
-                    f'The input must have two words, but got {len(word_pair)}. Please check your inputs.')
-            if not isinstance(word_pair[0], str) or not isinstance(word_pair[1], str):
-                raise RuntimeError(
-                    f'The types of text pair must be (str, str), but got'
-                    f' ({type(word_pair[0]).__name__}, {type(word_pair[1]).__name__}). Please check your inputs.')
+    embedding_name = "w2v.people_daily.target.word-char.dim300"
 
-            for word in word_pair:
-                if self.get_idx_from_word(word) == \
-                        self.get_idx_from_word(self.vocab.unk_token):
-                    raise RuntimeError(
-                        f'Word "{word}" is not in vocab. Please check your inputs.')
-            results.append(str(self.cosine_sim(*word_pair)))
-        return results
+    def __init__(self, *args, **kwargs):
+        super(Embedding, self).__init__(embedding_name=self.embedding_name, *args, **kwargs)

modules/text/embedding/w2v_people_daily_target_word-word_dim300/README.md

@@ -56,6 +56,25 @@ def dot(
 * `word_a`： 需要计算内积的单词a。
 * `word_b`： 需要计算内积的单词b。
 
+
+```python
+def get_vocab_path()
+```
+获取本地词表文件的路径信息。
+
+
+```python
+def get_tokenizer(*args, **kwargs)
+```
+获取当前模型的tokenizer，返回一个JiebaTokenizer的实例，当前只支持中文embedding模型。
+
+**参数**
+* `*args`： 额外传递的列表形式的参数。
+* `**kwargs`： 额外传递的字典形式的参数。
+
+关于额外参数的详情，可查看[paddlenlp.data.tokenizer.JiebaTokenizer](https://github.com/PaddlePaddle/models/blob/develop/PaddleNLP/paddlenlp/data/tokenizer.py)
+
+
 更多api详情和用法可参考[paddlenlp.embeddings](https://github.com/PaddlePaddle/models/tree/release/2.0-beta/PaddleNLP/paddlenlp/embeddings)
 
 ## 代码示例
@@ -125,3 +144,6 @@ paddlehub >= 2.0.0
 
   初始发布
 
+* 1.0.1
+
+  支持基于embedding的文本分类和序列标注finetune任务

modules/text/embedding/w2v_people_daily_target_word-word_dim300/module.py

@@ -12,44 +12,24 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from typing import List
 from paddlenlp.embeddings import TokenEmbedding
-from paddlehub.module.module import moduleinfo, serving
+from paddlehub.module.module import moduleinfo
+from paddlehub.module.nlp_module import EmbeddingModule
 
 
 @moduleinfo(
     name="w2v_people_daily_target_word-word_dim300",
-    version="1.0.0",
+    version="1.0.1",
     summary="",
     author="paddlepaddle",
     author_email="",
-    type="nlp/semantic_model")
+    type="nlp/semantic_model",
+    meta=EmbeddingModule)
 class Embedding(TokenEmbedding):
     """
     Embedding model
     """
-    def __init__(self, *args, **kwargs):
-        super(Embedding, self).__init__(embedding_name="w2v.people_daily.target.word-word.dim300", *args, **kwargs)
-
-    @serving
-    def calc_similarity(self, data: List[List[str]]):
-        """
-        Calculate similarities of giving word pairs.
-        """
-        results = []
-        for word_pair in data:
-            if len(word_pair) != 2:
-                raise RuntimeError(
-                    f'The input must have two words, but got {len(word_pair)}. Please check your inputs.')
-            if not isinstance(word_pair[0], str) or not isinstance(word_pair[1], str):
-                raise RuntimeError(
-                    f'The types of text pair must be (str, str), but got'
-                    f' ({type(word_pair[0]).__name__}, {type(word_pair[1]).__name__}). Please check your inputs.')
+    embedding_name = "w2v.people_daily.target.word-word.dim300"
 
-            for word in word_pair:
-                if self.get_idx_from_word(word) == \
-                        self.get_idx_from_word(self.vocab.unk_token):
-                    raise RuntimeError(
-                        f'Word "{word}" is not in vocab. Please check your inputs.')
-            results.append(str(self.cosine_sim(*word_pair)))
-        return results
+    def __init__(self, *args, **kwargs):
+        super(Embedding, self).__init__(embedding_name=self.embedding_name, *args, **kwargs)

modules/text/embedding/w2v_sikuquanshu_target_word-bigram_dim300/README.md

@@ -56,6 +56,25 @@ def dot(
 * `word_a`： 需要计算内积的单词a。
 * `word_b`： 需要计算内积的单词b。
 
+
+```python
+def get_vocab_path()
+```
+获取本地词表文件的路径信息。
+
+
+```python
+def get_tokenizer(*args, **kwargs)
+```
+获取当前模型的tokenizer，返回一个JiebaTokenizer的实例，当前只支持中文embedding模型。
+
+**参数**
+* `*args`： 额外传递的列表形式的参数。
+* `**kwargs`： 额外传递的字典形式的参数。
+
+关于额外参数的详情，可查看[paddlenlp.data.tokenizer.JiebaTokenizer](https://github.com/PaddlePaddle/models/blob/develop/PaddleNLP/paddlenlp/data/tokenizer.py)
+
+
 更多api详情和用法可参考[paddlenlp.embeddings](https://github.com/PaddlePaddle/models/tree/release/2.0-beta/PaddleNLP/paddlenlp/embeddings)
 
 ## 代码示例
@@ -125,3 +144,6 @@ paddlehub >= 2.0.0
 
   初始发布
 
+* 1.0.1
+
+  支持基于embedding的文本分类和序列标注finetune任务

modules/text/embedding/w2v_sikuquanshu_target_word-bigram_dim300/module.py

@@ -12,44 +12,24 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from typing import List
 from paddlenlp.embeddings import TokenEmbedding
-from paddlehub.module.module import moduleinfo, serving
+from paddlehub.module.module import moduleinfo
+from paddlehub.module.nlp_module import EmbeddingModule
 
 
 @moduleinfo(
     name="w2v_sikuquanshu_target_word-bigram_dim300",
-    version="1.0.0",
+    version="1.0.1",
     summary="",
     author="paddlepaddle",
     author_email="",
-    type="nlp/semantic_model")
+    type="nlp/semantic_model",
+    meta=EmbeddingModule)
 class Embedding(TokenEmbedding):
     """
     Embedding model
     """
-    def __init__(self, *args, **kwargs):
-        super(Embedding, self).__init__(embedding_name="w2v.sikuquanshu.target.word-bigram.dim300", *args, **kwargs)
-
-    @serving
-    def calc_similarity(self, data: List[List[str]]):
-        """
-        Calculate similarities of giving word pairs.
-        """
-        results = []
-        for word_pair in data:
-            if len(word_pair) != 2:
-                raise RuntimeError(
-                    f'The input must have two words, but got {len(word_pair)}. Please check your inputs.')
-            if not isinstance(word_pair[0], str) or not isinstance(word_pair[1], str):
-                raise RuntimeError(
-                    f'The types of text pair must be (str, str), but got'
-                    f' ({type(word_pair[0]).__name__}, {type(word_pair[1]).__name__}). Please check your inputs.')
+    embedding_name = "w2v.sikuquanshu.target.word-bigram.dim300"
 
-            for word in word_pair:
-                if self.get_idx_from_word(word) == \
-                        self.get_idx_from_word(self.vocab.unk_token):
-                    raise RuntimeError(
-                        f'Word "{word}" is not in vocab. Please check your inputs.')
-            results.append(str(self.cosine_sim(*word_pair)))
-        return results
+    def __init__(self, *args, **kwargs):
+        super(Embedding, self).__init__(embedding_name=self.embedding_name, *args, **kwargs)

modules/text/embedding/w2v_sikuquanshu_target_word-word_dim300/README.md

@@ -56,6 +56,25 @@ def dot(
 * `word_a`： 需要计算内积的单词a。
 * `word_b`： 需要计算内积的单词b。
 
+
+```python
+def get_vocab_path()
+```
+获取本地词表文件的路径信息。
+
+
+```python
+def get_tokenizer(*args, **kwargs)
+```
+获取当前模型的tokenizer，返回一个JiebaTokenizer的实例，当前只支持中文embedding模型。
+
+**参数**
+* `*args`： 额外传递的列表形式的参数。
+* `**kwargs`： 额外传递的字典形式的参数。
+
+关于额外参数的详情，可查看[paddlenlp.data.tokenizer.JiebaTokenizer](https://github.com/PaddlePaddle/models/blob/develop/PaddleNLP/paddlenlp/data/tokenizer.py)
+
+
 更多api详情和用法可参考[paddlenlp.embeddings](https://github.com/PaddlePaddle/models/tree/release/2.0-beta/PaddleNLP/paddlenlp/embeddings)
 
 ## 代码示例
@@ -125,3 +144,6 @@ paddlehub >= 2.0.0
 
   初始发布
 
+* 1.0.1
+
+  支持基于embedding的文本分类和序列标注finetune任务

modules/text/embedding/w2v_sikuquanshu_target_word-word_dim300/module.py

@@ -12,44 +12,24 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from typing import List
 from paddlenlp.embeddings import TokenEmbedding
-from paddlehub.module.module import moduleinfo, serving
+from paddlehub.module.module import moduleinfo
+from paddlehub.module.nlp_module import EmbeddingModule
 
 
 @moduleinfo(
     name="w2v_sikuquanshu_target_word-word_dim300",
-    version="1.0.0",
+    version="1.0.1",
     summary="",
     author="paddlepaddle",
     author_email="",
-    type="nlp/semantic_model")
+    type="nlp/semantic_model",
+    meta=EmbeddingModule)
 class Embedding(TokenEmbedding):
     """
     Embedding model
     """
-    def __init__(self, *args, **kwargs):
-        super(Embedding, self).__init__(embedding_name="w2v.sikuquanshu.target.word-word.dim300", *args, **kwargs)
-
-    @serving
-    def calc_similarity(self, data: List[List[str]]):
-        """
-        Calculate similarities of giving word pairs.
-        """
-        results = []
-        for word_pair in data:
-            if len(word_pair) != 2:
-                raise RuntimeError(
-                    f'The input must have two words, but got {len(word_pair)}. Please check your inputs.')
-            if not isinstance(word_pair[0], str) or not isinstance(word_pair[1], str):
-                raise RuntimeError(
-                    f'The types of text pair must be (str, str), but got'
-                    f' ({type(word_pair[0]).__name__}, {type(word_pair[1]).__name__}). Please check your inputs.')
+    embedding_name = "w2v.sikuquanshu.target.word-word.dim300"
 
-            for word in word_pair:
-                if self.get_idx_from_word(word) == \
-                        self.get_idx_from_word(self.vocab.unk_token):
-                    raise RuntimeError(
-                        f'Word "{word}" is not in vocab. Please check your inputs.')
-            results.append(str(self.cosine_sim(*word_pair)))
-        return results
+    def __init__(self, *args, **kwargs):
+        super(Embedding, self).__init__(embedding_name=self.embedding_name, *args, **kwargs)

modules/text/embedding/w2v_sogou_target_bigram-char_dim300/README.md

@@ -56,6 +56,25 @@ def dot(
 * `word_a`： 需要计算内积的单词a。
 * `word_b`： 需要计算内积的单词b。
 
+
+```python
+def get_vocab_path()
+```
+获取本地词表文件的路径信息。
+
+
+```python
+def get_tokenizer(*args, **kwargs)
+```
+获取当前模型的tokenizer，返回一个JiebaTokenizer的实例，当前只支持中文embedding模型。
+
+**参数**
+* `*args`： 额外传递的列表形式的参数。
+* `**kwargs`： 额外传递的字典形式的参数。
+
+关于额外参数的详情，可查看[paddlenlp.data.tokenizer.JiebaTokenizer](https://github.com/PaddlePaddle/models/blob/develop/PaddleNLP/paddlenlp/data/tokenizer.py)
+
+
 更多api详情和用法可参考[paddlenlp.embeddings](https://github.com/PaddlePaddle/models/tree/release/2.0-beta/PaddleNLP/paddlenlp/embeddings)
 
 ## 代码示例
@@ -125,3 +144,6 @@ paddlehub >= 2.0.0
 
   初始发布
 
+* 1.0.1
+
+  支持基于embedding的文本分类和序列标注finetune任务

modules/text/embedding/w2v_sogou_target_bigram-char_dim300/module.py

@@ -12,44 +12,24 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from typing import List
 from paddlenlp.embeddings import TokenEmbedding
-from paddlehub.module.module import moduleinfo, serving
+from paddlehub.module.module import moduleinfo
+from paddlehub.module.nlp_module import EmbeddingModule
 
 
 @moduleinfo(
     name="w2v_sogou_target_bigram-char_dim300",
-    version="1.0.0",
+    version="1.0.1",
     summary="",
     author="paddlepaddle",
     author_email="",
-    type="nlp/semantic_model")
+    type="nlp/semantic_model",
+    meta=EmbeddingModule)
 class Embedding(TokenEmbedding):
     """
     Embedding model
     """
-    def __init__(self, *args, **kwargs):
-        super(Embedding, self).__init__(embedding_name="w2v.sogou.target.bigram-char.dim300", *args, **kwargs)
-
-    @serving
-    def calc_similarity(self, data: List[List[str]]):
-        """
-        Calculate similarities of giving word pairs.
-        """
-        results = []
-        for word_pair in data:
-            if len(word_pair) != 2:
-                raise RuntimeError(
-                    f'The input must have two words, but got {len(word_pair)}. Please check your inputs.')
-            if not isinstance(word_pair[0], str) or not isinstance(word_pair[1], str):
-                raise RuntimeError(
-                    f'The types of text pair must be (str, str), but got'
-                    f' ({type(word_pair[0]).__name__}, {type(word_pair[1]).__name__}). Please check your inputs.')
+    embedding_name = "w2v.sogou.target.bigram-char.dim300"
 
-            for word in word_pair:
-                if self.get_idx_from_word(word) == \
-                        self.get_idx_from_word(self.vocab.unk_token):
-                    raise RuntimeError(
-                        f'Word "{word}" is not in vocab. Please check your inputs.')
-            results.append(str(self.cosine_sim(*word_pair)))
-        return results
+    def __init__(self, *args, **kwargs):
+        super(Embedding, self).__init__(embedding_name=self.embedding_name, *args, **kwargs)

modules/text/embedding/w2v_sogou_target_word-bigram_dim300/README.md

@@ -56,6 +56,25 @@ def dot(
 * `word_a`： 需要计算内积的单词a。
 * `word_b`： 需要计算内积的单词b。
 
+
+```python
+def get_vocab_path()
+```
+获取本地词表文件的路径信息。
+
+
+```python
+def get_tokenizer(*args, **kwargs)
+```
+获取当前模型的tokenizer，返回一个JiebaTokenizer的实例，当前只支持中文embedding模型。
+
+**参数**
+* `*args`： 额外传递的列表形式的参数。
+* `**kwargs`： 额外传递的字典形式的参数。
+
+关于额外参数的详情，可查看[paddlenlp.data.tokenizer.JiebaTokenizer](https://github.com/PaddlePaddle/models/blob/develop/PaddleNLP/paddlenlp/data/tokenizer.py)
+
+
 更多api详情和用法可参考[paddlenlp.embeddings](https://github.com/PaddlePaddle/models/tree/release/2.0-beta/PaddleNLP/paddlenlp/embeddings)
 
 ## 代码示例
@@ -125,3 +144,6 @@ paddlehub >= 2.0.0
 
   初始发布
 
+* 1.0.1
+
+  支持基于embedding的文本分类和序列标注finetune任务

modules/text/embedding/w2v_sogou_target_word-bigram_dim300/module.py

@@ -12,44 +12,24 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from typing import List
 from paddlenlp.embeddings import TokenEmbedding
-from paddlehub.module.module import moduleinfo, serving
+from paddlehub.module.module import moduleinfo
+from paddlehub.module.nlp_module import EmbeddingModule
 
 
 @moduleinfo(
     name="w2v_sogou_target_word-bigram_dim300",
-    version="1.0.0",
+    version="1.0.1",
     summary="",
     author="paddlepaddle",
     author_email="",
-    type="nlp/semantic_model")
+    type="nlp/semantic_model",
+    meta=EmbeddingModule)
 class Embedding(TokenEmbedding):
     """
     Embedding model
     """
-    def __init__(self, *args, **kwargs):
-        super(Embedding, self).__init__(embedding_name="w2v.sogou.target.word-bigram.dim300", *args, **kwargs)
-
-    @serving
-    def calc_similarity(self, data: List[List[str]]):
-        """
-        Calculate similarities of giving word pairs.
-        """
-        results = []
-        for word_pair in data:
-            if len(word_pair) != 2:
-                raise RuntimeError(
-                    f'The input must have two words, but got {len(word_pair)}. Please check your inputs.')
-            if not isinstance(word_pair[0], str) or not isinstance(word_pair[1], str):
-                raise RuntimeError(
-                    f'The types of text pair must be (str, str), but got'
-                    f' ({type(word_pair[0]).__name__}, {type(word_pair[1]).__name__}). Please check your inputs.')
+    embedding_name = "w2v.sogou.target.word-bigram.dim300"
 
-            for word in word_pair:
-                if self.get_idx_from_word(word) == \
-                        self.get_idx_from_word(self.vocab.unk_token):
-                    raise RuntimeError(
-                        f'Word "{word}" is not in vocab. Please check your inputs.')
-            results.append(str(self.cosine_sim(*word_pair)))
-        return results
+    def __init__(self, *args, **kwargs):
+        super(Embedding, self).__init__(embedding_name=self.embedding_name, *args, **kwargs)

modules/text/embedding/w2v_sogou_target_word-char_dim300/README.md

@@ -56,6 +56,25 @@ def dot(
 * `word_a`： 需要计算内积的单词a。
 * `word_b`： 需要计算内积的单词b。
 
+
+```python
+def get_vocab_path()
+```
+获取本地词表文件的路径信息。
+
+
+```python
+def get_tokenizer(*args, **kwargs)
+```
+获取当前模型的tokenizer，返回一个JiebaTokenizer的实例，当前只支持中文embedding模型。
+
+**参数**
+* `*args`： 额外传递的列表形式的参数。
+* `**kwargs`： 额外传递的字典形式的参数。
+
+关于额外参数的详情，可查看[paddlenlp.data.tokenizer.JiebaTokenizer](https://github.com/PaddlePaddle/models/blob/develop/PaddleNLP/paddlenlp/data/tokenizer.py)
+
+
 更多api详情和用法可参考[paddlenlp.embeddings](https://github.com/PaddlePaddle/models/tree/release/2.0-beta/PaddleNLP/paddlenlp/embeddings)
 
 ## 代码示例
@@ -125,3 +144,6 @@ paddlehub >= 2.0.0
 
   初始发布
 
+* 1.0.1
+
+  支持基于embedding的文本分类和序列标注finetune任务

modules/text/embedding/w2v_sogou_target_word-char_dim300/module.py

@@ -12,44 +12,24 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from typing import List
 from paddlenlp.embeddings import TokenEmbedding
-from paddlehub.module.module import moduleinfo, serving
+from paddlehub.module.module import moduleinfo
+from paddlehub.module.nlp_module import EmbeddingModule
 
 
 @moduleinfo(
     name="w2v_sogou_target_word-char_dim300",
-    version="1.0.0",
+    version="1.0.1",
     summary="",
     author="paddlepaddle",
     author_email="",
-    type="nlp/semantic_model")
+    type="nlp/semantic_model",
+    meta=EmbeddingModule)
 class Embedding(TokenEmbedding):
     """
     Embedding model
     """
-    def __init__(self, *args, **kwargs):
-        super(Embedding, self).__init__(embedding_name="w2v.sogou.target.word-char.dim300", *args, **kwargs)
-
-    @serving
-    def calc_similarity(self, data: List[List[str]]):
-        """
-        Calculate similarities of giving word pairs.
-        """
-        results = []
-        for word_pair in data:
-            if len(word_pair) != 2:
-                raise RuntimeError(
-                    f'The input must have two words, but got {len(word_pair)}. Please check your inputs.')
-            if not isinstance(word_pair[0], str) or not isinstance(word_pair[1], str):
-                raise RuntimeError(
-                    f'The types of text pair must be (str, str), but got'
-                    f' ({type(word_pair[0]).__name__}, {type(word_pair[1]).__name__}). Please check your inputs.')
+    embedding_name = "w2v.sogou.target.word-char.dim300"
 
-            for word in word_pair:
-                if self.get_idx_from_word(word) == \
-                        self.get_idx_from_word(self.vocab.unk_token):
-                    raise RuntimeError(
-                        f'Word "{word}" is not in vocab. Please check your inputs.')
-            results.append(str(self.cosine_sim(*word_pair)))
-        return results
+    def __init__(self, *args, **kwargs):
+        super(Embedding, self).__init__(embedding_name=self.embedding_name, *args, **kwargs)

modules/text/embedding/w2v_sogou_target_word-word_dim300/README.md

@@ -56,6 +56,25 @@ def dot(
 * `word_a`： 需要计算内积的单词a。
 * `word_b`： 需要计算内积的单词b。
 
+
+```python
+def get_vocab_path()
+```
+获取本地词表文件的路径信息。
+
+
+```python
+def get_tokenizer(*args, **kwargs)
+```
+获取当前模型的tokenizer，返回一个JiebaTokenizer的实例，当前只支持中文embedding模型。
+
+**参数**
+* `*args`： 额外传递的列表形式的参数。
+* `**kwargs`： 额外传递的字典形式的参数。
+
+关于额外参数的详情，可查看[paddlenlp.data.tokenizer.JiebaTokenizer](https://github.com/PaddlePaddle/models/blob/develop/PaddleNLP/paddlenlp/data/tokenizer.py)
+
+
 更多api详情和用法可参考[paddlenlp.embeddings](https://github.com/PaddlePaddle/models/tree/release/2.0-beta/PaddleNLP/paddlenlp/embeddings)
 
 ## 代码示例
@@ -125,3 +144,6 @@ paddlehub >= 2.0.0
 
   初始发布
 
+* 1.0.1
+
+  支持基于embedding的文本分类和序列标注finetune任务

modules/text/embedding/w2v_sogou_target_word-word_dim300/module.py

@@ -12,44 +12,24 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from typing import List
 from paddlenlp.embeddings import TokenEmbedding
-from paddlehub.module.module import moduleinfo, serving
+from paddlehub.module.module import moduleinfo
+from paddlehub.module.nlp_module import EmbeddingModule
 
 
 @moduleinfo(
     name="w2v_sogou_target_word-word_dim300",
-    version="1.0.0",
+    version="1.0.1",
     summary="",
     author="paddlepaddle",
     author_email="",
-    type="nlp/semantic_model")
+    type="nlp/semantic_model",
+    meta=EmbeddingModule)
 class Embedding(TokenEmbedding):
     """
     Embedding model
     """
-    def __init__(self, *args, **kwargs):
-        super(Embedding, self).__init__(embedding_name="w2v.sogou.target.word-word.dim300", *args, **kwargs)
-
-    @serving
-    def calc_similarity(self, data: List[List[str]]):
-        """
-        Calculate similarities of giving word pairs.
-        """
-        results = []
-        for word_pair in data:
-            if len(word_pair) != 2:
-                raise RuntimeError(
-                    f'The input must have two words, but got {len(word_pair)}. Please check your inputs.')
-            if not isinstance(word_pair[0], str) or not isinstance(word_pair[1], str):
-                raise RuntimeError(
-                    f'The types of text pair must be (str, str), but got'
-                    f' ({type(word_pair[0]).__name__}, {type(word_pair[1]).__name__}). Please check your inputs.')
+    embedding_name = "w2v.sogou.target.word-word.dim300"
 
-            for word in word_pair:
-                if self.get_idx_from_word(word) == \
-                        self.get_idx_from_word(self.vocab.unk_token):
-                    raise RuntimeError(
-                        f'Word "{word}" is not in vocab. Please check your inputs.')
-            results.append(str(self.cosine_sim(*word_pair)))
-        return results
+    def __init__(self, *args, **kwargs):
+        super(Embedding, self).__init__(embedding_name=self.embedding_name, *args, **kwargs)

modules/text/embedding/w2v_weibo_target_bigram-char_dim300/README.md

@@ -56,6 +56,25 @@ def dot(
 * `word_a`： 需要计算内积的单词a。
 * `word_b`： 需要计算内积的单词b。
 
+
+```python
+def get_vocab_path()
+```
+获取本地词表文件的路径信息。
+
+
+```python
+def get_tokenizer(*args, **kwargs)
+```
+获取当前模型的tokenizer，返回一个JiebaTokenizer的实例，当前只支持中文embedding模型。
+
+**参数**
+* `*args`： 额外传递的列表形式的参数。
+* `**kwargs`： 额外传递的字典形式的参数。
+
+关于额外参数的详情，可查看[paddlenlp.data.tokenizer.JiebaTokenizer](https://github.com/PaddlePaddle/models/blob/develop/PaddleNLP/paddlenlp/data/tokenizer.py)
+
+
 更多api详情和用法可参考[paddlenlp.embeddings](https://github.com/PaddlePaddle/models/tree/release/2.0-beta/PaddleNLP/paddlenlp/embeddings)
 
 ## 代码示例
@@ -125,3 +144,6 @@ paddlehub >= 2.0.0
 
   初始发布
 
+* 1.0.1
+
+  支持基于embedding的文本分类和序列标注finetune任务

modules/text/embedding/w2v_weibo_target_bigram-char_dim300/module.py

@@ -12,44 +12,24 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from typing import List
 from paddlenlp.embeddings import TokenEmbedding
-from paddlehub.module.module import moduleinfo, serving
+from paddlehub.module.module import moduleinfo
+from paddlehub.module.nlp_module import EmbeddingModule
 
 
 @moduleinfo(
     name="w2v_weibo_target_bigram-char_dim300",
-    version="1.0.0",
+    version="1.0.1",
     summary="",
     author="paddlepaddle",
     author_email="",
-    type="nlp/semantic_model")
+    type="nlp/semantic_model",
+    meta=EmbeddingModule)
 class Embedding(TokenEmbedding):
     """
     Embedding model
     """
-    def __init__(self, *args, **kwargs):
-        super(Embedding, self).__init__(embedding_name="w2v.weibo.target.bigram-char.dim300", *args, **kwargs)
-
-    @serving
-    def calc_similarity(self, data: List[List[str]]):
-        """
-        Calculate similarities of giving word pairs.
-        """
-        results = []
-        for word_pair in data:
-            if len(word_pair) != 2:
-                raise RuntimeError(
-                    f'The input must have two words, but got {len(word_pair)}. Please check your inputs.')
-            if not isinstance(word_pair[0], str) or not isinstance(word_pair[1], str):
-                raise RuntimeError(
-                    f'The types of text pair must be (str, str), but got'
-                    f' ({type(word_pair[0]).__name__}, {type(word_pair[1]).__name__}). Please check your inputs.')
+    embedding_name = "w2v.weibo.target.bigram-char.dim300"
 
-            for word in word_pair:
-                if self.get_idx_from_word(word) == \
-                        self.get_idx_from_word(self.vocab.unk_token):
-                    raise RuntimeError(
-                        f'Word "{word}" is not in vocab. Please check your inputs.')
-            results.append(str(self.cosine_sim(*word_pair)))
-        return results
+    def __init__(self, *args, **kwargs):
+        super(Embedding, self).__init__(embedding_name=self.embedding_name, *args, **kwargs)