Add embedding finetune demo (#1204)

* Add embedding seq-cls finetune demo and update api

* Update docs of pad_sequence and trunc_sequence

demo/text_classification/embedding/model.py

+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import os
+from typing import List
+
+import paddle
+import paddle.nn as nn
+import paddle.nn.functional as F
+
+import paddlenlp as nlp
+from paddlenlp.embeddings import TokenEmbedding
+from paddlenlp.data import JiebaTokenizer
+
+from paddlehub.utils.log import logger
+from paddlehub.utils.utils import pad_sequence, trunc_sequence
+
+
+class BoWModel(nn.Layer):
+    """
+    This class implements the Bag of Words Classification Network model to classify texts.
+    At a high level, the model starts by embedding the tokens and running them through
+    a word embedding. Then, we encode these epresentations with a `BoWEncoder`.
+    Lastly, we take the output of the encoder to create a final representation,
+    which is passed through some feed-forward layers to output a logits (`output_layer`).
+    Args:
+        vocab_size (obj:`int`): The vocabulary size.
+        emb_dim (obj:`int`, optional, defaults to 300):  The embedding dimension.
+        hidden_size (obj:`int`, optional, defaults to 128): The first full-connected layer hidden size.
+        fc_hidden_size (obj:`int`, optional, defaults to 96): The second full-connected layer hidden size.
+        num_classes (obj:`int`): All the labels that the data has.
+    """
+
+    def __init__(self,
+                 num_classes: int = 2,
+                 embedder: TokenEmbedding = None,
+                 tokenizer: JiebaTokenizer = None,
+                 hidden_size: int = 128,
+                 fc_hidden_size: int = 96,
+                 load_checkpoint: str = None,
+                 label_map: dict = None):
+        super().__init__()
+        self.embedder = embedder
+        self.tokenizer = tokenizer
+        self.label_map = label_map
+
+        emb_dim = self.embedder.embedding_dim
+        self.bow_encoder = nlp.seq2vec.BoWEncoder(emb_dim)
+        self.fc1 = nn.Linear(self.bow_encoder.get_output_dim(), hidden_size)
+        self.fc2 = nn.Linear(hidden_size, fc_hidden_size)
+        self.dropout = nn.Dropout(p=0.3, axis=1)
+        self.output_layer = nn.Linear(fc_hidden_size, num_classes)
+        self.criterion = nn.loss.CrossEntropyLoss()
+        self.metric = paddle.metric.Accuracy()
+
+        if load_checkpoint is not None and os.path.isfile(load_checkpoint):
+            state_dict = paddle.load(load_checkpoint)
+            self.set_state_dict(state_dict)
+            logger.info('Loaded parameters from %s' % os.path.abspath(load_checkpoint))
+
+    def training_step(self, batch: List[paddle.Tensor], batch_idx: int):
+        """
+        One step for training, which should be called as forward computation.
+        Args:
+            batch(:obj:List[paddle.Tensor]): The one batch data, which contains the model needed,
+                such as input_ids, sent_ids, pos_ids, input_mask and labels.
+            batch_idx(int): The index of batch.
+        Returns:
+            results(:obj: Dict) : The model outputs, such as loss and metrics.
+        """
+        _, avg_loss, metric = self(ids=batch[0], labels=batch[1])
+        self.metric.reset()
+        return {'loss': avg_loss, 'metrics': metric}
+
+    def validation_step(self, batch: List[paddle.Tensor], batch_idx: int):
+        """
+        One step for validation, which should be called as forward computation.
+        Args:
+            batch(:obj:List[paddle.Tensor]): The one batch data, which contains the model needed,
+                such as input_ids, sent_ids, pos_ids, input_mask and labels.
+            batch_idx(int): The index of batch.
+        Returns:
+            results(:obj: Dict) : The model outputs, such as metrics.
+        """
+        _, _, metric = self(ids=batch[0], labels=batch[1])
+        self.metric.reset()
+        return {'metrics': metric}
+
+    def forward(self, ids: paddle.Tensor, labels: paddle.Tensor = None):
+
+        # Shape: (batch_size, num_tokens, embedding_dim)
+        embedded_text = self.embedder(ids)
+
+        # Shape: (batch_size, embedding_dim)
+        summed = self.bow_encoder(embedded_text)
+        summed = self.dropout(summed)
+        encoded_text = paddle.tanh(summed)
+
+        # Shape: (batch_size, hidden_size)
+        fc1_out = paddle.tanh(self.fc1(encoded_text))
+        # Shape: (batch_size, fc_hidden_size)
+        fc2_out = paddle.tanh(self.fc2(fc1_out))
+        # Shape: (batch_size, num_classes)
+        logits = self.output_layer(fc2_out)
+
+        probs = F.softmax(logits, axis=1)
+        if labels is not None:
+            loss = self.criterion(logits, labels)
+            correct = self.metric.compute(probs, labels)
+            acc = self.metric.update(correct)
+            return probs, loss, {'acc': acc}
+        else:
+            return probs
+
+    def _batchify(self, data: List[List[str]], max_seq_len: int, batch_size: int):
+        examples = []
+        for item in data:
+            ids = self.tokenizer.encode(sentence=item[0])
+
+            if len(ids) > max_seq_len:
+                ids = trunc_sequence(ids, max_seq_len)
+            else:
+                pad_token = self.tokenizer.vocab.pad_token
+                pad_token_id = self.tokenizer.vocab.to_indices(pad_token)
+                ids = pad_sequence(ids, max_seq_len, pad_token_id)
+            examples.append(ids)
+
+        # Seperates data into some batches.
+        one_batch = []
+        for example in examples:
+            one_batch.append(example)
+            if len(one_batch) == batch_size:
+                yield one_batch
+                one_batch = []
+        if one_batch:
+            # The last batch whose size is less than the config batch_size setting.
+            yield one_batch
+
+    def predict(
+            self,
+            data: List[List[str]],
+            max_seq_len: int = 128,
+            batch_size: int = 1,
+            use_gpu: bool = False,
+            return_result: bool = True,
+    ):
+        paddle.set_device('gpu') if use_gpu else paddle.set_device('cpu')
+
+        batches = self._batchify(data, max_seq_len, batch_size)
+        results = []
+        self.eval()
+        for batch in batches:
+            ids = paddle.to_tensor(batch)
+            probs = self(ids)
+            idx = paddle.argmax(probs, axis=1).numpy()
+
+            if return_result:
+                idx = idx.tolist()
+                labels = [self.label_map[i] for i in idx]
+                results.extend(labels)
+            else:
+                results.extend(probs.numpy())
+
+        return results

demo/text_classification/embedding/predict.py

+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import paddlehub as hub
+from paddlenlp.data import JiebaTokenizer
+from model import BoWModel
+
+import ast
+import argparse
+
+
+parser = argparse.ArgumentParser(__doc__)
+parser.add_argument("--hub_embedding_name", type=str, default='w2v_baidu_encyclopedia_target_word-word_dim300', help="")
+parser.add_argument("--max_seq_len", type=int, default=128, help="Number of words of the longest seqence.")
+parser.add_argument("--batch_size", type=int, default=64, help="Total examples' number in batch for training.")
+parser.add_argument("--checkpoint", type=str, default='./checkpoint/best_model/model.pdparams', help="Model checkpoint")
+parser.add_argument("--use_gpu", type=ast.literal_eval, default=True, help="Whether use GPU for fine-tuning, input should be True or False")
+
+args = parser.parse_args()
+
+
+if __name__ == '__main__':
+    # Data to be prdicted
+    data = [
+        ["这个宾馆比较陈旧了，特价的房间也很一般。总体来说一般"],
+        ["交通方便；环境很好；服务态度很好 房间较小"],
+        ["还稍微重了点，可能是硬盘大的原故，还要再轻半斤就好了。其他要进一步验证。贴的几种膜气泡较多，用不了多久就要更换了，屏幕膜稍好点，但比没有要强多了。建议配赠几张膜让用用户自己贴。"],
+        ["前台接待太差，酒店有A B楼之分，本人check－in后，前台未告诉B楼在何处，并且B楼无明显指示；房间太小，根本不像4星级设施，下次不会再选择入住此店啦"],
+        ["19天硬盘就罢工了~~~算上运来的一周都没用上15天~~~可就是不能换了~~~唉~~~~你说这算什么事呀~~~"],
+    ]
+
+    label_map = {0: 'negative', 1: 'positive'}
+
+    embedder = hub.Module(name=args.hub_embedding_name)
+    tokenizer = embedder.get_tokenizer()
+    model = BoWModel(
+        embedder=embedder,
+        tokenizer=tokenizer,
+        load_checkpoint=args.checkpoint,
+        label_map=label_map)
+
+    results = model.predict(data, max_seq_len=args.max_seq_len, batch_size=args.batch_size, use_gpu=args.use_gpu, return_result=False)
+    for idx, text in enumerate(data):
+         print('Data: {} \t Lable: {}'.format(text[0], results[idx]))

demo/text_classification/embedding/train.py

+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import paddle
+import paddlehub as hub
+from paddlehub.datasets import ChnSentiCorp
+from paddlenlp.data import JiebaTokenizer
+from model import BoWModel
+
+import ast
+import argparse
+
+
+parser = argparse.ArgumentParser(__doc__)
+parser.add_argument("--hub_embedding_name", type=str, default='w2v_baidu_encyclopedia_target_word-word_dim300', help="")
+parser.add_argument("--num_epoch", type=int, default=10, help="Number of epoches for fine-tuning.")
+parser.add_argument("--learning_rate", type=float, default=5e-4, help="Learning rate used to train with warmup.")
+parser.add_argument("--max_seq_len", type=int, default=128, help="Number of words of the longest seqence.")
+parser.add_argument("--batch_size", type=int, default=64, help="Total examples' number in batch for training.")
+parser.add_argument("--checkpoint_dir", type=str, default='./checkpoint', help="Directory to model checkpoint")
+parser.add_argument("--save_interval", type=int, default=5, help="Save checkpoint every n epoch.")
+parser.add_argument("--use_gpu", type=ast.literal_eval, default=True, help="Whether use GPU for fine-tuning, input should be True or False")
+
+args = parser.parse_args()
+
+
+if __name__ == '__main__':
+    embedder = hub.Module(name=args.hub_embedding_name)
+    tokenizer = embedder.get_tokenizer()
+
+    train_dataset = ChnSentiCorp(tokenizer=tokenizer, max_seq_len=args.max_seq_len, mode='train')
+    dev_dataset = ChnSentiCorp(tokenizer=tokenizer, max_seq_len=args.max_seq_len, mode='dev')
+    test_dataset = ChnSentiCorp(tokenizer=tokenizer, max_seq_len=args.max_seq_len, mode='test')
+
+    model = BoWModel(embedder=embedder)
+    optimizer = paddle.optimizer.AdamW(
+        learning_rate=args.learning_rate, parameters=model.parameters())
+    trainer = hub.Trainer(model, optimizer, checkpoint_dir=args.checkpoint_dir, use_gpu=args.use_gpu)
+    trainer.train(
+        train_dataset,
+        epochs=args.num_epoch,
+        batch_size=args.batch_size,
+        eval_dataset=dev_dataset,
+        save_interval=args.save_interval,
+    )
+    trainer.evaluate(test_dataset, batch_size=args.batch_size)

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

@@ -15,6 +15,7 @@
 from typing import List
 from paddlenlp.embeddings import TokenEmbedding
 from paddlehub.module.module import moduleinfo, serving
+from paddlehub.module.nlp_module import EmbeddingModule
 
 
 @moduleinfo(
@@ -23,33 +24,13 @@ from paddlehub.module.module import moduleinfo, serving
     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-word.dim300", *args, **kwargs)
+    embedding_name = 'w2v.baidu_encyclopedia.target.word-word.dim300'
 
-    @serving
-    def calc_similarity(self, data: List[List[str]]):
-        """
+    def __init__(self, *args, **kwargs):
+        super(Embedding, self).__init__(embedding_name=self.embedding_name, *args, **kwargs)
\ No newline at end of file
-        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.')
-
-            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

paddlehub/datasets/base_nlp_dataset.py

@@ -20,13 +20,14 @@ import numpy as np
 import paddle
 
 from paddlehub.env import DATA_HOME
-from paddlehub.text.bert_tokenizer import BertTokenizer
-from paddlehub.text.tokenizer import CustomTokenizer
+from paddlenlp.transformers import PretrainedTokenizer
+from paddlenlp.data import JiebaTokenizer
 from paddlehub.utils.log import logger
-from paddlehub.utils.utils import download, reseg_token_label
+from paddlehub.utils.utils import download, reseg_token_label, pad_sequence, trunc_sequence
 from paddlehub.utils.xarfile import is_xarfile, unarchive
 
 
+
 class InputExample(object):
     """
     The input data structure of Transformer modules (BERT, ERNIE and so on).
@@ -72,7 +73,7 @@ class BaseNLPDataset(object):
 
     def __init__(self,
                  base_path: str,
-                 tokenizer: Union[BertTokenizer, CustomTokenizer],
+                 tokenizer: Union[PretrainedTokenizer, JiebaTokenizer],
                  max_seq_len: Optional[int] = 128,
                  mode: Optional[str] = "train",
                  data_file: Optional[str] = None,
@@ -81,7 +82,7 @@ class BaseNLPDataset(object):
         """
         Ags:
             base_path (:obj:`str`): The directory to the whole dataset.
-            tokenizer (:obj:`BertTokenizer` or :obj:`CustomTokenizer`):
+            tokenizer (:obj:`PretrainedTokenizer` or :obj:`JiebaTokenizer`):
                 It tokenizes the text and encodes the data as model needed.
             max_seq_len (:obj:`int`, `optional`, defaults to :128):
                 If set to a number, will limit the total sequence returned so that it has a maximum length.
@@ -159,7 +160,7 @@ class TextClassificationDataset(BaseNLPDataset, paddle.io.Dataset):
 
     def __init__(self,
                  base_path: str,
-                 tokenizer: Union[BertTokenizer, CustomTokenizer],
+                 tokenizer: Union[PretrainedTokenizer, JiebaTokenizer],
                  max_seq_len: int = 128,
                  mode: str = "train",
                  data_file: str = None,
@@ -169,7 +170,7 @@ class TextClassificationDataset(BaseNLPDataset, paddle.io.Dataset):
         """
         Ags:
             base_path (:obj:`str`): The directory to the whole dataset.
-            tokenizer (:obj:`BertTokenizer` or :obj:`CustomTokenizer`):
+            tokenizer (:obj:`PretrainedTokenizer` or :obj:`JiebaTokenizer`):
                 It tokenizes the text and encodes the data as model needed.
             max_seq_len (:obj:`int`, `optional`, defaults to :128):
                 If set to a number, will limit the total sequence returned so that it has a maximum length.
@@ -231,9 +232,22 @@ class TextClassificationDataset(BaseNLPDataset, paddle.io.Dataset):
         """
         records = []
         for example in examples:
+            if isinstance(self.tokenizer, PretrainedTokenizer):
                 record = self.tokenizer.encode(text=example.text_a, text_pair=example.text_b, max_seq_len=self.max_seq_len)
-            # CustomTokenizer will tokenize the text firstly and then lookup words in the vocab
-            # When all words are not found in the vocab, the text will be dropped.
+            elif isinstance(self.tokenizer, JiebaTokenizer):
+                pad_token = self.tokenizer.vocab.pad_token
+
+                ids = self.tokenizer.encode(sentence=example.text_a)
+                seq_len = min(len(ids), self.max_seq_len)
+                if len(ids) > self.max_seq_len:
+                    ids = trunc_sequence(ids, self.max_seq_len)
+                else:
+                    pad_token_id = self.tokenizer.vocab.to_indices(pad_token)
+                    ids = pad_sequence(ids, self.max_seq_len, pad_token_id)
+                record = {'text': ids, 'seq_len': seq_len}
+            else:
+                raise RuntimeError("Unknown type of self.tokenizer: {}, it must be an instance of  PretrainedTokenizer or JiebaTokenizer".format(type(self.tokenizer)))
+
             if not record:
                 logger.info(
                     "The text %s has been dropped as it has no words in the vocab after tokenization." % example.text_a)
@@ -245,19 +259,53 @@ class TextClassificationDataset(BaseNLPDataset, paddle.io.Dataset):
 
     def __getitem__(self, idx):
         record = self.records[idx]
+        if isinstance(self.tokenizer, PretrainedTokenizer):
             if 'label' in record.keys():
                 return np.array(record['input_ids']), np.array(record['segment_ids']), np.array(record['label'], dtype=np.int64)
             else:
                 return np.array(record['input_ids']), np.array(record['segment_ids'])
+        elif isinstance(self.tokenizer, JiebaTokenizer):
+            if 'label' in record.keys():
+                return np.array(record['text']), np.array(record['label'], dtype=np.int64)
+            else:
+                return np.array(record['text'])
+        else:
+            raise RuntimeError("Unknown type of self.tokenizer: {}, it must be an instance of  PretrainedTokenizer or JiebaTokenizer".format(type(self.tokenizer)))
 
     def __len__(self):
         return len(self.records)
 
 
 class SeqLabelingDataset(BaseNLPDataset, paddle.io.Dataset):
+    """
+    Ags:
+        base_path (:obj:`str`): The directory to the whole dataset.
+        tokenizer (:obj:`PretrainedTokenizer` or :obj:`JiebaTokenizer`):
+            It tokenizes the text and encodes the data as model needed.
+        max_seq_len (:obj:`int`, `optional`, defaults to :128):
+            If set to a number, will limit the total sequence returned so that it has a maximum length.
+        mode (:obj:`str`, `optional`, defaults to `train`):
+            It identifies the dataset mode (train, test or dev).
+        data_file(:obj:`str`, `optional`, defaults to :obj:`None`):
+            The data file name, which is relative to the base_path.
+        label_file(:obj:`str`, `optional`, defaults to :obj:`None`):
+            The label file name, which is relative to the base_path.
+            It is all labels of the dataset, one line one label.
+        label_list(:obj:`List[str]`, `optional`, defaults to :obj:`None`):
+            The list of all labels of the dataset
+        split_char(:obj:`str`, `optional`, defaults to :obj:`\002`):
+            The symbol used to split chars in text and labels
+        no_entity_label(:obj:`str`, `optional`, defaults to :obj:`O`):
+            The label used to mark no entities
+        ignore_label(:obj:`int`, `optional`, defaults to :-100):
+            If one token's label == ignore_label, it will be ignored when
+            calculating loss
+        is_file_with_header(:obj:bool, `optional`, default to :obj: False) :
+            Whether or not the file is with the header introduction.
+    """
     def __init__(self,
                  base_path: str,
-                 tokenizer: Union[BertTokenizer, CustomTokenizer],
+                 tokenizer: Union[PretrainedTokenizer, JiebaTokenizer],
                  max_seq_len: int = 128,
                  mode: str = "train",
                  data_file: str = None,
@@ -309,22 +357,46 @@ class SeqLabelingDataset(BaseNLPDataset, paddle.io.Dataset):
         """
         records = []
         for example in examples:
-            tokens, labels = reseg_token_label(
-                tokenizer=self.tokenizer,
-                tokens=example.text_a.split(self.split_char),
-                labels=example.label.split(self.split_char))
-            record = self.tokenizer.encode(
-                text=tokens, max_seq_len=self.max_seq_len)
-            # CustomTokenizer will tokenize the text firstly and then lookup words in the vocab
-            # When all words are not found in the vocab, the text will be dropped.
+            tokens = example.text_a.split(self.split_char)
+            labels = example.label.split(self.split_char)
+
+            # convert tokens into record
+            if isinstance(self.tokenizer, PretrainedTokenizer):
+                pad_token = self.tokenizer.pad_token
+
+                tokens, labels = reseg_token_label(tokenizer=self.tokenizer, tokens=tokens, labels=labels)
+                record = self.tokenizer.encode(text=tokens, max_seq_len=self.max_seq_len)
+            elif isinstance(self.tokenizer, JiebaTokenizer):
+                pad_token = self.tokenizer.vocab.pad_token
+
+                ids = [self.tokenizer.vocab.to_indices(token) for token in tokens]
+                seq_len = min(len(ids), self.max_seq_len)
+                if len(ids) > self.max_seq_len:
+                    ids = trunc_sequence(ids, self.max_seq_len)
+                else:
+                    pad_token_id = self.tokenizer.vocab.to_indices(pad_token)
+                    ids = pad_sequence(ids, self.max_seq_len, pad_token_id)
+
+                record = {'text': ids, 'seq_len': seq_len}
+            else:
+                raise RuntimeError("Unknown type of self.tokenizer: {}, it must be an instance of  PretrainedTokenizer or JiebaTokenizer".format(type(self.tokenizer)))
+
             if not record:
                 logger.info(
                     "The text %s has been dropped as it has no words in the vocab after tokenization."
                     % example.text_a)
                 continue
+
+            # convert labels into record
             if labels:
                 record["label"] = []
+                if isinstance(self.tokenizer, PretrainedTokenizer):
                     tokens_with_specical_token = self.tokenizer.convert_ids_to_tokens(record['input_ids'])
+                elif isinstance(self.tokenizer, JiebaTokenizer):
+                    tokens_with_specical_token = [self.tokenizer.vocab.to_tokens(id_) for id_ in record['text']]
+                else:
+                    raise RuntimeError("Unknown type of self.tokenizer: {}, it must be an instance of  PretrainedTokenizer or JiebaTokenizer".format(type(self.tokenizer)))
+
                 tokens_index = 0
                 for token in tokens_with_specical_token:
                     if tokens_index < len(
@@ -332,7 +404,7 @@ class SeqLabelingDataset(BaseNLPDataset, paddle.io.Dataset):
                         record["label"].append(
                             self.label_list.index(labels[tokens_index]))
                         tokens_index += 1
-                    elif token in [self.tokenizer.pad_token]:
+                    elif token in [pad_token]:
                         record["label"].append(self.ignore_label)  # label of special token
                     else:
                         record["label"].append(
@@ -342,10 +414,18 @@ class SeqLabelingDataset(BaseNLPDataset, paddle.io.Dataset):
 
     def __getitem__(self, idx):
         record = self.records[idx]
+        if isinstance(self.tokenizer, PretrainedTokenizer):
             if 'label' in record.keys():
                 return np.array(record['input_ids']), np.array(record['segment_ids']), np.array(record['seq_len']), np.array(record['label'], dtype=np.int64)
             else:
                 return np.array(record['input_ids']), np.array(record['segment_ids']), np.array(record['seq_len'])
+        elif isinstance(self.tokenizer, JiebaTokenizer):
+            if 'label' in record.keys():
+                return np.array(record['text']), np.array(record['seq_len']), np.array(record['label'], dtype=np.int64)
+            else:
+                return np.array(record['text']), np.array(record['seq_len'])
+        else:
+            raise RuntimeError("Unknown type of self.tokenizer: {}, it must be an instance of  PretrainedTokenizer or JiebaTokenizer".format(type(self.tokenizer)))
 
     def __len__(self):
         return len(self.records)

paddlehub/module/nlp_module.py

@@ -32,6 +32,9 @@ from paddlehub.module.module import serving, RunModule, runnable
 from paddlehub.utils.log import logger
 from paddlehub.utils.utils import reseg_token_label
 
+from paddlenlp.embeddings.token_embedding import EMBEDDING_HOME, EMBEDDING_URL_ROOT
+from paddlenlp.data import JiebaTokenizer
+
 __all__ = [
     'PretrainedModel',
     'register_base_model',
@@ -510,6 +513,7 @@ class TransformerModule(RunModule, TextServing):
             batch_size: int = 1,
             use_gpu: bool = False
     ):
+
         """
         Predicts the data labels.
 
@@ -563,3 +567,63 @@ class TransformerModule(RunModule, TextServing):
                 ])
 
         return results
+
+
+class EmbeddingServing(object):
+    """
+    A base class for embedding model which supports serving.
+    """
+    @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.')
+
+            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
+
+
+class EmbeddingModule(RunModule, EmbeddingServing):
+    """
+    The base class for Embedding models.
+    """
+    base_url = 'https://paddlenlp.bj.bcebos.com/models/embeddings/'
+
+    def _download_vocab(self):
+        """
+        Download vocab from url
+        """
+        url = EMBEDDING_URL_ROOT + '/' + f'vocab.{self.embedding_name}'
+        get_path_from_url(url, EMBEDDING_HOME)
+
+    def get_vocab_path(self):
+        """
+        Get local vocab path
+        """
+        vocab_path = os.path.join(EMBEDDING_HOME, f'vocab.{self.embedding_name}')
+        if not os.path.exists(vocab_path):
+            self._download_vocab()
+        return vocab_path
+
+    def get_tokenizer(self, *args, **kwargs):
+        """
+        Get tokenizer of embedding module
+        """
+        if self.embedding_name.endswith('.en'):  # English
+            raise NotImplementedError  # TODO: (chenxiaojie) add tokenizer of English embedding
+        else:                                    # Chinese
+            return JiebaTokenizer(self.vocab)

paddlehub/utils/utils.py

@@ -367,3 +367,23 @@ def reseg_token_label(tokenizer, tokens: List[str], labels: List[str] = None):
             if len(sub_token) < 2:
                 continue
         return ret_tokens, None
+
+
+def pad_sequence(ids: List[int], max_seq_len: int, pad_token_id: int):
+    '''
+    Pads a sequence to max_seq_len
+    '''
+    assert len(ids) <= max_seq_len, \
+        f'The input length {len(ids)} is greater than max_seq_len {max_seq_len}. '\
+        'Please check the input list and max_seq_len if you really want to pad a sequence.'
+    return ids[:] + [pad_token_id]*(max_seq_len-len(ids))
+
+
+def trunc_sequence(ids: List[int], max_seq_len: int):
+    '''
+    Truncates a sequence to max_seq_len
+    '''
+    assert len(ids) >= max_seq_len, \
+        f'The input length {len(ids)} is less than max_seq_len {max_seq_len}. ' \
+        'Please check the input list and max_seq_len if you really want to truncate a sequence.'
+    return ids[:max_seq_len]