add senta demo

python/examples/senta/README.md

+# Chinese sentence sentiment classification
+
+## Get model files and sample data
+```
+sh get_data.sh
+```
+
+## Start rpc service
+
+
+## Start http service
+```
+python senta_web_service.py senta_bilstm_model/ workdir 9292
+```
+
+## Client prediction
+```
+curl -H "Content-Type:application/json" -X POST -d '{"words": "天气不错 | 0", "fetch":["sentence_feature"]}' http://127.0.0.1:9393/senta/prediction
+```

python/examples/senta/get_data.sh

+wget https://paddle-serving.bj.bcebos.com/paddle_hub_models/text/SentimentAnalysis/senta_bilstm.tar.gz --no-check-certificate
+tar -xzvf senta_bilstm.tar.gz
+
+wget https://baidu-nlp.bj.bcebos.com/sentiment_classification-dataset-1.0.0.tar.gz
+tar -zxvf sentiment_classification-dataset-1.0.0.tar.gz

python/examples/senta/senta_reader.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 sys
+import io
+
+
+class SentaReader():
+    def __init__(self, vocab_path, max_seq_len=20):
+        self.max_seq_len = max_seq_len
+        self.word_dict = self.load_vocab(vocab_path)
+
+    def load_vocab(self, vocab_path):
+        """
+        load the given vocabulary
+        """
+        vocab = {}
+        with io.open(vocab_path, 'r', encoding='utf8') as f:
+            wid = 0
+            for line in f:
+                if line.strip() not in vocab:
+                    vocab[line.strip()] = wid
+                    wid += 1
+        vocab["<unk>"] = len(vocab)
+        return vocab
+
+    def process(self, cols):
+        unk_id = len(self.word_dict)
+        pad_id = 0
+        wids = [
+            self.word_dict[x] if x in self.word_dict else unk_id for x in cols
+        ]
+        seq_len = len(wids)
+        if seq_len < self.max_seq_len:
+            for i in range(self.max_seq_len - seq_len):
+                wids.append(pad_id)
+        else:
+            wids = wids[:self.max_seq_len]
+            seq_len = self.max_seq_len
+
+        return wids