update (#3718)

PaddleNLP/sentiment_classification/README.md

@@ -118,10 +118,10 @@ Running type options:
 
 Model config options:
   --model_type {bow_net,cnn_net,lstm_net,bilstm_net,gru_net,textcnn_net}
-                        Model type to run the task. Default: textcnn_net.
+                        Model type to run the task. Default: bilstm_net.
   --init_checkpoint INIT_CHECKPOINT
                         Init checkpoint to resume training from. Default: .
-  --save_checkpoint_dir SAVE_CHECKPOINT_DIR
+  --checkpoints SAVE_CHECKPOINT_DIR
                         Directory path to save checkpoints Default: .
 ...
 """

PaddleNLP/sentiment_classification/download.py

+# Copyright (c) 2019 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.
+
+"""
+Download script, download dataset and pretrain models.
+"""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import io
+import os
+import sys
+import time
+import hashlib
+import tarfile
+import requests
+
+def usage():
+    desc = ("\nDownload datasets and pretrained models for Sentiment Classification task.\n"
+        "Usage:\n"
+        "   1. python download.py dataset\n"
+        "   2. python download.py model\n")
+    print(desc)
+
+def extract(fname, dir_path):
+    """
+    Extract tar.gz file
+    """
+    try:
+        tar = tarfile.open(fname, "r:gz")
+        file_names = tar.getnames()
+        for file_name in file_names:
+            tar.extract(file_name, dir_path)
+            print(file_name)
+        tar.close()
+    except Exception as e:
+        raise e
+
+def download(url, filename):
+    """
+    Download file
+    """
+    retry = 0
+    retry_limit = 3
+    chunk_size = 4096
+    while not (os.path.exists(filename):
+        if retry < retry_limit:
+            retry += 1
+        else:
+            raise RuntimeError("Cannot download dataset ({0}) with retry {1} times.".
+                format(url, retry_limit))
+        try:
+            start = time.time()
+            size = 0
+            res = requests.get(url, stream=True)
+            filesize = int(res.headers['content-length'])
+            if res.status_code == 200:
+                print("[Filesize]: %0.2f MB" % (filesize / 1024 / 1024))
+                # save by chunk
+                with io.open(filename, "wb") as fout:
+                    for chunk in res.iter_content(chunk_size=chunk_size):
+                        if chunk:
+                            fout.write(chunk)
+                            size += len(chunk)
+                            pr = '>' * int(size * 50 / filesize)
+                            print('\r[Process ]: %s%.2f%%' % (pr, float(size / filesize*100)), end='')
+            end = time.time()
+            print("\n[CostTime]: %.2f s" % (end - start))
+        except Exception as e:
+            print(e)
+
+def download_dataset(dir_path):
+    BASE_URL = "https://baidu-nlp.bj.bcebos.com/"
+    DATASET_NAME = "sentiment_classification-dataset-1.0.0.tar.gz"
+    file_path = os.path.join(dir_path, DATASET_NAME)
+    url = BASE_URL + DATASET_NAME
+    
+    if not os.path.exists(dir_path):
+        os.makedirs(dir_path)
+    # download dataset
+    print("Downloading dataset: %s" % url)
+    download(url, file_path)
+    # extract dataset
+    print("Extracting dataset: %s" % file_path)
+    extract(file_path, dir_path)
+    os.remove(file_path)
+
+def download_model(dir_path):
+    BASE_URL = "https://baidu-nlp.bj.bcebos.com/"
+    MODEL_NAME = "sentiment_classification-1.0.0.tar.gz"
+    if not os.path.exists(dir_path):
+        os.makedirs(dir_path)
+    url = BASE_URL + MODEL_NAME
+    model_path = os.path.join(dir_path, model)
+    print("Downloading model: %s" % url)
+    # download model
+    download(url, model_path, MODEL_NAME)
+    # extract model.tar.gz
+    print("Extracting model: %s" % model_path)
+    extract(model_path, dir_path)
+    os.remove(model_path)
+
+if __name__ == "__main__":
+    if len(sys) != 2:
+        usage()
+        sys.exit(1)
+    
+    if sys.argv[1] == "dataset":
+        pwd = os.path.join(os.path.dirname(__file__), "./")
+        download_dataset(pwd)
+    elif sys.argv[1] == "model":
+        pwd = os.path.join(os.path.dirname(__file__), "./models")
+        download_model(pwd)
+    else:
+        usage()

PaddleNLP/sentiment_classification/inference_model_ernie.py

@@ -14,7 +14,7 @@ import utils
 import reader
 from run_ernie_classifier import ernie_pyreader
 from models.representation.ernie import ErnieConfig
-from models.representation.ernie import ernie_encoder
+from models.representation.ernie import ernie_encoder, ernie_encoder_with_paddle_hub
 from preprocess.ernie import task_reader
 
 def do_save_inference_model(args):
@@ -39,8 +39,11 @@ def do_save_inference_model(args):
             infer_pyreader, ernie_inputs, labels = ernie_pyreader(
                 args,
                 pyreader_name="infer_reader")
-
-            embeddings = ernie_encoder(ernie_inputs, ernie_config=ernie_config)
+            
+            if args.use_paddle_hub:
+                embeddings = ernie_encoder_with_paddle_hub(ernie_inputs, args.max_seq_len)
+            else:
+                embeddings = ernie_encoder(ernie_inputs, ernie_config=ernie_config)
 
             probs = create_model(args,
                     embeddings,