diff --git a/docker-compose-gpu.yml b/docker-compose-gpu.yml
index bab364c..40cc513 100644
--- a/docker-compose-gpu.yml
+++ b/docker-compose-gpu.yml
@@ -71,7 +71,7 @@ services:
       context: ./src
       dockerfile: Dockerfile-gpu
     runtime: nvidia
-    command: ['celery', '-A', 'app.celeryapp:michaniki_celery_app', 'worker', '-l', 'info']
+    command: ['celery', '-A', 'app.celeryapp:michaniki_celery_app', 'worker', '-l', 'info','--max-tasks-per-child','1']
     volumes:
       - ./src:/opt/src
     networks:
diff --git a/src/app/apis/SentimentV1/API_helpers_nlp.py b/src/app/apis/SentimentV1/API_helpers_nlp.py
index f088cb3..8cf1c5e 100644
--- a/src/app/apis/SentimentV1/API_helpers_nlp.py
+++ b/src/app/apis/SentimentV1/API_helpers_nlp.py
@@ -64,7 +64,7 @@ def download_a_dir_from_s3(bucket_name, local_path):
     logging.info("* Helper: Text Loaded at: {}".format(output_path))
     return output_path
 
-def download_test_file_from_s3(bucket_name, bucket_prefix, local_path):
+def download_test_file_from_s3(bucket_name, local_path):
     """
     download the folder from S3
 
diff --git a/src/app/models/SentimentV1/sentimentV1_transfer_retraining.py b/src/app/models/SentimentV1/sentimentV1_transfer_retraining.py
index 4bdbc96..e4271bb 100644
--- a/src/app/models/SentimentV1/sentimentV1_transfer_retraining.py
+++ b/src/app/models/SentimentV1/sentimentV1_transfer_retraining.py
@@ -15,6 +15,7 @@
 import run_classifier
 import tokenization
 import tensorflow as tf
+import csv
 
 import settings
 import datetime
@@ -28,6 +29,39 @@
 class BertTransferLeaner:
     def __init__(self, model_name):
         self.model_name = model_name
+    
+    def getlabel(self,data_dir):
+        with tf.gfile.Open(os.path.join(data_dir, "train.tsv"),"r") as f:
+            reader = csv.reader(f, delimiter="\t", quotechar=None)
+            label = []
+            for line in reader:
+                line_label = line[1]
+                if line_label not in label:
+                    label.append(line_label)
+        return label
+    
+    def cal_test_examples(self,data_dir,firstlabel,set_type):
+        examples = []
+        with tf.gfile.Open(os.path.join(data_dir, "test.tsv"), "r") as f:
+            reader = csv.reader(f, delimiter="\t", quotechar=None)
+            lines = []
+            for line in reader:
+                lines.append(line)
+        for (i, line) in enumerate(lines):
+            # Only the test set has a header
+            if set_type == "test" and i == 0:
+                continue
+            guid = "%s-%s" % (set_type, i)
+            if set_type == "test":
+                text_a = tokenization.convert_to_unicode(line[1])
+                label = firstlabel
+            else:
+                text_a = tokenization.convert_to_unicode(line[3])
+                label = tokenization.convert_to_unicode(line[1])
+            examples.append(
+                run_classifier.InputExample(guid=guid, text_a=text_a, text_b=None, label=label))
+        return examples
+
 
     def traineval_model(self, local_dir,
                        nb_epoch,
@@ -71,7 +105,9 @@ def traineval_model(self, local_dir,
         bert_config = modeling.BertConfig.from_json_file(BERT_CONFIG_FILE)
         tf.gfile.MakeDirs(OUTPUT_DIR)
         processor = run_classifier.ColaProcessor()
-        label_list = processor.get_labels()
+        #label_list = processor.get_labels()
+        label_list = self.getlabel(DATA_DIR)
+        print label_list
         tokenizer = tokenization.FullTokenizer(
         vocab_file=VOCAB_FILE, do_lower_case=DO_LOWER_CASE)
 
@@ -215,7 +251,9 @@ def test_model(self, local_dir,
         bert_config = modeling.BertConfig.from_json_file(BERT_CONFIG_FILE)
         tf.gfile.MakeDirs(OUTPUT_DIR)
         processor = run_classifier.ColaProcessor()
-        label_list = processor.get_labels()
+        #label_list = processor.get_labels()
+        label_list = self.getlabel(DATA_DIR)
+        print label_list
         tokenizer = tokenization.FullTokenizer(
         vocab_file=VOCAB_FILE, do_lower_case=DO_LOWER_CASE)
 
@@ -254,7 +292,8 @@ def test_model(self, local_dir,
             eval_batch_size=EVAL_BATCH_SIZE,
             predict_batch_size=PREDICT_BATCH_SIZE)
         
-        predict_examples = processor.get_test_examples(DATA_DIR)
+        #predict_examples = processor.get_test_examples(DATA_DIR)
+        predict_examples = self.cal_test_examples(DATA_DIR,label_list[0],"test")
         num_actual_predict_examples = len(predict_examples)
         predict_file = os.path.join(OUTPUT_DIR, "predict.tf_record")
         run_classifier.file_based_convert_examples_to_features(predict_examples, label_list,