rm reduntant code

fluid/PaddleNLP/sequence_tagging_for_ner/infer.py

@@ -64,7 +64,7 @@ def infer(model_path, batch_size, test_data_file, vocab_file, target_file,
 
 if __name__ == "__main__":
     infer(
-        model_path="models/params_pass_16",
+        model_path="models/params_pass_0",
         batch_size=6,
         test_data_file="data/test",
         vocab_file="data/vocab.txt",

fluid/PaddleNLP/sequence_tagging_for_ner/train.py

@@ -65,7 +65,7 @@ def main(train_data_file,
         input=feature_out, param_attr=fluid.ParamAttr(name='crfw'))
 
     (precision, recall, f1_score, num_infer_chunks, num_label_chunks,
-     num_correct_chunks) = fluid.layers.chunk_eval(
+    num_correct_chunks) = fluid.layers.chunk_eval(
         input=crf_decode,
         label=target,
         chunk_scheme="IOB",
@@ -74,14 +74,9 @@ def main(train_data_file,
 
     inference_program = fluid.default_main_program().clone(for_test=True)
     test_fetch_list = [num_infer_chunks, num_label_chunks, num_correct_chunks]
-    sgd_optimizer = fluid.optimizer.SGD(learning_rate=1e-2)
+    sgd_optimizer = fluid.optimizer.SGD(learning_rate=1e-3)
     sgd_optimizer.minimize(avg_cost)
 
-
-    #print(inference_program)
-    #exit(0)
-    #test_fetch_list = [num_infer_chunks, num_label_chunks, num_correct_chunks]
-
     if "CE_MODE_X" not in os.environ:
         train_reader = paddle.batch(
             paddle.reader.shuffle(
@@ -115,7 +110,6 @@ def main(train_data_file,
     for pass_id in six.moves.xrange(num_passes):
         chunk_evaluator.reset()
         for batch_id, data in enumerate(train_reader()):
-            #print(data)
             cost_var, nums_infer, nums_label, nums_correct = exe.run(
                 fluid.default_main_program(),
                 feed=feeder.feed(data),
@@ -151,8 +145,8 @@ def main(train_data_file,
 
 if __name__ == "__main__":
     main(
-        train_data_file="data/train.txt",
-        test_data_file="data/test.txt",
+        train_data_file="data/train",
+        test_data_file="data/test",
         vocab_file="data/vocab.txt",
         target_file="data/target.txt",
         emb_file="data/wordVectors.txt",