diff --git a/reproduction/chinese_word_seg/cws.cfg b/reproduction/chinese_word_seg/cws.cfg
new file mode 100644
index 00000000..ded4f623
--- /dev/null
+++ b/reproduction/chinese_word_seg/cws.cfg
@@ -0,0 +1,29 @@
+[train]
+epochs = 2
+batch_size = 32
+pickle_path = "./save/"
+validate = true
+save_best_dev = true
+model_saved_path = "./save/"
+rnn_hidden_units = 100
+rnn_layers = 2
+rnn_bi_direction = true
+word_emb_dim = 100
+dropout = 0.5
+use_crf = true
+use_cuda = true
+
+[test]
+save_output = true
+validate_in_training = true
+save_dev_input = false
+save_loss = true
+batch_size = 64
+pickle_path = "./save/"
+rnn_hidden_units = 100
+rnn_layers = 1
+rnn_bi_direction = true
+word_emb_dim = 100
+dropout = 0.5
+use_crf = true
+use_cuda = true
diff --git a/reproduction/chinese_word_seg/cws_train.py b/reproduction/chinese_word_seg/cws_train.py
new file mode 100644
index 00000000..6616ff5f
--- /dev/null
+++ b/reproduction/chinese_word_seg/cws_train.py
@@ -0,0 +1,110 @@
+import sys
+
+sys.path.append("..")
+
+from fastNLP.loader.config_loader import ConfigLoader, ConfigSection
+from fastNLP.core.trainer import POSTrainer
+from fastNLP.loader.dataset_loader import TokenizeDatasetLoader, BaseLoader
+from fastNLP.loader.preprocess import POSPreprocess, load_pickle
+from fastNLP.saver.model_saver import ModelSaver
+from fastNLP.loader.model_loader import ModelLoader
+from fastNLP.core.tester import POSTester
+from fastNLP.models.sequence_modeling import SeqLabeling
+from fastNLP.core.inference import Inference
+
+data_name = "pku_training.utf8"
+cws_data_path = "/home/zyfeng/data/pku_training.utf8"
+pickle_path = "./save/"
+data_infer_path = "data_for_tests/people_infer.txt"
+
+
+def infer():
+    # Load infer configuration, the same as test
+    test_args = ConfigSection()
+    ConfigLoader("config.cfg", "").load_config("./data_for_tests/config", {"POS_test": test_args})
+
+    # fetch dictionary size and number of labels from pickle files
+    word2index = load_pickle(pickle_path, "word2id.pkl")
+    test_args["vocab_size"] = len(word2index)
+    index2label = load_pickle(pickle_path, "id2class.pkl")
+    test_args["num_classes"] = len(index2label)
+
+    # Define the same model
+    model = SeqLabeling(test_args)
+
+    # Dump trained parameters into the model
+    ModelLoader.load_pytorch(model, "./data_for_tests/saved_model.pkl")
+    print("model loaded!")
+
+    # Data Loader
+    raw_data_loader = BaseLoader(data_name, data_infer_path)
+    infer_data = raw_data_loader.load_lines()
+
+    # Inference interface
+    infer = Inference(pickle_path)
+    results = infer.predict(model, infer_data)
+
+    print(results)
+    print("Inference finished!")
+
+
+def train():
+    # Config Loader
+    train_args = ConfigSection()
+    test_args = ConfigSection()
+    ConfigLoader("good_name", "good_path").load_config("./cws.cfg", {"train": train_args, "test": test_args})
+
+    # Data Loader
+    loader = TokenizeDatasetLoader(data_name, cws_data_path)
+    train_data = loader.load_pku()
+
+    # Preprocessor
+    p = POSPreprocess(train_data, pickle_path)
+    train_args["vocab_size"] = p.vocab_size
+    train_args["num_classes"] = p.num_classes
+
+    # Trainer
+    trainer = POSTrainer(train_args)
+
+    # Model
+    model = SeqLabeling(train_args)
+
+    # Start training
+    trainer.train(model)
+    print("Training finished!")
+
+    # Saver
+    saver = ModelSaver("./save/saved_model.pkl")
+    saver.save_pytorch(model)
+    print("Model saved!")
+
+
+def test():
+    # Config Loader
+    train_args = ConfigSection()
+    ConfigLoader("config.cfg", "").load_config("./data_for_tests/config", {"POS": train_args})
+
+    # Define the same model
+    model = SeqLabeling(train_args)
+
+    # Dump trained parameters into the model
+    ModelLoader.load_pytorch(model, "./data_for_tests/saved_model.pkl")
+    print("model loaded!")
+
+    # Load test configuration
+    test_args = ConfigSection()
+    ConfigLoader("config.cfg", "").load_config("./data_for_tests/config", {"POS_test": test_args})
+
+    # Tester
+    tester = POSTester(test_args)
+
+    # Start testing
+    tester.test(model)
+
+    # print test results
+    print(tester.show_matrices())
+    print("model tested!")
+
+
+if __name__ == "__main__":
+    train()