diff --git a/reproduction/matching/matching.py b/reproduction/matching/matching.py
deleted file mode 100644
index 8251b3bc..00000000
--- a/reproduction/matching/matching.py
+++ /dev/null
@@ -1,44 +0,0 @@
-import os
-
-import torch
-
-from fastNLP.core import Trainer, Tester, Adam, AccuracyMetric, Const
-
-from fastNLP.io.dataset_loader import MatchingLoader
-
-from reproduction.matching.model.bert import BertForNLI
-from reproduction.matching.model.esim import ESIMModel
-
-
-bert_dirs = 'path/to/bert/dir'
-
-# load data set
-# data_info = MatchingLoader(data_format='snli', for_model='bert', bert_dir=bert_dirs).process(...
-data_info = MatchingLoader(data_format='snli', for_model='esim').process(
-    {'train': './data/snli/snli_1.0_train.jsonl',
-     'dev': './data/snli/snli_1.0_dev.jsonl',
-     'test': './data/snli/snli_1.0_test.jsonl'},
-    input_field=[Const.TARGET]
-)
-
-# model = BertForNLI(bert_dir=bert_dirs)
-model = ESIMModel(data_info.embeddings['elmo'],)
-
-trainer = Trainer(train_data=data_info.datasets['train'], model=model,
-                  optimizer=Adam(lr=1e-4, model_params=model.parameters()),
-                  batch_size=torch.cuda.device_count() * 24, n_epochs=20, print_every=-1,
-                  dev_data=data_info.datasets['dev'],
-                  metrics=AccuracyMetric(), metric_key='acc', device=[i for i in range(torch.cuda.device_count())],
-                  check_code_level=-1)
-trainer.train(load_best_model=True)
-
-tester = Tester(
-    data=data_info.datasets['test'],
-    model=model,
-    metrics=AccuracyMetric(),
-    batch_size=torch.cuda.device_count() * 12,
-    device=[i for i in range(torch.cuda.device_count())],
-)
-tester.test()
-
-