diff --git a/examples/multi-task/evaluate-slot.py b/examples/multi-task/evaluate-slot.py
new file mode 100644
index 0000000000000000000000000000000000000000..d598e96b5f35abd03696ccb1870b94e6eb89a57c
--- /dev/null
+++ b/examples/multi-task/evaluate-slot.py
@@ -0,0 +1,78 @@
+#  -*- coding: utf-8 -*-
+
+import json
+
+
+def load_label_map(map_dir="./data/atis/atis_slot/label_map.json"):
+    """
+    :param map_dir: dict indictuing chunk type
+    :return:
+    """
+    return json.load(open(map_dir, "r"))
+
+
+def cal_chunk(total_res, total_label):
+    assert len(total_label) == len(total_res), "prediction result doesn't match to labels, {}, {}".format(len(total_res),len(total_label))
+    num_labels = 0
+    num_corr = 0
+    num_infers = 0
+    for res, label in zip(total_res, total_label):
+        assert len(res) == len(label), "prediction result doesn't match to labels, {}, {}".format(len(res),len(label))
+        num_labels += sum([0 if i == 6 else 1 for i in label])
+        num_corr += sum([1 if label[i] == res[i] and label[i] != 6 else 0 for i in range(len(label))])
+        num_infers += sum([0 if i == 6 else 1 for i in res])
+
+    precision = num_corr * 1.0 / num_infers if num_infers > 0 else 0.0
+    recall = num_corr * 1.0 / num_labels if num_labels > 0 else 0.0
+    f1 = 2 * precision * recall / (precision + recall) if precision + recall > 0 else 0.0
+
+    return precision, recall, f1
+
+
+def res_evaluate(res_dir="./outputs/predict-slot/predictions.json", data_dir="./data/atis/atis_slot/test.tsv"):
+    label_map = load_label_map()
+
+    total_label = []
+    with open(data_dir, "r") as file:
+        first_flag = True
+        for line in file:
+            if first_flag:
+                first_flag = False
+                continue
+            line = line.strip("\n")
+            if len(line) == 0:
+                continue
+            line = line.split("\t")
+            if len(line) < 2:
+                continue
+            labels = line[1][:-1].split("\x02")
+            total_label.append(labels)
+    total_label = [[label_map[j] for j in i] for i in total_label]
+
+    total_res = []
+    with open(res_dir, "r") as file:
+        cnt = 0
+        for line in file:
+            line = line.strip("\n")
+            if len(line) == 0:
+                continue
+            try:
+                res_arr = json.loads(line)
+
+                if len(total_label[cnt]) < len(res_arr):
+                    total_res.append(res_arr[1: 1 + len(total_label[cnt])])
+                elif len(total_label[cnt]) == len(res_arr):
+                    total_res.append(res_arr)
+                else:
+                    total_res.append(res_arr)
+                    total_label[cnt] = total_label[cnt][: len(res_arr)]
+            except:
+                print("json format error: {}".format(cnt))
+                print(line)
+
+            cnt += 1
+
+    precision, recall, f1 = cal_chunk(total_res, total_label)
+    print("precision: {}, recall: {}, f1: {}".format(precision, recall, f1))
+
+res_evaluate()