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clozet/README.md

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+# Cloze Test Dataset
+
+### Data Example
+
+```
+{
+	"story": 
+		"一只猫望着窗外飞翔的鸟儿馋涎欲滴，但自己又捕捉不到。于是它便想了一个法子。它给那些鸟儿们寄去请柬，邀请他们来参加自己的生日宴会。<mask>鸟儿一进来，猫就关上了门。鸟儿们彻底入了虎穴，被猫一只一只抓来吃掉了。", 
+	"plot0": 
+		"可是没有一只鸟儿愿意来。", 
+	"plot1": 
+		"有些单纯的鸟儿赴宴来了。", 
+	"label": 
+		"1"
+}
+```
+
+- "story" (`str`)：input story，`<mask>` means the removed sentence
+- "plot0" (`str`)：candidate #0
+- "plot1" (`str`)：candidate #1
+- "label" (`str`): 0 means candidate #0 is correct, while 1 means candidate #1 is correct.
+
+
+
+### Citation
+
+```
+@misc{guan2021lot,
+      title={LOT: A Benchmark for Evaluating Chinese Long Text Understanding and Generation}, 
+      author={Jian Guan and Zhuoer Feng and Yamei Chen and Ruilin He and Xiaoxi Mao and Changjie Fan and Minlie Huang},
+      year={2021},
+      eprint={2108.12960},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}
+```
+
+
+
+### Evaluation
+
+The prediction result should have the same format with `test.jsonl`
+
+```shell
+python eval.py prediction_file test.jsonl
+```
+
+
+
+We use accuracy as the evaluation metric. The output of the script `eval.py` is a dictionary as follows:
+
+```python
+{"accuracy": _}
+```
+

clozet/eval.py

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+import json
+import argparse
+import sys
+import numpy as np
+import jieba
+from nltk.translate.bleu_score import sentence_bleu, SmoothingFunction
+from nltk import ngrams
+
+
+
+def load_file(filename):
+    data = []
+    with open(filename, "r") as f:
+        for line in f.readlines():
+            data.append(json.loads(line))
+        f.close()
+    return data
+
+def proline(line):
+    return " ".join([w for w in jieba.cut("".join(line.strip().split()))])
+
+
+def compute(golden_file, pred_file, return_dict=True):
+    golden_data = load_file(golden_file)
+    pred_data = load_file(pred_file)
+
+    if len(golden_data) != len(pred_data):
+        raise RuntimeError("Wrong Predictions")
+
+    num = 0
+    for g, p in zip(golden_data, pred_data):
+        if isinstance(g["label"], str):
+            l = int(g["label"].strip())
+        elif isinstance(g["label"], int):
+            l = g["label"]
+        else:
+            raise Exception("Data type error")
+
+        if isinstance(p["label"], str):
+            p = int(p["label"].strip())            
+        elif isinstance(p["label"], int):
+            p = p["label"]            
+        else:
+            raise Exception("Data type error")
+        if l == p:
+            num += 1
+
+    return {'accuracy': float(num)/len(golden_data)}
+
+def main():
+    argv = sys.argv
+    print("预测结果：{}, 测试集: {}".format(argv[1], argv[2]))
+    print(compute(argv[2], argv[1]))
+
+
+if __name__ == '__main__':
+    main()