Update agieval.py
Browse files- agieval.py +3 -1
agieval.py
CHANGED
|
@@ -3,7 +3,9 @@ import json
|
|
| 3 |
import ast
|
| 4 |
import pandas as pd
|
| 5 |
import csv
|
| 6 |
-
|
|
|
|
|
|
|
| 7 |
english_qa_datasets = ["lsat-ar", "lsat-lr", "lsat-rc", "logiqa-en", "sat-math", "sat-en", "aqua-rat",
|
| 8 |
"sat-en-without-passage", "gaokao-english"]
|
| 9 |
chinese_qa_datasets = ["logiqa-zh", "jec-qa-kd", "jec-qa-ca", "gaokao-chinese", "gaokao-geography", "gaokao-history",
|
|
|
|
| 3 |
import ast
|
| 4 |
import pandas as pd
|
| 5 |
import csv
|
| 6 |
+
_DESCRIPTION = """\
|
| 7 |
+
The dataset is an amendment and re-annotation of LogiQA in 2020, a large-scale logical reasoning reading comprehension dataset adapted from the Chinese Civil Service Examination. We increase the data size, refine the texts with manual translation by professionals, and improve the quality by removing items with distinctive cultural features like Chinese idioms. Furthermore, we conduct a fine-grained annotation on the dataset and turn it into a two-way natural language inference (NLI) task, resulting in 35k premise-hypothesis pairs with gold labels, making it the first large-scale NLI dataset for complex logical reasoning
|
| 8 |
+
"""
|
| 9 |
english_qa_datasets = ["lsat-ar", "lsat-lr", "lsat-rc", "logiqa-en", "sat-math", "sat-en", "aqua-rat",
|
| 10 |
"sat-en-without-passage", "gaokao-english"]
|
| 11 |
chinese_qa_datasets = ["logiqa-zh", "jec-qa-kd", "jec-qa-ca", "gaokao-chinese", "gaokao-geography", "gaokao-history",
|