Datasets:
Size:
10K - 100K
License:
alter default config
Browse files
jsick.py
CHANGED
@@ -40,6 +40,11 @@ class JSICKDataset(ds.GeneratorBasedBuilder):
|
|
40 |
version=VERSION,
|
41 |
description="hoge",
|
42 |
),
|
|
|
|
|
|
|
|
|
|
|
43 |
ds.BuilderConfig(
|
44 |
name="stress",
|
45 |
version=VERSION,
|
@@ -50,6 +55,26 @@ class JSICKDataset(ds.GeneratorBasedBuilder):
|
|
50 |
def _info(self) -> ds.DatasetInfo:
|
51 |
labels = ds.ClassLabel(names=["entailment", "neutral", "contradiction"])
|
52 |
if self.config.name == "base":
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
53 |
features = ds.Features(
|
54 |
{
|
55 |
"pair_ID": ds.Value("int32"),
|
@@ -99,6 +124,17 @@ class JSICKDataset(ds.GeneratorBasedBuilder):
|
|
99 |
df: pd.DataFrame = pd.read_table(data_path, sep="\t", header=0)
|
100 |
|
101 |
if self.config.name == "base":
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
102 |
return [
|
103 |
ds.SplitGenerator(
|
104 |
name=ds.Split.TRAIN,
|
|
|
40 |
version=VERSION,
|
41 |
description="hoge",
|
42 |
),
|
43 |
+
ds.BuilderConfig(
|
44 |
+
name="original",
|
45 |
+
version=VERSION,
|
46 |
+
description="hoge",
|
47 |
+
),
|
48 |
ds.BuilderConfig(
|
49 |
name="stress",
|
50 |
version=VERSION,
|
|
|
55 |
def _info(self) -> ds.DatasetInfo:
|
56 |
labels = ds.ClassLabel(names=["entailment", "neutral", "contradiction"])
|
57 |
if self.config.name == "base":
|
58 |
+
features = ds.Features(
|
59 |
+
{
|
60 |
+
"id": ds.Value("int32"),
|
61 |
+
"premise": ds.Value("string"),
|
62 |
+
"hypothesis": ds.Value("string"),
|
63 |
+
"label": labels,
|
64 |
+
"score": ds.Value("float32"),
|
65 |
+
"sentence_A_En": ds.Value("string"),
|
66 |
+
"sentence_B_En": ds.Value("string"),
|
67 |
+
"entailment_label_En": labels,
|
68 |
+
"relatedness_score_En": ds.Value("float32"),
|
69 |
+
"corr_entailment_labelAB_En": ds.Value("string"),
|
70 |
+
"corr_entailment_labelBA_En": ds.Value("string"),
|
71 |
+
"image_ID": ds.Value("string"),
|
72 |
+
"original_caption": ds.Value("string"),
|
73 |
+
"semtag_short": ds.Value("string"),
|
74 |
+
"semtag_long": ds.Value("string"),
|
75 |
+
}
|
76 |
+
)
|
77 |
+
elif self.config.name == "original":
|
78 |
features = ds.Features(
|
79 |
{
|
80 |
"pair_ID": ds.Value("int32"),
|
|
|
124 |
df: pd.DataFrame = pd.read_table(data_path, sep="\t", header=0)
|
125 |
|
126 |
if self.config.name == "base":
|
127 |
+
df = df.rename(
|
128 |
+
columns={
|
129 |
+
"pair_ID": "id",
|
130 |
+
"sentence_A_Ja": "premise",
|
131 |
+
"sentence_B_Ja": "hypothesis",
|
132 |
+
"entailment_label_Ja": "label",
|
133 |
+
"relatedness_score_Ja": "score",
|
134 |
+
}
|
135 |
+
)
|
136 |
+
|
137 |
+
if self.config.name in ["base", "original"]:
|
138 |
return [
|
139 |
ds.SplitGenerator(
|
140 |
name=ds.Split.TRAIN,
|