:+1: fix config name

README.md

@@ -33,6 +33,8 @@ task_ids:
   - [Table of Contents](#table-of-contents)
   - [Dataset Description](#dataset-description)
     - [Dataset Summary](#dataset-summary)
+      - [Japanese Sentences Involving Compositional Knowledge (JSICK) Dataset.](#japanese-sentences-involving-compositional-knowledge-jsick-dataset)
+      - [JSICK-stress Test set](#jsick-stress-test-set)
     - [Languages](#languages)
   - [Dataset Structure](#dataset-structure)
     - [Data Instances](#data-instances)
@@ -40,7 +42,7 @@ task_ids:
       - [stress](#stress)
     - [Data Fields](#data-fields)
       - [base](#base-1)
-      - [original](#original)
+      - [stress](#stress-1)
     - [Data Splits](#data-splits)
     - [Annotations](#annotations)
   - [Additional Information](#additional-information)
@@ -59,10 +61,26 @@ task_ids:
 
 From official [GitHub](https://github.com/verypluming/JSICK):
 
-Japanese Sentences Involving Compositional Knowledge (JSICK) Dataset.
+#### Japanese Sentences Involving Compositional Knowledge (JSICK) Dataset.
+
 JSICK is the Japanese NLI and STS dataset by manually translating the English dataset [SICK (Marelli et al., 2014)](https://aclanthology.org/L14-1314/) into Japanese.
 We hope that our dataset will be useful in research for realizing more advanced models that are capable of appropriately performing multilingual compositional inference.
 
+
+#### JSICK-stress Test set
+
+The JSICK-stress test set is a dataset to investigate whether models capture word order and case particles in Japanese.
+The JSICK-stress test set is provided by transforming syntactic structures of sentence pairs in JSICK, where we analyze whether models are attentive to word order and case particles to predict entailment labels and similarity scores.
+
+The JSICK test set contains 1666, 797, and 1006 sentence pairs (A, B) whose premise sentences A (the column `sentence_A_Ja_origin`) include the basic word order involving
+ga-o (nominative-accusative), ga-ni (nominative-dative), and ga-de (nominative-instrumental/locative) relations, respectively.
+
+We provide the JSICK-stress test set by transforming syntactic structures of these pairs by the following three ways:
+- `scrum_ga_o`: a scrambled pair, where the word order of premise sentences A is scrambled into o-ga, ni-ga, and de-ga order, respectively.
+- `ex_ga_o`:  a rephrased pair, where the only case particles (ga, o, ni, de) in the premise A are swapped
+- `del_ga_o`: a rephrased pair, where the only case particles (ga, o, ni) in the premise A are deleted
+
+
 ### Languages
 
 The language data in JSICK is in Japanese and English.
@@ -81,11 +99,11 @@ dataset: ds.DatasetDict = ds.load_dataset("hpprc/jsick")
 print(dataset)
 # DatasetDict({
 #     train: Dataset({
-#         features: ['id', 'premise', 'hypothesis', 'label', 'score', 'sentence_A_En', 'sentence_B_En', 'entailment_label_En', 'relatedness_score_En', 'corr_entailment_labelAB_En', 'corr_entailment_labelBA_En', 'image_ID', 'original_caption', 'semtag_short', 'semtag_long'],
+#         features: ['id', 'premise', 'hypothesis', 'label', 'score', 'premise_en', 'hypothesis_en', 'label_en', 'score_en', 'corr_entailment_labelAB_En', 'corr_entailment_labelBA_En', 'image_ID', 'original_caption', 'semtag_short', 'semtag_long'],
 #         num_rows: 4500
 #     })
 #     test: Dataset({
-#         features: ['id', 'premise', 'hypothesis', 'label', 'score', 'sentence_A_En', 'sentence_B_En', 'entailment_label_En', 'relatedness_score_En', 'corr_entailment_labelAB_En', 'corr_entailment_labelBA_En', 'image_ID', 'original_caption', 'semtag_short', 'semtag_long'],
+#         features: ['id', 'premise', 'hypothesis', 'label', 'score', 'premise_en', 'hypothesis_en', 'label_en', 'score_en', 'corr_entailment_labelAB_En', 'corr_entailment_labelBA_En', 'image_ID', 'original_caption', 'semtag_short', 'semtag_long'],
 #         num_rows: 4927
 #     })
 # })
@@ -106,21 +124,22 @@ print(dataset)
 An example of looks as follows:
 
 ```json
-{'id': 1,
-'premise': '子供たちのグループが庭で遊んでいて、後ろの方には年を取った男性が立っている',
-'hypothesis': '庭にいる男の子たちのグループが遊んでいて、男性が後ろの方に立っている',
-'label': 1,
-'score': 3.700000047683716,
-'sentence_A_En': 'A group of kids is playing in a yard and an old man is standing in the background',
-'sentence_B_En': 'A group of boys in a yard is playing and a man is standing in the background',
-'entailment_label_En': 1,
-'relatedness_score_En': 4.5,
-'corr_entailment_labelAB_En': 'nan',
-'corr_entailment_labelBA_En': 'nan',
-'image_ID': '3155657768_b83a7831e5.jpg',
-'original_caption': 'A group of children playing in a yard , a man in the background .',
-'semtag_short': 'nan',
-'semtag_long': 'nan',
+{
+    'id': 1,
+    'premise': '子供たちのグループが庭で遊んでいて、後ろの方には年を取った男性が立っている',
+    'hypothesis': '庭にいる男の子たちのグループが遊んでいて、男性が後ろの方に立っている',
+    'label': 1, // (neutral)
+    'score': 3.700000047683716,
+    'premise_en': 'A group of kids is playing in a yard and an old man is standing in the background',
+    'hypothesis_en': 'A group of boys in a yard is playing and a man is standing in the background',
+    'label_en': 1, // (neutral)
+    'score_en': 4.5,
+    'corr_entailment_labelAB_En': 'nan',
+    'corr_entailment_labelBA_En': 'nan',
+    'image_ID': '3155657768_b83a7831e5.jpg',
+    'original_caption': 'A group of children playing in a yard , a man in the background .',
+    'semtag_short': 'nan',
+    'semtag_long': 'nan',
 }
 ```
 
@@ -129,7 +148,18 @@ An example of looks as follows:
 An example of looks as follows:
 
 ```json
-{'id': '5818_de_d', 'premise': '女性火の近くダンスをしている', 'hypothesis': '火の近くでダンスをしている女性は一人もいない', 'label': 2, 'score': 4.0, 'sentence_A_Ja_origin': '女性が火の近くでダンスをしている', 'entailment_label_origin': 2, 'relatedness_score_Ja_origin': 3.700000047683716, 'rephrase_type': 'd', 'case_particles': 'de'}
+{
+    'id': '5818_de_d',
+    'premise': '女性火の近くダンスをしている',
+    'hypothesis': '火の近くでダンスをしている女性は一人もいない',
+    'label': 2,  // (contradiction)
+    'score': 4.0,
+    'sentence_A_Ja_origin': '女性が火の近くでダンスをしている',
+    'entailment_label_origin': 2,
+    'relatedness_score_Ja_origin': 3.700000047683716,
+    'rephrase_type': 'd',
+    'case_particles': 'de'
+}
 ```
 
 ### Data Fields
@@ -138,33 +168,51 @@ An example of looks as follows:
 
 A version adopting the column names of a typical NLI dataset.
 
-- `id`: The number of the sentence pair.
-- `premise`: The premise (sentence_A_Ja).
-- `hypothesis`: The hypothesis (sentence_B_Ja).
-- `label`: The correct label for this sentence pair (either `entailment` or `non-entailment`); in the setting described in the paper, non-entailment = neutral + contradiction (entailment_label_Ja).
-- `heuristics`: The heuristics (structural pattern) tag. The tags are: subsequence, constituent, full-overlap, order-subset, and mixed-subset.
-- `number_of_NPs`: The number of noun phrase in a sentence.
-- `semtag`: The linguistic phenomena tag.
+| Name                       | Description                                                                                                                          |
+| -------------------------- | ------------------------------------------------------------------------------------------------------------------------------------ |
+| id                         | ids (the same with original SICK)                                                                                                    |
+| premise                    | first sentence in Japanese                                                                                                           |
+| hypothesis                 | second sentence in Japanese                                                                                                          |
+| label                      | entailment label in Japanese                                                                                                         |
+| score                      | relatedness score in the range [1-5] in Japanese                                                                                     |
+| premise_en                 | first sentence in English                                                                                                            |
+| hypothesis_en              | second sentence in English                                                                                                           |
+| label_en                   | original entailment label in English                                                                                                 |
+| score_en                   | original relatedness score in the range [1-5] in English                                                                             |
+| semtag_short               | linguistic phenomena tags in Japanese                                                                                                |
+| semtag_long                | details of linguistic phenomena tags in Japanese                                                                                     |
+| image_ID                   | original image in [8K ImageFlickr dataset](https://www.kaggle.com/datasets/adityajn105/flickr8k)                                     |
+| original_caption           | original caption in [8K ImageFlickr dataset](https://www.kaggle.com/datasets/adityajn105/flickr8k)                                   |
+| corr_entailment_labelAB_En | corrected entailment label from A to B in English by [(Karouli et al., 2017)](http://vcvpaiva.github.io/includes/pubs/2017-iwcs.pdf) |
+| corr_entailment_labelBA_En | corrected entailment label from B to A in English by [(Karouli et al., 2017)](http://vcvpaiva.github.io/includes/pubs/2017-iwcs.pdf) |
+
+
+#### stress
 
-#### original
 
-The original version retaining the unaltered column names.
 
-- `id`: The number of the sentence pair.
-- `sentence_A_Ja`: The premise.
-- `sentence_B_Ja`: The hypothesis.
-- `entailment_label_Ja`: The correct label for this sentence pair (either `entailment` or `non-entailment`); in the setting described in the paper, non-entailment = neutral + contradiction
-- `heuristics`: The heuristics (structural pattern) tag. The tags are: subsequence, constituent, full-overlap, order-subset, and mixed-subset.
-- `number_of_NPs`: The number of noun phrase in a sentence.
-- `semtag`: The linguistic phenomena tag.
+| Name                        | Description                                                                                       |
+| --------------------------- | ------------------------------------------------------------------------------------------------- |
+| id                          | ids (the same with original SICK)                                                                 |
+| premise                     | first sentence in Japanese                                                                        |
+| hypothesis                  | second sentence in Japanese                                                                       |
+| label                       | entailment label in Japanese                                                                      |
+| score                       | relatedness score in the range [1-5] in Japanese                                                  |
+| sentence_A_Ja_origin        | the original premise sentences A from the JSICK test set.                                         |
+| entailment_label_origin     | the original entailment labels                                                                    |
+| relatedness_score_Ja_origin | the original relatedness scores                                                                   |
+| rephrase_type               | the type of transformation applied to the syntactic structures of the sentence pairs              |
+| case_particles              | the grammatical particles in Japanese that indicate the function or role of a noun in a sentence. |
 
 
 ### Data Splits
 
-| name     |  train | validation | test |
-| -------- | -----: | ---------: | ---: |
-| base     | 13,680 |            |  720 |
-| original | 13,680 |            |  720 |
+| name            | train | validation | test |
+| --------------- | ----: | ---------: | ---: |
+| base            |  4500 |            | 4927 |
+| original        |  4500 |            | 4927 |
+| stress          |       |            |  900 |
+| stress-original |       |            |  900 |
 
 
 

jsick.py

@@ -41,12 +41,12 @@ class JSICKDataset(ds.GeneratorBasedBuilder):
         ds.BuilderConfig(
             name="base",
             version=VERSION,
-            description="hoge",
+            description="A version adopting the column names of a typical NLI dataset.",
         ),
         ds.BuilderConfig(
             name="original",
             version=VERSION,
-            description="hoge",
+            description="The original version retaining the unaltered column names.",
         ),
         ds.BuilderConfig(
             name="stress",
@@ -119,7 +119,7 @@ class JSICKDataset(ds.GeneratorBasedBuilder):
                 }
             )
 
-        elif self.config.name == "stress_original":
+        elif self.config.name == "stress-original":
             features = ds.Features(
                 {
                     "pair_ID": ds.Value("string"),
@@ -146,13 +146,13 @@ class JSICKDataset(ds.GeneratorBasedBuilder):
     def _split_generators(self, dl_manager: ds.DownloadManager):
         if self.config.name in ["base", "original"]:
             url = _URLS["base"]
-        elif self.config.name in ["stress", "stress_original"]:
+        elif self.config.name in ["stress", "stress-original"]:
             url = _URLS["stress"]
 
         data_path = dl_manager.download_and_extract(url)
         df: pd.DataFrame = pd.read_table(data_path, sep="\t", header=0)
 
-        if self.config.name in ["stress", "stress_original"]:
+        if self.config.name in ["stress", "stress-original"]:
             df = df[
                 [
                     "pair_ID",
@@ -195,7 +195,7 @@ class JSICKDataset(ds.GeneratorBasedBuilder):
                 ),
             ]
 
-        elif self.config.name in ["stress", "stress_original"]:
+        elif self.config.name in ["stress", "stress-original"]:
             return [
                 ds.SplitGenerator(
                     name=ds.Split.TEST,