complete the readme, fill in the loader

README.md

@@ -10,7 +10,7 @@ licenses:
 multilinguality:
 - monolingual
 size_categories:
-- 1K<n<10K
+- n<1K
 source_datasets:
 - original
 task_categories:
@@ -80,19 +80,19 @@ Russian, as spoken on the Meduza website (i.e. from multiple countries) (`bcp47:
 
 #### zulu_stance
 
-- **Size of downloaded dataset files:** 212.54 KiB
-- **Size of the generated dataset:** 186.76 KiB
-- **Total amount of disk used:**  399.30KiB
+- **Size of downloaded dataset files:** 349.79 KiB
+- **Size of the generated dataset:** 366.11 KiB
+- **Total amount of disk used:**  715.90 KiB
 
 An example of 'train' looks as follows.
 
 ```
 {
   'id': '0', 
-  'text': 'ubukhulu be-islam buba sobala lapho i-smartphone ifaka i-ramayana njengo-ramadan. #semst', 
-  'target': 'Atheism', 
-  'stance': 1}
-
+  'text': 'Волки, волки!!', 
+  'title': 'Минобороны обвинило «гражданского сотрудника» в публикации скриншота из игры вместо фото террористов. И показало новое «неоспоримое подтверждение»', 
+  'stance': 3
+}
 ```
 
 
@@ -104,57 +104,56 @@ An example of 'train' looks as follows.
 - `stance`: a class label representing the stance the text expresses towards the target. Full tagset with indices:
 
 ```
-                            0: "FAVOR",
-                            1: "AGAINST",
-                            2: "NONE",
+                            0: "support",
+                            1: "deny",
+                            2: "query",
+                            3: "comment",
 ```
 
 ### Data Splits
 
 |  name   |train|
 |---------|----:|
-|zulu_stance|1343 sentences|
+|rustance|958 sentences|
 
 ## Dataset Creation
 
 ### Curation Rationale
 
-To enable stance detection in Zulu and also to measure domain transfer in translation
+Toy data for training and especially evaluating stance prediction in Russian
 
 ### Source Data
 
 #### Initial Data Collection and Normalization
 
-The original data is taken from [Semeval2016 task 6: Detecting stance in tweets.](https://aclanthology.org/S16-1003/),
-and then translated manually to Zulu.
+The data is comments scraped from a Russian news site not situated in Russia, [Meduza](https://meduza.io/), in 2018.
 
 #### Who are the source language producers?
 
-English-speaking Twitter users.
+Russian speakers including from the Russian diaspora, especially Latvia
 
 ### Annotations
 
 #### Annotation process
 
-See [Semeval2016 task 6: Detecting stance in tweets.](https://aclanthology.org/S16-1003/); the annotations are taken from there.
+Annotators labelled comments for supporting, denying, querying or just commenting on a news article.
 
 #### Who are the annotators?
 
-See [Semeval2016 task 6: Detecting stance in tweets.](https://aclanthology.org/S16-1003/); the annotations are taken from there.
+Russian native speakers, IT education, male, 20s.
 
 ### Personal and Sensitive Information
 
-The data was public at the time of collection. User names are preserved.
+The data was public at the time of collection. No PII removal has been performed.
 
 ## Considerations for Using the Data
 
 ### Social Impact of Dataset
 
-There's a risk of user-deleted content being in this data. The data has NOT been vetted for any content, so there's a risk of [harmful text](https://arxiv.org/abs/2204.14256) content.
+There's a risk of misinformative content being in this data. The data has NOT been vetted for any content, so there's a risk of [harmful text](https://arxiv.org/abs/2204.14256) content.
 
 ### Discussion of Biases
 
-While the data is in Zulu, the source text is not from or about Zulu-speakers, and so still expresses the social biases and topics found in English-speaking Twitter users. Further, some of the topics are USA-specific. The sentiments and ideas in this dataset do not represent Zulu speakers.
 
 ### Other Known Limitations
 
@@ -173,11 +172,13 @@ The authors distribute this data under Creative Commons attribution license, CC-
 ### Citation Information
 
 ```
-@inproceedings{dlamini_zulu_stance,
-  title={Bridging the Domain Gap for Stance Detection for the Zulu language},
-  author={Dlamini, Gcinizwe and Bekkouch, Imad Eddine Ibrahim and Khan, Adil and Derczynski, Leon},
-  booktitle={Proceedings of IEEE IntelliSys},
-  year={2022}
+@inproceedings{lozhnikov2018stance,
+  title={Stance prediction for russian: data and analysis},
+  author={Lozhnikov, Nikita and Derczynski, Leon and Mazzara, Manuel},
+  booktitle={International Conference in Software Engineering for Defence Applications},
+  pages={176--186},
+  year={2018},
+  organization={Springer}
 }
 ```
 

rustance.py

@@ -16,7 +16,7 @@
 # Lint as: python3
 """Introduction to the CoNLL-2003 Shared Task: Language-Independent Named Entity Recognition"""
 
-import json
+import csv
 import os
 
 import datasets
@@ -26,54 +26,42 @@ logger = datasets.logging.get_logger(__name__)
 
 
 _CITATION = """\
-@inproceedings{dlamini_zulu_stance,
-  title={Bridging the Domain Gap for Stance Detection for the Zulu language},
-  author={Dlamini, Gcinizwe and Bekkouch, Imad Eddine Ibrahim and Khan, Adil and Derczynski, Leon},
-  booktitle={Proceedings of IEEE IntelliSys},
-  year={2022}
+@inproceedings{lozhnikov2018stance,
+  title={Stance prediction for russian: data and analysis},
+  author={Lozhnikov, Nikita and Derczynski, Leon and Mazzara, Manuel},
+  booktitle={International Conference in Software Engineering for Defence Applications},
+  pages={176--186},
+  year={2018},
+  organization={Springer}
 }
 """
 
 _DESCRIPTION = """\
-This is a stance detection dataset in the Zulu language. The data is translated to Zulu by Zulu native speakers, from English source texts.
-
-Misinformation has become a major concern in recent last years given its 
-spread across our information sources. In the past years, many NLP tasks have
-been introduced in this area, with some systems reaching good results on 
-English language datasets. Existing AI based approaches for fighting 
-misinformation in literature suggest automatic stance detection as an integral
-first step to success. Our paper aims at utilizing this progress made for
-English to transfers that knowledge into other languages, which is a 
-non-trivial task due to the domain gap between English and the target 
-languages. We propose a black-box non-intrusive method that utilizes techniques
-from Domain Adaptation to reduce the domain gap, without requiring any human
-expertise in the target language, by leveraging low-quality data in both a
-supervised and unsupervised manner. This allows us to rapidly achieve similar
-results for stance detection for the Zulu language, the target language in
-this work, as are found for English. We also provide a stance detection dataset
-in the Zulu language. 
+This is a stance prediction dataset in Russian. The dataset contains comments on news articles,
+and rows are a comment, the title of the news article it responds to, and the stance of the comment
+towards the article.
 """
 
-_URL = "ZUstance.json"
+_URL = "rustance_dataset.csv"
 
 
-class ZuluStanceConfig(datasets.BuilderConfig):
-    """BuilderConfig for ZuluStance"""
+class RuStanceConfig(datasets.BuilderConfig):
+    """BuilderConfig for RuStance"""
 
     def __init__(self, **kwargs):
-        """BuilderConfig ZuluStance.
+        """BuilderConfig RuStance.
 
         Args:
           **kwargs: keyword arguments forwarded to super.
         """
-        super(ZuluStanceConfig, self).__init__(**kwargs)
+        super(RuStanceConfig, self).__init__(**kwargs)
 
 
-class ZuluStance(datasets.GeneratorBasedBuilder):
-    """ZuluStance dataset."""
+class RuStance(datasets.GeneratorBasedBuilder):
+    """RuStance dataset."""
 
     BUILDER_CONFIGS = [
-        ZuluStanceConfig(name="zulu-stance", version=datasets.Version("1.0.0"), description="Stance dataset in Zulu"),
+        RuStanceConfig(name="rustance", version=datasets.Version("1.0.0"), description="Stance dataset in Russian"),
     ]
 
     def _info(self):
@@ -83,18 +71,19 @@ class ZuluStance(datasets.GeneratorBasedBuilder):
                 {
                     "id": datasets.Value("string"),
                     "text": datasets.Value("string"),
-                    "target": datasets.Value("string"),
+                    "title": datasets.Value("string"),
                     "stance": datasets.features.ClassLabel(
                         names=[
-                            "FAVOR",
-                            "AGAINST",
-                            "NONE",
+                            "support",
+                            "deny",
+                            "query",
+                            "comment",
                         ]
                     )
                 }
             ),
             supervised_keys=None,
-            homepage="https://arxiv.org/abs/2205.03153",
+            homepage="https://link.springer.com/chapter/10.1007/978-3-030-14687-0_16",
             citation=_CITATION,
         )
 
@@ -109,12 +98,22 @@ class ZuluStance(datasets.GeneratorBasedBuilder):
     def _generate_examples(self, filepath):
         logger.info("⏳ Generating examples from = %s", filepath)
         with open(filepath, encoding="utf-8") as f:
+            rustance_reader = csv.DictReader(f, delimiter=";", quotechar='"')
             guid = 0
-            zustance_dataset = json.load(f)
-            for instance in zustance_dataset:
+            for instance in rustance_reader:
                 instance["id"] = str(guid)
-                instance["text"] = instance.pop("Tweet")
-                instance["target"] = instance.pop("Target")
+                if instance['Stance'] == "s":
+                    instance['Stance'] = "support"
+                elif instance['Stance'] == "d":
+                    instance['Stance'] = "deny" 
+                elif instance['Stance'] == "q":
+                    instance['Stance'] = "query" 
+                elif instance['Stance'] == "c":
+                    instance['Stance'] = "comment" 
+
+                instance["text"] = instance.pop("Text")
+                instance["title"] = instance.pop("Title")
                 instance["stance"] = instance.pop("Stance")
+
                 yield guid, instance
                 guid += 1