minor fixes

README.md

@@ -8,7 +8,7 @@ size_categories:
 - 1K<n<10K
 ---
 
-# Dataset Card for Dataset Name
+# Dataset Card for RuSpellGold
 
 ## Dataset Description
 
@@ -19,7 +19,7 @@ size_categories:
 
 ### Dataset Summary
 
-Multidomain Russian Spellcheck dataset is a benchmark of 1711 sentence pairs dedicated to a problem of automatic spelling correction in Russian language. The dataset is gathered from five different domains including news, Russian classic literature, social media texts, open web and strategic documents. It has been passed through two-stage manual labeling process with native speakers as annotators to correct spelling violation and preserve original style of text at the same time. 
+RuSpellGold is a benchmark of 1711 sentence pairs dedicated to a problem of automatic spelling correction in Russian language. The dataset is gathered from five different domains including news, Russian classic literature, social media texts, open web and strategic documents. It has been passed through two-stage manual labeling process with native speakers as annotators to correct spelling violation and preserve original style of text at the same time. 
 
 ## Dataset Structure
 
@@ -40,14 +40,6 @@ Russian.
 }
 ```
 
-The example in English for illustration purposes:
-```
-{
-  "sources": "Видела в городе афиши, анонсрующие ее концерт.",
-  "corrections": "Видела в городе афиши, анонсирующие её концерт",
-  "domain": "aranea"
-}
-```
 
 ### Data Fields
 

russian_multidomain_spellcheck.py

@@ -0,0 +1,101 @@
+import json
+from typing import List
+
+import datasets
+
+
+_DESCRIPTION = """
+                RuSpellGold is a benchmark of 1711 sentence pairs 
+                dedicated to a problem of automatic spelling correction in Russian language. 
+                The dataset is gathered from five different domains including news, Russian classic literature,
+                social media texts, open web and strategic documents. 
+                It has been passed through two-stage manual labeling process with native speakers as annotators
+                to correct spelling violation and preserve original style of text at the same time.
+                """
+
+_LICENSE = "apache-2.0"
+
+
+class RuSpellGoldConfig(datasets.BuilderConfig):
+    """BuilderConfig for RuFacts."""
+
+    def __init__(self, data_urls, features, **kwargs):
+        """BuilderConfig for RuFacts.
+        Args:
+        features: *list[string]*, list of the features that will appear in the
+            feature dict. Should not include "label".
+        data_urls: *dict[string]*, urls to download the zip file from.
+        **kwargs: keyword arguments forwarded to super.
+        """
+        super(RuFactsConfig, self).__init__(version=datasets.Version("0.0.1"), **kwargs)
+        self.data_urls = data_urls
+        self.features = features
+
+
+class RuSpellGold(datasets.GeneratorBasedBuilder):
+    """RuFacts dataset."""
+
+    BUILDER_CONFIGS = [
+        RuFactsConfig(
+            name="raw",
+            data_urls={
+                "train": "raw/train.json",
+                "validation": "raw/validation.json",
+                "test": "raw/test.json",
+            },
+            features=["idx", "evidence", "claim", "label"],
+        ),
+    ]
+
+    def _info(self) -> datasets.DatasetInfo:
+        features = {
+            "idx": datasets.Value("int64"),
+            "evidence": datasets.Value("string"),
+            "claim": datasets.Value("string"),
+            "label": datasets.features.ClassLabel(names=["consistent", "inconsistent"]),
+        }
+        return datasets.DatasetInfo(
+            features=datasets.Features(features),
+            description=_DESCRIPTION,
+            license=_LICENSE,
+        )
+
+    def _split_generators(
+        self, dl_manager: datasets.DownloadManager
+    ) -> List[datasets.SplitGenerator]:
+        urls_to_download = self.config.data_urls
+        downloaded_files = dl_manager.download_and_extract(urls_to_download)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "data_file": downloaded_files["train"],
+                    "split": datasets.Split.TRAIN,
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "data_file": downloaded_files["validation"],
+                    "split": datasets.Split.VALIDATION,
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "data_file": downloaded_files["test"],
+                    "split": datasets.Split.TEST,
+                },
+            ),
+        ]
+
+    def _generate_examples(self, data_file, split):
+        with open(data_file, encoding="utf-8") as f:
+            key = 0
+            for line in f:
+                row = json.loads(line)
+
+                example = {feature: row[feature] for feature in self.config.features}
+                yield key, example
+
+                key += 1