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KB.py

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+import json
+import math
+from typing import List
+
+import datasets
+
+
+logger = datasets.logging.get_logger(__name__)
+
+
+_CITATION = ''
+
+_DESCRIPTION = """\
+Выдержки из руководства пользователя для генерации ответов по запросу пользователя.\
+"""
+_LICENSE = ''
+
+_URL = "https://huggingface.co/datasets/SIA86/KB/blob/main/"
+_URLS = {
+    "train": _URL + "kb.json",
+}
+
+class LFQAKBConfig(datasets.BuilderConfig):
+    """BuilderConfig for LFQAKnowledgeBase"""
+
+    def __init__(
+        self, language="ru", snippets_length=50, snippets_overlap=25, **kwargs
+    ):
+        """BuilderConfig for WikiSnippets.
+        Args:
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(LFQAKBConfig, self).__init__(**kwargs)
+        self.language = language
+        self.snippets_length = snippets_length
+        self.snippets_overlap = snippets_overlap
+
+
+class WikiSnippets(datasets.GeneratorBasedBuilder):
+    
+    BUILDER_CONFIGS = LFQAKBConfig(
+            name="LFQAKnowledgeBase",
+            version=datasets.Version("1.0.0"),
+            language="en",
+            snippets_length=50,
+            snippets_overlap=25,
+        )
+
+    test_dummy_data = False
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "id": datasets.Value("int32g"),
+                    "title": datasets.Value("string"),
+                    "heading1": datasets.Value("string"),
+                    "heading2": datasets.Value("string"),
+                    "heading3": datasets.Value("string"),
+                    "heading4": datasets.Value("string"),
+                    "heading5": datasets.Value("string"),
+                    "text": datasets.Value("string"),
+                    
+                }
+            ),
+            supervised_keys=None,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
+        urls_to_download = self._URLS
+        downloaded_files = dl_manager.download_and_extract(urls_to_download)
+
+        return [
+            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files}),
+        ]
+
+    def _generate_examples(self, filepath):
+        """This function returns the examples in the raw (text) form."""
+        logger.info("generating examples from = %s", filepath)
+        with open(filepath) as f:
+            lfqa = json.load(f)
+            for article in lfqa:
+                id_ = article['id']
+                title = article['title']
+                heading1 = article['heading1']
+                heading2 = article['heading2']
+                heading3 = article['heading3']
+                heading4 = article['heading4']
+                heading5 = article['heading5']
+                text = article['text']
+                # Features currently used are "context", "question", and "answers".
+                # Others are extracted here for the ease of future expansions.
+                yield id_, {
+                    "title": title,
+                    "heading1": heading1,
+                    "heading2": heading2,
+                    "heading3": heading3,
+                    "heading4": heading4,
+                    "heading5": heading5,
+                    "text": text
+                    }