Upload dataset_card.py with huggingface_hub

dataset_card.py

@@ -0,0 +1,121 @@
+import datasets
+
+_DESCRIPTION = """
+Uzbek Language Dataset Collection - Bu o'zbek tili uchun eng keng ko'lamli va keng qamrovli dataset to'plami hisoblanadi. 
+Dataset turli manbalardan to'plangan va NLP modellari, til modellari va boshqa AI ilovalar uchun mo'ljallangan.
+
+Dataset 4ta asosiy qismdan iborat:
+- Community OSCAR Uzbek (1.1GB): OSCAR Common Crawl datasidan o'zbek tilidagi matnlar
+- Custom Uzbek (2.1GB): Maxsus to'plangan va qayta ishlangan o'zbek matnlari
+- OSCAR Uzbek (38MB): OSCAR 2301 rasmiy uzbek dataset'idan so'z ro'yxatlari
+- Merge (122MB): Birlashtirilgan lug'atlar va frequency lists
+
+Jami hajmi: ~3.4GB
+Jami satr soni: 4.7+ million lines
+"""
+
+_HOMEPAGE = "https://huggingface.co/datasets/xkas2001/uzbek-language-dataset"
+
+_LICENSE = "apache-2.0"
+
+_URLS = {
+    "oscar_community": "community-oscar-uzbek/all_metadata_text.txt",
+    "custom_uzbek": "custom-uzbek/parsed_with_imlo.txt", 
+    "custom_uzbek_no_emoji": "custom-uzbek/parsed_with_imlo_without_emoji.txt",
+    "top5_quality": "community-oscar-uzbek/top5_metadata_text.txt",
+    "frequency_list": "merge/frequency_list.txt",
+    "unique_words": "merge/unique_words.txt"
+}
+
+class UzbekLanguageDataset(datasets.GeneratorBasedBuilder):
+    """Uzbek Language Dataset Collection."""
+
+    VERSION = datasets.Version("1.0.0")
+
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(
+            name="oscar_community",
+            version=VERSION,
+            description="OSCAR Community data from Common Crawl (2014-2023)"
+        ),
+        datasets.BuilderConfig(
+            name="custom_uzbek",
+            version=VERSION, 
+            description="Custom curated Uzbek texts with spelling correction"
+        ),
+        datasets.BuilderConfig(
+            name="top5_quality",
+            version=VERSION,
+            description="Top 5 quality texts from OSCAR Community"
+        ),
+        datasets.BuilderConfig(
+            name="word_lists",
+            version=VERSION,
+            description="Combined word frequency and unique word lists"
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = "oscar_community"
+
+    def _info(self):
+        if self.config.name == "word_lists":
+            features = datasets.Features({
+                "word": datasets.Value("string"),
+                "frequency": datasets.Value("int32")
+            })
+        else:
+            features = datasets.Features({
+                "text": datasets.Value("string")
+            })
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+        )
+
+    def _split_generators(self, dl_manager):
+        if self.config.name == "word_lists":
+            frequency_file = dl_manager.download(_URLS["frequency_list"])
+            return [
+                datasets.SplitGenerator(
+                    name=datasets.Split.TRAIN,
+                    gen_kwargs={"filepath": frequency_file, "split": "frequency"}
+                )
+            ]
+        else:
+            url_key = self.config.name
+            if url_key not in _URLS:
+                url_key = "oscar_community"
+            
+            text_file = dl_manager.download(_URLS[url_key])
+            return [
+                datasets.SplitGenerator(
+                    name=datasets.Split.TRAIN,
+                    gen_kwargs={"filepath": text_file, "split": "train"}
+                )
+            ]
+
+    def _generate_examples(self, filepath, split):
+        if split == "frequency":
+            with open(filepath, encoding="utf-8") as f:
+                for idx, line in enumerate(f):
+                    line = line.strip()
+                    if '\t' in line:
+                        parts = line.split('\t', 1)
+                        if len(parts) == 2:
+                            word, freq = parts
+                            try:
+                                yield idx, {
+                                    "word": word,
+                                    "frequency": int(freq)
+                                }
+                            except ValueError:
+                                continue
+        else:
+            with open(filepath, encoding="utf-8") as f:
+                for idx, line in enumerate(f):
+                    line = line.strip()
+                    if line:
+                        yield idx, {"text": line}