add gen file

README.md

@@ -1,13 +1,24 @@
 ---
+language:
+- ja
 license: cc0-1.0
 task_categories:
 - text-generation
-language:
-- ja
 dataset_info:
   features:
-    - name: text
-      dtype: string
+  - name: text
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 177901962
+    num_examples: 161401
+  download_size: 107585837
+  dataset_size: 177901962
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: data/train-*
 ---
 
 oscar 2023をfilterしたもの  

gen.py

@@ -0,0 +1,105 @@
+import collections
+import io
+import zstandard
+import json
+
+from dataclasses import dataclass
+
+import datasets
+
+
+logger = datasets.logging.get_logger(__name__)
+
+
+@dataclass
+class Identification:
+    label: str
+    prob: float
+
+
+_DESCRIPTION = """
+"""
+
+_URL = ""
+
+_LICENSE = """
+"""
+
+_CITATION = """
+"""
+
+_BASE_DATA_PAT_FORMAT_STR = ""
+_BASE_CHECKSUM_FILE_NAME = "checksum.sha256"
+
+class Oscar2301JAConfig(datasets.BuilderConfig):
+    def __init__(self, **kwargs):
+        """BuilderConfig for OSCAR.
+        Args:
+            language (str): It has to contain 2-letter or 3-letter coded strings. For example: "se", "hu", "eml"
+            **kwargs: Keyword arguments forwarded to super.
+        """
+            
+        description = (
+            f"filter OSCAR 2023 dataset ja"
+        )
+        super(Oscar2301JAConfig, self).__init__(
+            name="oscar_2023_ja_filtered", description=description, **kwargs
+        )
+
+
+class Oscar2301(datasets.GeneratorBasedBuilder):    
+    BUILDER_CONFIGS = [
+        Oscar2301JAConfig(
+            version=datasets.Version("2023.1.0"),
+        )
+    ]
+    BUILDER_CONFIG_CLASS = Oscar2301JAConfig
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "id": datasets.Value("int64"),
+                    "text": datasets.Value("string")                  
+                }
+            ),
+            supervised_keys=None,
+            homepage=_URL,
+            citation=_CITATION,
+            license=_LICENSE,
+        )
+
+    def _split_generators(self, dl_manager):
+        checksum_path = self.config.base_data_path + _BASE_CHECKSUM_FILE_NAME
+        checksum_file = dl_manager.download(checksum_path)
+        with open(checksum_file, encoding="utf-8") as f:
+            data_filenames = [line.split()[1] for line in f if line]
+            data_urls = [
+                self.config.base_data_path + data_filename
+                for data_filename in data_filenames
+            ]
+        doc_files = dl_manager.download(
+            [url for url in data_urls if url.endswith(".jsonl.zst")]
+        )
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN, gen_kwargs={"doc_files": doc_files}
+            ),
+        ]
+
+    def _generate_examples(self, doc_files):
+        """This function returns the examples in the raw (text) form by iterating on all the files."""
+        id_ = 0
+        for doc_path in doc_files:
+            logger.info("generating examples from = %s", doc_path)
+
+            with open(doc_path, "rb") as fh:
+                dctx = zstandard.ZstdDecompressor()
+                stream_reader = dctx.stream_reader(fh)
+                buffered_reader = io.BufferedReader(stream_reader)
+                text_stream = io.TextIOWrapper(buffered_reader, encoding="utf-8")
+                for line in text_stream:
+                    doc = json.loads(line)
+                    yield id_, {"id": id_, "text": doc["text"]}
+                    id_ += 1