first with data loader

italian_tweets_1M.py

@@ -0,0 +1,86 @@
+"""Norwegian Colossal Corpus v2 dataset."""
+import gzip
+import json
+import datasets
+
+logger = datasets.logging.get_logger(__name__)
+_DESCRIPTION = """\\nItalian tweets."""
+_DATA_URL = "https://huggingface.co/datasets/pere/italian_tweets_1M/resolve/main/data/{split_suffix}-shard-{index:04d}-of-{n_shards:04d}.json.gz"
+_N_SHARDS_PER_SPLIT = {
+    "train": 1, "validation": 1
+}
+
+
+class italian_tweets_1MConfig(datasets.BuilderConfig):
+    """BuilderConfig for NbNn."""
+
+    def __init__(self, *args, **kwargs):
+        """BuilderConfig for NbNn.
+        Args:
+            **kwargs: keyword arguments forwarded to super.
+        """
+        super().__init__(
+            *args,
+            name="italian_tweets_1M",
+            **kwargs,
+        )
+
+
+class italian_tweets_1M(datasets.GeneratorBasedBuilder):
+    """Norwegian Colossal Corpus v2."""
+    BUILDER_CONFIGS = [italian_tweets_1MConfig()]
+    BUILDER_CONFIG_CLASS = italian_tweets_1MConfig
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "id": datasets.Value("string"),
+                    "doc_type": datasets.Value("string"),
+                    "publish_year":  datasets.Value("int32"),
+                    "lang_fasttext":  datasets.Value("string"),
+                    "lang_fasttext_conf":  datasets.Value("string"),
+                    "text":  datasets.Value("string"),
+
+                }
+            ),
+            supervised_keys=None,
+            homepage=_URL,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        data_urls = {}
+        for split in ["train", "validation"]:
+            data_urls[split] = [
+                _DATA_URL.format(
+                    language=self.config.name,
+                    split_suffix=split,
+                    index=index,
+                    n_shards=_N_SHARDS_PER_SPLIT[split],
+                )
+                for index in range(1, _N_SHARDS_PER_SPLIT[split] + 1)
+            ]
+        train_downloaded_files = dl_manager.download(data_urls["train"])
+        validation_downloaded_files = dl_manager.download(data_urls["validation"])
+
+        return [
+            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepaths": train_downloaded_files}),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION, gen_kwargs={"filepaths": validation_downloaded_files}
+            ),
+
+        ]
+
+    def _generate_examples(self, filepaths):
+        """This function returns the examples in the raw (text) form by iterating on all the files."""
+        id_ = 0
+        for filepath in filepaths:
+            logger.info("generating examples from = %s", filepath)
+            with gzip.open(open(filepath, "rb"), "rt", encoding="utf-8") as f:
+                for line in f:
+                    if line:
+                        example = json.loads(line)
+                        yield id_, example
+                        id_ += 1