feat(weibo.py): update loadding script

.gitattributes

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+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text
+# Audio files - uncompressed
+*.pcm filter=lfs diff=lfs merge=lfs -text
+*.sam filter=lfs diff=lfs merge=lfs -text
+*.raw filter=lfs diff=lfs merge=lfs -text
+# Audio files - compressed
+*.aac filter=lfs diff=lfs merge=lfs -text
+*.flac filter=lfs diff=lfs merge=lfs -text
+*.mp3 filter=lfs diff=lfs merge=lfs -text
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+*.wav filter=lfs diff=lfs merge=lfs -text
+# Image files - uncompressed
+*.bmp filter=lfs diff=lfs merge=lfs -text
+*.gif filter=lfs diff=lfs merge=lfs -text
+*.png filter=lfs diff=lfs merge=lfs -text
+*.tiff filter=lfs diff=lfs merge=lfs -text
+# Image files - compressed
+*.jpg filter=lfs diff=lfs merge=lfs -text
+*.jpeg filter=lfs diff=lfs merge=lfs -text
+*.webp filter=lfs diff=lfs merge=lfs -text
+# BMES files - uncompressed
+*.bmes filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+license: apache-2.0
+---

data/test.char.bmes

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+size 93851

data/train.char.bmes

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data/validation.char.bmes

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+size 91251

weibo.py

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+import datasets
+from datasets.download.download_manager import DownloadManager
+
+_DESCRIPTION = """\
+The Weibo NER dataset is a Chinese Named Entity Recognition dataset 
+drawn from the social media website Sina Weibo.
+"""
+
+_CITATION = """\
+@inproceedings{peng-dredze-2015-named,
+    title = "Named Entity Recognition for {C}hinese 
+        Social Media with Jointly Trained Embeddings",
+    author = "Peng, Nanyun  and Dredze, Mark",
+    booktitle = "Proceedings of the 2015 Conference on 
+        Empirical Methods in Natural Language Processing",
+    month = sep,
+    year = "2015",
+    address = "Lisbon, Portugal",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/D15-1064",
+    doi = "10.18653/v1/D15-1064",
+    pages = "548--554",
+}
+"""
+
+_URL = "https://huggingface.co/datasets/minskiter/weibo/resolve/main/"
+_URLS = {
+    "train": _URL + "data/train.char.bmes",
+    "validation": _URL + "data/validation.char.bmes",
+    "test": _URL + "data/test.char.bmes",
+}
+
+
+class WeiboNamedEntities(datasets.GeneratorBasedBuilder):
+    VERSION = datasets.Version("1.0.0")
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "text": datasets.Value("string"),
+                    "labels": datasets.Sequence(
+                        datasets.features.ClassLabel(
+                            names=[
+                                'O',
+                                'B-PER.NAM',
+                                'I-PER.NAM',
+                                'E-PER.NAM',
+                                'S-PER.NAM',
+                                'B-ORG.NAM',
+                                'I-ORG.NAM',
+                                'E-ORG.NAM',
+                                'S-ORG.NAM',
+                                'B-LOC.NAM',
+                                'I-LOC.NAM',
+                                'E-LOC.NAM',
+                                'S-LOC.NAM',
+                                'B-GPE.NAM',
+                                'I-GPE.NAM',
+                                'E-GPE.NAM',
+                                'S-GPE.NAM',
+                                'B-PER.NOM',
+                                'I-PER.NOM',
+                                'E-PER.NOM',
+                                'S-PER.NOM',
+                                'B-ORG.NOM',
+                                'I-ORG.NOM',
+                                'E-ORG.NOM',
+                                'S-ORG.NOM',
+                                'B-LOC.NOM',
+                                'I-LOC.NOM',
+                                'E-LOC.NOM',
+                                'S-LOC.NOM',
+                                'B-GPE.NOM',
+                                'I-GPE.NOM',
+                                'E-GPE.NOM',
+                                'S-GPE.NOM',
+                            ]
+                        )
+                    ),
+                }
+            ),
+            supervised_keys=None,
+            homepage="https://aclanthology.org/D15-1064/",
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager: DownloadManager):
+        urls_to_download = _URLS
+        download_files = dl_manager.download_and_extract(urls_to_download)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"filepath": download_files["train"]},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={"filepath": download_files["validation"]},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={"filepath": download_files["test"]},
+            ),
+        ]
+
+    def _default_example(self):
+        return {"text": [], "labels": []}
+
+    def _generate_examples(self, filepath):
+        with open(filepath, "r", encoding="utf-8") as f:
+            example = self._default_example()
+            _id = 0
+            for line in f:
+                if len(line.strip()) == 0:
+                    if len(example["text"]) > 0:
+                        example["text"] = "".join(example["text"])
+                        yield _id, example
+                        example = self._default_example()
+                        _id += 1
+                    continue
+                char, label = line.split(" ")
+                char = char.strip()
+                if char == "":
+                    char = " "
+                label = label.strip()
+                example["text"].append(char)
+                example["labels"].append(label)
+            if len(example["text"]) > 0:
+                example["text"] = "".join(example["text"])
+                yield _id, example