class_docstr: @inproceedings{peng2015ner,
  title={Named Entity Recognition for Chinese Social Media with Jointly Trained Embeddings},
  author={Peng, Nanyun and Dredze, Mark},
  booktitle={Processings of the Conference on Empirical Methods in Natural Language Processing (EMNLP)},
  pages={548–-554},
  year={2015}, File={https://www.aclweb.org/anthology/D15-1064/}, }
  
  @inproceedings{peng2016improving,
  title={Improving named entity recognition for Chinese social media with word segmentation representation learning},
  author={Peng, Nanyun and Dredze, Mark},
  booktitle={Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (ACL)},
  volume={2},
  pages={149--155},
  year={2016}, File={https://www.aclweb.org/anthology/P16-2025/}, }
  
  @inproceedings{he-sun-2017-f,
  title = "{F}-Score Driven Max Margin Neural Network for Named Entity Recognition in {C}hinese Social Media",
  author = "He, Hangfeng  and
  Sun, Xu",
  booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers",
  month = apr,
  year = "2017",
  address = "Valencia, Spain",
  publisher = "Association for Computational Linguistics",
  url = "https://www.aclweb.org/anthology/E17-2113",
  pages = "713--718",
  }
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code: def get_examples(self, data_dir, split):
      path = os.path.join(data_dir, f"{split}.jsonline")
  
      with open(path, encoding="utf8") as f:
          for line in f:
              example_json = json.loads(line)
              for span in example_json["span_list"]:
                  example = InputExample(
                      meta={
                          "context": "".join(example_json["tokens"]),
                          "entity": "".join(
                              example_json["tokens"][
                                  span["start"] : span["end"] + 1
                              ]
                          ),
                          "options": self.labels_mapped,
                      },
                      tgt_text=self.get_label(span["type"]),
                  )
                  examples.append(example)
  
imports: from .processor import *
label_desc: Use image node_id 2 for calling the Weibo_NER obj's underlying member method code with example usage: obj.get_examples(data_dir, split) without return types