class_docstr: This is a dataset for classifying citation intents in academic papers.
class_docstr_tok: ["This","is","a","dataset","for","classifying","citation","intents","in","academic","papers","."]
docstr: Yields examples.
docstr_tok: ["Yields","examples","."]
code: def _generate_examples(self, path=None):
      """Yields examples."""
      with epath.Path(path).open() as f:
          unique_ids = {}
          for line in f:
              d = json.loads(line)
              unique_id = str(d["unique_id"])
              if unique_id in unique_ids:
                  continue
              unique_ids[unique_id] = True
              yield unique_id, {
                  "string": d["string"],
                  "label": str(d["label"]),
                  "sectionName": str(d["sectionName"]),
                  "citingPaperId": str(d["citingPaperId"]),
                  "citedPaperId": str(d["citedPaperId"]),
                  "excerpt_index": int(d["excerpt_index"]),
                  "isKeyCitation": bool(d["isKeyCitation"]),
                  "label2": str(d.get("label2", "none")),
                  "citeEnd": _safe_int(d["citeEnd"]),
                  "citeStart": _safe_int(d["citeStart"]),
                  "source": str(d["source"]),
                  "label_confidence": float(
                      d.get("label_confidence", 0.0)
                  ),
                  "label2_confidence": float(
                      d.get("label2_confidence", 0.0)
                  ),
                  "id": str(d["id"]),
              }
  
file_path: datasets/tensorflow_datasets/datasets/scicite/scicite_dataset_builder.py
imports: import json
  import os
  from etils import epath
  import numpy
  import tensorflow_datasets.public_api
label_desc: Use image node_id 3 for calling the Builder obj's underlying member method code with example usage: obj._generate_examples(path) without return types