README.md

metadata

license: other
license_name: other
license_link: LICENSE
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: validation
        path: data/validation-*
      - split: test
        path: data/test-*
dataset_info:
  features:
    - name: id
      dtype: string
    - name: tokens
      sequence: string
    - name: pos_tags
      sequence:
        class_label:
          names:
            '0': '"'
            '1': ''''''
            '2': '#'
            '3': $
            '4': (
            '5': )
            '6': ','
            '7': .
            '8': ':'
            '9': '``'
            '10': CC
            '11': CD
            '12': DT
            '13': EX
            '14': FW
            '15': IN
            '16': JJ
            '17': JJR
            '18': JJS
            '19': LS
            '20': MD
            '21': NN
            '22': NNP
            '23': NNPS
            '24': NNS
            '25': NN|SYM
            '26': PDT
            '27': POS
            '28': PRP
            '29': PRP$
            '30': RB
            '31': RBR
            '32': RBS
            '33': RP
            '34': SYM
            '35': TO
            '36': UH
            '37': VB
            '38': VBD
            '39': VBG
            '40': VBN
            '41': VBP
            '42': VBZ
            '43': WDT
            '44': WP
            '45': WP$
            '46': WRB
    - name: chunk_tags
      sequence:
        class_label:
          names:
            '0': O
            '1': B-ADJP
            '2': I-ADJP
            '3': B-ADVP
            '4': I-ADVP
            '5': B-CONJP
            '6': I-CONJP
            '7': B-INTJ
            '8': I-INTJ
            '9': B-LST
            '10': I-LST
            '11': B-NP
            '12': I-NP
            '13': B-PP
            '14': I-PP
            '15': B-PRT
            '16': I-PRT
            '17': B-SBAR
            '18': I-SBAR
            '19': B-UCP
            '20': I-UCP
            '21': B-VP
            '22': I-VP
    - name: ner_tags
      sequence:
        class_label:
          names:
            '0': O
            '1': B-PER
            '2': I-PER
            '3': B-ORG
            '4': I-ORG
            '5': B-LOC
            '6': I-LOC
            '7': B-MISC
            '8': I-MISC
    - name: ner_labels
      sequence: string
    - name: sentence
      dtype: string
    - name: entities
      struct:
        - name: LOC
          sequence: string
        - name: MISC
          sequence: string
        - name: ORG
          sequence: string
        - name: PER
          sequence: string
  splits:
    - name: train
      num_bytes: 9823344
      num_examples: 14041
    - name: validation
      num_bytes: 2461842
      num_examples: 3250
    - name: test
      num_bytes: 2248983
      num_examples: 3453
  download_size: 3369931
  dataset_size: 14534169
task_categories:
  - text-generation
  - token-classification
language:
  - en
size_categories:
  - 10K<n<100K

Dataset Card for CoNLL-2003 with NER Workflow Enhancements

This dataset is a modified version of the CoNLL-2003 dataset, enhanced to support an LLM-based Named Entity Recognition (NER) workflow. Two new columns, sentence and entities, have been added. You can find the code used to generate this version together with the data files.

Named Entities

As in the original CoNLL-2003 task, this dataset focuses on four types of named entities:

• Persons (PER)
• Locations (LOC)
• Organizations (ORG)
• Miscellaneous entities (MISC) that do not belong to the previous three groups.

Workflow

The LLM is provided with the sentence and a prompt containing NER task instructions. It is then asked to output a dictionary with the identified Named Entities.

Example

For the sentence:

EU rejects German call to boycott British lamb.

The expected output is:

{
  "entities": {
    "LOC": [],
    "MISC": ["German", "British"],
    "ORG": ["EU"],
    "PER": []
  }
}

Original Dataset

You can find the original dataset and its associated documentation at the CoNLL-2003 dataset on Hugging Face.