README.md

metadata

annotations_creators:
  - expert-generated
language_creators:
  - found
language:
  - zh
license:
  - unknown
multilinguality:
  - monolingual
size_categories:
  - 1K<n<10K
source_datasets:
  - original
task_categories:
  - token-classification
task_ids:
  - named-entity-recognition
paperswithcode_id: nlp-model-tune
pretty_name: NER Model Tune
train-eval-index:
  - config: default
    task: token-classification
    task_id: entity_extraction
    splits:
      train_split: train
      eval_split: test
    col_mapping:
      tokens: tokens
      ner_tags: tags
    metrics:
      - type: seqeval
        name: seqeval
dataset_info:
  features:
    - name: id
      dtype: string
    - name: tokens
      sequence: string
    - name: ner_tags
      sequence:
        class_label:
          names:
            '0': O,
            '1': B-CARDINAL,
            '2': B-DATE,
            '3': B-EVENT,
            '4': B-FAC,
            '5': B-GPE,
            '6': B-LANGUAGE,
            '7': B-LAW,
            '8': B-LOC,
            '9': B-MONEY,
            '10': B-NORP,
            '11': B-ORDINAL,
            '12': B-ORG,
            '13': B-PERCENT,
            '14': B-PERSON,
            '15': B-PRODUCT,
            '16': B-QUANTITY,
            '17': B-TIME,
            '18': B-WORK_OF_ART,
            '19': I-CARDINAL,
            '20': I-DATE,
            '21': I-EVENT,
            '22': I-FAC,
            '23': I-GPE,
            '24': I-LANGUAGE,
            '25': I-LAW,
            '26': I-LOC,
            '27': I-MONEY,
            '28': I-NORP,
            '29': I-ORDINAL,
            '30': I-ORG,
            '31': I-PERCENT,
            '32': I-PERSON,
            '33': I-PRODUCT,
            '34': I-QUANTITY,
            '35': I-TIME,
            '36': I-WORK_OF_ART,
            '37': E-CARDINAL,
            '38': E-DATE,
            '39': E-EVENT,
            '40': E-FAC,
            '41': E-GPE,
            '42': E-LANGUAGE,
            '43': E-LAW,
            '44': E-LOC,
            '45': E-MONEY,
            '46': E-NORP,
            '47': E-ORDINAL,
            '48': E-ORG,
            '49': E-PERCENT,
            '50': E-PERSON,
            '51': E-PRODUCT,
            '52': E-QUANTITY,
            '53': E-TIME,
            '54': E-WORK_OF_ART,
            '55': S-CARDINAL,
            '56': S-DATE,
            '57': S-EVENT,
            '58': S-FAC,
            '59': S-GPE,
            '60': S-LANGUAGE,
            '61': S-LAW,
            '62': S-LOC,
            '63': S-MONEY,
            '64': S-NORP,
            '65': S-ORDINAL,
            '66': S-ORG,
            '67': S-PERCENT,
            '68': S-PERSON,
            '69': S-PRODUCT,
            '70': S-QUANTITY,
            '71': S-TIME,
            '72': S-WORK_OF_ART
  splits:
    - name: train
      num_bytes: 568
      num_examples: 1
  download_size: 568
  dataset_size: 568

Dataset Card for "NER Model Tune"

Table of Contents

• Dataset Description
  • Dataset Summary
  • Supported Tasks and Leaderboards
  • Languages
• Dataset Structure
  • Data Instances
  • Data Fields
  • Data Splits
• Dataset Creation
  • Curation Rationale
  • Source Data
  • Annotations
  • Personal and Sensitive Information
• Considerations for Using the Data
  • Social Impact of Dataset
  • Discussion of Biases
  • Other Known Limitations
• Additional Information
  • Dataset Curators
  • Licensing Information
  • Citation Information
  • Contributions

Dataset Description

• Homepage: None
• Repository: https://huggingface.co/datasets/ayuhamaro/nlp-model-tune
• Paper: [More Information Needed]
• Leaderboard: If the dataset supports an active leaderboard, add link here
• Point of Contact: [More Information Needed]

Dataset Summary

[More Information Needed]

Supported Tasks and Leaderboards

[More Information Needed]

Languages

[More Information Needed]

Dataset Structure

Data Instances

[More Information Needed]

Data Fields

[More Information Needed]

Data Splits

[More Information Needed]

Dataset Creation

Curation Rationale

[More Information Needed]

Source Data

[More Information Needed]

Initial Data Collection and Normalization

[More Information Needed]

Who are the source language producers?

[More Information Needed]

Annotations

[More Information Needed]

Annotation process

[More Information Needed]

Who are the annotators?

[More Information Needed]

Personal and Sensitive Information

[More Information Needed]

Considerations for Using the Data

Social Impact of Dataset

[More Information Needed]

Discussion of Biases

[More Information Needed]

Other Known Limitations

[More Information Needed]

Additional Information

Dataset Curators

[More Information Needed]

Licensing Information

[More Information Needed]

Citation Information

[More Information Needed]

Contributions