metadata

dataset_info:
  features:
    - name: transcription
      dtype: string
    - name: glosses
      dtype: string
    - name: translation
      dtype: string
    - name: glottocode
      dtype: string
    - name: id
      dtype: string
    - name: source
      dtype: string
    - name: metalang_glottocode
      dtype: string
    - name: is_segmented
      dtype: string
    - name: language
      dtype: string
    - name: metalang
      dtype: string
  splits:
    - name: train
      num_bytes: 93769783
      num_examples: 418718
    - name: train_ID
      num_bytes: 25048415
      num_examples: 104928
    - name: eval_ID
      num_bytes: 2732125
      num_examples: 11138
    - name: test_ID
      num_bytes: 2869258
      num_examples: 11940
    - name: train_OOD
      num_bytes: 1817406
      num_examples: 7356
    - name: eval_OOD
      num_bytes: 249722
      num_examples: 984
    - name: test_OOD
      num_bytes: 240556
      num_examples: 972
  download_size: 38002540
  dataset_size: 126727265
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: train_ID
        path: data/train_ID-*
      - split: eval_ID
        path: data/eval_ID-*
      - split: test_ID
        path: data/test_ID-*
      - split: train_OOD
        path: data/train_OOD-*
      - split: eval_OOD
        path: data/eval_OOD-*
      - split: test_OOD
        path: data/test_OOD-*

Multilingual IGT

A compilation of various sources of interlinear glossed text (IGT) across nearly two thousand languages in a standardized format.

Dataset Details

Dataset Description

License: CC BY 4.0

Dataset Sources [optional]

Repository: https://github.com/foltaProject/glosslm/settings
Paper [optional]: Coming soon...

Direct Use

Training models for IGT generation
Linguistic analysis of IGT across languages
Use of IGT in downstream applications such as machine translation

Dataset Structure

[More Information Needed]

Dataset Creation

Source Data

IMTVault 1.1 (https://imtvault.org)[https://imtvault.org]
ODIN (http://depts.washington.edu/uwcl/odin/)[http://depts.washington.edu/uwcl/odin/]
APiCS (https://apics-online.info)[https://apics-online.info]
UraTyp (https://uralic.clld.org)[https://uralic.clld.org]
Guarani Corpus (https://guaranicorpus.usc.edu)[https://guaranicorpus.usc.edu]
2023 SIGMORPHON Shared Task (https://github.com/sigmorphon/2023GlossingST)[https://github.com/sigmorphon/2023GlossingST]

Data Collection and Processing

[More Information Needed]

Who are the source data producers?

[More Information Needed]

Annotations [optional]

Annotation process

[More Information Needed]

Who are the annotators?

[More Information Needed]

Personal and Sensitive Information

[More Information Needed]

Bias, Risks, and Limitations

[More Information Needed]

Recommendations

Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.

Citation [optional]

BibTeX:

[More Information Needed]

APA:

[More Information Needed]

Glossary [optional]

[More Information Needed]

More Information [optional]

[More Information Needed]

Dataset Card Authors [optional]

[More Information Needed]

Dataset Card Contact

[More Information Needed]