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
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Who are the annotators?
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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:
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APA:
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Glossary [optional]
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Dataset Card Authors [optional]
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Dataset Card Contact
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