--- 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]