en (Indo-European), ru (Indo-European), zh (Sino-Tibetan), de (Indo-European), es (Indo-European), fr (Indo-European), ja (Japonic), it (Indo-European), pt (Indo-European), el (Indo-European), ko (Koreanic), fi (Uralic), id (Austronesian), tr (Turkic), ar (Afro-Asiatic), vi (Austroasiatic), th (Tai–Kadai), bg (Indo-European), ca (Indo-European), hi (Indo-European), et (Uralic), bn (Indo-European), ta (Dravidian), ur (Indo-European), sw (Niger–Congo), te (Dravidian), eu (Language isolate), my (Sino-Tibetan), ht (Creole), qu (Quechuan)
Training Details
Data Sources:
30 languages corpus
Data Volume:
500 billion sub-tokens
Model Architecture:
multilingual autoregressive model with 564M parameters
Note: green Score (e.g. "73.2") means that the model is better than facebook/xglm-564M.
Rank the Xglm 564M Capabilities
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Instruction Following and Task Automation
Factuality and Completeness of Knowledge
Censorship and Alignment
Data Analysis and Insight Generation
Text Generation
Text Summarization and Feature Extraction
Code Generation
Multi-Language Support and Translation
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