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Document Classification on MLDoc 1.0 (test)

0.959Accuracy (DE)MultiFiT
0.806120.845810.88550.92519Sep 10, 2019

Evaluation results

MethodLinks
MultiFiTSetting=Supervised, Training Examples=1,000 target language2019.090.9590.96070.94750.90250.90030.87650.9252
MultiFiT, no wikiSetting=Supervised, Training Examples=1,000 target language, Pre-training=No Wikipedia tokens2019.090.95230.95070.94650.8930.88630.87520.9003
Monolingual BERTSetting=Supervised, Training Examples=1,000 target language2019.090.9493—————0.9217
MultiBERTSetting=Supervised, Training Examples=1,000 target language2019.090.940.95150.9320.85820.87480.86850.9072
MultiCCASetting=Supervised, Training Examples=1,000 target language2019.090.9370.94450.92050.85550.85350.85650.873
LASER, paperSetting=Supervised, Training Examples=1,000 target language2019.090.9270.88750.9080.85930.85150.84650.8898
MultiFiT, pseudoSetting=Zero-shot, Training Examples=1,000 source language, Bootstrapping=Pseudo-labels2019.090.91620.7910.89420.76020.69570.67830.8248
MultiFiTSetting=Supervised, Training Examples=100 target language2019.090.9090.890.85030.80120.80550.73550.8802
LASER, codeSetting=Zero-shot, Training Examples=1,000 source language2019.090.87650.75480.840.71180.64580.66580.7665
LASER, paperSetting=Zero-shot, Training Examples=1,000 source language2019.090.86250.7930.7830.7020.60950.67250.7098
MultiBERTSetting=Zero-shot, Training Examples=1,000 source language2019.090.82350.74980.83030.68270.64580.71580.6617
MultiCCASetting=Zero-shot, Training Examples=1,000 source language2019.090.8120.7250.72380.69380.67630.6080.7473