Cross Lingual Ner On Conll German

Résultats

Résultats de performance de divers modèles sur ce benchmark

Nom du modèle	F1	Paper Title	Repository
mBERT	69.56	Beto, Bentz, Becas: The Surprising Cross-Lingual Effectiveness of BERT	-
XLM-R large	74.5	Model and Data Transfer for Cross-Lingual Sequence Labelling in Zero-Resource Settings	-
SMTS Multi avg	74.97	Single-/Multi-Source Cross-Lingual NER via Teacher-Student Learning on Unlabeled Data in Target Language	-
Zero shot mBERT 3	72.44	Towards Lingua Franca Named Entity Recognition with BERT	-
MAN-MoE+CharCNN+UMWE	56	Multi-Source Cross-Lingual Model Transfer: Learning What to Share	-
SMTS Multi sim	75.33	Single-/Multi-Source Cross-Lingual NER via Teacher-Student Learning on Unlabeled Data in Target Language	-
UniTrans	74.82	UniTrans: Unifying Model Transfer and Data Transfer for Cross-Lingual Named Entity Recognition with Unlabeled Data	-
Base Model	70.79	Enhanced Meta-Learning for Cross-lingual Named Entity Recognition with Minimal Resources	-
Meta-Cross	73.16	Enhanced Meta-Learning for Cross-lingual Named Entity Recognition with Minimal Resources	-
SMTS Single	73.22	Single-/Multi-Source Cross-Lingual NER via Teacher-Student Learning on Unlabeled Data in Target Language	-

0 of 10 row(s) selected.