This model is a fine-tuned version of
xlm-roberta-base on an XTREME dataset.
It achieves the following results on the evaluation set:
I used subset of the Cross-lingual TRansfer Evaluation of
Multilingual Encoders (XTREME) benchmark called WikiANN or PAN-X.2 This
dataset consists of Wikipedia articles in many languages, including the four most
commonly spoken languages in Switzerland: German (62.9%), French (22.9%), Ital‐
ian (8.4%), and English (5.9%). Each article is annotated with LOC (location), PER
(person), and ORG (organization) tags in the “inside-outside-beginning” (IOB2) for‐
mat. In this format, a B- prefix indicates the beginning of an entity, and consecutive
tokens belonging to the same entity are given an I- prefix.
