RuBERT Entity Embedder (Russian, cased, 12‑layer, 768‑hidden, 12‑heads, 180M parameters) is based on
DeepPavlov's RuBERT-base-cased
. It is fine-tuned as a Siamese neural network to build effective token embeddings of 29 entity classes on Russian [1]. The fine-tuning procedure is the first stage of two-stage fine-tuning of a BERT-based language model for more robust named entity recognition [2].
[1]:
Artemova
,
E.
,
Zmeev
,
M.
,
Loukachevitch
,
N.V.
,
Rozhkov
,
I.S.
,
Batura
,
T.
,
Ivanov
,
V.
, &
Tutubalina
,
E.
(2022).
RuNNE-2022 Shared Task: Recognizing Nested Named Entities
. Proceedings of the International Conference “Dialogue 2022”.
https://www.dialog-21.ru/media/5747/artemovaelplusetal109.pdf
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