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hmteams/teams-base-historic-multilingual-discriminator

sourceHugging Faceapache-2.0updated 2y agoView on Hugging Face

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hmTEAMS

![🤗](https://github.com/stefan-it/hmTEAMS)

Historic Multilingual and Monolingual TEAMS Models. The following languages are covered:

—English (British Library Corpus - Books)
—German (Europeana Newspaper)
—French (Europeana Newspaper)
—Finnish (Europeana Newspaper, Digilib)
—Swedish (Europeana Newspaper, Digilib)
—Dutch (Delpher Corpus)
—Norwegian (NCC Corpus)

Architecture

We pretrain a "Training ELECTRA Augmented with Multi-word Selection" (TEAMS) model:

hmTEAMS Overview

Results

We perform experiments on various historic NER datasets, such as HIPE-2022 or ICDAR Europeana. All details incl. hyper-parameters can be found here.

Small Benchmark

We test our pretrained language models on various datasets from HIPE-2020, HIPE-2022 and Europeana. The following table shows an overview of used datasets.

Language	Dataset	Additional Dataset
English	AjMC	-
German	AjMC	-
French	AjMC	ICDAR-Europeana
Finnish	NewsEye	-
Swedish	NewsEye	-
Dutch	ICDAR-Europeana	-

Results

Model	English AjMC	German AjMC	French AjMC	Finnish NewsEye	Swedish NewsEye	Dutch ICDAR	French ICDAR	Avg.
hmBERT (32k) Schweter et al.	85.36 ± 0.94	89.08 ± 0.09	85.10 ± 0.60	77.28 ± 0.37	82.85 ± 0.83	82.11 ± 0.61	77.21 ± 0.16	82.71
hmTEAMS (Ours)	86.41 ± 0.36	88.64 ± 0.42	85.41 ± 0.67	79.27 ± 1.88	82.78 ± 0.60	88.21 ± 0.39	78.03 ± 0.39	84.11

Release

Our pretrained hmTEAMS model can be obtained from the Hugging Face Model Hub:

Acknowledgements

We thank Luisa März, Katharina Schmid and Erion Çano for their fruitful discussions about Historic Language Models.

Research supported with Cloud TPUs from Google's TPU Research Cloud (TRC). Many Thanks for providing access to the TPUs ❤️