---
license: apache-2.0
language:
- en
- de
- fr
- fi
- sv
- nl
- nb
- nn
- 'no'
---

# hmTEAMS

[![🤗](https://github.com/stefan-it/hmTEAMS/raw/main/logo.jpeg "🤗")](https://github.com/stefan-it/hmTEAMS)

Historic Multilingual and Monolingual [TEAMS](https://aclanthology.org/2021.findings-acl.219/) 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](https://aclanthology.org/2021.findings-acl.219/)) model:

![hmTEAMS Overview](https://github.com/stefan-it/hmTEAMS/raw/main/hmteams_overview.svg)

# 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](https://github.com/stefan-it/hmTEAMS/tree/main/bench).

## 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](https://github.com/hipe-eval/HIPE-2022-data/blob/main/documentation/README-ajmc.md)       | -                                                                                |
| German   | [AjMC](https://github.com/hipe-eval/HIPE-2022-data/blob/main/documentation/README-ajmc.md)       | -                                                                                |
| French   | [AjMC](https://github.com/hipe-eval/HIPE-2022-data/blob/main/documentation/README-ajmc.md)       | [ICDAR-Europeana](https://github.com/stefan-it/historic-domain-adaptation-icdar) |
| Finnish  | [NewsEye](https://github.com/hipe-eval/HIPE-2022-data/blob/main/documentation/README-newseye.md) | -                                                                                |
| Swedish  | [NewsEye](https://github.com/hipe-eval/HIPE-2022-data/blob/main/documentation/README-newseye.md) | -                                                                                |
| Dutch    | [ICDAR-Europeana](https://github.com/stefan-it/historic-domain-adaptation-icdar)                 | -                                                                                |

# Results

| Model                                                                                  | English AjMC | German AjMC  | French AjMC  | Finnish NewsEye | Swedish NewsEye | Dutch ICDAR  | French ICDAR | Avg.      |
|----------------------------------------------------------------------------------------|--------------|--------------|--------------|-----------------|-----------------|--------------|--------------|-----------|
| hmBERT (32k) [Schweter et al.](https://ceur-ws.org/Vol-3180/paper-87.pdf)              | 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:

* [hmTEAMS Discriminator (**this model**)](https://huggingface.co/hmteams/teams-base-historic-multilingual-discriminator)
* [hmTEAMS Generator](https://huggingface.co/hmteams/teams-base-historic-multilingual-generator)

# Acknowledgements

We thank [Luisa März](https://github.com/LuisaMaerz), [Katharina Schmid](https://github.com/schmika) and
[Erion Çano](https://github.com/erionc) for their fruitful discussions about Historic Language Models.

Research supported with Cloud TPUs from Google's [TPU Research Cloud](https://sites.research.google/trc/about/) (TRC).
Many Thanks for providing access to the TPUs ❤️