---
language: en
license: mit
tags:
- flair
- token-classification
- sequence-tagger-model
base_model: hmteams/teams-base-historic-multilingual-discriminator
widget:
- text: Cp . Eur . Phoen . 240 , 1 , αἷμα ddiov φλέγέι .
---

# Fine-tuned Flair Model on AjMC English NER Dataset (HIPE-2022)

This Flair model was fine-tuned on the
[AjMC English](https://github.com/hipe-eval/HIPE-2022-data/blob/main/documentation/README-ajmc.md)
NER Dataset using hmTEAMS as backbone LM.

The AjMC dataset consists of NE-annotated historical commentaries in the field of Classics,
and was created in the context of the [Ajax MultiCommentary](https://mromanello.github.io/ajax-multi-commentary/)
project.

The following NEs were annotated: `pers`, `work`, `loc`, `object`, `date` and `scope`.

# Results

We performed a hyper-parameter search over the following parameters with 5 different seeds per configuration:

* Batch Sizes: `[8, 4]`
* Learning Rates: `[0.00015, 0.00016]`

And report micro F1-score on development set:

| Configuration     | Run 1        | Run 2        | Run 3        | Run 4        | Run 5        | Avg.         |
|-------------------|--------------|--------------|--------------|--------------|--------------|--------------|
| bs4-e10-lr0.00016 | [0.842][1]   | [0.8548][2]  | [0.8407][3]  | [0.8431][4]  | [0.8443][5]  | 84.5 ± 0.51  |
| bs4-e10-lr0.00015 | [0.8376][6]  | [0.8343][7]  | [0.8495][8]  | [0.8394][9]  | [0.837][10]  | 83.96 ± 0.52 |
| bs8-e10-lr0.00015 | [0.8172][11] | [0.8242][12] | [0.8217][13] | [0.8367][14] | [0.8323][15] | 82.64 ± 0.71 |
| bs8-e10-lr0.00016 | [0.8178][16] | [0.8205][17] | [0.8126][18] | [0.8339][19] | [0.8264][20] | 82.22 ± 0.73 |

[1]: https://hf.co/hmbench/hmbench-ajmc-en-hmbyt5-bs4-wsFalse-e10-lr0.00016-poolingfirst-layers-1-crfFalse-1
[2]: https://hf.co/hmbench/hmbench-ajmc-en-hmbyt5-bs4-wsFalse-e10-lr0.00016-poolingfirst-layers-1-crfFalse-2
[3]: https://hf.co/hmbench/hmbench-ajmc-en-hmbyt5-bs4-wsFalse-e10-lr0.00016-poolingfirst-layers-1-crfFalse-3
[4]: https://hf.co/hmbench/hmbench-ajmc-en-hmbyt5-bs4-wsFalse-e10-lr0.00016-poolingfirst-layers-1-crfFalse-4
[5]: https://hf.co/hmbench/hmbench-ajmc-en-hmbyt5-bs4-wsFalse-e10-lr0.00016-poolingfirst-layers-1-crfFalse-5
[6]: https://hf.co/hmbench/hmbench-ajmc-en-hmbyt5-bs4-wsFalse-e10-lr0.00015-poolingfirst-layers-1-crfFalse-1
[7]: https://hf.co/hmbench/hmbench-ajmc-en-hmbyt5-bs4-wsFalse-e10-lr0.00015-poolingfirst-layers-1-crfFalse-2
[8]: https://hf.co/hmbench/hmbench-ajmc-en-hmbyt5-bs4-wsFalse-e10-lr0.00015-poolingfirst-layers-1-crfFalse-3
[9]: https://hf.co/hmbench/hmbench-ajmc-en-hmbyt5-bs4-wsFalse-e10-lr0.00015-poolingfirst-layers-1-crfFalse-4
[10]: https://hf.co/hmbench/hmbench-ajmc-en-hmbyt5-bs4-wsFalse-e10-lr0.00015-poolingfirst-layers-1-crfFalse-5
[11]: https://hf.co/hmbench/hmbench-ajmc-en-hmbyt5-bs8-wsFalse-e10-lr0.00015-poolingfirst-layers-1-crfFalse-1
[12]: https://hf.co/hmbench/hmbench-ajmc-en-hmbyt5-bs8-wsFalse-e10-lr0.00015-poolingfirst-layers-1-crfFalse-2
[13]: https://hf.co/hmbench/hmbench-ajmc-en-hmbyt5-bs8-wsFalse-e10-lr0.00015-poolingfirst-layers-1-crfFalse-3
[14]: https://hf.co/hmbench/hmbench-ajmc-en-hmbyt5-bs8-wsFalse-e10-lr0.00015-poolingfirst-layers-1-crfFalse-4
[15]: https://hf.co/hmbench/hmbench-ajmc-en-hmbyt5-bs8-wsFalse-e10-lr0.00015-poolingfirst-layers-1-crfFalse-5
[16]: https://hf.co/hmbench/hmbench-ajmc-en-hmbyt5-bs8-wsFalse-e10-lr0.00016-poolingfirst-layers-1-crfFalse-1
[17]: https://hf.co/hmbench/hmbench-ajmc-en-hmbyt5-bs8-wsFalse-e10-lr0.00016-poolingfirst-layers-1-crfFalse-2
[18]: https://hf.co/hmbench/hmbench-ajmc-en-hmbyt5-bs8-wsFalse-e10-lr0.00016-poolingfirst-layers-1-crfFalse-3
[19]: https://hf.co/hmbench/hmbench-ajmc-en-hmbyt5-bs8-wsFalse-e10-lr0.00016-poolingfirst-layers-1-crfFalse-4
[20]: https://hf.co/hmbench/hmbench-ajmc-en-hmbyt5-bs8-wsFalse-e10-lr0.00016-poolingfirst-layers-1-crfFalse-5

The [training log](training.log) and TensorBoard logs are also uploaded to the model hub.

More information about fine-tuning can be found [here](https://github.com/stefan-it/hmBench).

# 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 ❤️