|
--- |
|
language: de |
|
license: mit |
|
tags: |
|
- flair |
|
- token-classification |
|
- sequence-tagger-model |
|
- hetzner |
|
- hetzner-gex44 |
|
- hetzner-gpu |
|
base_model: deepset/gbert-base |
|
widget: |
|
- text: Wesentliche Tätigkeiten der Compliance-Funktion wurden an die Mercurtainment |
|
AG , Düsseldorf , ausgelagert . |
|
--- |
|
|
|
# Fine-tuned Flair Model on CO-Fun NER Dataset |
|
|
|
This Flair model was fine-tuned on the |
|
[CO-Fun](https://arxiv.org/abs/2403.15322) NER Dataset using GBERT Base as backbone LM. |
|
|
|
## Dataset |
|
|
|
The [Company Outsourcing in Fund Prospectuses (CO-Fun) dataset](https://arxiv.org/abs/2403.15322) consists of |
|
948 sentences with 5,969 named entity annotations, including 2,340 Outsourced Services, 2,024 Companies, 1,594 Locations |
|
and 11 Software annotations. |
|
|
|
Overall, the following named entities are annotated: |
|
|
|
* `Auslagerung` (engl. outsourcing) |
|
* `Unternehmen` (engl. company) |
|
* `Ort` (engl. location) |
|
* `Software` |
|
|
|
## Fine-Tuning |
|
|
|
The latest [Flair version](https://github.com/flairNLP/flair/tree/42ea3f6854eba04387c38045f160c18bdaac07dc) is used for |
|
fine-tuning. |
|
|
|
A hyper-parameter search over the following parameters with 5 different seeds per configuration is performed: |
|
|
|
* Batch Sizes: [`16`, `8`] |
|
* Learning Rates: [`3e-05`, `5e-05`] |
|
|
|
More details can be found in this [repository](https://github.com/stefan-it/co-funer). All models are fine-tuned on a |
|
[Hetzner GX44](https://www.hetzner.com/dedicated-rootserver/matrix-gpu/) with an NVIDIA RTX 4000. |
|
|
|
## Results |
|
|
|
A hyper-parameter search with 5 different seeds per configuration is performed and micro F1-score on development set |
|
is reported: |
|
|
|
| Configuration | Seed 1 | Seed 2 | Seed 3 | Seed 4 | Seed 5 | Average | |
|
|--------------------|-----------------|--------------|--------------|--------------|--------------|-----------------| |
|
| `bs8-e10-lr5e-05` | [0.9477][1] | [0.935][2] | [0.9517][3] | [0.9443][4] | [0.9342][5] | 0.9426 ± 0.0077 | |
|
| `bs16-e10-lr5e-05` | [0.9214][6] | [0.9364][7] | [0.9334][8] | [0.9489][9] | [0.9257][10] | 0.9332 ± 0.0106 | |
|
| `bs8-e10-lr3e-05` | [**0.928**][11] | [0.9248][12] | [0.9421][13] | [0.9295][14] | [0.9263][15] | 0.9301 ± 0.0069 | |
|
| `bs16-e10-lr3e-05` | [0.918][16] | [0.9256][17] | [0.9331][18] | [0.9273][19] | [0.9196][20] | 0.9247 ± 0.0061 | |
|
|
|
[1]: https://hf.co/stefan-it/flair-co-funer-gbert_base-bs8-e10-lr5e-05-1 |
|
[2]: https://hf.co/stefan-it/flair-co-funer-gbert_base-bs8-e10-lr5e-05-2 |
|
[3]: https://hf.co/stefan-it/flair-co-funer-gbert_base-bs8-e10-lr5e-05-3 |
|
[4]: https://hf.co/stefan-it/flair-co-funer-gbert_base-bs8-e10-lr5e-05-4 |
|
[5]: https://hf.co/stefan-it/flair-co-funer-gbert_base-bs8-e10-lr5e-05-5 |
|
[6]: https://hf.co/stefan-it/flair-co-funer-gbert_base-bs16-e10-lr5e-05-1 |
|
[7]: https://hf.co/stefan-it/flair-co-funer-gbert_base-bs16-e10-lr5e-05-2 |
|
[8]: https://hf.co/stefan-it/flair-co-funer-gbert_base-bs16-e10-lr5e-05-3 |
|
[9]: https://hf.co/stefan-it/flair-co-funer-gbert_base-bs16-e10-lr5e-05-4 |
|
[10]: https://hf.co/stefan-it/flair-co-funer-gbert_base-bs16-e10-lr5e-05-5 |
|
[11]: https://hf.co/stefan-it/flair-co-funer-gbert_base-bs8-e10-lr3e-05-1 |
|
[12]: https://hf.co/stefan-it/flair-co-funer-gbert_base-bs8-e10-lr3e-05-2 |
|
[13]: https://hf.co/stefan-it/flair-co-funer-gbert_base-bs8-e10-lr3e-05-3 |
|
[14]: https://hf.co/stefan-it/flair-co-funer-gbert_base-bs8-e10-lr3e-05-4 |
|
[15]: https://hf.co/stefan-it/flair-co-funer-gbert_base-bs8-e10-lr3e-05-5 |
|
[16]: https://hf.co/stefan-it/flair-co-funer-gbert_base-bs16-e10-lr3e-05-1 |
|
[17]: https://hf.co/stefan-it/flair-co-funer-gbert_base-bs16-e10-lr3e-05-2 |
|
[18]: https://hf.co/stefan-it/flair-co-funer-gbert_base-bs16-e10-lr3e-05-3 |
|
[19]: https://hf.co/stefan-it/flair-co-funer-gbert_base-bs16-e10-lr3e-05-4 |
|
[20]: https://hf.co/stefan-it/flair-co-funer-gbert_base-bs16-e10-lr3e-05-5 |
|
|
|
The result in bold shows the performance of the current viewed model. |
|
|
|
Additionally, the Flair [training log](training.log) and [TensorBoard logs](../../tensorboard) are also uploaded to the model |
|
hub. |
