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