readme: add initial version of model card
Browse filesHey,
this PR adds the initial version of model card.
README.md
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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: google-bert/bert-base-german-cased
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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 German BERT 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` | [0.9346][1] | [0.9388][2] | [0.9301][3] | [0.9291][4] | [0.9346][5] | 0.9334 ± 0.0039 |
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| `bs16-e10-lr5e-05` | [0.9316][6] | [**0.9328**][7] | [0.9341][8] | [0.9315][9] | [0.9248][10] | 0.931 ± 0.0036 |
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| `bs8-e10-lr3e-05` | [0.9234][11] | [0.9391][12] | [0.9207][13] | [0.9191][14] | [0.9394][15] | 0.9283 ± 0.0101 |
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| `bs16-e10-lr3e-05` | [0.9136][16] | [0.9269][17] | [0.9231][18] | [0.9251][19] | [0.9247][20] | 0.9227 ± 0.0053 |
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[1]: https://hf.co/stefan-it/flair-co-funer-german_bert_base-bs8-e10-lr5e-05-1
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[2]: https://hf.co/stefan-it/flair-co-funer-german_bert_base-bs8-e10-lr5e-05-2
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[3]: https://hf.co/stefan-it/flair-co-funer-german_bert_base-bs8-e10-lr5e-05-3
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[4]: https://hf.co/stefan-it/flair-co-funer-german_bert_base-bs8-e10-lr5e-05-4
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[5]: https://hf.co/stefan-it/flair-co-funer-german_bert_base-bs8-e10-lr5e-05-5
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[6]: https://hf.co/stefan-it/flair-co-funer-german_bert_base-bs16-e10-lr5e-05-1
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[7]: https://hf.co/stefan-it/flair-co-funer-german_bert_base-bs16-e10-lr5e-05-2
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[8]: https://hf.co/stefan-it/flair-co-funer-german_bert_base-bs16-e10-lr5e-05-3
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[9]: https://hf.co/stefan-it/flair-co-funer-german_bert_base-bs16-e10-lr5e-05-4
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[10]: https://hf.co/stefan-it/flair-co-funer-german_bert_base-bs16-e10-lr5e-05-5
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[11]: https://hf.co/stefan-it/flair-co-funer-german_bert_base-bs8-e10-lr3e-05-1
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[12]: https://hf.co/stefan-it/flair-co-funer-german_bert_base-bs8-e10-lr3e-05-2
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[13]: https://hf.co/stefan-it/flair-co-funer-german_bert_base-bs8-e10-lr3e-05-3
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[14]: https://hf.co/stefan-it/flair-co-funer-german_bert_base-bs8-e10-lr3e-05-4
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[15]: https://hf.co/stefan-it/flair-co-funer-german_bert_base-bs8-e10-lr3e-05-5
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[16]: https://hf.co/stefan-it/flair-co-funer-german_bert_base-bs16-e10-lr3e-05-1
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[17]: https://hf.co/stefan-it/flair-co-funer-german_bert_base-bs16-e10-lr3e-05-2
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[18]: https://hf.co/stefan-it/flair-co-funer-german_bert_base-bs16-e10-lr3e-05-3
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[19]: https://hf.co/stefan-it/flair-co-funer-german_bert_base-bs16-e10-lr3e-05-4
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[20]: https://hf.co/stefan-it/flair-co-funer-german_bert_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.
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