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readme: add initial version of model card

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Hey,

this PR adds the initial version of model card.

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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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+
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+ # Fine-tuned Flair Model on CO-Fun NER Dataset
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+
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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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+
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+ ## Dataset
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+
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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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+
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+ Overall, the following named entities are annotated:
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+
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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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+
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+ ## Fine-Tuning
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+
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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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+
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+ A hyper-parameter search over the following parameters with 5 different seeds per configuration is performed:
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+
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+ * Batch Sizes: [`16`, `8`]
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+ * Learning Rates: [`3e-05`, `5e-05`]
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+
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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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+
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+ ## Results
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+
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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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+
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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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+
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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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+
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+ The result in bold shows the performance of the current viewed model.
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+
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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.