readme: add initial version of model card

Hey,

this PR adds the initial version of model card.

README.md

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+---
+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.