readme: add initial version of model card (#2)

- readme: add initial version of model card (252ec25e5359314e70982d8fd9636ee9670d2f7d)

README.md

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+---
+language: de
+license: mit
+tags:
+- flair
+- token-classification
+- sequence-tagger-model
+base_model: deepset/gbert-base
+widget:
+- text: PASt ( KvD ) - Polizeipräsidium Westhessen [ Newsroom ] Wiesbaden ( ots )
+    - Am Sonntag , den 27.01.2019 führte die Autobahnpolizei Wiesbaden in Zusammenarbeit
+    mit der Präsidialwache in der Zeit von 11:00 - 16:00 Uhr eine Geschwindigkeitsmessung
+    in der Baustelle der A66 am Wiesbadener Kreuz durch .
+---
+
+# Fine-tuned Flair Model on German MobIE Dataset with AutoTrain
+
+This Flair model was fine-tuned on the
+[German MobIE](https://aclanthology.org/2021.konvens-1.22/)
+NER Dataset using GBERT Base as backbone LM and the 🚀 [AutoTrain](https://github.com/huggingface/autotrain-advanced)
+library.
+
+## Dataset
+
+The [German MobIE](https://github.com/DFKI-NLP/MobIE) dataset is a German-language dataset, which is human-annotated
+with 20 coarse- and fine-grained entity types and entity linking information for geographically linkable entities. The
+dataset consists of 3,232 social media texts and traffic reports with 91K tokens, and contains 20.5K annotated
+entities, 13.1K of which are linked to a knowledge base.
+
+The following named entities are annotated:
+
+* `location-stop`
+* `trigger`
+* `organization-company`
+* `location-city`
+* `location`
+* `event-cause`
+* `location-street`
+* `time`
+* `date`
+* `number`
+* `duration`
+* `organization`
+* `person`
+* `set`
+* `distance`
+* `disaster-type`
+* `money`
+* `org-position`
+* `percent`
+
+## Fine-Tuning
+
+The latest [Flair version](https://github.com/flairNLP/flair/tree/42ea3f6854eba04387c38045f160c18bdaac07dc) is used for
+fine-tuning. Additionally, the model is trained with the
+[FLERT (Schweter and Akbik (2020)](https://arxiv.org/abs/2011.06993) approach, because the MobIE dataset thankfully
+comes with document boundary information marker.
+
+A hyper-parameter search over the following parameters with 5 different seeds per configuration is performed:
+
+* Batch Sizes: [`16`]
+* Learning Rates: [`5e-05`, `3e-05`]
+
+All models are trained with the awesome [AutoTrain Advanced](https://github.com/huggingface/autotrain-advanced) from
+Hugging Face. More details can be found in this [repository](https://github.com/stefan-it/autotrain-flair-mobie).
+
+## 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         |
+|--------------------|-------------|-----------------|-------------|-------------|--------------|-----------------|
+| `bs16-e10-lr5e-05` | [0.8446][1] | [0.8495][2]     | [0.8455][3] | [0.8419][4] | [0.8476][5]  | 0.8458 ± 0.0029 |
+| `bs16-e10-lr3e-05` | [0.8392][6] | [**0.8445**][7] | [0.8495][8] | [0.8381][9] | [0.8449][10] | 0.8432 ± 0.0046 |
+
+[1]: https://hf.co/stefan-it/autotrain-flair-mobie-gbert_base-bs16-e10-lr5e-05-1
+[2]: https://hf.co/stefan-it/autotrain-flair-mobie-gbert_base-bs16-e10-lr5e-05-2
+[3]: https://hf.co/stefan-it/autotrain-flair-mobie-gbert_base-bs16-e10-lr5e-05-3
+[4]: https://hf.co/stefan-it/autotrain-flair-mobie-gbert_base-bs16-e10-lr5e-05-4
+[5]: https://hf.co/stefan-it/autotrain-flair-mobie-gbert_base-bs16-e10-lr5e-05-5
+[6]: https://hf.co/stefan-it/autotrain-flair-mobie-gbert_base-bs16-e10-lr3e-05-1
+[7]: https://hf.co/stefan-it/autotrain-flair-mobie-gbert_base-bs16-e10-lr3e-05-2
+[8]: https://hf.co/stefan-it/autotrain-flair-mobie-gbert_base-bs16-e10-lr3e-05-3
+[9]: https://hf.co/stefan-it/autotrain-flair-mobie-gbert_base-bs16-e10-lr3e-05-4
+[10]: https://hf.co/stefan-it/autotrain-flair-mobie-gbert_base-bs16-e10-lr3e-05-5
+
+The result in bold shows the performance of this model.
+
+Additionally, the Flair [training log](training.log) and [TensorBoard logs](tensorboard) are also uploaded to the model
+hub.