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

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- readme: add initial version of model card (6a1d881124582391c311e2f721f30702f1d2bea9)

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