readme: add initial version

README.md

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+---
+language:
+- en
+library_name: flair
+pipeline_tag: token-classification
+base_model: FacebookAI/xlm-roberta-large
+widget:
+  - text: According to the BBC George Washington went to Washington.
+---
+
+# Flair NER Model trained on CleanCoNLL Dataset
+
+This (unofficial) Flair NER model was trained on the awesome [CleanCoNLL](https://aclanthology.org/2023.emnlp-main.533/) dataset.
+
+The CleanCoNLL dataset was proposed by Susanna Rücker and Alan Akbik and introduces a corrected version of the classic CoNLL-03 dataset, with updated and more consistent NER labels.
+
+## Fine-Tuning
+
+We use XLM-RoBERTa Large as backbone language model and the following hyper-parameters for fine-tuning:
+
+| Hyper-Parameter | Value  |
+|:--------------- |:-------|
+| Batch Size      | `4`    |
+| Learning Rate   | `5-06` |
+| Max. Epochs     | `10`   |
+
+Additionally, the [FLERT](https://arxiv.org/abs/2011.06993) approach is used for fine-tuning the model. [Training logs](training.log) and [TensorBoard](../../tensorboard) are also available for each model.
+
+## Results
+
+We report micro F1-Score on development (in brackets) and test set for five runs with different seeds:
+
+| [Seed 1][1]     | [Seed 2][2]     | [Seed 3][3]     | [Seed 4][4]     | [Seed 5][5]     | Avg.
+|:--------------- |:--------------- |:--------------- |:--------------- |:--------------- |:--------------- |
+| (97.34) / 97.00 | (97.26) / 96.90 | (97.66) / 97.02 | (97.42) / 96.96 | (97.46) / 96.99 | (97.43) / 96.97 |
+
+Rücker and Akbik report 96.98 on three different runs, so our results are very close to their reported performance!
+
+[1]: https://huggingface.co/stefan-it/flair-clean-conll-1
+[2]: https://huggingface.co/stefan-it/flair-clean-conll-2
+[3]: https://huggingface.co/stefan-it/flair-clean-conll-3
+[4]: https://huggingface.co/stefan-it/flair-clean-conll-4
+[5]: https://huggingface.co/stefan-it/flair-clean-conll-5
+
+# Flair Demo
+
+The following snippet shows how to use the CleanCoNLL NER models with Flair:
+
+```python
+from flair.data import Sentence
+from flair.models import SequenceTagger
+
+# load tagger
+tagger = SequenceTagger.load("stefan-it/flair-clean-conll-1")
+
+# make example sentence
+sentence = Sentence("According to the BBC George Washington went to Washington.")
+
+# predict NER tags
+tagger.predict(sentence)
+
+# print sentence
+print(sentence)
+
+# print predicted NER spans
+print('The following NER tags are found:')
+# iterate over entities and print
+for entity in sentence.get_spans('ner'):
+    print(entity)
+```