readme: add links to fine-tuned models

README.md

@@ -24,18 +24,24 @@ We use XLM-RoBERTa Large as backbone language model and the following hyper-para
 | Learning Rate   | `5-06` |
 | Max. Epochs     | `10`   |
 
-Additionally, the [FLERT](https://arxiv.org/abs/2011.06993) approach is used for fine-tuning the model.
+Additionally, the [FLERT](https://arxiv.org/abs/2011.06993) approach is used for fine-tuning the model. [Training logs](training.log) and [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          | Seed 2          | Seed 3          | Seed 4          | Seed 5          | Avg.
+| [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: