Update README.md

README.md

@@ -27,7 +27,7 @@ decay of 1e − 5 and a maximal learning of 5e − 4. I train the model using a
 To fine-tune the model, I use several datasets, including:
 - A manually labeled [multi-label database of German ad-hoc announcements](https://arxiv.org/pdf/2311.07598.pdf) containing 31,771 sentences, each associated with up to 20 possible topics.
 - An extractive question-answering dataset based on the SQuAD format, which was created using 3,044 ad-hoc announcements processed by OpenAI's ChatGPT to generate and answer questions (see [here](https://huggingface.co/datasets/scherrmann/adhoc_quad)).
-- The [financial phrase bank](https://arxiv.org/abs/1307.5336) of Malo et al. (2013) for sentiment classification, translated to German using [DeepL](https://www.deepl.com/translator) (see [here](https://huggingface.co/datasets/scherrmann/financial_phrasebank_75agree_german))
+- The [financial phrase bank](https://arxiv.org/abs/1307.5336) of Malo et al. (2013) for sentiment classification, translated to German using [DeepL](https://www.deepl.com/translator) (see [here](https://huggingface.co/datasets/scherrmann/financial_phrasebank_75agree_german)).
 
 ### Benchmark Results
 The further pre-trained German FinBERT model demonstrated the following performances on finance-specific downstream tasks: