Update README.md

README.md

@@ -32,7 +32,7 @@ The pre-training dataset consists of documents from different domains:
 | Legal | OpenLegalData: German cases and laws | 5.4GB | 308,228 | 1B |
 | Medical | Smaller public datasets | 253MB | 179,776 | 50M |
 | Medical | CC medical texts | 3.6GB | 2,000,000 | 682M |
-| Medical | Medicine Dissertations | 1.4GB | 14,496 | 295M |
+| Medical | Medical Dissertations | 1.4GB | 14,496 | 295M |
 | Medical | Pubmed abstracts | 8.5GB | 21,044,382 | 1.7B |
 | Medical | MIMIC III | 2.6GB | 24,221,834 | 695M |
 | Medical | PMC-Patients-ReCDS | 2.1GB | 1,743,344 | 414M |
@@ -44,7 +44,7 @@ The pre-training dataset consists of documents from different domains:
 ## Benchmark
 
 In a comprehensive benchmark, we evaluated existing German models and our own. The benchmark included a variety of task types, such as question answering, 
-classification, and named entity recognition (NER). In addition, we introduced a new task focused on hate speech detection, using two existing datasets. 
+classification, and named entity recognition (NER). In addition, we introduced a new task focused on hate speech detection using two existing datasets. 
 When the datasets provided training, development, and test sets, we used them accordingly.
 
 
@@ -61,7 +61,7 @@ The following table presents the F1 scores:
 |  GottBERT  | 87.15±0.19 | 72.76±0.378 | 51.12±1.20 | 74.25±0.80 | **78.18**±0.11 | 65.71±0.01 | 74.60±4.75 | 88.61±0.23 | 74.05±0.51 |
 | GeBERTa-base | **88.06**±0.22 | **78.54**±0.32 | **53.16**±1.39 | **74.83**±0.36 | 78.13±0.15 | **68.37**±1.11 | **81.85**±5.23 | **89.14**±0.32 | **76.51**±0.32 |
 
-<sup>1</sup>Is not published yet but described in the [MedBERT.de paper](https://arxiv.org/abs/2303.08179).
+<sup>1</sup>Is not published yet but is described in the [MedBERT.de paper](https://arxiv.org/abs/2303.08179).
 
 ## Publication