Update README.md

README.md

@@ -160,19 +160,19 @@ Note: For French and Italian, the training data remains in German, while the tes
 
 ### Results
 
-Making use of an unsupervised training approach, Swissbert for Sentence Embeddings achieves comparable results as the best-performing multilingual Sentence-BERT model in the semantic textual similarity task for German and outperforms it in the French text classification task.
-
-| Evaluation task        |swissbert |         |sentence swissbert|         |all-mpnet-base-v2|         |
-|------------------------|----------|---------|------------------|---------|-----------------|---------|
-|                        |accuracy  |f1-score |accuracy          |f1-score |accuracy         |f1-score |
-| Semantic Similarity DE | 83.80    | -       |**93.70**         |    -    |  87.70          |    -    |
-| Semantic Similarity FR | 82.30    | -       |**92.90**         |    -    |  91.10          |    -    |
-| Semantic Similarity IT | 83.00    | -       |**91.20**         |    -    |  89.80          |    -    |
-| Semantic Similarity RM | 78.80    | -       |**90.80**         |    -    |  67.90          |    -    |
-| Text Classification DE | 95.76    |  91.99  | 96.36            |**92.11**|  95.61          |  91.20  |
-| Text Classification FR | 94.55    | 88.52   | 95.76            |**90.94**|  94.55          |  89.82  |
-| Text Classification IT | 93.48    | 88.29   | 95.44            |  90.44  |  95.91          |**92.05**|
-| Text Classification RM |          |         |                  |         |                 |         |
+Making use of an unsupervised training approach, Swissbert for Sentence Embeddings achieves comparable results as the best-performing multilingual Sentence-BERT model (distiluse-base-multilingual-cased) in the semantic textual similarity task for German and outperforms it in the French text classification task.
+
+| Evaluation task        |swissbert |         |sentence swissbert|         |Sentence-BERT|         |
+|------------------------|----------|---------|------------------|---------|-------------|---------|
+|                        |accuracy  |f1-score |accuracy          |f1-score |accuracy     |f1-score |
+| Semantic Similarity DE | 83.80    | -       |**93.70**         |    -    |  87.70      |    -    |
+| Semantic Similarity FR | 82.30    | -       |**92.90**         |    -    |  91.10      |    -    |
+| Semantic Similarity IT | 83.00    | -       |**91.20**         |    -    |  89.80      |    -    |
+| Semantic Similarity RM | 78.80    | -       |**90.80**         |    -    |  67.90      |    -    |
+| Text Classification DE | 95.76    |  91.99  | 96.36            |**92.11**|  95.61      |  91.20  |
+| Text Classification FR | 94.55    | 88.52   | 95.76            |**90.94**|  94.55      |  89.82  |
+| Text Classification IT | 93.48    | 88.29   | 95.44            |  90.44  |  95.91      |**92.05**|
+| Text Classification RM |          |         |                  |         |             |         |
 
 #### Baseline