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README.md
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### Results
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| Evaluation task |swissbert | |swissbert for SE| |
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| |accuracy |f1-score |accuracy |f1-score |
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| Semantic Similarity DE | 83.80 | - |**87.70** | - |
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| Semantic Similarity FR | 82.30 | -
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| Semantic Similarity IT | 83.00 | - `
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| Text Classification DE | 95.76 |**91.99**| 94.70 | 89.43 |
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| Text Classification FR | 94.55 | 88.52 | 95.30 |**89.91**|
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| Text Classification FR | 93.48 | 88.29 | 94.85
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### Results
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| Evaluation task |swissbert | |swissbert for SE| |Sentence-BERT| |
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| |accuracy |f1-score |accuracy |f1-score |accuracy |f1-score |
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| Semantic Similarity DE | 83.80 | - |**87.70** | - |**87.70** | - |
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| Semantic Similarity FR | 82.30 | - | 84.02 | - |**91.10** | - |
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| Semantic Similarity IT | 83.00 | - ` | 84.00 | - |**89.80** | - |
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| Text Classification DE | 95.76 |**91.99**| 94.70 | 89.43 | 95.61 | 91.20 |
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| Text Classification FR | 94.55 | 88.52 | 95.30 |**89.91**| 94.55 | 89.82 |
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| Text Classification FR | 93.48 | 88.29 | 94.85 | 90.36 | 95.91 |**92.05**|
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