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Add example for computing scores of learning outcome esco skill pairs

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@@ -5,3 +5,18 @@ license: mit
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  # pascalhuerten/bge_reranker_skillfit
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  This is a finetuning of **BAAI/bge-reranker-base** on a german dataset containing positive and negative skill labels and learning outcomes of courses as the query.
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  # pascalhuerten/bge_reranker_skillfit
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  This is a finetuning of **BAAI/bge-reranker-base** on a german dataset containing positive and negative skill labels and learning outcomes of courses as the query.
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+ This model is trained to perform well on calculating relevance scores for learning outcome and esco skill pairs in german language.
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+
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+ ## Using FlagEmbedding
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+ ```
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+ pip install -U FlagEmbedding
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+ ```
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+
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+ Get relevance scores (higher scores indicate more relevance):
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+ ```python
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+ from FlagEmbedding import FlagReranker
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+ reranker = FlagReranker('pascalhuerten/bge_reranker_skillfit', use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation
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+
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+ scores = reranker.compute_score([['Einführung in die Arbeitsweise von WordPress', 'WordPress'], ['Einführung in die Arbeitsweise von WordPress', 'Software für Content-Management-Systeme nutzen'], ['Einführung in die Arbeitsweise von WordPress', 'Website-Sichtbarkeit erhöhen']])
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+ print(scores)
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+ ```