README.md

---
license: mit
---

# pascalhuerten/bge_reranker_skillfit

## Overview

This model 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. The model is trained to perform well on calculating relevance scores for learning outcome and esco skill pairs in German language.

## Using FlagEmbedding

```
pip install -U FlagEmbedding
```

Get relevance scores (higher scores indicate more relevance):
```python
from FlagEmbedding import FlagReranker
reranker = FlagReranker('pascalhuerten/bge_reranker_skillfit', use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation

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']])
print(scores)
```

## Interpretation of Scores

The scores computed by the model tend to range from -12 to 12, with higher scores indicating more relevance. Scores greater than 0 tend to be good fits.