**INFO**: The model is being continuously updated.

The model is a [multilingual-e5-base](https://huggingface.co/intfloat/multilingual-e5-base) model fine-tuned with the task of semantic textual similarity in mind.

## Model Training
The model has been fine-tuned on the German subsets of the following datasets:
- [German paraphrase corpus by Philip May](https://huggingface.co/datasets/deutsche-telekom/ger-backtrans-paraphrase)
- [paws-x](https://huggingface.co/datasets/paws-x)
- [stsb_multi_mt](https://huggingface.co/datasets/stsb_multi_mt)

The training procedure can be divided into two stages:
- training on paraphrase datasets with the Multiple Negatives Ranking Loss
- training on semantic textual similarity datasets using the Cosine Similarity Loss

# Results
The model achieves the following results:
- 0.920 on stsb's validation subset
- 0.904 on stsb's test subset
{"language": ["de"], "license": "mit", "datasets": ["deutsche-telekom/ger-backtrans-paraphrase", "paws-x", "stsb_multi_mt"], "model-index": [{"name": "e5-base-sts-en-de", "results": [{"task": {"type": "semantic textual similarity"}, "dataset": {"name": "stsb_multi_mt", "type": "stsb_multi_mt"}, "metrics": [{"type": "spearmanr", "value": 0.904}]}]}]}
transformers safetensors xlm-roberta feature-extraction de dataset:deutsche-telekom/ger-backtrans-paraphrase dataset:paws-x dataset:stsb_multi_mt license:mit model-index endpoints_compatible region:us