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README.md
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The successor of [German_Semantic_STS_V2](https://huggingface.co/aari1995/German_Semantic_STS_V2) is here and comes with loads of cool new features! Feel free to provide feedback on the model and what you would like to see next.
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**Note:** To run this model properly,
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## Major updates and USPs:
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- **Pooling Function:** Moving away from mean pooling towards using the CLS token. Generally seems to learn better after the stage-2 pretraining and allows for more flexibility.
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- **License:** Apache 2.0
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## Usage:
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```python
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from sentence_transformers import SentenceTransformer
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# Get the similarity scores for the embeddings
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similarities = model.similarity(embeddings, embeddings)
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```
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### Full Model Architecture
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**Q: Is this Model better than V2?**
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**A:** In terms of flexibility-definitely. In terms of data-yes as well, as it is more up-to-date. In terms of benchmark they differ, while V3 is better for longer texts, V2 works very well for shorter texts. Keeping in mind that many benchmarks also do not cover cultural knowledge too well.
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**Q: How does the model perform vs. multilingual models?**
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**A:** There are really great multilingual models that will be very useful for many use-cases. This model shines with its cultural knowledge and knowledge about German people and behaviour.
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**Q: What is the trade-off when reducing the embedding size?**
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The successor of [German_Semantic_STS_V2](https://huggingface.co/aari1995/German_Semantic_STS_V2) is here and comes with loads of cool new features! Feel free to provide feedback on the model and what you would like to see next.
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**Note:** To run this model properly, see "Usage".
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## Major updates and USPs:
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- **Pooling Function:** Moving away from mean pooling towards using the CLS token. Generally seems to learn better after the stage-2 pretraining and allows for more flexibility.
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- **License:** Apache 2.0
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(If you are looking for even better performance on tasks, but with a German knowledge-cutoff around 2020, check out [German_Semantic_V3b](https://huggingface.co/aari1995/German_Semantic_V3))
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## Usage:
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This model has some build-in functionality that is rather hidden. To profit from it, use this code:
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```python
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from sentence_transformers import SentenceTransformer
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# Get the similarity scores for the embeddings
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similarities = model.similarity(embeddings, embeddings)
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```
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### Full Model Architecture
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**Q: Is this Model better than V2?**
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**A:** In terms of flexibility-definitely. In terms of data-yes as well, as it is more up-to-date. In terms of benchmark they differ, while V3 is better for longer texts, V2 works very well for shorter texts. Keeping in mind that many benchmarks also do not cover cultural knowledge too well.
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If you are fine with the model not knowing about developments after early 2020, I'd suggest you use [German_Semantic_V3b](https://huggingface.co/aari1995/German_Semantic_V3).
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**Q: How does the model perform vs. multilingual models?**
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**A:** There are really great multilingual models that will be very useful for many use-cases. This model shines with its cultural knowledge and knowledge about German people and behaviour.
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**Q: What is the trade-off when reducing the embedding size?**
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