---
library_name: sentence-transformers
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
base_model_relation: merge
widget:
- source_sentence: 'search_query: i love autotrain'
  sentences:
  - 'search_query: huggingface auto train'
  - 'search_query: hugging face auto train'
  - 'search_query: i love autotrain'
pipeline_tag: sentence-similarity
license: mit
language:
- de
- en
---

# e1-EMB-German-Preview-v-0.1

This is a merged [sentence-transformers](https://www.SBERT.net) model. It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.

## Model Details

### Model Description
- **Model Type:** Sentence Transformer
<!-- - **Base model:** [Unknown](https://huggingface.co/unknown) -->
- **Maximum Sequence Length:** 8192 tokens
- **Output Dimensionality:** 1024 tokens
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->

### Model Sources

- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)

### Full Model Architecture

```
SentenceTransformer(
  (0): Transformer({'max_seq_length': 8192, 'do_lower_case': False}) with Transformer model: XLMRobertaModel 
  (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
  (2): Normalize()
)
```

## Evaluation MTEB-Tasks 

### Classification
- AmazonCounterfactualClassification
- AmazonReviewsClassification
- MassiveIntentClassification
- MassiveScenarioClassification
- MTOPDomainClassification
- MTOPIntentClassification

### Pair Classification
- FalseFriendsGermanEnglish
- PawsXPairClassification

### Retrieval
- GermanQuAD-Retrieval
- GermanDPR

### STS (Semantic Textual Similarity)
- GermanSTSBenchmark

#### Comparison 

| TASK                                | Snowflake | e1-EMB-German | e1-EMB-German vs. Snowflake  |
|-------------------------------------|-----------|----------------------|-------------------------|
| AmazonCounterfactualClassification  | 0.6587    | **0.7152**           | 5.65%                        |
| AmazonReviewsClassification         | 0.3697    | **0.4577**           | 8.80%                        |
| FalseFriendsGermanEnglish           | 0.5360    | **0.5378**           | 0.18%                        |
| GermanQuAD-Retrieval                | 0.9423    | **0.9456**           | 0.33%                        |
| GermanSTSBenchmark                  | 0.7499    | **0.8558**           | 10.59%                       |
| MassiveIntentClassification         | 0.6778    | **0.6826**           | 0.48%                        |
| MassiveScenarioClassification       | 0.7375    | **0.7494**           | 1.19%                        |
| GermanDPR                           | 0.8367    | **0.8330**           | -0.37%                       |
| MTOPDomainClassification            | 0.9080    | **0.9259**           | 1.79%                        |
| MTOPIntentClassification            | 0.6675    | **0.7143**           | 4.68%                        |
| PawsXPairClassification             | 0.5887    | **0.5803**           | -0.84%                       |


### Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

```bash
pip install -U sentence-transformers
```

Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("embraceableAI/e1-EMB-German-Preview-v-0.1")
# Run inference
sentences = [
    'The weather is lovely today.',
    "It's so sunny outside!",
    'He drove to the stadium.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```

<!--
### Direct Usage (Transformers)

<details><summary>Click to see the direct usage in Transformers</summary>

</details>
-->

<!--
### Downstream Usage (Sentence Transformers)

You can finetune this model on your own dataset.

<details><summary>Click to expand</summary>

</details>
-->

<!--
### Out-of-Scope Use

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-->

<!--
## Bias, Risks and Limitations

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### Recommendations

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## Training Details

### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.2.1
- Transformers: 4.44.2
- PyTorch: 2.4.1+cu121
- Accelerate: 0.34.2
- Datasets: 3.0.1
- Tokenizers: 0.19.1

## Citation

```
@misc{bge-m3,
      title={BGE M3-Embedding: Multi-Lingual, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillation}, 
      author={Jianlv Chen and Shitao Xiao and Peitian Zhang and Kun Luo and Defu Lian and Zheng Liu},
      year={2024},
      eprint={2402.03216},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
```


## The embracebaleAI Team
[Marcel Rosiak](https://de.linkedin.com/in/marcel-rosiak)
[Soumya Paul](https://de.linkedin.com/in/soumya-paul-1636a68a)
[Siavash Mollaebrahim](https://de.linkedin.com/in/siavash-mollaebrahim-4084b5153?trk=people-guest_people_search-card)