README.md

---
library_name: sentence-transformers
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
base_model: WhereIsAI/UAE-Large-V1
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
datasets:
- avemio/German-RAG-EMBEDDING-TRIPLES-HESSIAN-AI
license: mit
language:
- de
- en
---

# German-RAG-UAE-LARGE-V1-TRIPLES-HESSIAN-AI

This is a [sentence-transformers](https://www.SBERT.net) model trained on this [Dataset](https://huggingface.co/datasets/avemio/German-RAG-Embedding-Triples-Hessian-AI) with roughly 300k Triple-Samples. 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:** 512 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': 512, 'do_lower_case': False}) with Transformer model: BertModel 
  (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

| TASK                                | [UAE](https://huggingface.co/WhereIsAI/UAE-Large-V1/)   | German-RAG-UAE | [Merged-UAE](https://huggingface.co/avemio/German-RAG-UAE-LARGE-V1-TRIPLES-MERGED-HESSIAN-AI/) | German-RAG vs. UAE | Merged vs. UAE |
|-------------------------------------|-------|----------|------------|--------------|----------------|
| AmazonCounterfactualClassification | **0.5650** | 0.5449   | 0.5401     | -2.01%       | -2.48%         |
| AmazonReviewsClassification         | 0.2738 | 0.2745   | **0.2782**     | 0.08%        | 0.44%          |
| FalseFriendsGermanEnglish           | **0.4808** | 0.4777   | 0.4703     | -0.32%       | -1.05%         |
| GermanQuAD-Retrieval                | 0.7811 | 0.8353   | **0.8628**     | 5.42%        | 8.18%          |
| GermanSTSBenchmark                  | 0.6421 | 0.6568   | **0.6754**     | 1.47%        | 3.33%          |
| MassiveIntentClassification         | **0.5139** | 0.4884   | 0.4714     | -2.55%       | -4.25%         |
| MassiveScenarioClassification       | 0.6062 | 0.5837   | **0.6111**     | -2.25%       | 0.49%          |
| GermanDPR                           | 0.6750 | 0.7210   | **0.7507**     | 4.60%        | 7.57%          |
| MTOPDomainClassification            | 0.7625 | 0.7450   | **0.7686**     | -1.75%       | 0.61%          |
| MTOPIntentClassification            | **0.4994** | 0.4516   | 0.4413     | -4.77%       | -5.80%         |
| PawsXPairClassification             | **0.5452** | 0.5077   | 0.5162     | -3.76%       | -2.90%         |


## Evaluation on German-RAG-EMBEDDING-BENCHMARK

Accuracy is calculated by evaluating if the relevant context is the highest ranking embedding of the whole context array.
See Eval-Dataset and Evaluation Code [here](https://huggingface.co/datasets/avemio/German-RAG-EMBEDDING-BENCHMARK)

| Model Name                                       | Accuracy  |
|-------------------------------------------------|-----------|
| [bge-m3](https://huggingface.co/BAAI/bge-m3  )                                     | 0.8806    |
| [UAE-Large-V1](https://huggingface.co/WhereIsAI/UAE-Large-V1)                            | 0.8393    |
| [German-RAG-BGE-M3-TRIPLES-HESSIAN-AI](https://huggingface.co/avemio/German-RAG-BGE-M3-TRIPLES-HESSIAN-AI)             | 0.8857    |
| [German-RAG-BGE-M3-TRIPLES-MERGED-HESSIAN-AI](https://huggingface.co/avemio/German-RAG-BGE-M3-TRIPLES-MERGED-HESSIAN-AI)      | **0.8866** |
| [German-RAG-BGE-M3-MERGED-x-SNOWFLAKE-ARCTIC-HESSIAN-AI](https://huggingface.co/avemio/German-RAG-BGE-M3-MERGED-x-SNOWFLAKE-ARCTIC-HESSIAN-AI)   | **0.8866** |
| [German-RAG-UAE-LARGE-V1-TRIPLES-HESSIAN-AI](https://huggingface.co/avemio/German-RAG-UAE-LARGE-V1-TRIPLES-HESSIAN-AI)       | 0.8763    |
| [German-RAG-UAE-LARGE-V1-TRIPLES-MERGED-HESSIAN-AI](https://huggingface.co/avemio/German-RAG-UAE-LARGE-V1-TRIPLES-MERGED-HESSIAN-AI)   | 0.8771    |

## Usage

### 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("avemio/German-RAG-UAE-LARGE-V1-TRIPLES-HESSIAN-AI")
# 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)

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</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

*List how the model may foreseeably be misused and address what users ought not to do with the model.*
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## Bias, Risks and Limitations

*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
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### Recommendations

*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
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## Training Details

### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.2.1
- Transformers: 4.44.2
- PyTorch: 2.5.0+cu121
- Accelerate: 0.34.2
- Datasets: 2.19.0
- Tokenizers: 0.19.1

## Citation

```
@article{li2023angle,
  title={AnglE-optimized Text Embeddings},
  author={Li, Xianming and Li, Jing},
  journal={arXiv preprint arXiv:2309.12871},
  year={2023}
}
```



## The German-RAG AI 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)
[Zain ul Haq](https://de.linkedin.com/in/zain-ul-haq-31ba35196)