sentence-transformers safetensors xlm-roberta sentence-similarity feature-extraction dataset:avemio/German-RAG-EMBEDDING-TRIPLES-HESSIAN-AI arxiv:2402.03216 base_model:BAAI/bge-m3 base_model:finetune:BAAI/bge-m3 autotrain_compatible text-embeddings-inference endpoints_compatible region:us
---
base_model: BAAI/bge-m3
datasets:
- avemio/German-RAG-EMBEDDING-TRIPLES-HESSIAN-AI
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
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'
---

# German-RAG-BGE-M3-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.
It was merged with the Base-Model [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3) again to maintain performance on other languages again.

## 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 between Base-Model ([BGE-M3](https://huggingface.co/BAAI/bge-m3)), Finetuned Model ([German-RAG-BGE](https://huggingface.co/avemio/German-RAG-BGE-M3-TRIPLES-HESSIAN-AI)) and Merged Model with Base-Model ([Merged-BGE](https://huggingface.co/avemio/German-RAG-BGE-M3-TRIPLES-MERGED-HESSIAN-AI/))

| TASK                                | [BGE-M3](https://huggingface.co/BAAI/bge-m3)   | German-RAG-BGE | [Merged-BGE](https://huggingface.co/avemio/German-RAG-BGE-M3-TRIPLES-MERGED-HESSIAN-AI/) | German-RAG vs. BGE | Merged vs. BGE |
|-------------------------------------|-------|----------|------------|--------------|----------------|
| AmazonCounterfactualClassification | 0.6908 | 0.5449   | **0.7111**     | -14.59%      | 2.03%          |
| AmazonReviewsClassification         | **0.4634** | 0.2745   | 0.4571     | -18.89%      | -0.63%         |
| FalseFriendsGermanEnglish           | **0.5343** | 0.4777   | 0.5338     | -5.67%       | -0.05%         |
| GermanQuAD-Retrieval                | **0.9444** | 0.8714   | 0.9311     | -7.30%       | -1.33%         |
| GermanSTSBenchmark                  | 0.8079 | 0.7921   | **0.8218**     | -1.58%       | 1.39%          |
| MassiveIntentClassification         | **0.6575** | 0.4884   | 0.6522     | -16.90%      | -0.52%         |
| MassiveScenarioClassification       | 0.7355 | 0.5837   | **0.7381**     | -15.19%      | 0.25%          |
| GermanDPR                           | **0.8265** | 0.7210   | 0.8159     | -10.54%      | -1.06%         |
| MTOPDomainClassification            | 0.9121 | 0.7450   | **0.9139**     | -16.71%      | 0.17%          |
| MTOPIntentClassification            | **0.6808** | 0.4516   | 0.6684     | -22.92%      | -1.25%         |
| PawsXPairClassification             | 0.5678 | 0.5077   | **0.5710**     | -6.01%       | 0.33%          |

#### Comparison between Base-Model ([BGE-M3](https://huggingface.co/BAAI/bge-m3)), Merged Model with Base-Model ([Merged-BGE](https://huggingface.co/avemio/German-RAG-BGE-M3-TRIPLES-MERGED-HESSIAN-AI/)) and our Merged-Model merged with [Snowflake/snowflake-arctic-embed-l-v2.0](https://huggingface.co/Snowflake/snowflake-arctic-embed-l-v2.0)

| TASK                                | [BGE-M3](https://huggingface.co/BAAI/bge-m3)   | [Merged-BGE](https://huggingface.co/avemio/German-RAG-BGE-M3-TRIPLES-MERGED-HESSIAN-AI/) | [Merged-Snowflake](https://huggingface.co/avemio/German-RAG-BGE-M3-MERGED-x-SNOWFLAKE-ARCTIC-HESSIAN-AI/) | Merged-BGE vs. BGE | Merged-Snowflake vs. BGE | Merged-Snowflake vs. Merged-BGE |
|-------------------------------------|-------|------------|------------------|--------------------|--------------------------|---------------------------------|
| AmazonCounterfactualClassification | 0.6908 | 0.7111     | **0.7152**           | 2.94%             | 3.53%                   | 0.58%                          |
| AmazonReviewsClassification         | **0.4634** | 0.4571     | 0.4577           | -1.36%            | -1.23%                  | 0.13%                          |
| FalseFriendsGermanEnglish           | 0.5343 | 0.5338     | **0.5378**           | -0.09%            | 0.66%                   | 0.75%                          |
| GermanQuAD-Retrieval                | 0.9444 | 0.9311     | **0.9456**           | -1.41%            | 0.13%                   | 1.56%                          |
| GermanSTSBenchmark                  | 0.8079 | 0.8218     | **0.8558**           | 1.72%             | 5.93%                   | 4.14%                          |
| MassiveIntentClassification         | 0.6575 | 0.6522     | **0.6826**           | -0.81%            | 3.82%                   | 4.66%                          |
| MassiveScenarioClassification       | 0.7355 | 0.7381     | **0.7494**           | 0.35%             | 1.89%                   | 1.53%                          |
| GermanDPR                           | 0.8265 | 0.8159     | **0.8330**           | -1.28%            | 0.79%                   | 2.10%                          |
| MTOPDomainClassification            | 0.9121 | 0.9139     | **0.9259**           | 0.20%             | 1.52%                   | 1.31%                          |
| MTOPIntentClassification            | 0.6808 | 0.6684     | **0.7143**           | -1.82%            | 4.91%                   | 6.87%                          |
| PawsXPairClassification             | 0.5678 | 0.5710     | **0.5803**           | 0.56%             | 2.18%                   | 1.63%                          |


## 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-BGE-M3-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)

<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

*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->

<!--
## 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.*
-->

<!--
### Recommendations

*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->

## 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 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)
