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---
library_name: sentence-transformers
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
base_model: BAAI/bge-m3
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/GRAG-EMBEDDING-TRIPLES-HESSIAN-AI
---
<img src="https://www.grag.ai/wp-content/uploads/2024/12/GRAG-ICON-TO-WORDLOGO-Animation_Loop-small-ezgif.com-video-to-gif-converter.gif" alt="GRAG Logo" width="400" style="margin-left:'auto' margin-right:'auto' display:'block'"/>
# GRAG-BGE-M3-TRIPLES-HESSIAN-AI
This is a [sentence-transformers](https://www.SBERT.net) model trained on this [Dataset](https://huggingface.co/datasets/avemio/GRAG-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 ([GRAG-BGE](https://huggingface.co/avemio/GRAG-BGE-M3-TRIPLES-HESSIAN-AI)) and Merged Model with Base-Model ([Merged-BGE](https://huggingface.co/avemio/GRAG-BGE-M3-TRIPLES-MERGED-HESSIAN-AI/))
| TASK | [BGE-M3](https://huggingface.co/BAAI/bge-m3) | GRAG-BGE | [Merged-BGE](https://huggingface.co/avemio/GRAG-BGE-M3-TRIPLES-MERGED-HESSIAN-AI/) | GRAG 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/GRAG-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/GRAG-BGE-M3-TRIPLES-MERGED-HESSIAN-AI/) | [Merged-Snowflake](https://huggingface.co/avemio/GRAG-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 GRAG-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/GRAG-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 |
| [GRAG-BGE-M3-TRIPLES-HESSIAN-AI](https://huggingface.co/avemio/GRAG-BGE-M3-TRIPLES-HESSIAN-AI) | 0.8857 |
| [GRAG-BGE-M3-TRIPLES-MERGED-HESSIAN-AI](https://huggingface.co/avemio/GRAG-BGE-M3-TRIPLES-MERGED-HESSIAN-AI) | **0.8866** |
| [GRAG-BGE-M3-MERGED-x-SNOWFLAKE-ARCTIC-HESSIAN-AI](https://huggingface.co/avemio/GRAG-BGE-M3-MERGED-x-SNOWFLAKE-ARCTIC-HESSIAN-AI) | **0.8866** |
| [GRAG-UAE-LARGE-V1-TRIPLES-HESSIAN-AI](https://huggingface.co/avemio/GRAG-UAE-LARGE-V1-TRIPLES-HESSIAN-AI) | 0.8763 |
| [GRAG-UAE-LARGE-V1-TRIPLES-MERGED-HESSIAN-AI](https://huggingface.co/avemio/GRAG-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/GRAG-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]
```
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### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
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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}
}
```
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