German-RAG-BGE-M3-TRIPLES-MERGED-HESSIAN-AI

This is a sentence-transformers model trained on this Dataset 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 again to maintain performance on other languages again.

Model Details

Model Description

• Model Type: Sentence Transformer

• Maximum Sequence Length: 8192 tokens
• Output Dimensionality: 1024 tokens
• Similarity Function: Cosine Similarity

Model Sources

• Documentation: Sentence Transformers Documentation
• Repository: Sentence Transformers on GitHub
• Hugging Face: Sentence Transformers on Hugging Face

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), Finetuned Model (German-RAG-BGE) and Merged Model with Base-Model (Merged-BGE)

Comparison between Base-Model (BGE-M3), Merged Model with Base-Model (Merged-BGE) and our Merged-Model merged with Snowflake/snowflake-arctic-embed-l-v2.0

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

Usage

Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

pip install -U sentence-transformers

Then you can load this model and run inference.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("avemio/German-RAG-BGE-M3-TRIPLES-MERGED-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]

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 Soumya Paul Siavash Mollaebrahim Zain ul Haq