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.

Model Details

Model Description

Model Type: Sentence Transformer

Maximum Sequence Length: 512 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': 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	German-RAG-UAE	Merged-UAE	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

Model Name	Accuracy
bge-m3	0.8806
UAE-Large-V1	0.8393
German-RAG-BGE-M3-TRIPLES-HESSIAN-AI	0.8857
German-RAG-BGE-M3-TRIPLES-MERGED-HESSIAN-AI	0.8866
German-RAG-BGE-M3-MERGED-x-SNOWFLAKE-ARCTIC-HESSIAN-AI	0.8866
German-RAG-UAE-LARGE-V1-TRIPLES-HESSIAN-AI	0.8763
German-RAG-UAE-LARGE-V1-TRIPLES-MERGED-HESSIAN-AI	0.8771

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.

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]

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 Soumya Paul Siavash Mollaebrahim Zain ul Haq