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GRAG-BGE-M3-TRIPLES-HESSIAN-AI

Sentence Similarity
sentence-transformers
Safetensors
xlm-roberta
feature-extraction
text-embeddings-inference
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metadata
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
GRAG Logo

GRAG-BGE-M3-TRIPLES-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

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 (GRAG-BGE) and Merged Model with Base-Model (Merged-BGE)

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

TASK BGE-M3 Merged-BGE Merged-Snowflake 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

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

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]

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}
}