Bge M3 Msmarco V3 Sbert

🚀 SentenceTransformer based on BAAI/bge-m3

This model is a fine-tuned version of BAAI/bge-m3 using sentence-transformers. It maps sentences and paragraphs into a 1024-dimensional dense vector space and can be applied to semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.

🚀 Quick Start

This model can be used directly for sentence embedding and similarity calculation. First, you need to install the sentence-transformers library, and then load the model for inference.

✨ Features

• High-dimensional Vector Space: Maps sentences and paragraphs to a 1024-dimensional dense vector space.
• Multiple Applications: Can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, etc.
• Fine-tuned Model: Fine-tuned from BAAI/bge-m3 to better adapt to specific tasks.

📦 Installation

First, install the sentence-transformers library:

pip install -U sentence-transformers

💻 Usage Examples

Basic Usage

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("BlackBeenie/bge-m3-msmarco-v3-sbert")
# Run inference
sentences = [
    'who is christopher kyle',
    'Chris Kyle American Sniper. Christopher Scott Kyle was born and raised in Texas and was a United States Navy SEAL from 1999 to 2009. He is currently known as the most successful sniper in American military history. According to his book American Sniper, he had 160 confirmed kills (which was from 255 claimed kills).',
    "'American Sniper' Chris Kyle's wife thanks audiences for 'watching the hard stuff'. Taya Kyle has told of her gratitude to audiences for supporting the film about her dead husband Chris Kyle, a Navy Seal played by Bradley Cooper.",
]
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]

📚 Documentation

Model Details

Model Description

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': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)

Training Details

Training Dataset

Unnamed Dataset

• Size: 498,970 training samples
• Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>sentence_2</code>
• Approximate statistics based on the first 1000 samples:

  	sentence_0	sentence_1	sentence_2
  type	string	string	string
  details	min: 4 tokens mean: 9.93 tokens max: 37 tokens	min: 17 tokens mean: 90.01 tokens max: 239 tokens	min: 16 tokens mean: 86.47 tokens max: 229 tokens

• Samples:

  sentence_0	sentence_1	sentence_2
  how much does it cost to paint a interior house	Interior House Painting Cost Factors. Generally, it will take a minimum of two gallons of paint to cover a room. At the highest end, paint will cost anywhere between $30 and $60 per gallon and come in three different finishes: flat, semi-gloss or high-gloss.Flat finishes are the least shiny and are best suited for areas requiring frequent cleaning.rovide a few details about your project and receive competitive quotes from local pros. The average national cost to paint a home interior is $1,671, with most homeowners spending between $966 and $2,426.	Question DetailsAsked on 3/12/2014. Guest_... How much does it cost per square foot to paint the interior of a house? We just bought roughly a 1500 sg ft townhouse and want to get the entire house, including ceilings painted (including a roughly 400 sq ft finished basement not included in square footage).
  when is s corp taxes due	If you form a corporate entity for your small business, regardless of whether it's taxed as a C or S corporation, a tax return must be filed with the Internal Revenue Service on its due date each year. Corporate tax returns are always due on the 15th day of the third month following the close of the tax year. The actual day that the tax return filing deadline falls on, however, isn't the same for every corporation.	But if you haven’t, don’t panic: the majority of forms aren’t due quite yet. Most tax forms have an annual January 31 due date. Your tax forms are considered on time if the form is properly addressed and mailed on or before that date. If the regular due date falls on a Saturday, Sunday, or legal holiday – which is the case in 2015 for both January and February due dates – issuers have until the next business day.
  what are disaccharides	Disaccharides are formed when two monosaccharides are joined together and a molecule of water is removed, a process known as dehydration reaction. For example; milk sugar (lactose) is made from glucose and galactose whereas the sugar from sugar cane and sugar beets (sucrose) is made from glucose and fructose.altose, another notable disaccharide, is made up of two glucose molecules. The two monosaccharides are bonded via a dehydration reaction (also called a condensation reaction or dehydration synthesis) that leads to the loss of a molecule of water and formation of a glycosidic bond.	Other disaccharides include (diagrams p. 364): Sucrose, common table sugar, has a glycosidic bond linking the anomeric hydroxyls of glucose and fructose. Because the configuration at the anomeric carbon of glucose is a (O points down from the ring), the linkage is designated a(12).

• Loss: MultipleNegativesRankingLoss with these parameters:

  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  

Training Hyperparameters

Non-Default Hyperparameters

• eval_strategy: steps
• per_device_train_batch_size: 32
• per_device_eval_batch_size: 32
• num_train_epochs: 5
• fp16: True
• multi_dataset_batch_sampler: round_robin

All Hyperparameters

Training Logs

Framework Versions

• Python: 3.11.11
• Sentence Transformers: 3.4.1
• Transformers: 4.48.3
• PyTorch: 2.5.1+cu124
• Accelerate: 1.3.0
• Datasets: 3.3.2
• Tokenizers: 0.21.0

📄 License

No license information provided in the original document.

🔧 Technical Details

The model maps sentences and paragraphs to a 1024-dimensional dense vector space, using the MultipleNegativesRankingLoss loss function and cosine similarity as the similarity metric. During training, a series of hyperparameters were set, such as eval_strategy, per_device_train_batch_size, etc., to optimize the model performance.

📄 Citation

BibTeX

Sentence Transformers

@inproceedings{reimers-2019-sentence-bert,
    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
    author = "Reimers, Nils and Gurevych, Iryna",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
    month = "11",
    year = "2019",
    publisher = "Association for Computational Linguistics",
    url = "https://arxiv.org/abs/1908.10084",
}

MultipleNegativesRankingLoss

@misc{henderson2017efficient,
    title={Efficient Natural Language Response Suggestion for Smart Reply},
    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
    year={2017},
    eprint={1705.00652},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}