aloobun / d-mxbai-L8-embed

SentenceTransformer based on Model Distillation

I initialize the student model by pruning the teacher model, retaining only 8 layers, in an attempt to create a lighter, faster version.

WIP

Model Details

Model Description

• Model Type: Sentence Transformer
• Base model: mixedbread-ai/mxbai-embed-large-v1
• Maximum Sequence Length: 512 tokens
• Output Dimensionality: 1024 dimensions
• 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})
)

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("sentence_transformers_model_id")
# Run inference
sentences = [
    'A black dog is drinking next to a brown and white dog that is looking at an orange ball in the lake, whilst a horse and rider passes behind.',
    'Some men with jerseys are in a bar, watching a soccer match.',
    'There are two people running around a track in lane three and the one wearing a blue shirt with a green thing over the eyes is just barely ahead of the guy wearing an orange shirt and sunglasses.',
]
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]

Evaluation

Metrics

Semantic Similarity

• Datasets: sts-dev and sts-test
• Evaluated with EmbeddingSimilarityEvaluator

Knowledge Distillation

• Evaluated with MSEEvaluator

Training Details

Training Dataset

Unnamed Dataset

• Size: 1,195,425 training samples
• Columns: sentence and label
• Approximate statistics based on the first 1000 samples:

  	sentence	label
  type	string	list
  details	min: 4 tokens mean: 12.24 tokens max: 52 tokens	size: 1024 elements

• Samples:

  sentence	label
  A person on a horse jumps over a broken down airplane.	[-0.012967385351657867, 0.3716000020503998, 0.252520889043808, 0.7052643299102783, -0.15118499100208282, ...]
  Children smiling and waving at camera	[0.15414997935295105, 0.6666896939277649, -0.3150098919868469, 1.0102407932281494, 0.4113735556602478, ...]
  A boy is jumping on skateboard in the middle of a red bridge.	[-0.2989530563354492, 0.8571284413337708, -0.48532426357269287, 0.8935043215751648, 0.28524795174598694, ...]

• Loss: MSELoss

Evaluation Dataset

Unnamed Dataset

• Size: 10,000 evaluation samples
• Columns: sentence and label
• Approximate statistics based on the first 1000 samples:

  	sentence	label
  type	string	list
  details	min: 5 tokens mean: 13.23 tokens max: 57 tokens	size: 1024 elements

• Samples:

  sentence	label
  Two women are embracing while holding to go packages.	[-0.35094621777534485, 0.4337681233882904, 0.22905530035495758, 0.9438946843147278, -1.0199058055877686, ...]
  Two young children in blue jerseys, one with the number 9 and one with the number 2 are standing on wooden steps in a bathroom and washing their hands in a sink.	[-0.37593328952789307, 0.6690596342086792, -0.14921458065509796, 0.7559019923210144, -0.4093412756919861, ...]
  A man selling donuts to a customer during a world exhibition event held in the city of Angeles	[0.21969863772392273, 0.5065202713012695, -0.25664886832237244, 0.2569092810153961, -0.05940837413072586, ...]

• Loss: MSELoss

Training Hyperparameters

Non-Default Hyperparameters

• eval_strategy : steps
• per_device_train_batch_size : 64
• per_device_eval_batch_size : 64
• learning_rate : 0.0001
• num_train_epochs : 1
• warmup_ratio : 0.1
• fp16 : True
• load_best_model_at_end : True

All Hyperparameters

Training Logs

• The bold row denotes the saved checkpoint.

Framework Versions

• Python: 3.10.14
• Sentence Transformers: 3.3.1
• Transformers: 4.46.3
• PyTorch: 2.4.0
• Accelerate: 1.1.1
• Datasets: 3.1.0
• Tokenizers: 0.20.3

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",
}

MSELoss

@inproceedings{reimers-2020-multilingual-sentence-bert,
    title = "Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation",
    author = "Reimers, Nils and Gurevych, Iryna",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing",
    month = "11",
    year = "2020",
    publisher = "Association for Computational Linguistics",
    url = "https://arxiv.org/abs/2004.09813",
}