SentenceTransformer based on BAAI/bge-small-en-v1.5

This is a sentence-transformers model finetuned from BAAI/bge-small-en-v1.5. It maps sentences & paragraphs to a 384-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
• Base model: BAAI/bge-small-en-v1.5
• Maximum Sequence Length: 512 tokens
• Output Dimensionality: 384 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': True, 'architecture': 'BertModel'})
  (1): Pooling({'word_embedding_dimension': 384, '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()
)

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 = [
    'Can I automate the Gain image over time to create dynamic volume changes?',
    'Document_title: Harmor \nFile_name: plugins/Harmor.htm\nHeading_hierarchy: [Harmor -> About images and planes]\nAnchor_id: [none]\nThere are independent images that control the Pitch/Frequency and Gain of partials. Together these can create any sound, just as sampler can. In the image window the vertical dimension\n  is frequency (each line of pixels is a single partial), while the horizontal dimension is time.',
    'Document_title: PoiZone V2 \nFile_name: plugins/PoiZone.htm\nHeading_hierarchy: [PoiZone V2 -> Voicing]\nAnchor_id: [none]\n• 2 main oscillators for subtractive synthesis: SAW and PULSE shapes, pulse width adjustable. • 1 NOISE Oscillator. • Variable polyphony (1 to 32 voices).',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.3468, 0.0824],
#         [0.3468, 1.0000, 0.0908],
#         [0.0824, 0.0908, 1.0000]])

Evaluation

Metrics

Information Retrieval

• Evaluated with InformationRetrievalEvaluator

Training Details

Training Dataset

Unnamed Dataset

• Size: 29,840 training samples
• Columns: sentence_0 and sentence_1
• Approximate statistics based on the first 1000 samples:

  	sentence_0	sentence_1
  type	string	string
  details	min: 9 tokens mean: 18.0 tokens max: 33 tokens	min: 29 tokens mean: 310.93 tokens max: 512 tokens

• Samples:

  sentence_0	sentence_1
  How do I load a *.SPEECH preset into the Sampler plugin?	Document_title: Speech Preset (.SPEECH) File_name: fformats_sample_speech.htm Heading_hierarchy: [Speech Preset (.SPEECH)] Anchor_id: [none] The Speech synthesizer processes text to create computerized or Vocoder-like vocals to your projects. The *.SPEECH presets are supported by all native FL Studio plugins that use custom samples for synthesizing, i.e. Sampler , Granulizer , Fruity Slicer and Fruity Scratcher .
  Can I use Speech presets with the Fruity Granulizer to create vocal effects?	Document_title: Speech Preset (.SPEECH) File_name: fformats_sample_speech.htm Heading_hierarchy: [Speech Preset (.SPEECH)] Anchor_id: [none] The Speech synthesizer processes text to create computerized or Vocoder-like vocals to your projects. The *.SPEECH presets are supported by all native FL Studio plugins that use custom samples for synthesizing, i.e. Sampler , Granulizer , Fruity Slicer and Fruity Scratcher .
  What kind of vocals can the Speech synthesizer create from text?	Document_title: Speech Preset (.SPEECH) File_name: fformats_sample_speech.htm Heading_hierarchy: [Speech Preset (.SPEECH)] Anchor_id: [none] The Speech synthesizer processes text to create computerized or Vocoder-like vocals to your projects. The *.SPEECH presets are supported by all native FL Studio plugins that use custom samples for synthesizing, i.e. Sampler , Granulizer , Fruity Slicer and Fruity Scratcher .

• Loss: MultipleNegativesRankingLoss with these parameters:
  json Copy

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

Training Hyperparameters

Non-Default Hyperparameters

• eval_strategy: steps
• per_device_train_batch_size: 10
• per_device_eval_batch_size: 10
• num_train_epochs: 2
• multi_dataset_batch_sampler: round_robin

All Hyperparameters

Training Logs

Framework Versions

• Python: 3.12.12
• Sentence Transformers: 5.2.3
• Transformers: 5.0.0
• PyTorch: 2.10.0+cu128
• Accelerate: 1.12.0
• Datasets: 4.0.0
• Tokenizers: 0.22.2

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}
}