Veriscrape Book Test Sbert Bs128 Lr5e 05 Ep3 Euclidean Sntrue Spfalse Hn1

🚀 SentenceTransformer based on Alibaba-NLP/gte-base-en-v1.5

This SentenceTransformer model, finetuned from Alibaba-NLP/gte-base-en-v1.5, maps sentences and paragraphs to a 768 - dimensional dense vector space. It can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.

✨ Features

• Maps sentences and paragraphs to a 768 - dimensional dense vector space.
• Suitable for multiple NLP tasks such as semantic textual similarity and clustering.

📦 Installation

First, you need to 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("albertus-sussex/veriscrape-book-test-sbert-bs128_lr5e-05_ep3_euclidean_snTrue_spFalse_hn1")
# Run inference
sentences = [
    'Midnight',
    'The Bone Parade',
    '12/01/2005',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# 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: NewModel 
  (1): Pooling({'word_embedding_dimension': 768, '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})
)

Evaluation

Metrics

Triplet

Evaluated with TripletEvaluator

Silhouette

Evaluated with veriscrape.training.SilhouetteEvaluator

Triplet

Evaluated with TripletEvaluator

Silhouette

Evaluated with veriscrape.training.SilhouetteEvaluator

Training Details

Training Dataset

Unnamed Dataset

• Size: 84,524 training samples
• Columns: anchor, positive, negative, pos_attr_name, and neg_attr_name
• Approximate statistics based on the first 1000 samples:

  	anchor	positive	negative	pos_attr_name	neg_attr_name
  type	string	string	string	string	string
  details	min: 3 tokens mean: 6.97 tokens max: 37 tokens	min: 3 tokens mean: 7.09 tokens max: 28 tokens	min: 3 tokens mean: 6.31 tokens max: 23 tokens	min: 3 tokens mean: 3.77 tokens max: 5 tokens	min: 3 tokens mean: 3.8 tokens max: 5 tokens

• Samples:

  anchor	positive	negative	pos_attr_name	neg_attr_name
  09/01/1997	12/01/1977	2010	publication_date	title
  9780060275730	9780829748772	HarperCollins Publishers Ltd	isbn_13	publisher
  9780609809648	9780764551956	HarperCollins Publishers	isbn_13	author

• Loss: veriscrape.training.AttributeTripletLoss with these parameters:

  {
      "distance_metric": "TripletDistanceMetric.EUCLIDEAN",
      "triplet_margin": 5
  }
  

Evaluation Dataset

Unnamed Dataset

• Size: 9,392 evaluation samples
• Columns: anchor, positive, negative, pos_attr_name, and neg_attr_name
• Approximate statistics based on the first 1000 samples:

  	anchor	positive	negative	pos_attr_name	neg_attr_name
  type	string	string	string	string	string
  details	min: 3 tokens mean: 6.85 tokens max: 27 tokens	min: 3 tokens mean: 6.98 tokens max: 44 tokens	min: 3 tokens mean: 6.08 tokens max: 18 tokens	min: 3 tokens mean: 3.75 tokens max: 5 tokens	min: 3 tokens mean: 3.8 tokens max: 5 tokens

• Samples:

  anchor	positive	negative	pos_attr_name	neg_attr_name
  9780764200564	: 9780590458467	1984	isbn_13	publication_date
  Penguin Group USA	Signet	9781600243912	publisher	isbn_13
  Alphabet Juice	Space	9780807871133	title	isbn_13

• Loss: veriscrape.training.AttributeTripletLoss with these parameters:

  {
      "distance_metric": "TripletDistanceMetric.EUCLIDEAN",
      "triplet_margin": 5
  }
  

Training Hyperparameters

Non - Default Hyperparameters

• eval_strategy: epoch
• per_device_train_batch_size: 128
• per_device_eval_batch_size: 128
• warmup_ratio: 0.1

All Hyperparameters

Training Logs

Framework Versions

• Python: 3.10.16
• Sentence Transformers: 3.4.1
• Transformers: 4.45.2
• PyTorch: 2.5.1+cu124
• Accelerate: 1.5.2
• Datasets: 3.1.0
• Tokenizers: 0.20.3

📄 License

No license information provided in the original document.

📄 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",
}

AttributeTripletLoss

@misc{hermans2017defense,
    title={In Defense of the Triplet Loss for Person Re-Identification},
    author={Alexander Hermans and Lucas Beyer and Bastian Leibe},
    year={2017},
    eprint={1703.07737},
    archivePrefix={arXiv},
    primaryClass={cs.CV}
}