JFernandoGRE / gtelarge-duplicates-judgenames

MPNet base trained on AllNLI triplets

This is a sentence-transformers model finetuned from thenlper/gte-large . 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.

Model Details

Model Description

• Model Type: Sentence Transformer
• Base model: thenlper/gte-large
• Maximum Sequence Length: 512 tokens
• Output Dimensionality: 1024 dimensions
• Similarity Function: Cosine Similarity
• Language: en
• License: apache-2.0

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': 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})
  (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("JFernandoGRE/gtelarge-duplicates-judgenames")
# Run inference
sentences = [
    'AmySt. Eve J.',
    'BARBARA J. ROTHSTEIN',
    'ALGENON I. MARBLEY',
]
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

Training Dataset

Unnamed Dataset

• Size: 5,538 training samples
• Columns: sentence1 , sentence2 , and label
• Approximate statistics based on the first 1000 samples:

  	sentence1	sentence2	label
  type	string	string	int
  details	min: 4 tokens mean: 9.43 tokens max: 39 tokens	min: 4 tokens mean: 7.67 tokens max: 18 tokens	0: ~73.50% 1: ~26.50%

• Samples:

  sentence1	sentence2	label
  REGGIE B. WALTON	REGGIEBWALTON	1
  REGGIE WALTON	REGGIEBWALTON	1
  TREGGIE B. WALTON	REGGIEBWALTON	0

• Loss: OnlineContrastiveLoss

Evaluation Dataset

Unnamed Dataset

• Size: 7,218 evaluation samples
• Columns: sentence1 , sentence2 , and label
• Approximate statistics based on the first 1000 samples:

  	sentence1	sentence2	label
  type	string	string	int
  details	min: 4 tokens mean: 10.23 tokens max: 38 tokens	min: 4 tokens mean: 7.99 tokens max: 25 tokens	0: ~78.10% 1: ~21.90%

• Samples:

  sentence1	sentence2	label
  St. Eve	*St Eve	1
  Amy J St. Eve	*St Eve	1
  Amy J. St Eve	*St Eve	1

• Loss: OnlineContrastiveLoss

Training Hyperparameters

Non-Default Hyperparameters

• eval_strategy : steps
• per_device_train_batch_size : 16
• per_device_eval_batch_size : 16
• learning_rate : 1e-05
• warmup_ratio : 0.182
• fp16 : True

All Hyperparameters

Training Logs

Framework Versions

• Python: 3.10.12
• Sentence Transformers: 3.3.1
• Transformers: 4.46.3
• PyTorch: 2.5.1+cu121
• 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",
}