Modernbert Embed Base Legal Matryoshka 2

🚀 ModernBERT Embed base Legal Matryoshka

This model is a fine - tuned sentence - transformers model derived from [nomic - ai/modernbert - embed - base](https://huggingface.co/nomic - ai/modernbert - embed - base) on a json dataset. It maps sentences and paragraphs to a 768 - dimensional dense vector space, which can be applied in semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and other tasks.

🚀 Quick Start

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("manishh16/modernbert-embed-base-legal-matryoshka-2")
# Run inference
sentences = [
    'protests pursuant to 28 U.S.C. § 1491(b).  See 28 U.S.C. § 1491(b).  Section 1491(b)(1) grants the \n17 \n \ncourt jurisdiction over protests filed “by an interested party objecting to a solicitation by a Federal \nagency for bids or proposals for a proposed contract... or any alleged violation of statute or',
    'Under which U.S. Code section are the protests filed?',
    "Which agency's declaration is mentioned?",
]
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]

✨ Features

• Maps sentences and paragraphs to a 768 - dimensional dense vector space.
• Applicable for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, etc.

📦 Installation

Install the Sentence Transformers library using the following command:

pip install -U sentence-transformers

💻 Usage Examples

Basic Usage

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("manishh16/modernbert-embed-base-legal-matryoshka-2")
# Run inference
sentences = [
    'protests pursuant to 28 U.S.C. § 1491(b).  See 28 U.S.C. § 1491(b).  Section 1491(b)(1) grants the \n17 \n \ncourt jurisdiction over protests filed “by an interested party objecting to a solicitation by a Federal \nagency for bids or proposals for a proposed contract... or any alleged violation of statute or',
    'Under which U.S. Code section are the protests filed?',
    "Which agency's declaration is mentioned?",
]
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](https://github.com/UKPLab/sentence - transformers)
• Hugging Face: [Sentence Transformers on Hugging Face](https://huggingface.co/models?library = sentence - transformers)

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 8192, 'do_lower_case': False}) with Transformer model: ModernBertModel 
  (1): Pooling({'word_embedding_dimension': 768, '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()
)

Evaluation

Information Retrieval (dim_768)

• Dataset: dim_768
• Evaluated with InformationRetrievalEvaluator with these parameters:

  {
      "truncate_dim": 768
  }
  

Information Retrieval (dim_512)

• Dataset: dim_512
• Evaluated with InformationRetrievalEvaluator with these parameters:

  {
      "truncate_dim": 512
  }
  

Information Retrieval (dim_256)

• Dataset: dim_256
• Evaluated with InformationRetrievalEvaluator with these parameters:

  {
      "truncate_dim": 256
  }
  

Information Retrieval (dim_128)

• Dataset: dim_128
• Evaluated with InformationRetrievalEvaluator with these parameters:

  {
      "truncate_dim": 128
  }
  

Information Retrieval (dim_64)

• Dataset: dim_64
• Evaluated with InformationRetrievalEvaluator with these parameters:

  {
      "truncate_dim": 64
  }
  

Training Details

Training Dataset

json

• Dataset: json
• Size: 5,822 training samples
• Columns: positive and anchor

📄 License

The license of this model is apache - 2.0.