Modernbert Base Sts

🚀 SentenceTransformer based on answerdotai/ModernBERT-base

This is a Sentence Transformer model fine-tuned from answerdotai/ModernBERT-base on the stsb dataset. It maps sentences and paragraphs to a 768 - dimensional dense vector space, which can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.

🚀 Quick Start

This Sentence Transformer model is fine - tuned from [answerdotai/ModernBERT - base](https://huggingface.co/answerdotai/ModernBERT - base) on the [stsb](https://huggingface.co/datasets/sentence - transformers/stsb) dataset. It can map text to a 768 - dimensional vector space for various NLP tasks.

✨ Features

• Semantic Similarity: Capable of calculating semantic similarity between sentences.
• Feature Extraction: Can extract 768 - dimensional feature vectors from sentences and paragraphs.
• Multiple Applications: Applicable to semantic search, paraphrase mining, text classification, clustering, etc.

📦 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("nickprock/ModernBERT-base-sts")
# Run inference
sentences = [
    'While Queen may refer to both Queen regent (sovereign) or Queen consort, the King has always been the sovereign.',
    'There is a very good reason not to refer to the Queen\'s spouse as "King" - because they aren\'t the King.',
    'A man sitting on the floor in a room is strumming a guitar.',
]
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 Information

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})
)

Evaluation

Metrics - Semantic Similarity

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

Training Details

Training Dataset - stsb

• Dataset: [stsb](https://huggingface.co/datasets/sentence - transformers/stsb) at [ab7a5ac](https://huggingface.co/datasets/sentence - transformers/stsb/tree/ab7a5ac0e35aa22088bdcf23e7fd99b220e53308)
• Size: 5,749 training samples
• Columns: sentence1, sentence2, and score
• Approximate statistics based on the first 1000 samples:

  	sentence1	sentence2	score
  type	string	string	float
  details	min: 6 tokens mean: 10.16 tokens max: 28 tokens	min: 6 tokens mean: 10.12 tokens max: 25 tokens	min: 0.0 mean: 0.45 max: 1.0

• Samples:

  sentence1	sentence2	score
  A plane is taking off.	An air plane is taking off.	1.0
  A man is playing a large flute.	A man is playing a flute.	0.76
  A man is spreading shreded cheese on a pizza.	A man is spreading shredded cheese on an uncooked pizza.	0.76

• Loss: CoSENTLoss with parameters:

  {
      "scale": 20.0,
      "similarity_fct": "pairwise_cos_sim"
  }
  

Evaluation Dataset - stsb

• Dataset: [stsb](https://huggingface.co/datasets/sentence - transformers/stsb) at [ab7a5ac](https://huggingface.co/datasets/sentence - transformers/stsb/tree/ab7a5ac0e35aa22088bdcf23e7fd99b220e53308)
• Size: 1,500 evaluation samples
• Columns: sentence1, sentence2, and score
• Approximate statistics based on the first 1000 samples:

  	sentence1	sentence2	score
  type	string	string	float
  details	min: 5 tokens mean: 15.11 tokens max: 44 tokens	min: 6 tokens mean: 15.1 tokens max: 50 tokens	min: 0.0 mean: 0.42 max: 1.0

• Samples:

  sentence1	sentence2	score
  A man with a hard hat is dancing.	A man wearing a hard hat is dancing.	1.0
  A young child is riding a horse.	A child is riding a horse.	0.95
  A man is feeding a mouse to a snake.	The man is feeding a mouse to the snake.	1.0

• Loss: CoSENTLoss with parameters:

  {
      "scale": 20.0,
      "similarity_fct": "pairwise_cos_sim"
  }
  

Training Hyperparameters

Non - Default Hyperparameters

• eval_strategy: steps
• per_device_train_batch_size: 16
• per_device_eval_batch_size: 16
• num_train_epochs: 4
• warmup_ratio: 0.1
• fp16: True

All Hyperparameters

Training Logs

Framework Versions

• Python: 3.10.12
• Sentence Transformers: 3.4.0.dev0
• Transformers: 4.49.0.dev0
• PyTorch: 2.4.1+cu121
• Accelerate: 0.34.2
• Datasets: 3.2.0
• Tokenizers: 0.21.0

📄 License

No license information is 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",
}

CoSENTLoss

@online{kexuefm-8847,
    title={CoSENT: A more efficient sentence vector scheme than Sentence-BERT},
    author={Su Jianlin},
    year={2022},
    month={Jan},
    url={https://kexue.fm/archives/8847},
}