Modernbert Base Gooaq

🚀 SentenceTransformer based on answerdotai/ModernBERT-base

This model is a sentence-transformers model fine - tuned from answerdotai/ModernBERT-base on the gooaq dataset. It maps sentences and paragraphs to a 768 - dimensional dense vector space. This 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.
• Can be applied in multiple natural language processing tasks such as semantic textual similarity, semantic search, etc.
• Fine - tuned on the gooaq dataset.

📦 Installation

First 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("tomaarsen/ModernBERT-base-gooaq")
# Run inference
sentences = [
    'are you human korean novela?',
    "Are You Human? (Korean: 너도 인간이니; RR: Neodo Inganini; lit. Are You Human Too?) is a 2018 South Korean television series starring Seo Kang - jun and Gong Seung - yeon. It aired on KBS2's Mondays and Tuesdays at 22:00 (KST) time slot, from June 4 to August 7, 2018.",
    'A relative of European pear varieties like Bartlett and Anjou, the Asian pear is great used in recipes or simply eaten out of hand. It retains a crispness that works well in slaws and salads, and it holds its shape better than European pears when baked and cooked.',
]
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': 512, '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})
)

🔧 Technical Details

This model has been finetuned using train_st_gooaq.py using an RTX 3090, although only 10GB of VRAM was used.

📄 Evaluation

Metrics

Information Retrieval

• Datasets: NanoNQ and NanoMSMARCO
• Evaluated with InformationRetrievalEvaluator

The model has the following performance metrics on different datasets:

NanoNQ

NanoMSMARCO

NanoBEIR mean