E5 All Nli Triplet Matryoshka

🚀 SentenceTransformer based on intfloat/multilingual-e5-small

This is a Sentence Transformer model fine - tuned from intfloat/multilingual-e5-small. It maps sentences and paragraphs to a 384 - dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.

🚀 Quick Start

This model can be used directly for semantic tasks after installation. First, you need to install the Sentence Transformers library, and then load the model for inference.

✨ Features

• Semantic Mapping: Maps sentences and paragraphs to a 384 - dimensional dense vector space.
• Multiple Applications: Can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, etc.

📦 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("Omartificial-Intelligence-Space/E5-Matro")
# Run inference
sentences = [
    'يجلس شاب ذو شعر أشقر على الحائط يقرأ جريدة بينما تمر امرأة وفتاة شابة.',
    'ذكر شاب ينظر إلى جريدة بينما تمر إمرأتان بجانبه',
    'الشاب نائم بينما الأم تقود ابنتها إلى الحديقة',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# 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: BertModel 
  (1): Pooling({'word_embedding_dimension': 384, '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

Metrics

Semantic Similarity (Dataset: sts-test-384)

Evaluated with EmbeddingSimilarityEvaluator

Semantic Similarity (Dataset: sts-test-256)

Evaluated with EmbeddingSimilarityEvaluator

Semantic Similarity (Dataset: sts-test-128)

Evaluated with EmbeddingSimilarityEvaluator

Semantic Similarity (Dataset: sts-test-64)

Evaluated with EmbeddingSimilarityEvaluator