Sentence Transformers Multilingual E5 Large

🚀 smart-tribune/sentence-transformers-multilingual-e5-large

This is a sentence-transformers model that maps sentences and paragraphs to a 1024-dimensional dense vector space, which can be used for tasks such as clustering or semantic search.

🚀 Quick Start

This is a sentence-transformers model. It maps sentences and paragraphs to a 1024-dimensional dense vector space and can be used for tasks like clustering or semantic search.

The model is based on intfloat/multilingual-e5-base, with a pooling (mean) and normalization layer added, and saved in a sentence transformers version.

📦 Installation

Using this model becomes easy when you have sentence-transformers installed:

pip install -U sentence-transformers

💻 Usage Examples

Basic Usage

from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]

model = SentenceTransformer('smart-tribune/sentence-transformers-multilingual-e5-large')
embeddings = model.encode(sentences)
print(embeddings)

📚 Documentation

Evaluation Results

In progress

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: XLMRobertaModel 
  (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})
  (2): Normalize()
)

📄 License

This project is licensed under the MIT license.