multilingual-e5-large-instruct-pms-16384

This model is a 42.73% smaller version of intfloat/multilingual-e5-large-instruct optimized for French language via vocabulary size reduction using the trimming method.
This trimmed model should perform similarly to the original model with only 16,384 tokens and a much smaller memory footprint. However, it may not perform well for other languages as tokens not commonly used in the selected languages were removed from the vocabulary.

Model Statistics

Metric	Original	Trimmed	Reduction
Vocabulary size	250,002 tokens	16,384 tokens	93.44%
Model size	559,890,432 params	320,665,600 params	42.73%

Mining Dataset Statistics

• Number of texts used for mining: 200,000 texts
• Dataset: alphaedgeai/fineweb-2-trimming

Usage

from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("alphaedgeai/multilingual-e5-large-instruct-pms-16384")
# Run inference with queries and documents
query = "My query"
documents = [
    "Chunk 1",
    "Chunk 2",
    "Chunk 3",
]
query_embeddings = model.encode_query(query)
document_embeddings = model.encode_document(documents)
print(query_embeddings.shape, document_embeddings.shape)
# Compute similarities to determine a ranking
similarities = model.similarity(query_embeddings, document_embeddings)
print(similarities)

Citation

Multilingual E5

@article{wang2024multilingual,
  title={Multilingual E5 Text Embeddings: A Technical Report},
  author={Wang, Liang and Yang, Nan and Huang, Xiaolong and Yang, Linjun and Majumder, Rangan and Wei, Furu},
  journal={arXiv preprint arXiv:2402.05672},
  year={2024}
}