Sentence Similarity
sentence-transformers
Safetensors
French
English
new
passage-retrieval
pruned
custom_code
text-embeddings-inference
Instructions to use ijohn07/french-english-gte-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use ijohn07/french-english-gte-base with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("ijohn07/french-english-gte-base", trust_remote_code=True) sentences = [ "C'est une personne heureuse", "C'est un chien heureux", "C'est une personne très heureuse", "Aujourd'hui est une journée ensoleillée" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
🇫🇷 french-gte-multilingual-base
This model is a 51.4% smaller version of Alibaba-NLP/gte-multilingual-base for the French and English language, created using the mtem-pruner space.
This pruned model should perform similarly to the original model for French and English language tasks with a much smaller memory footprint. However, it may not perform well for other languages present in the original multilingual model as tokens not commonly used in French and English were removed from the original multilingual model's vocabulary.
Usage
You can use this model with the Transformers library:
from transformers import AutoModel, AutoTokenizer
model_name = "ijohn07/french-english-gte-base"
model = AutoModel.from_pretrained(model_name, trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True, use_fast=True)
Or with the sentence-transformers library:
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("ijohn07/french-english-gte-base")
Credits: cc @antoinelouis
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Model tree for ijohn07/french-english-gte-base
Base model
Alibaba-NLP/gte-multilingual-base