Sentence Similarity
sentence-transformers
Safetensors
Spanish
new
passage-retrieval
pruned
custom_code
text-embeddings-inference
Instructions to use CarlosRCDev/spanish-gte-multilingual-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use CarlosRCDev/spanish-gte-multilingual-base with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("CarlosRCDev/spanish-gte-multilingual-base", trust_remote_code=True) sentences = [ "Esa es una persona feliz", "Ese es un perro feliz", "Esa es una persona muy feliz", "Hoy es un dÃa soleado" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
🇪🇸 spanish-gte-multilingual-base
This model is a 50.7% smaller version of Alibaba-NLP/gte-multilingual-base for the Spanish language, created using the mtem-pruner space.
This pruned model should perform similarly to the original model for Spanish 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 Spanish 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 = "CarlosRCDev/spanish-gte-multilingual-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("CarlosRCDev/spanish-gte-multilingual-base")
Credits: cc @antoinelouis
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Base model
Alibaba-NLP/gte-multilingual-base