Sentence Similarity
sentence-transformers
Safetensors
Italian
xlm-roberta
passage-retrieval
pruned
text-embeddings-inference
Instructions to use Kleva-ai/italian-bge-m3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Kleva-ai/italian-bge-m3 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Kleva-ai/italian-bge-m3") sentences = [ "Questa è una persona felice", "Questo è un cane felice", "Questa è una persona molto felice", "Oggi è una giornata di sole" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
🇮🇹 italian-bge-m3
This model is a 38.3% smaller version of BAAI/bge-m3 and comes with the Italian language only.
This pruned model should perform as the original model for Italian language tasks with a much smaller memory footprint.
Usage
You can use this model with the Transformers library:
from transformers import AutoModel, AutoTokenizer
model_name = "Kleva-ai/italian-bge-m3"
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("Kleva-ai/italian-bge-m3")
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Credits: cc [@antoinelouis]
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BAAI/bge-m3