sentence bert base italian xxl uncased EU AI Act Compliance Profile

sentence-bert-base-italian-xxl-cased

This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. It derives from dbmdz/bert-base-italian-xxl-uncased, check its model card for more info.

Usage (Sentence-Transformers)

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

Then you can use the model like this:

from sentence_transformers import SentenceTransformer
sentences = ["Una ragazza si acconcia i capelli.", "Una ragazza si sta spazzolando i capelli."]

model = SentenceTransformer('nickprock/sentence-bert-base-italian-xxl-uncased')
embeddings = model.encode(sentences)
print(embeddings)

Usage (FastEmbed)

Using this model becomes easy when you have FastEmbed installed:

Then you can use the model like this:

from fastembed import TextEmbedding
from fastembed.common.model_description import PoolingType, ModelSource

sentences = ["Una ragazza si acconcia i capelli.", "Una ragazza si sta spazzolando i capelli."]

TextEmbedding.add_custom_model(
    model="nickprock/sentence-bert-base-italian-xxl-uncased",
    pooling=PoolingType.MEAN,
    normalization=True,
    sources=ModelSource(hf="nickprock/sentence-bert-base-italian-xxl-uncased"),  # can be used with an `url` to load files from a private storage
    dim=768,
    model_file="onnx/model_qint8_avx512_vnni.onnx",  # can be used to load an already supported model with another optimization or quantization, e.g. onnx/model_O4.onnx
)

model = TextEmbedding(model_name="nickprock/sentence-bert-base-italian-xxl-uncased")
embeddings = list(model.embed(sentences))
print(embeddings)

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