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@@ -41,6 +41,37 @@ print(embeddings)
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  ```
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  ## Usage (HuggingFace Transformers)
 
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  ```
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+ ## Usage (FastEmbed)
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+
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+ Using this model becomes easy when you have [FastEmbed](https://github.com/qdrant/fastembed) installed:
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+
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+ ```
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+ pip install fastembed
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+ ```
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+
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+ Then you can use the model like this:
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+
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+ ```python
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+ from fastembed import TextEmbedding
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+ from fastembed.common.model_description import PoolingType, ModelSource
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+
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+ sentences = ["Una ragazza si acconcia i capelli.", "Una ragazza si sta spazzolando i capelli."]
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+
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+ TextEmbedding.add_custom_model(
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+ model="nickprock/multi-sentence-BERTino",
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+ pooling=PoolingType.MEAN,
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+ normalization=True,
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+ sources=ModelSource(hf="nickprock/multi-sentence-BERTino"), # can be used with an `url` to load files from a private storage
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+ dim=768,
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+ 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
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+ )
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+
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+ model = TextEmbedding(model_name="nickprock/multi-sentence-BERTino")
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+ embeddings = list(model.embed(sentences))
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+ print(embeddings)
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+
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+ ```
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+
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  ## Usage (HuggingFace Transformers)