Feature Extraction
sentence-transformers
Safetensors
Transformers
English
luxical-one
sentence-similarity
luxical
custom_code
Instructions to use DatologyAI/luxical-one with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use DatologyAI/luxical-one with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("DatologyAI/luxical-one", trust_remote_code=True) sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Transformers
How to use DatologyAI/luxical-one with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="DatologyAI/luxical-one", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("DatologyAI/luxical-one", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
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# Luxical One
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# Luxical One
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This is a small lexical-dense text embedding model distilled for English-language symmetrical full-document text similarity and classification applications from [`Snowflake/snowflake-arctic-embed-m-v2.0`](https://huggingface.co/Snowflake/snowflake-arctic-embed-m-v2.0).
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## Quickstart -- Transformers
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Luxical One includes a basic HuggingFace integration that supports model inference. GPU inference is not supported (GPU "acceleration" would not provide a meaningful boost on most systems due to how fast and small the model is).
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You must have the `luxical` package installed to run this model, and you must trust the remote code bundled with the model. Currently only cpython versions 3.11-3.13 and macos/linux operating systems are supported.
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``` shell
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pip install luxical
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```
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``` python
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from transformers import AutoModel
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from pathlib import Path
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# Load from the Huggingface Hub.
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hub_model = AutoModel.from_pretrained(
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"datologyai/luxical-one",
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trust_remote_code=True,
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)
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# Embed some text.
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emb = local_model(["Luxical integrates with Huggingface."]).embeddings
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```
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