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--- |
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license: mit |
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task_categories: |
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- feature-extraction |
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tags: |
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- multilingual |
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- llm |
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- linguistics |
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- embeddings |
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--- |
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This dataset contains the computed language latent vectors (binary vectors, Euclidean vectors, and distances) as presented in the paper [Deep Language Geometry: Constructing a Metric Space from LLM Weights](https://huggingface.co/papers/2508.11676). |
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The paper introduces a novel framework that utilizes the internal weight activations of Large Language Models (LLMs) to construct a metric space of languages. This dataset makes the automatically derived high-dimensional vector representations for 106 languages publicly available, capturing intrinsic language characteristics that reflect linguistic phenomena. |
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**Paper:** [Deep Language Geometry: Constructing a Metric Space from LLM Weights](https://huggingface.co/papers/2508.11676) |
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**Code:** [https://github.com/mshamrai/deep-language-geometry](https://github.com/mshamrai/deep-language-geometry) |
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**Gradio Analysis Tool (Hugging Face Space):** [https://huggingface.co/spaces/mshamrai/language-metric-analysis](https://huggingface.co/spaces/mshamrai/language-metric-analysis) |
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### Dataset Contents |
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The dataset includes: |
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- Calculated binary vectors |
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- Euclidean vectors |
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- Distances between languages |
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These components can be used to analyze and visualize inter-language connections and linguistic families. |
