Row 63112

Row ID: 63112 | Dataset Entry | Axioma AXP Content Repository

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Yes, it definitely makes sense.

Language models learn a latent space where similar texts are mapped close together (I always think of this concept in terms of Eigenvectors). This space implicitly captures semantic relationships between words and concepts in your dataset. In high dimensions, data often resides on lower-dimensional structures (i.e. a manifold). By approximating the manifold *within the latent space*, you're essentially uncovering the underlying organization of your text data.

You've probably already figured out that manifolds tend to be much easier to analyze and visualize compared to the full, high-dimensional latent space. This can allow you to identify patterns and clusters that might be hidden in the raw data. Just be aware that you this isn't *guaranteed* to work.

FieldValue
text Yes, it definitely makes sense. Language models learn a latent space where similar texts are mapped close together (I always think of this concept in terms of Eigenvectors). This space implicitly captures semantic relationships between words and concepts in your dataset. In high dimensions, data often resides on lower-dimensional structures (i.e. a manifold). By approximating the manifold *within the latent space*, you're essentially uncovering the underlying organization of your text data. Yo…
label r/deeplearning
dataType comment
communityName r/deeplearning
datetime 2024-05-23
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Raw Record

{
  "text": "Yes, it definitely makes sense.\n\nLanguage models learn a latent space where similar texts are mapped close together (I always think of this concept in terms of Eigenvectors). This space implicitly captures semantic relationships between words and concepts in your dataset. In high dimensions, data often resides on lower-dimensional structures (i.e. a manifold). By approximating the manifold *within the latent space*, you're essentially uncovering the underlying organization of your text data.\n\nYou've probably already figured out that manifolds tend to be much easier to analyze and visualize compared to the full, high-dimensional latent space. This can allow you to identify patterns and clusters that might be hidden in the raw data. Just be aware that you this isn't *guaranteed* to work.",
  "label": "r/deeplearning",
  "dataType": "comment",
  "communityName": "r/deeplearning",
  "datetime": "2024-05-23",
  "username_encoded": "Z0FBQUFBQm5Lak1hZGtxTzZneE1XUmRYdXhHZmRscFdHVG93djQ1alIyN0JYcFZPNHl1RDdKOW5iWXN4enBURkM4aHV6Tk0tWHpYT3k4VmFZcE5hZmRYZVY1UWtYeDRtVHc9PQ==",
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}
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