Row 44440

Row ID: 44440 | Dataset Entry | Axioma AXP Content Repository

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You can think of SEFR as a linear classifier with a twist: it basically thresholding samples based on a similarity score in respect to cleverly averaged negative and positive samples seen during training. Amrite u/CriticalofReviewer2 ??

FieldValue
text You can think of SEFR as a linear classifier with a twist: it basically thresholding samples based on a similarity score in respect to cleverly averaged negative and positive samples seen during training. Amrite u/CriticalofReviewer2 ??
label r/machinelearning
dataType comment
communityName r/MachineLearning
datetime 2024-05-22
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url_encoded Z0FBQUFBQm5Lak9lNUQ5Vng3cUR0T3o4SXZ3b3NYSC1qU2xBZXBnYzdQd0d3bnBmcjFLUnhhNmhKdmRwVXR1X0ZjQmdvQTFFUk1KZk9lUVh5LWJnTGtoZ3p4bmZXQjFHVEFGZTJGZVBqdEJ3OElfQ01pQ2dLRkNtYmpucW5fY3hOSlpFNnp1XzAwYVZhTWVsbExQUmRwVUtaRXJpZWkwUkt5Q3VFVUFYXzhINTh6c1k1bUxtRWxTNHE4dmFZRUVHU2hzN3hCUUFpX2VtTDljaHJ2WHpxejNPcVRheWtxUktIb29xMm5XVTlXTGJQd2owcVVrZFJ4az0=

Raw Record

{
  "text": "You can think of SEFR as a linear classifier with a twist: it basically thresholding samples based on a similarity score in respect to cleverly averaged negative and positive samples seen during training. Amrite u/CriticalofReviewer2 ??",
  "label": "r/machinelearning",
  "dataType": "comment",
  "communityName": "r/MachineLearning",
  "datetime": "2024-05-22",
  "username_encoded": "Z0FBQUFBQm5Lak1PcHpPRE1HQUFOQmVGWWpVelRTeHc1RDllVmc1b2ROSklGLXExZXdhVFVod0ctVUJXRFlIa2JReXZCM25rWHhIZWxWdjhzWjZ4aWpDZkItM25CZ2hlSmc9PQ==",
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}
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