Row 44157

Row ID: 44157 | Dataset Entry | Axioma AXP Content Repository

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i need sparse lazily initialized to 0s embeddings that don't need the prior knowledge of the size of the "dictionary"

or are there better ways to treat integer data (some kind of IDs) that works kinda like classes, that will be used together with text embeddings? (also the model will often be trained when there is new data, potentially with more of the IDs, and it could stumble upon unseen IDs when used)

FieldValue
text i need sparse lazily initialized to 0s embeddings that don't need the prior knowledge of the size of the "dictionary" or are there better ways to treat integer data (some kind of IDs) that works kinda like classes, that will be used together with text embeddings? (also the model will often be trained when there is new data, potentially with more of the IDs, and it could stumble upon unseen IDs when used)
label r/pytorch
dataType post
communityName r/pytorch
datetime 2024-05-22
username_encoded Z0FBQUFBQm5Lak1PVFNmRFdMNGIzTmxpanN2QzJUbTRrenVvY1NRZXpURHdLQ29GZVd1N0FTWGp5dEh1UXRXR1M2NmdCWE1XOGg5T0xnVF8yRVYxcFota2JBSC1rZTF2cVE9PQ==
url_encoded Z0FBQUFBQm5Lak9lSmNIM0Z2ZUFfdHItczlLQ0k4Zi1DU2xIM2toclJoR016dHFZemRfZGFnNF9JOTBCcjQtRFFCdWp4MmxVbUpxX0hmTkMxYTF3OG90aXpVYUJRTGpub2RaRm8xQTd2Z192cjc2QldIM0M4TW5xMmpIOThlOFRwQ2J3bVZLNXphcW92VWxIVVhKN3lmUTBzOGRfQkZWOTBpb2VsQU1Fbk0zNzRrM1ctZTF4MFU4czh2TTJGa091VVFJZUs4TWxUX182

Raw Record

{
  "text": "i need sparse lazily initialized to 0s embeddings that don't need the prior knowledge of the size of the \"dictionary\"\n\nor are there better ways to treat integer data (some kind of IDs) that works kinda like classes, that will be used together with text embeddings? (also the model will often be trained when there is new data, potentially with more of the IDs, and it could stumble upon unseen IDs when used)",
  "label": "r/pytorch",
  "dataType": "post",
  "communityName": "r/pytorch",
  "datetime": "2024-05-22",
  "username_encoded": "Z0FBQUFBQm5Lak1PVFNmRFdMNGIzTmxpanN2QzJUbTRrenVvY1NRZXpURHdLQ29GZVd1N0FTWGp5dEh1UXRXR1M2NmdCWE1XOGg5T0xnVF8yRVYxcFota2JBSC1rZTF2cVE9PQ==",
  "url_encoded": "Z0FBQUFBQm5Lak9lSmNIM0Z2ZUFfdHItczlLQ0k4Zi1DU2xIM2toclJoR016dHFZemRfZGFnNF9JOTBCcjQtRFFCdWp4MmxVbUpxX0hmTkMxYTF3OG90aXpVYUJRTGpub2RaRm8xQTd2Z192cjc2QldIM0M4TW5xMmpIOThlOFRwQ2J3bVZLNXphcW92VWxIVVhKN3lmUTBzOGRfQkZWOTBpb2VsQU1Fbk0zNzRrM1ctZTF4MFU4czh2TTJGa091VVFJZUs4TWxUX182"
}
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