Comments (3)
gasvn
commented on August 25, 2024
In our implementation, each task is equipped with a specific number of prompt tokens, denoted as prompt_num. The code is designed to jointly train on all datasets simultaneously.
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Zero-coder
commented on August 25, 2024
Thanks your splendid work,No offense！Then why it's called prompt_num since it's have no semantic meaning relationship with "forecasting" or "classification". Hope for an response.❤
from units.
gasvn
commented on August 25, 2024
Not sure I understand your question well. The prompt_num has the meaningful of the number of prompt tokens used for each data source.
from units.
Related Issues (20)
- How to run self supervised pretraining using UniTS on custom data? HOT 1
- The initialization of Category Embedding HOT 1
- Why there is no loss.backward in training process? HOT 1
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- Should requirements.txt be updated including more packages? HOT 2
- Using UniTS on Unlabeled Data for Anomaly Detection Task HOT 2
- During the run of the UniTS_pretrain_x128.sh script the loss value encountered nan. HOT 1
- Validation set HOT 2
- Dataset Download HOT 1
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- Hello! Asking about Inference. HOT 2
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- the meaning of prompt_num HOT 2
- About anomaly detection HOT 1
- If the pre-training of UniTS includes a test dataset, can it still be called ZS? HOT 10
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