|
2024-05-29 18:45 - Cuda check |
|
2024-05-29 18:45 - True |
|
2024-05-29 18:45 - 1 |
|
2024-05-29 18:45 - Configue Model and tokenizer |
|
2024-05-29 18:45 - Memory usage in 0.00 GB |
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2024-05-29 18:45 - Dataset loaded successfully: |
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train-Jingmei/Pandemic_Wiki |
|
test -Jingmei/Pandemic_WHO |
|
2024-05-29 18:46 - Tokenize data: DatasetDict({ |
|
train: Dataset({ |
|
features: ['input_ids', 'attention_mask'], |
|
num_rows: 2152 |
|
}) |
|
test: Dataset({ |
|
features: ['input_ids', 'attention_mask'], |
|
num_rows: 8264 |
|
}) |
|
}) |
|
2024-05-29 18:49 - Split data into chunks:DatasetDict({ |
|
train: Dataset({ |
|
features: ['input_ids', 'attention_mask'], |
|
num_rows: 24863 |
|
}) |
|
test: Dataset({ |
|
features: ['input_ids', 'attention_mask'], |
|
num_rows: 198964 |
|
}) |
|
}) |
|
2024-05-29 18:49 - Setup PEFT |
|
2024-05-29 18:49 - Setup optimizer |
|
2024-05-29 18:49 - Start training |
|
2024-05-29 18:57 - Cuda check |
|
2024-05-29 18:57 - True |
|
2024-05-29 18:57 - 1 |
|
2024-05-29 18:57 - Configue Model and tokenizer |
|
2024-05-29 18:57 - Memory usage in 25.17 GB |
|
2024-05-29 18:57 - Dataset loaded successfully: |
|
train-Jingmei/Pandemic_Wiki |
|
test -Jingmei/Pandemic_WHO |
|
2024-05-29 18:57 - Tokenize data: DatasetDict({ |
|
train: Dataset({ |
|
features: ['input_ids', 'attention_mask'], |
|
num_rows: 2152 |
|
}) |
|
test: Dataset({ |
|
features: ['input_ids', 'attention_mask'], |
|
num_rows: 8264 |
|
}) |
|
}) |
|
2024-05-29 18:57 - Split data into chunks:DatasetDict({ |
|
train: Dataset({ |
|
features: ['input_ids', 'attention_mask'], |
|
num_rows: 24863 |
|
}) |
|
test: Dataset({ |
|
features: ['input_ids', 'attention_mask'], |
|
num_rows: 198964 |
|
}) |
|
}) |
|
2024-05-29 18:57 - Setup PEFT |
|
2024-05-29 18:57 - Setup optimizer |
|
2024-05-29 18:57 - Start training |
|
2024-05-29 19:04 - Cuda check |
|
2024-05-29 19:04 - True |
|
2024-05-29 19:04 - 1 |
|
2024-05-29 19:04 - Configue Model and tokenizer |
|
2024-05-29 19:04 - Memory usage in 25.17 GB |
|
2024-05-29 19:04 - Dataset loaded successfully: |
|
train-Jingmei/Pandemic_Wiki |
|
test -Jingmei/Pandemic_WHO |
|
2024-05-29 19:04 - Tokenize data: DatasetDict({ |
|
train: Dataset({ |
|
features: ['input_ids', 'attention_mask'], |
|
num_rows: 2152 |
|
}) |
|
test: Dataset({ |
|
features: ['input_ids', 'attention_mask'], |
|
num_rows: 8264 |
|
}) |
|
}) |
|
2024-05-29 19:04 - Split data into chunks:DatasetDict({ |
|
train: Dataset({ |
|
features: ['input_ids', 'attention_mask'], |
|
num_rows: 24863 |
|
}) |
|
test: Dataset({ |
|
features: ['input_ids', 'attention_mask'], |
|
num_rows: 198964 |
|
}) |
|
}) |
|
2024-05-29 19:04 - Setup PEFT |
|
2024-05-29 19:04 - Setup optimizer |
|
2024-05-29 19:04 - Start training |
|
2024-05-29 19:10 - Cuda check |
|
2024-05-29 19:10 - True |
|
2024-05-29 19:10 - 1 |
|
2024-05-29 19:10 - Configue Model and tokenizer |
|
2024-05-29 19:10 - Memory usage in 25.17 GB |
|
2024-05-29 19:10 - Dataset loaded successfully: |
|
train-Jingmei/Pandemic_Wiki |
|
test -Jingmei/Pandemic_WHO |
|
2024-05-29 19:10 - Tokenize data: DatasetDict({ |
|
train: Dataset({ |
|
features: ['input_ids', 'attention_mask'], |
|
num_rows: 2152 |
|
}) |
|
test: Dataset({ |
|
features: ['input_ids', 'attention_mask'], |
|
num_rows: 8264 |
|
}) |
|
}) |
|
2024-05-29 19:10 - Split data into chunks:DatasetDict({ |
|
train: Dataset({ |
|
features: ['input_ids', 'attention_mask'], |
|
num_rows: 24863 |
|
}) |
|
test: Dataset({ |
|
features: ['input_ids', 'attention_mask'], |
|
num_rows: 198964 |
|
}) |
|
}) |
|
2024-05-29 19:10 - Setup PEFT |
|
2024-05-29 19:10 - Setup optimizer |
|
2024-05-29 19:10 - Start training |
|
2024-05-29 19:16 - Cuda check |
|
2024-05-29 19:16 - True |
|
2024-05-29 19:16 - 1 |
|
2024-05-29 19:16 - Configue Model and tokenizer |
|
2024-05-29 19:16 - Memory usage in 25.17 GB |
|
2024-05-29 19:16 - Dataset loaded successfully: |
|
train-Jingmei/Pandemic_Wiki |
|
test -Jingmei/Pandemic_WHO |
|
2024-05-29 19:16 - Tokenize data: DatasetDict({ |
|
train: Dataset({ |
|
features: ['input_ids', 'attention_mask'], |
|
num_rows: 2152 |
|
}) |
|
test: Dataset({ |
|
features: ['input_ids', 'attention_mask'], |
|
num_rows: 8264 |
|
}) |
|
}) |
|
2024-05-29 19:16 - Split data into chunks:DatasetDict({ |
|
train: Dataset({ |
|
features: ['input_ids', 'attention_mask'], |
|
num_rows: 24863 |
|
}) |
|
test: Dataset({ |
|
features: ['input_ids', 'attention_mask'], |
|
num_rows: 198964 |
|
}) |
|
}) |
|
2024-05-29 19:16 - Setup PEFT |
|
2024-05-29 19:16 - Setup optimizer |
|
2024-05-29 19:16 - Start training |
|
2024-05-29 19:22 - Cuda check |
|
2024-05-29 19:22 - True |
|
2024-05-29 19:22 - 1 |
|
2024-05-29 19:22 - Configue Model and tokenizer |
|
2024-05-29 19:22 - Memory usage in 25.17 GB |
|
2024-05-29 19:22 - Dataset loaded successfully: |
|
train-Jingmei/Pandemic_Wiki |
|
test -Jingmei/Pandemic_WHO |
|
2024-05-29 19:22 - Tokenize data: DatasetDict({ |
|
train: Dataset({ |
|
features: ['input_ids', 'attention_mask'], |
|
num_rows: 2152 |
|
}) |
|
test: Dataset({ |
|
features: ['input_ids', 'attention_mask'], |
|
num_rows: 8264 |
|
}) |
|
}) |
|
2024-05-29 19:22 - Split data into chunks:DatasetDict({ |
|
train: Dataset({ |
|
features: ['input_ids', 'attention_mask'], |
|
num_rows: 24863 |
|
}) |
|
test: Dataset({ |
|
features: ['input_ids', 'attention_mask'], |
|
num_rows: 198964 |
|
}) |
|
}) |
|
2024-05-29 19:22 - Setup PEFT |
|
2024-05-29 19:22 - Setup optimizer |
|
2024-05-29 19:22 - Start training |
|
2024-05-29 19:29 - Cuda check |
|
2024-05-29 19:29 - True |
|
2024-05-29 19:29 - 1 |
|
2024-05-29 19:29 - Configue Model and tokenizer |
|
2024-05-29 19:29 - Memory usage in 25.17 GB |
|
2024-05-29 19:29 - Dataset loaded successfully: |
|
train-Jingmei/Pandemic_Wiki |
|
test -Jingmei/Pandemic_WHO |
|
2024-05-29 19:29 - Tokenize data: DatasetDict({ |
|
train: Dataset({ |
|
features: ['input_ids', 'attention_mask'], |
|
num_rows: 2152 |
|
}) |
|
test: Dataset({ |
|
features: ['input_ids', 'attention_mask'], |
|
num_rows: 8264 |
|
}) |
|
}) |
|
2024-05-29 19:29 - Split data into chunks:DatasetDict({ |
|
train: Dataset({ |
|
features: ['input_ids', 'attention_mask'], |
|
num_rows: 24863 |
|
}) |
|
test: Dataset({ |
|
features: ['input_ids', 'attention_mask'], |
|
num_rows: 198964 |
|
}) |
|
}) |
|
2024-05-29 19:29 - Setup PEFT |
|
2024-05-29 19:29 - Setup optimizer |
|
2024-05-29 19:29 - Start training |
|
2024-05-29 22:44 - Training complete!!! |
|
2024-05-29 23:26 - Cuda check |
|
2024-05-29 23:26 - True |
|
2024-05-29 23:26 - 1 |
|
2024-05-29 23:26 - Configue Model and tokenizer |
|
2024-05-29 23:26 - Memory usage in 25.17 GB |
|
2024-05-29 23:26 - Dataset loaded successfully: |
|
train-Jingmei/Pandemic_CDC |
|
test -Jingmei/Pandemic_WHO |
|
2024-05-29 23:26 - Tokenize data: DatasetDict({ |
|
train: Dataset({ |
|
features: ['input_ids', 'attention_mask'], |
|
num_rows: 2152 |
|
}) |
|
test: Dataset({ |
|
features: ['input_ids', 'attention_mask'], |
|
num_rows: 8264 |
|
}) |
|
}) |
|
2024-05-29 23:26 - Split data into chunks:DatasetDict({ |
|
train: Dataset({ |
|
features: ['input_ids', 'attention_mask'], |
|
num_rows: 24863 |
|
}) |
|
test: Dataset({ |
|
features: ['input_ids', 'attention_mask'], |
|
num_rows: 198964 |
|
}) |
|
}) |
|
2024-05-29 23:26 - Setup PEFT |
|
2024-05-29 23:26 - Setup optimizer |
|
2024-05-29 23:26 - Start training |
|
2024-05-30 01:58 - Training complete!!! |
|
2024-05-30 02:00 - Cuda check |
|
2024-05-30 02:00 - True |
|
2024-05-30 02:00 - 1 |
|
2024-05-30 02:00 - Configue Model and tokenizer |
|
2024-05-30 02:00 - Memory usage in 25.17 GB |
|
2024-05-30 02:00 - Dataset loaded successfully: |
|
train-Jingmei/Pandemic_CDC |
|
test -Jingmei/Pandemic_WHO |
|
2024-05-30 02:03 - Tokenize data: DatasetDict({ |
|
train: Dataset({ |
|
features: ['input_ids', 'attention_mask'], |
|
num_rows: 15208 |
|
}) |
|
test: Dataset({ |
|
features: ['input_ids', 'attention_mask'], |
|
num_rows: 8264 |
|
}) |
|
}) |
|
2024-05-30 02:08 - Split data into chunks:DatasetDict({ |
|
train: Dataset({ |
|
features: ['input_ids', 'attention_mask'], |
|
num_rows: 364678 |
|
}) |
|
test: Dataset({ |
|
features: ['input_ids', 'attention_mask'], |
|
num_rows: 198964 |
|
}) |
|
}) |
|
2024-05-30 02:08 - Setup PEFT |
|
2024-05-30 02:08 - Setup optimizer |
|
2024-05-30 02:08 - Start training |
|
2024-05-30 17:35 - Training complete!!! |
|
2024-05-30 19:45 - Cuda check |
|
2024-05-30 19:45 - True |
|
2024-05-30 19:45 - 1 |
|
2024-05-30 19:45 - Configue Model and tokenizer |
|
2024-05-30 19:45 - Memory usage in 25.17 GB |
|
2024-05-30 19:45 - Dataset loaded successfully: |
|
train-Jingmei/Pandemic_ECDC |
|
test -Jingmei/Pandemic_WHO |
|
2024-05-30 19:46 - Tokenize data: DatasetDict({ |
|
train: Dataset({ |
|
features: ['input_ids', 'attention_mask'], |
|
num_rows: 7008 |
|
}) |
|
test: Dataset({ |
|
features: ['input_ids', 'attention_mask'], |
|
num_rows: 8264 |
|
}) |
|
}) |
|
2024-05-30 19:49 - Split data into chunks:DatasetDict({ |
|
train: Dataset({ |
|
features: ['input_ids', 'attention_mask'], |
|
num_rows: 103936 |
|
}) |
|
test: Dataset({ |
|
features: ['input_ids', 'attention_mask'], |
|
num_rows: 198964 |
|
}) |
|
}) |
|
2024-05-30 19:49 - Setup PEFT |
|
2024-05-30 19:49 - Setup optimizer |
|
2024-05-30 19:49 - Start training |
|
2024-05-30 19:49 - Training complete!!! |
|
2024-05-30 20:16 - Cuda check |
|
2024-05-30 20:16 - True |
|
2024-05-30 20:16 - 1 |
|
2024-05-30 20:16 - Configue Model and tokenizer |
|
2024-05-30 20:16 - Memory usage in 25.17 GB |
|
2024-05-30 20:16 - Dataset loaded successfully: |
|
train-Jingmei/Pandemic_ECDC |
|
test -Jingmei/Pandemic_WHO |
|
2024-05-30 20:16 - Tokenize data: DatasetDict({ |
|
train: Dataset({ |
|
features: ['input_ids', 'attention_mask'], |
|
num_rows: 7008 |
|
}) |
|
test: Dataset({ |
|
features: ['input_ids', 'attention_mask'], |
|
num_rows: 8264 |
|
}) |
|
}) |
|
2024-05-30 20:16 - Split data into chunks:DatasetDict({ |
|
train: Dataset({ |
|
features: ['input_ids', 'attention_mask'], |
|
num_rows: 103936 |
|
}) |
|
test: Dataset({ |
|
features: ['input_ids', 'attention_mask'], |
|
num_rows: 198964 |
|
}) |
|
}) |
|
2024-05-30 20:16 - Setup PEFT |
|
2024-05-30 20:16 - Setup optimizer |
|
2024-05-30 20:16 - Resume from checkpoint - ./trainer_Wiki_lora/checkpoint-560 |
|
2024-05-30 20:16 - Training complete!!! |
|
2024-05-30 20:30 - Cuda check |
|
2024-05-30 20:30 - True |
|
2024-05-30 20:30 - 1 |
|
2024-05-30 20:30 - Configue Model and tokenizer |
|
2024-05-30 20:31 - Memory usage in 25.17 GB |
|
2024-05-30 20:31 - Dataset loaded successfully: |
|
train-Jingmei/Pandemic_Books |
|
test -Jingmei/Pandemic_WHO |
|
2024-05-30 20:33 - Tokenize data: DatasetDict({ |
|
train: Dataset({ |
|
features: ['input_ids', 'attention_mask'], |
|
num_rows: 5966 |
|
}) |
|
test: Dataset({ |
|
features: ['input_ids', 'attention_mask'], |
|
num_rows: 8264 |
|
}) |
|
}) |
|
2024-05-30 20:36 - Split data into chunks:DatasetDict({ |
|
train: Dataset({ |
|
features: ['input_ids', 'attention_mask'], |
|
num_rows: 388208 |
|
}) |
|
test: Dataset({ |
|
features: ['input_ids', 'attention_mask'], |
|
num_rows: 198964 |
|
}) |
|
}) |
|
2024-05-30 20:36 - Setup PEFT |
|
2024-05-30 20:36 - Setup optimizer |
|
2024-05-30 20:36 - Resume from checkpoint |
|
2024-05-31 00:03 - Training complete!!! |
|
2024-05-31 00:20 - Cuda check |
|
2024-05-31 00:20 - True |
|
2024-05-31 00:20 - 1 |
|
2024-05-31 00:20 - Configue Model and tokenizer |
|
2024-05-31 00:20 - Memory usage in 25.17 GB |
|
2024-05-31 00:32 - Dataset loaded successfully: |
|
train-Jingmei/Pandemic_PMC |
|
test -Jingmei/Pandemic_WHO |
|
2024-05-31 01:26 - Tokenize data: DatasetDict({ |
|
train: Dataset({ |
|
features: ['input_ids', 'attention_mask'], |
|
num_rows: 469701 |
|
}) |
|
test: Dataset({ |
|
features: ['input_ids', 'attention_mask'], |
|
num_rows: 8264 |
|
}) |
|
}) |
|
2024-05-31 03:25 - Split data into chunks:DatasetDict({ |
|
train: Dataset({ |
|
features: ['input_ids', 'attention_mask'], |
|
num_rows: 13199845 |
|
}) |
|
test: Dataset({ |
|
features: ['input_ids', 'attention_mask'], |
|
num_rows: 198964 |
|
}) |
|
}) |
|
2024-05-31 03:25 - Setup PEFT |
|
2024-05-31 03:25 - Setup optimizer |
|
2024-05-31 03:25 - Resume from checkpoint |
|
|