The cache for model files in Transformers v4.22.0 has been updated. Migrating your old cache. This is a one-time only operation. You can interrupt this and resume the migration later on by calling `transformers.utils.move_cache()`. 0it [00:00, ?it/s] 0it [00:00, ?it/s] /opt/conda/lib/python3.10/site-packages/transformers/deepspeed.py:23: FutureWarning: transformers.deepspeed module is deprecated and will be removed in a future version. Please import deepspeed modules directly from transformers.integrations warnings.warn( 2024-07-08 23:40:11.901364: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered 2024-07-08 23:40:11.901476: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered 2024-07-08 23:40:12.037646: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. /opt/conda/lib/python3.10/site-packages/datasets/load.py:929: FutureWarning: The repository for data contains custom code which must be executed to correctly load the dataset. You can inspect the repository content at /kaggle/working/amr-tst-indo/AMRBART-id/fine-tune/data_interface/data.py You can avoid this message in future by passing the argument `trust_remote_code=True`. Passing `trust_remote_code=True` will be mandatory to load this dataset from the next major release of `datasets`. warnings.warn( Generating train split: 0 examples [00:00, ? examples/s] Generating train split: 1 examples [00:00, 3.88 examples/s] Generating train split: 1238 examples [00:00, 4413.17 examples/s] Generating train split: 3103 examples [00:00, 9215.76 examples/s] Generating train split: 5000 examples [00:00, 12212.22 examples/s] Generating train split: 6917 examples [00:00, 14396.35 examples/s] Generating train split: 8743 examples [00:00, 15592.99 examples/s] Generating train split: 10624 examples [00:00, 16576.68 examples/s] Generating train split: 12518 examples [00:00, 17294.16 examples/s] Generating train split: 14390 examples [00:01, 17722.72 examples/s] Generating train split: 16255 examples [00:01, 18000.93 examples/s] Generating train split: 18144 examples [00:01, 18266.76 examples/s] Generating train split: 20031 examples [00:01, 18445.34 examples/s] Generating train split: 22000 examples [00:01, 18592.67 examples/s] Generating train split: 24000 examples [00:01, 18643.93 examples/s] Generating train split: 26000 examples [00:01, 18720.36 examples/s] Generating train split: 28572 examples [00:01, 17966.60 examples/s] Generating train split: 31098 examples [00:01, 17567.47 examples/s] Generating train split: 33000 examples [00:02, 17780.66 examples/s] Generating train split: 35000 examples [00:02, 17998.09 examples/s] Generating train split: 37000 examples [00:02, 18205.04 examples/s] Generating train split: 39000 examples [00:02, 18353.06 examples/s] Generating train split: 40897 examples [00:02, 18520.89 examples/s] Generating train split: 43695 examples [00:02, 18565.96 examples/s] Generating train split: 45569 examples [00:02, 18609.57 examples/s] Generating train split: 48302 examples [00:02, 18465.48 examples/s] Generating train split: 51055 examples [00:03, 18425.68 examples/s] Generating train split: 53000 examples [00:03, 18353.57 examples/s] Generating train split: 55000 examples [00:03, 18412.27 examples/s] Generating train split: 56898 examples [00:03, 18558.86 examples/s] Generating train split: 59657 examples [00:03, 18494.69 examples/s] Generating train split: 61542 examples [00:03, 18584.74 examples/s] Generating train split: 63437 examples [00:03, 18679.22 examples/s] Generating train split: 65340 examples [00:03, 18773.84 examples/s] Generating train split: 68161 examples [00:03, 18783.21 examples/s] Generating train split: 71000 examples [00:04, 18763.67 examples/s] Generating train split: 73000 examples [00:04, 18754.05 examples/s] Generating train split: 75000 examples [00:04, 18734.06 examples/s] Generating train split: 77000 examples [00:04, 18703.78 examples/s] Generating train split: 79000 examples [00:04, 18733.53 examples/s] Generating train split: 81000 examples [00:04, 18740.79 examples/s] Generating train split: 83000 examples [00:04, 18673.70 examples/s] Generating train split: 85000 examples [00:04, 18696.05 examples/s] Generating train split: 87000 examples [00:04, 18626.21 examples/s] Generating train split: 88999 examples [00:05, 18811.30 examples/s] Generating train split: 91753 examples [00:05, 18639.98 examples/s] Generating train split: 92867 examples [00:05, 17484.13 examples/s] Running tokenizer on train dataset: 0%| | 0/92867 [00:00