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( 2024-07-14 08:43:45.068999: 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 2024-07-14 08:43:45.069097: 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 2024-07-14 08:43:45.179167: 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: 900 examples [00:00, 6387.15 examples/s] Generating train split: 2220 examples [00:00, 9815.64 examples/s] Generating train split: 4015 examples [00:00, 13212.94 examples/s] Generating train split: 5861 examples [00:00, 15172.75 examples/s] Generating train split: 7684 examples [00:00, 16232.50 examples/s] Generating train split: 9523 examples [00:00, 16948.52 examples/s] Generating train split: 11410 examples [00:00, 17565.17 examples/s] Generating train split: 14023 examples [00:00, 17502.61 examples/s] Generating train split: 15808 examples [00:00, 17346.06 examples/s] Generating train split: 17592 examples [00:01, 17483.60 examples/s] Generating train split: 19434 examples [00:01, 17751.28 examples/s] Generating train split: 21320 examples [00:01, 18070.43 examples/s] Generating train split: 23204 examples [00:01, 18293.39 examples/s] Generating train split: 25071 examples [00:01, 18401.97 examples/s] Generating train split: 27000 examples [00:01, 18435.90 examples/s] Generating train split: 29000 examples [00:01, 18505.72 examples/s] Generating train split: 30999 examples [00:01, 18740.92 examples/s] Generating train split: 33769 examples [00:01, 18628.11 examples/s] Generating train split: 36539 examples [00:02, 18568.61 examples/s] Generating train split: 39297 examples [00:02, 18502.83 examples/s] Generating train split: 41992 examples [00:02, 18312.98 examples/s] Generating train split: 43846 examples [00:02, 18363.85 examples/s] Generating train split: 45697 examples [00:02, 18400.24 examples/s] Generating train split: 47554 examples [00:02, 18442.95 examples/s] Generating train split: 50298 examples [00:02, 18386.03 examples/s] Generating train split: 53008 examples [00:03, 18272.99 examples/s] Generating train split: 54901 examples [00:03, 18431.63 examples/s] Generating train split: 57582 examples [00:03, 18236.20 examples/s] Generating train split: 60351 examples [00:03, 18306.67 examples/s] Generating train split: 62199 examples [00:03, 18345.48 examples/s] Generating train split: 64067 examples [00:03, 18428.67 examples/s] Generating train split: 66000 examples [00:03, 18498.50 examples/s] Generating train split: 67997 examples [00:03, 18702.40 examples/s] Generating train split: 70791 examples [00:03, 18671.13 examples/s] Generating train split: 73559 examples [00:04, 18593.01 examples/s] Generating train split: 76291 examples [00:04, 18464.65 examples/s] Generating train split: 79060 examples [00:04, 18460.17 examples/s] Generating train split: 81000 examples [00:04, 18456.65 examples/s] Generating train split: 82877 examples [00:04, 18532.86 examples/s] Generating train split: 85620 examples [00:04, 18443.12 examples/s] Generating train split: 88375 examples [00:04, 18415.40 examples/s] Generating train split: 91110 examples [00:05, 18353.51 examples/s] Generating train split: 92867 examples [00:05, 17906.92 examples/s] Running tokenizer on train dataset: 0%| | 0/92867 [00:00