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( 2024-07-15 17:01:05.602639: 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 2024-07-15 17:01:05.602754: 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 2024-07-15 17:01:05.746442: 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, 4.14 examples/s] Generating train split: 1150 examples [00:00, 4268.49 examples/s] Generating train split: 2925 examples [00:00, 8938.75 examples/s] Generating train split: 4475 examples [00:00, 11109.61 examples/s] Generating train split: 6020 examples [00:00, 12499.18 examples/s] Generating train split: 7686 examples [00:00, 13804.83 examples/s] Generating train split: 9217 examples [00:00, 14268.63 examples/s] Generating train split: 10877 examples [00:00, 14947.91 examples/s] Generating train split: 12454 examples [00:01, 15196.49 examples/s] Generating train split: 14085 examples [00:01, 15532.74 examples/s] Generating train split: 15806 examples [00:01, 16038.66 examples/s] Generating train split: 18244 examples [00:01, 16122.11 examples/s] Generating train split: 19956 examples [00:01, 16391.08 examples/s] Generating train split: 22358 examples [00:01, 16249.55 examples/s] Generating train split: 24000 examples [00:01, 16205.28 examples/s] Generating train split: 25735 examples [00:01, 16512.02 examples/s] Generating train split: 28188 examples [00:01, 16449.87 examples/s] Generating train split: 29914 examples [00:02, 16617.73 examples/s] Generating train split: 32393 examples [00:02, 16579.17 examples/s] Generating train split: 34063 examples [00:02, 16607.27 examples/s] Generating train split: 35794 examples [00:02, 16792.75 examples/s] Generating train split: 38211 examples [00:02, 16543.32 examples/s] Generating train split: 39946 examples [00:02, 16747.96 examples/s] Generating train split: 42424 examples [00:02, 16663.96 examples/s] Generating train split: 44151 examples [00:02, 16815.06 examples/s] Generating train split: 45916 examples [00:03, 17031.79 examples/s] Generating train split: 48309 examples [00:03, 16639.61 examples/s] Generating train split: 50000 examples [00:03, 16611.13 examples/s] Generating train split: 51724 examples [00:03, 16776.43 examples/s] Generating train split: 54093 examples [00:03, 16414.88 examples/s] Generating train split: 56367 examples [00:03, 15983.87 examples/s] Generating train split: 58528 examples [00:03, 15463.65 examples/s] Generating train split: 60657 examples [00:03, 15055.69 examples/s] Generating train split: 62231 examples [00:04, 15212.31 examples/s] Generating train split: 63916 examples [00:04, 15619.60 examples/s] Generating train split: 65581 examples [00:04, 15888.28 examples/s] Generating train split: 68000 examples [00:04, 15801.92 examples/s] Generating train split: 69654 examples [00:04, 15986.04 examples/s] Generating train split: 72064 examples [00:04, 16012.57 examples/s] Generating train split: 73813 examples [00:04, 16381.70 examples/s] Generating train split: 76215 examples [00:04, 16251.89 examples/s] Generating train split: 78000 examples [00:05, 16472.05 examples/s] Generating train split: 79784 examples [00:05, 16826.87 examples/s] Generating train split: 82312 examples [00:05, 16832.96 examples/s] Generating train split: 84947 examples [00:05, 16944.10 examples/s] Generating train split: 87449 examples [00:05, 16854.49 examples/s] Generating train split: 90000 examples [00:05, 16752.95 examples/s] Generating train split: 91715 examples [00:05, 16841.88 examples/s] Generating train split: 92867 examples [00:05, 15591.14 examples/s] Running tokenizer on train dataset: 0%| | 0/92867 [00:00