sbatch-4324537-06-10.16-33.log

Using the `WANDB_DISABLED` environment variable is deprecated and will be removed in v5. Use the --report_to flag to control the integrations used for logging result (for instance --report_to none).
06/10/2024 22:36:58 - WARNING - __main__ - Process rank: 0, device: cuda:0, n_gpu: 1, distributed training: False, 16-bits training: False
06/10/2024 22:36:58 - INFO - __main__ - Training/evaluation parameters Seq2SeqTrainingArguments(
_n_gpu=1,
adafactor=False,
adam_beta1=0.9,
adam_beta2=0.999,
adam_epsilon=1e-08,
auto_find_batch_size=False,
bf16=False,
bf16_full_eval=False,
data_seed=None,
dataloader_drop_last=False,
dataloader_num_workers=0,
dataloader_persistent_workers=False,
dataloader_pin_memory=True,
ddp_backend=None,
ddp_broadcast_buffers=None,
ddp_bucket_cap_mb=None,
ddp_find_unused_parameters=None,
ddp_timeout=1800,
debug=[],
deepspeed=None,
disable_tqdm=False,
dispatch_batches=None,
do_eval=False,
do_predict=True,
do_train=False,
eval_accumulation_steps=None,
eval_delay=0,
eval_steps=None,
evaluation_strategy=no,
fp16=False,
fp16_backend=auto,
fp16_full_eval=False,
fp16_opt_level=O1,
fsdp=[],
fsdp_config={'min_num_params': 0, 'xla': False, 'xla_fsdp_grad_ckpt': False},
fsdp_min_num_params=0,
fsdp_transformer_layer_cls_to_wrap=None,
full_determinism=False,
generation_config=None,
generation_max_length=None,
generation_num_beams=2,
gradient_accumulation_steps=1,
gradient_checkpointing=False,
gradient_checkpointing_kwargs=None,
greater_is_better=None,
group_by_length=False,
half_precision_backend=auto,
hub_always_push=False,
hub_model_id=None,
hub_private_repo=False,
hub_strategy=every_save,
hub_token=<HUB_TOKEN>,
ignore_data_skip=False,
include_inputs_for_metrics=False,
include_num_input_tokens_seen=False,
include_tokens_per_second=False,
jit_mode_eval=False,
label_names=None,
label_smoothing_factor=0.0,
learning_rate=5e-05,
length_column_name=input_length,
load_best_model_at_end=False,
local_rank=0,
log_level=passive,
log_level_replica=warning,
log_on_each_node=True,
logging_dir=/beegfs/scratch/user/blee/project_3/models/NLU.mt5-base.task_type-1.fine_tune.gpu_a100-40g+.node-1x1.bsz-64.epochs-22.metric-ema.metric_lang-all/checkpoint-30407/eval/NLU/runs/Jun10_22-36-57_tholus-7.int.europe.naverlabs.com,
logging_first_step=False,
logging_nan_inf_filter=True,
logging_steps=500,
logging_strategy=steps,
lr_scheduler_kwargs={},
lr_scheduler_type=linear,
max_grad_norm=1.0,
max_steps=-1,
metric_for_best_model=None,
mp_parameters=,
neftune_noise_alpha=None,
no_cuda=False,
num_train_epochs=3.0,
optim=adamw_torch,
optim_args=None,
output_dir=/beegfs/scratch/user/blee/project_3/models/NLU.mt5-base.task_type-1.fine_tune.gpu_a100-40g+.node-1x1.bsz-64.epochs-22.metric-ema.metric_lang-all/checkpoint-30407/eval/NLU,
overwrite_output_dir=False,
past_index=-1,
per_device_eval_batch_size=32,
per_device_train_batch_size=8,
predict_with_generate=True,
prediction_loss_only=False,
push_to_hub=False,
push_to_hub_model_id=None,
push_to_hub_organization=None,
push_to_hub_token=<PUSH_TO_HUB_TOKEN>,
ray_scope=last,
remove_unused_columns=True,
report_to=['tensorboard'],
resume_from_checkpoint=None,
run_name=/beegfs/scratch/user/blee/project_3/models/NLU.mt5-base.task_type-1.fine_tune.gpu_a100-40g+.node-1x1.bsz-64.epochs-22.metric-ema.metric_lang-all/checkpoint-30407/eval/NLU,
save_on_each_node=False,
save_only_model=False,
save_safetensors=True,
save_steps=500,
save_strategy=steps,
save_total_limit=None,
seed=42,
skip_memory_metrics=True,
sortish_sampler=False,
split_batches=False,
tf32=None,
torch_compile=False,
torch_compile_backend=None,
torch_compile_mode=None,
torchdynamo=None,
tpu_metrics_debug=False,
tpu_num_cores=None,
use_cpu=False,
use_ipex=False,
use_legacy_prediction_loop=False,
use_mps_device=False,
warmup_ratio=0.0,
warmup_steps=0,
weight_decay=0.0,
)
Loading Dataset Infos from /beegfs/scratch/user/blee/hugging-face/models/modules/datasets_modules/datasets/massive_slu/f9c095e36aa8a498aff90ba642b0b428b56191e41f2c80e78e378689cdb36617
06/10/2024 22:36:58 - INFO - datasets.info - Loading Dataset Infos from /beegfs/scratch/user/blee/hugging-face/models/modules/datasets_modules/datasets/massive_slu/f9c095e36aa8a498aff90ba642b0b428b56191e41f2c80e78e378689cdb36617
Overwrite dataset info from restored data version if exists.
06/10/2024 22:36:58 - INFO - datasets.builder - Overwrite dataset info from restored data version if exists.
Loading Dataset info from /beegfs/scratch/user/blee/hugging-face/models/datasets/massive_slu/multilingual-test/1.0.0/f9c095e36aa8a498aff90ba642b0b428b56191e41f2c80e78e378689cdb36617
06/10/2024 22:36:58 - INFO - datasets.info - Loading Dataset info from /beegfs/scratch/user/blee/hugging-face/models/datasets/massive_slu/multilingual-test/1.0.0/f9c095e36aa8a498aff90ba642b0b428b56191e41f2c80e78e378689cdb36617
Found cached dataset massive_slu (/beegfs/scratch/user/blee/hugging-face/models/datasets/massive_slu/multilingual-test/1.0.0/f9c095e36aa8a498aff90ba642b0b428b56191e41f2c80e78e378689cdb36617)
06/10/2024 22:36:58 - INFO - datasets.builder - Found cached dataset massive_slu (/beegfs/scratch/user/blee/hugging-face/models/datasets/massive_slu/multilingual-test/1.0.0/f9c095e36aa8a498aff90ba642b0b428b56191e41f2c80e78e378689cdb36617)
Loading Dataset info from /beegfs/scratch/user/blee/hugging-face/models/datasets/massive_slu/multilingual-test/1.0.0/f9c095e36aa8a498aff90ba642b0b428b56191e41f2c80e78e378689cdb36617
06/10/2024 22:36:58 - INFO - datasets.info - Loading Dataset info from /beegfs/scratch/user/blee/hugging-face/models/datasets/massive_slu/multilingual-test/1.0.0/f9c095e36aa8a498aff90ba642b0b428b56191e41f2c80e78e378689cdb36617
[INFO|configuration_utils.py:737] 2024-06-10 22:36:58,618 >> loading configuration file /beegfs/scratch/user/blee/project_3/models/NLU.mt5-base.task_type-1.fine_tune.gpu_a100-40g+.node-1x1.bsz-64.epochs-22.metric-ema.metric_lang-all/checkpoint-30407/config.json
[INFO|configuration_utils.py:802] 2024-06-10 22:36:58,633 >> Model config MT5Config {
  "_name_or_path": "/beegfs/scratch/user/blee/project_3/models/NLU.mt5-base.task_type-1.fine_tune.gpu_a100-40g+.node-1x1.bsz-64.epochs-22.metric-ema.metric_lang-all/checkpoint-30407",
  "architectures": [
    "MT5ForConditionalGeneration"
  ],
  "classifier_dropout": 0.0,
  "d_ff": 2048,
  "d_kv": 64,
  "d_model": 768,
  "decoder_start_token_id": 0,
  "dense_act_fn": "gelu_new",
  "dropout": 0.2,
  "dropout_rate": 0.1,
  "eos_token_id": 1,
  "feed_forward_proj": "gated-gelu",
  "initializer_factor": 1.0,
  "is_encoder_decoder": true,
  "is_gated_act": true,
  "layer_norm_epsilon": 1e-06,
  "model_type": "mt5",
  "num_decoder_layers": 12,
  "num_heads": 12,
  "num_layers": 12,
  "output_past": true,
  "pad_token_id": 0,
  "relative_attention_max_distance": 128,
  "relative_attention_num_buckets": 32,
  "tie_word_embeddings": false,
  "tokenizer_class": "T5Tokenizer",
  "torch_dtype": "float32",
  "transformers_version": "4.37.0.dev0",
  "use_cache": true,
  "vocab_size": 250112
}

[INFO|tokenization_utils_base.py:2024] 2024-06-10 22:36:58,656 >> loading file spiece.model
[INFO|tokenization_utils_base.py:2024] 2024-06-10 22:36:58,658 >> loading file tokenizer.json
[INFO|tokenization_utils_base.py:2024] 2024-06-10 22:36:58,660 >> loading file added_tokens.json
[INFO|tokenization_utils_base.py:2024] 2024-06-10 22:36:58,662 >> loading file special_tokens_map.json
[INFO|tokenization_utils_base.py:2024] 2024-06-10 22:36:58,665 >> loading file tokenizer_config.json
[INFO|modeling_utils.py:3373] 2024-06-10 22:36:59,181 >> loading weights file /beegfs/scratch/user/blee/project_3/models/NLU.mt5-base.task_type-1.fine_tune.gpu_a100-40g+.node-1x1.bsz-64.epochs-22.metric-ema.metric_lang-all/checkpoint-30407/model.safetensors
[INFO|configuration_utils.py:826] 2024-06-10 22:36:59,353 >> Generate config GenerationConfig {
  "decoder_start_token_id": 0,
  "eos_token_id": 1,
  "pad_token_id": 0
}

[INFO|modeling_utils.py:4224] 2024-06-10 22:37:04,608 >> All model checkpoint weights were used when initializing MT5ForConditionalGeneration.

[INFO|modeling_utils.py:4232] 2024-06-10 22:37:04,610 >> All the weights of MT5ForConditionalGeneration were initialized from the model checkpoint at /beegfs/scratch/user/blee/project_3/models/NLU.mt5-base.task_type-1.fine_tune.gpu_a100-40g+.node-1x1.bsz-64.epochs-22.metric-ema.metric_lang-all/checkpoint-30407.
If your task is similar to the task the model of the checkpoint was trained on, you can already use MT5ForConditionalGeneration for predictions without further training.
[INFO|configuration_utils.py:779] 2024-06-10 22:37:04,625 >> loading configuration file /beegfs/scratch/user/blee/project_3/models/NLU.mt5-base.task_type-1.fine_tune.gpu_a100-40g+.node-1x1.bsz-64.epochs-22.metric-ema.metric_lang-all/checkpoint-30407/generation_config.json
[INFO|configuration_utils.py:826] 2024-06-10 22:37:04,627 >> Generate config GenerationConfig {
  "decoder_start_token_id": 0,
  "eos_token_id": 1,
  "pad_token_id": 0
}


Running tokenizer on prediction dataset:   0%|          | 0/2974 [00:00<?, ? examples/s]Caching processed dataset at /beegfs/scratch/user/blee/hugging-face/models/datasets/massive_slu/multilingual-test/1.0.0/f9c095e36aa8a498aff90ba642b0b428b56191e41f2c80e78e378689cdb36617/cache-525dddbb93e613dc.arrow
06/10/2024 22:37:04 - INFO - datasets.arrow_dataset - Caching processed dataset at /beegfs/scratch/user/blee/hugging-face/models/datasets/massive_slu/multilingual-test/1.0.0/f9c095e36aa8a498aff90ba642b0b428b56191e41f2c80e78e378689cdb36617/cache-525dddbb93e613dc.arrow

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06/10/2024 22:37:05 - INFO - datasets.arrow_dataset - Caching processed dataset at /beegfs/scratch/user/blee/hugging-face/models/datasets/massive_slu/multilingual-test/1.0.0/f9c095e36aa8a498aff90ba642b0b428b56191e41f2c80e78e378689cdb36617/cache-0ff2f782f6ce550d.arrow

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06/10/2024 22:37:05 - INFO - datasets.arrow_dataset - Caching processed dataset at /beegfs/scratch/user/blee/hugging-face/models/datasets/massive_slu/multilingual-test/1.0.0/f9c095e36aa8a498aff90ba642b0b428b56191e41f2c80e78e378689cdb36617/cache-db517c5fbde3f93f.arrow

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06/10/2024 22:37:05 - INFO - datasets.arrow_dataset - Caching processed dataset at /beegfs/scratch/user/blee/hugging-face/models/datasets/massive_slu/multilingual-test/1.0.0/f9c095e36aa8a498aff90ba642b0b428b56191e41f2c80e78e378689cdb36617/cache-b1a23ce3c266ed7e.arrow

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06/10/2024 22:37:05 - INFO - datasets.arrow_dataset - Caching processed dataset at /beegfs/scratch/user/blee/hugging-face/models/datasets/massive_slu/multilingual-test/1.0.0/f9c095e36aa8a498aff90ba642b0b428b56191e41f2c80e78e378689cdb36617/cache-d14f4edcd32d40f9.arrow

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06/10/2024 22:37:06 - INFO - datasets.arrow_dataset - Caching processed dataset at /beegfs/scratch/user/blee/hugging-face/models/datasets/massive_slu/multilingual-test/1.0.0/f9c095e36aa8a498aff90ba642b0b428b56191e41f2c80e78e378689cdb36617/cache-c32b0d4edcd745d1.arrow

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06/10/2024 22:37:06 - INFO - datasets.arrow_dataset - Caching processed dataset at /beegfs/scratch/user/blee/hugging-face/models/datasets/massive_slu/multilingual-test/1.0.0/f9c095e36aa8a498aff90ba642b0b428b56191e41f2c80e78e378689cdb36617/cache-65b7a58a8f4b62ba.arrow

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06/10/2024 22:37:06 - INFO - datasets.arrow_dataset - Caching processed dataset at /beegfs/scratch/user/blee/hugging-face/models/datasets/massive_slu/multilingual-test/1.0.0/f9c095e36aa8a498aff90ba642b0b428b56191e41f2c80e78e378689cdb36617/cache-8571e8bc453b7162.arrow

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06/10/2024 22:37:06 - INFO - datasets.arrow_dataset - Caching processed dataset at /beegfs/scratch/user/blee/hugging-face/models/datasets/massive_slu/multilingual-test/1.0.0/f9c095e36aa8a498aff90ba642b0b428b56191e41f2c80e78e378689cdb36617/cache-c74febb30a64318a.arrow

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06/10/2024 22:37:06 - INFO - datasets.arrow_dataset - Caching processed dataset at /beegfs/scratch/user/blee/hugging-face/models/datasets/massive_slu/multilingual-test/1.0.0/f9c095e36aa8a498aff90ba642b0b428b56191e41f2c80e78e378689cdb36617/cache-4c2b08ae5bc14226.arrow

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06/10/2024 22:37:07 - INFO - datasets.arrow_dataset - Caching processed dataset at /beegfs/scratch/user/blee/hugging-face/models/datasets/massive_slu/multilingual-test/1.0.0/f9c095e36aa8a498aff90ba642b0b428b56191e41f2c80e78e378689cdb36617/cache-5a36e19b80c96113.arrow

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06/10/2024 22:37:07 - INFO - datasets.arrow_dataset - Caching processed dataset at /beegfs/scratch/user/blee/hugging-face/models/datasets/massive_slu/multilingual-test/1.0.0/f9c095e36aa8a498aff90ba642b0b428b56191e41f2c80e78e378689cdb36617/cache-b7c60de80d7c7840.arrow

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06/10/2024 22:37:07 - INFO - datasets.arrow_dataset - Caching processed dataset at /beegfs/scratch/user/blee/hugging-face/models/datasets/massive_slu/multilingual-test/1.0.0/f9c095e36aa8a498aff90ba642b0b428b56191e41f2c80e78e378689cdb36617/cache-ae561d58d8bc6128.arrow

Running tokenizer on prediction dataset: 100%|██████████| 2974/2974 [00:00<00:00, 26368.84 examples/s]
Running tokenizer on prediction dataset: 100%|██████████| 2974/2974 [00:00<00:00, 23629.43 examples/s]
06/10/2024 22:37:09 - WARNING - accelerate.utils.other - Detected kernel version 4.18.0, which is below the recommended minimum of 5.5.0; this can cause the process to hang. It is recommended to upgrade the kernel to the minimum version or higher.
06/10/2024 22:37:10 - INFO - __main__ - *** Predict ***
06/10/2024 22:37:10 - INFO - __main__ - *** test_en_US ***
[INFO|trainer.py:718] 2024-06-10 22:37:10,687 >> The following columns in the test set don't have a corresponding argument in `MT5ForConditionalGeneration.forward` and have been ignored: intent_str, annot_utt, locale, id. If intent_str, annot_utt, locale, id are not expected by `MT5ForConditionalGeneration.forward`,  you can safely ignore this message.
[INFO|trainer.py:3199] 2024-06-10 22:37:10,695 >> ***** Running Prediction *****
[INFO|trainer.py:3201] 2024-06-10 22:37:10,696 >>   Num examples = 2974
[INFO|trainer.py:3204] 2024-06-10 22:37:10,697 >>   Batch size = 32
[WARNING|logging.py:314] 2024-06-10 22:37:10,704 >> You're using a T5TokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.

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***** predict_test_en_US metrics *****
  predict_ex_match_acc         =     0.7317
  predict_ex_match_acc_stderr  =     0.0081
  predict_intent_acc           =     0.8894
  predict_intent_acc_stderr    =     0.0058
  predict_loss                 =     0.1316
  predict_runtime              = 0:00:44.50
  predict_samples              =       2974
  predict_samples_per_second   =     66.819
  predict_slot_micro_f1        =     0.8224
  predict_slot_micro_f1_stderr =     0.0027
  predict_steps_per_second     =      2.089
06/10/2024 22:37:55 - INFO - __main__ - *** test_es_ES ***
[INFO|trainer.py:718] 2024-06-10 22:37:55,406 >> The following columns in the test set don't have a corresponding argument in `MT5ForConditionalGeneration.forward` and have been ignored: intent_str, annot_utt, locale, id. If intent_str, annot_utt, locale, id are not expected by `MT5ForConditionalGeneration.forward`,  you can safely ignore this message.
[INFO|trainer.py:3199] 2024-06-10 22:37:55,409 >> ***** Running Prediction *****
[INFO|trainer.py:3201] 2024-06-10 22:37:55,409 >>   Num examples = 2974
[INFO|trainer.py:3204] 2024-06-10 22:37:55,410 >>   Batch size = 32

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***** predict_test_es_ES metrics *****
  predict_ex_match_acc         =     0.6722
  predict_ex_match_acc_stderr  =     0.0086
  predict_intent_acc           =     0.8692
  predict_intent_acc_stderr    =     0.0062
  predict_loss                 =      0.159
  predict_runtime              = 0:00:48.47
  predict_samples              =       2974
  predict_samples_per_second   =     61.347
  predict_slot_micro_f1        =       0.76
  predict_slot_micro_f1_stderr =     0.0029
  predict_steps_per_second     =      1.918
06/10/2024 22:38:44 - INFO - __main__ - *** test_de_DE ***
[INFO|trainer.py:718] 2024-06-10 22:38:44,125 >> The following columns in the test set don't have a corresponding argument in `MT5ForConditionalGeneration.forward` and have been ignored: intent_str, annot_utt, locale, id. If intent_str, annot_utt, locale, id are not expected by `MT5ForConditionalGeneration.forward`,  you can safely ignore this message.
[INFO|trainer.py:3199] 2024-06-10 22:38:44,127 >> ***** Running Prediction *****
[INFO|trainer.py:3201] 2024-06-10 22:38:44,128 >>   Num examples = 2974
[INFO|trainer.py:3204] 2024-06-10 22:38:44,128 >>   Batch size = 32

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***** predict_test_de_DE metrics *****
  predict_ex_match_acc         =      0.696
  predict_ex_match_acc_stderr  =     0.0084
  predict_intent_acc           =     0.8665
  predict_intent_acc_stderr    =     0.0062
  predict_loss                 =     0.1518
  predict_runtime              = 0:00:42.34
  predict_samples              =       2974
  predict_samples_per_second   =     70.231
  predict_slot_micro_f1        =     0.7999
  predict_slot_micro_f1_stderr =     0.0029
  predict_steps_per_second     =      2.196
06/10/2024 22:39:26 - INFO - __main__ - *** test_fr_FR ***
[INFO|trainer.py:718] 2024-06-10 22:39:26,669 >> The following columns in the test set don't have a corresponding argument in `MT5ForConditionalGeneration.forward` and have been ignored: intent_str, annot_utt, locale, id. If intent_str, annot_utt, locale, id are not expected by `MT5ForConditionalGeneration.forward`,  you can safely ignore this message.
[INFO|trainer.py:3199] 2024-06-10 22:39:26,671 >> ***** Running Prediction *****
[INFO|trainer.py:3201] 2024-06-10 22:39:26,672 >>   Num examples = 2974
[INFO|trainer.py:3204] 2024-06-10 22:39:26,672 >>   Batch size = 32

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***** predict_test_fr_FR metrics *****
  predict_ex_match_acc         =     0.6725
  predict_ex_match_acc_stderr  =     0.0086
  predict_intent_acc           =     0.8716
  predict_intent_acc_stderr    =     0.0061
  predict_loss                 =     0.1494
  predict_runtime              = 0:00:47.79
  predict_samples              =       2974
  predict_samples_per_second   =     62.224
  predict_slot_micro_f1        =       0.76
  predict_slot_micro_f1_stderr =     0.0029
  predict_steps_per_second     =      1.946
06/10/2024 22:40:14 - INFO - __main__ - *** test_pt_PT ***
[INFO|trainer.py:718] 2024-06-10 22:40:14,689 >> The following columns in the test set don't have a corresponding argument in `MT5ForConditionalGeneration.forward` and have been ignored: intent_str, annot_utt, locale, id. If intent_str, annot_utt, locale, id are not expected by `MT5ForConditionalGeneration.forward`,  you can safely ignore this message.
[INFO|trainer.py:3199] 2024-06-10 22:40:14,691 >> ***** Running Prediction *****
[INFO|trainer.py:3201] 2024-06-10 22:40:14,692 >>   Num examples = 2974
[INFO|trainer.py:3204] 2024-06-10 22:40:14,692 >>   Batch size = 32

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***** predict_test_pt_PT metrics *****
  predict_ex_match_acc         =      0.688
  predict_ex_match_acc_stderr  =     0.0085
  predict_intent_acc           =     0.8742
  predict_intent_acc_stderr    =     0.0061
  predict_loss                 =     0.1455
  predict_runtime              = 0:00:46.29
  predict_samples              =       2974
  predict_samples_per_second   =      64.24
  predict_slot_micro_f1        =      0.777
  predict_slot_micro_f1_stderr =     0.0029
  predict_steps_per_second     =      2.009
06/10/2024 22:41:01 - INFO - __main__ - *** test_pl_PL ***
[INFO|trainer.py:718] 2024-06-10 22:41:01,217 >> The following columns in the test set don't have a corresponding argument in `MT5ForConditionalGeneration.forward` and have been ignored: intent_str, annot_utt, locale, id. If intent_str, annot_utt, locale, id are not expected by `MT5ForConditionalGeneration.forward`,  you can safely ignore this message.
[INFO|trainer.py:3199] 2024-06-10 22:41:01,222 >> ***** Running Prediction *****
[INFO|trainer.py:3201] 2024-06-10 22:41:01,222 >>   Num examples = 2974
[INFO|trainer.py:3204] 2024-06-10 22:41:01,223 >>   Batch size = 32

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***** predict_test_pl_PL metrics *****
  predict_ex_match_acc         =     0.6577
  predict_ex_match_acc_stderr  =     0.0087
  predict_intent_acc           =     0.8753
  predict_intent_acc_stderr    =     0.0061
  predict_loss                 =     0.1657
  predict_runtime              = 0:00:41.03
  predict_samples              =       2974
  predict_samples_per_second   =     72.472
  predict_slot_micro_f1        =     0.7381
  predict_slot_micro_f1_stderr =     0.0034
  predict_steps_per_second     =      2.266
06/10/2024 22:41:42 - INFO - __main__ - *** test_nl_NL ***
[INFO|trainer.py:718] 2024-06-10 22:41:42,453 >> The following columns in the test set don't have a corresponding argument in `MT5ForConditionalGeneration.forward` and have been ignored: intent_str, annot_utt, locale, id. If intent_str, annot_utt, locale, id are not expected by `MT5ForConditionalGeneration.forward`,  you can safely ignore this message.
[INFO|trainer.py:3199] 2024-06-10 22:41:42,456 >> ***** Running Prediction *****
[INFO|trainer.py:3201] 2024-06-10 22:41:42,456 >>   Num examples = 2974
[INFO|trainer.py:3204] 2024-06-10 22:41:42,457 >>   Batch size = 32

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***** predict_test_nl_NL metrics *****
  predict_ex_match_acc         =     0.6974
  predict_ex_match_acc_stderr  =     0.0084
  predict_intent_acc           =     0.8769
  predict_intent_acc_stderr    =      0.006
  predict_loss                 =     0.1494
  predict_runtime              = 0:00:44.48
  predict_samples              =       2974
  predict_samples_per_second   =     66.861
  predict_slot_micro_f1        =     0.7816
  predict_slot_micro_f1_stderr =     0.0029
  predict_steps_per_second     =      2.091
06/10/2024 22:42:27 - INFO - __main__ - *** test_hu_HU ***
[INFO|trainer.py:718] 2024-06-10 22:42:27,132 >> The following columns in the test set don't have a corresponding argument in `MT5ForConditionalGeneration.forward` and have been ignored: intent_str, annot_utt, locale, id. If intent_str, annot_utt, locale, id are not expected by `MT5ForConditionalGeneration.forward`,  you can safely ignore this message.
[INFO|trainer.py:3199] 2024-06-10 22:42:27,134 >> ***** Running Prediction *****
[INFO|trainer.py:3201] 2024-06-10 22:42:27,134 >>   Num examples = 2974
[INFO|trainer.py:3204] 2024-06-10 22:42:27,135 >>   Batch size = 32

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***** predict_test_hu_HU metrics *****
  predict_ex_match_acc         =     0.6866
  predict_ex_match_acc_stderr  =     0.0085
  predict_intent_acc           =     0.8668
  predict_intent_acc_stderr    =     0.0062
  predict_loss                 =     0.1564
  predict_runtime              = 0:00:41.00
  predict_samples              =       2974
  predict_samples_per_second   =     72.526
  predict_slot_micro_f1        =     0.7878
  predict_slot_micro_f1_stderr =     0.0031
  predict_steps_per_second     =      2.268
06/10/2024 22:43:08 - INFO - __main__ - *** test_ru_RU ***
[INFO|trainer.py:718] 2024-06-10 22:43:08,343 >> The following columns in the test set don't have a corresponding argument in `MT5ForConditionalGeneration.forward` and have been ignored: intent_str, annot_utt, locale, id. If intent_str, annot_utt, locale, id are not expected by `MT5ForConditionalGeneration.forward`,  you can safely ignore this message.
[INFO|trainer.py:3199] 2024-06-10 22:43:08,345 >> ***** Running Prediction *****
[INFO|trainer.py:3201] 2024-06-10 22:43:08,345 >>   Num examples = 2974
[INFO|trainer.py:3204] 2024-06-10 22:43:08,346 >>   Batch size = 32

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***** predict_test_ru_RU metrics *****
  predict_ex_match_acc         =     0.7028
  predict_ex_match_acc_stderr  =     0.0084
  predict_intent_acc           =     0.8769
  predict_intent_acc_stderr    =      0.006
  predict_loss                 =     0.1495
  predict_runtime              = 0:00:42.44
  predict_samples              =       2974
  predict_samples_per_second   =     70.064
  predict_slot_micro_f1        =     0.7874
  predict_slot_micro_f1_stderr =     0.0031
  predict_steps_per_second     =      2.191
06/10/2024 22:43:50 - INFO - __main__ - *** test_tr_TR ***
[INFO|trainer.py:718] 2024-06-10 22:43:50,993 >> The following columns in the test set don't have a corresponding argument in `MT5ForConditionalGeneration.forward` and have been ignored: intent_str, annot_utt, locale, id. If intent_str, annot_utt, locale, id are not expected by `MT5ForConditionalGeneration.forward`,  you can safely ignore this message.
[INFO|trainer.py:3199] 2024-06-10 22:43:50,995 >> ***** Running Prediction *****
[INFO|trainer.py:3201] 2024-06-10 22:43:50,996 >>   Num examples = 2974
[INFO|trainer.py:3204] 2024-06-10 22:43:50,996 >>   Batch size = 32

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***** predict_test_tr_TR metrics *****
  predict_ex_match_acc         =     0.6856
  predict_ex_match_acc_stderr  =     0.0085
  predict_intent_acc           =     0.8675
  predict_intent_acc_stderr    =     0.0062
  predict_loss                 =     0.1582
  predict_runtime              = 0:00:39.89
  predict_samples              =       2974
  predict_samples_per_second   =      74.54
  predict_slot_micro_f1        =     0.7798
  predict_slot_micro_f1_stderr =     0.0033
  predict_steps_per_second     =      2.331
06/10/2024 22:44:31 - INFO - __main__ - *** test_vi_VN ***
[INFO|trainer.py:718] 2024-06-10 22:44:31,087 >> The following columns in the test set don't have a corresponding argument in `MT5ForConditionalGeneration.forward` and have been ignored: intent_str, annot_utt, locale, id. If intent_str, annot_utt, locale, id are not expected by `MT5ForConditionalGeneration.forward`,  you can safely ignore this message.
[INFO|trainer.py:3199] 2024-06-10 22:44:31,090 >> ***** Running Prediction *****
[INFO|trainer.py:3201] 2024-06-10 22:44:31,091 >>   Num examples = 2974
[INFO|trainer.py:3204] 2024-06-10 22:44:31,091 >>   Batch size = 32

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***** predict_test_vi_VN metrics *****
  predict_ex_match_acc         =     0.6557
  predict_ex_match_acc_stderr  =     0.0087
  predict_intent_acc           =     0.8608
  predict_intent_acc_stderr    =     0.0063
  predict_loss                 =       0.13
  predict_runtime              = 0:00:58.58
  predict_samples              =       2974
  predict_samples_per_second   =     50.767
  predict_slot_micro_f1        =     0.7441
  predict_slot_micro_f1_stderr =     0.0027
  predict_steps_per_second     =      1.588
06/10/2024 22:45:29 - INFO - __main__ - *** test_ar_SA ***
[INFO|trainer.py:718] 2024-06-10 22:45:29,940 >> The following columns in the test set don't have a corresponding argument in `MT5ForConditionalGeneration.forward` and have been ignored: intent_str, annot_utt, locale, id. If intent_str, annot_utt, locale, id are not expected by `MT5ForConditionalGeneration.forward`,  you can safely ignore this message.
[INFO|trainer.py:3199] 2024-06-10 22:45:29,942 >> ***** Running Prediction *****
[INFO|trainer.py:3201] 2024-06-10 22:45:29,943 >>   Num examples = 2974
[INFO|trainer.py:3204] 2024-06-10 22:45:29,943 >>   Batch size = 32

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***** predict_test_ar_SA metrics *****
  predict_ex_match_acc         =     0.6422
  predict_ex_match_acc_stderr  =     0.0088
  predict_intent_acc           =     0.8255
  predict_intent_acc_stderr    =      0.007
  predict_loss                 =     0.1855
  predict_runtime              = 0:00:40.74
  predict_samples              =       2974
  predict_samples_per_second   =     72.983
  predict_slot_micro_f1        =     0.7601
  predict_slot_micro_f1_stderr =     0.0034
  predict_steps_per_second     =      2.282
06/10/2024 22:46:10 - INFO - __main__ - *** test_ko_KR ***
[INFO|trainer.py:718] 2024-06-10 22:46:10,882 >> The following columns in the test set don't have a corresponding argument in `MT5ForConditionalGeneration.forward` and have been ignored: intent_str, annot_utt, locale, id. If intent_str, annot_utt, locale, id are not expected by `MT5ForConditionalGeneration.forward`,  you can safely ignore this message.
[INFO|trainer.py:3199] 2024-06-10 22:46:10,885 >> ***** Running Prediction *****
[INFO|trainer.py:3201] 2024-06-10 22:46:10,886 >>   Num examples = 2974
[INFO|trainer.py:3204] 2024-06-10 22:46:10,886 >>   Batch size = 32

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***** predict_test_ko_KR metrics *****
  predict_ex_match_acc         =     0.6967
  predict_ex_match_acc_stderr  =     0.0084
  predict_intent_acc           =     0.8642
  predict_intent_acc_stderr    =     0.0063
  predict_loss                 =     0.1606
  predict_runtime              = 0:00:35.57
  predict_samples              =       2974
  predict_samples_per_second   =     83.608
  predict_slot_micro_f1        =     0.8051
  predict_slot_micro_f1_stderr =     0.0033
  predict_steps_per_second     =      2.614