NLU-Speech-MASSIVE-finetune / sbatch-4324537-06-10.16-33.log
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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
Running tokenizer on prediction dataset: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 2974/2974 [00:00<00:00, 10152.18 examples/s] Running tokenizer on prediction dataset: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 2974/2974 [00:00<00:00, 9881.17 examples/s]
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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
Running tokenizer on prediction dataset: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 2974/2974 [00:00<00:00, 23379.87 examples/s] Running tokenizer on prediction dataset: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 2974/2974 [00:00<00:00, 22426.85 examples/s]
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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
Running tokenizer on prediction dataset: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 2974/2974 [00:00<00:00, 24733.67 examples/s] Running tokenizer on prediction dataset: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 2974/2974 [00:00<00:00, 23464.22 examples/s]
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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
Running tokenizer on prediction dataset: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 2974/2974 [00:00<00:00, 11672.89 examples/s] Running tokenizer on prediction dataset: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 2974/2974 [00:00<00:00, 11022.17 examples/s]
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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
Running tokenizer on prediction dataset: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 2974/2974 [00:00<00:00, 12128.52 examples/s] Running tokenizer on prediction dataset: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 2974/2974 [00:00<00:00, 11483.51 examples/s]
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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
Running tokenizer on prediction dataset: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 2974/2974 [00:00<00:00, 23271.39 examples/s] Running tokenizer on prediction dataset: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 2974/2974 [00:00<00:00, 21331.30 examples/s]
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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
Running tokenizer on prediction dataset: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 2974/2974 [00:00<00:00, 23459.63 examples/s] Running tokenizer on prediction dataset: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 2974/2974 [00:00<00:00, 18122.89 examples/s]
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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
Running tokenizer on prediction dataset: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 2974/2974 [00:00<00:00, 11319.17 examples/s] Running tokenizer on prediction dataset: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 2974/2974 [00:00<00:00, 10715.31 examples/s]
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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
Running tokenizer on prediction dataset: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 2974/2974 [00:00<00:00, 24163.00 examples/s] Running tokenizer on prediction dataset: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 2974/2974 [00:00<00:00, 20604.87 examples/s]
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-5a36e19b80c96113.arrow
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
Running tokenizer on prediction dataset: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 2974/2974 [00:00<00:00, 20147.34 examples/s] Running tokenizer on prediction dataset: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 2974/2974 [00:00<00:00, 12138.36 examples/s]
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-b7c60de80d7c7840.arrow
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
Running tokenizer on prediction dataset: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 2974/2974 [00:00<00:00, 25152.92 examples/s] Running tokenizer on prediction dataset: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 2974/2974 [00:00<00:00, 22962.73 examples/s]
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-ae561d58d8bc6128.arrow
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%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 2974/2974 [00:00<00:00, 26368.84 examples/s] Running tokenizer on prediction dataset: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 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
0%| | 0/93 [00:00<?, ?it/s] 2%|▏ | 2/93 [00:00<00:22, 3.96it/s] 3%|β–Ž | 3/93 [00:00<00:27, 3.25it/s] 4%|▍ | 4/93 [00:01<00:29, 3.01it/s] 5%|β–Œ | 5/93 [00:01<00:35, 2.46it/s] 6%|β–‹ | 6/93 [00:02<00:36, 2.41it/s] 8%|β–Š | 7/93 [00:02<00:35, 2.44it/s] 9%|β–Š | 8/93 [00:03<00:35, 2.43it/s] 10%|β–‰ | 9/93 [00:03<00:37, 2.24it/s] 11%|β–ˆ | 10/93 [00:04<00:36, 2.27it/s] 12%|β–ˆβ– | 11/93 [00:04<00:35, 2.32it/s] 13%|β–ˆβ–Ž | 12/93 [00:04<00:34, 2.33it/s] 14%|β–ˆβ– | 13/93 [00:05<00:35, 2.27it/s] 15%|β–ˆβ–Œ | 14/93 [00:06<00:40, 1.93it/s] 16%|β–ˆβ–Œ | 15/93 [00:06<00:39, 1.96it/s] 17%|β–ˆβ–‹ | 16/93 [00:07<00:39, 1.97it/s] 18%|β–ˆβ–Š | 17/93 [00:07<00:40, 1.90it/s] 19%|β–ˆβ–‰ | 18/93 [00:08<00:37, 1.98it/s] 20%|β–ˆβ–ˆ | 19/93 [00:08<00:38, 1.94it/s] 22%|β–ˆβ–ˆβ– | 20/93 [00:09<00:36, 2.02it/s] 23%|β–ˆβ–ˆβ–Ž | 21/93 [00:09<00:37, 1.93it/s] 24%|β–ˆβ–ˆβ–Ž | 22/93 [00:10<00:36, 1.93it/s] 25%|β–ˆβ–ˆβ– | 23/93 [00:10<00:35, 1.99it/s] 26%|β–ˆβ–ˆβ–Œ | 24/93 [00:10<00:32, 2.12it/s] 27%|β–ˆβ–ˆβ–‹ | 25/93 [00:12<01:01, 1.11it/s] 28%|β–ˆβ–ˆβ–Š | 26/93 [00:13<00:51, 1.30it/s] 29%|β–ˆβ–ˆβ–‰ | 27/93 [00:13<00:42, 1.55it/s] 30%|β–ˆβ–ˆβ–ˆ | 28/93 [00:14<00:37, 1.73it/s] 31%|β–ˆβ–ˆβ–ˆ | 29/93 [00:14<00:33, 1.89it/s] 32%|β–ˆβ–ˆβ–ˆβ– | 30/93 [00:14<00:31, 1.98it/s] 33%|β–ˆβ–ˆβ–ˆβ–Ž | 31/93 [00:15<00:33, 1.86it/s] 34%|β–ˆβ–ˆβ–ˆβ– | 32/93 [00:16<00:30, 2.01it/s] 35%|β–ˆβ–ˆβ–ˆβ–Œ | 33/93 [00:16<00:30, 1.98it/s] 37%|β–ˆβ–ˆβ–ˆβ–‹ | 34/93 [00:17<00:30, 1.93it/s] 38%|β–ˆβ–ˆβ–ˆβ–Š | 35/93 [00:17<00:28, 2.04it/s] 39%|β–ˆβ–ˆβ–ˆβ–Š | 36/93 [00:17<00:25, 2.24it/s] 40%|β–ˆβ–ˆβ–ˆβ–‰ | 37/93 [00:18<00:22, 2.47it/s] 41%|β–ˆβ–ˆβ–ˆβ–ˆ | 38/93 [00:18<00:24, 2.23it/s] 42%|β–ˆβ–ˆβ–ˆβ–ˆβ– | 39/93 [00:19<00:23, 2.28it/s] 43%|β–ˆβ–ˆβ–ˆβ–ˆβ–Ž | 40/93 [00:19<00:24, 2.14it/s] 44%|β–ˆβ–ˆβ–ˆβ–ˆβ– | 41/93 [00:20<00:26, 1.97it/s] 45%|β–ˆβ–ˆβ–ˆβ–ˆβ–Œ | 42/93 [00:20<00:27, 1.83it/s] 46%|β–ˆβ–ˆβ–ˆβ–ˆβ–Œ | 43/93 [00:21<00:28, 1.78it/s] 47%|β–ˆβ–ˆβ–ˆβ–ˆβ–‹ | 44/93 [00:22<00:26, 1.82it/s] 48%|β–ˆβ–ˆβ–ˆβ–ˆβ–Š | 45/93 [00:22<00:24, 1.95it/s] 49%|β–ˆβ–ˆβ–ˆβ–ˆβ–‰ | 46/93 [00:22<00:24, 1.94it/s] 51%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆ | 47/93 [00:23<00:25, 1.81it/s] 52%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ– | 48/93 [00:24<00:23, 1.92it/s] 53%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Ž | 49/93 [00:24<00:21, 2.03it/s] 54%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ– | 50/93 [00:24<00:20, 2.06it/s] 55%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ– | 51/93 [00:25<00:21, 2.00it/s] 56%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Œ | 52/93 [00:26<00:22, 1.86it/s] 57%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‹ | 53/93 [00:26<00:20, 1.91it/s] 58%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Š | 54/93 [00:27<00:20, 1.92it/s] 59%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‰ | 55/93 [00:27<00:19, 1.98it/s] 60%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ | 56/93 [00:27<00:17, 2.15it/s] 61%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ– | 57/93 [00:28<00:17, 2.03it/s] 62%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ– | 58/93 [00:28<00:16, 2.07it/s] 63%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Ž | 59/93 [00:29<00:15, 2.14it/s] 65%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ– | 60/93 [00:29<00:16, 2.01it/s] 66%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Œ | 61/93 [00:30<00:15, 2.08it/s] 67%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‹ | 62/93 [00:30<00:14, 2.11it/s] 68%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Š | 63/93 [00:31<00:14, 2.10it/s] 69%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‰ | 64/93 [00:33<00:27, 1.06it/s] 70%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‰ | 65/93 [00:33<00:22, 1.23it/s] 71%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ | 66/93 [00:34<00:20, 1.34it/s] 72%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ– | 67/93 [00:34<00:17, 1.52it/s] 73%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Ž | 68/93 [00:35<00:16, 1.49it/s] 74%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ– | 69/93 [00:36<00:14, 1.62it/s] 75%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Œ | 70/93 [00:36<00:13, 1.72it/s] 76%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‹ | 71/93 [00:37<00:12, 1.80it/s] 77%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‹ | 72/93 [00:37<00:11, 1.86it/s] 78%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Š | 73/93 [00:38<00:10, 1.91it/s] 80%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‰ | 74/93 [00:38<00:09, 1.91it/s] 81%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ | 75/93 [00:39<00:08, 2.03it/s] 82%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ– | 76/93 [00:39<00:08, 2.07it/s] 83%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Ž | 77/93 [00:39<00:07, 2.20it/s] 84%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ– | 78/93 [00:40<00:06, 2.29it/s] 85%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ– | 79/93 [00:40<00:07, 1.95it/s] 86%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Œ | 80/93 [00:41<00:07, 1.86it/s] 87%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‹ | 81/93 [00:42<00:06, 1.93it/s] 88%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Š | 82/93 [00:42<00:05, 2.11it/s] 89%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‰ | 83/93 [00:42<00:04, 2.06it/s] 90%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ | 84/93 [00:43<00:04, 1.88it/s] 91%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–| 85/93 [00:44<00:04, 1.99it/s] 92%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–| 86/93 [00:44<00:03, 2.09it/s] 94%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Ž| 87/93 [00:44<00:02, 2.01it/s] 95%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–| 88/93 [00:45<00:02, 2.00it/s] 96%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Œ| 89/93 [00:45<00:01, 2.04it/s] 97%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‹| 90/93 [00:46<00:01, 2.09it/s] 98%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Š| 91/93 [00:46<00:01, 2.00it/s] 99%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‰| 92/93 [00:47<00:00, 2.03it/s] 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 93/93 [00:47<00:00, 2.13it/s] 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 93/93 [00:48<00:00, 1.94it/s]
***** 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
0%| | 0/93 [00:00<?, ?it/s] 2%|▏ | 2/93 [00:00<00:20, 4.47it/s] 3%|β–Ž | 3/93 [00:00<00:24, 3.64it/s] 4%|▍ | 4/93 [00:01<00:26, 3.31it/s] 5%|β–Œ | 5/93 [00:01<00:30, 2.91it/s] 6%|β–‹ | 6/93 [00:01<00:31, 2.74it/s] 8%|β–Š | 7/93 [00:02<00:31, 2.69it/s] 9%|β–Š | 8/93 [00:02<00:31, 2.66it/s] 10%|β–‰ | 9/93 [00:03<00:33, 2.54it/s] 11%|β–ˆ | 10/93 [00:03<00:32, 2.53it/s] 12%|β–ˆβ– | 11/93 [00:03<00:32, 2.55it/s] 13%|β–ˆβ–Ž | 12/93 [00:04<00:31, 2.56it/s] 14%|β–ˆβ– | 13/93 [00:04<00:32, 2.49it/s] 15%|β–ˆβ–Œ | 14/93 [00:05<00:36, 2.14it/s] 16%|β–ˆβ–Œ | 15/93 [00:05<00:36, 2.16it/s] 17%|β–ˆβ–‹ | 16/93 [00:06<00:32, 2.34it/s] 18%|β–ˆβ–Š | 17/93 [00:06<00:34, 2.22it/s] 19%|β–ˆβ–‰ | 18/93 [00:07<00:32, 2.29it/s] 20%|β–ˆβ–ˆ | 19/93 [00:07<00:32, 2.29it/s] 22%|β–ˆβ–ˆβ– | 20/93 [00:07<00:30, 2.38it/s] 23%|β–ˆβ–ˆβ–Ž | 21/93 [00:08<00:29, 2.46it/s] 24%|β–ˆβ–ˆβ–Ž | 22/93 [00:08<00:30, 2.29it/s] 25%|β–ˆβ–ˆβ– | 23/93 [00:09<00:31, 2.26it/s] 26%|β–ˆβ–ˆβ–Œ | 24/93 [00:09<00:29, 2.35it/s] 27%|β–ˆβ–ˆβ–‹ | 25/93 [00:10<00:30, 2.25it/s] 28%|β–ˆβ–ˆβ–Š | 26/93 [00:10<00:30, 2.22it/s] 29%|β–ˆβ–ˆβ–‰ | 27/93 [00:10<00:28, 2.33it/s] 30%|β–ˆβ–ˆβ–ˆ | 28/93 [00:11<00:26, 2.47it/s] 31%|β–ˆβ–ˆβ–ˆ | 29/93 [00:11<00:25, 2.51it/s] 32%|β–ˆβ–ˆβ–ˆβ– | 30/93 [00:12<00:25, 2.49it/s] 33%|β–ˆβ–ˆβ–ˆβ–Ž | 31/93 [00:12<00:25, 2.40it/s] 34%|β–ˆβ–ˆβ–ˆβ– | 32/93 [00:12<00:25, 2.39it/s] 35%|β–ˆβ–ˆβ–ˆβ–Œ | 33/93 [00:13<00:27, 2.22it/s] 37%|β–ˆβ–ˆβ–ˆβ–‹ | 34/93 [00:13<00:25, 2.27it/s] 38%|β–ˆβ–ˆβ–ˆβ–Š | 35/93 [00:14<00:24, 2.32it/s] 39%|β–ˆβ–ˆβ–ˆβ–Š | 36/93 [00:14<00:22, 2.48it/s] 40%|β–ˆβ–ˆβ–ˆβ–‰ | 37/93 [00:14<00:21, 2.64it/s] 41%|β–ˆβ–ˆβ–ˆβ–ˆ | 38/93 [00:15<00:22, 2.49it/s] 42%|β–ˆβ–ˆβ–ˆβ–ˆβ– | 39/93 [00:15<00:21, 2.49it/s] 43%|β–ˆβ–ˆβ–ˆβ–ˆβ–Ž | 40/93 [00:16<00:21, 2.45it/s] 44%|β–ˆβ–ˆβ–ˆβ–ˆβ– | 41/93 [00:16<00:22, 2.33it/s] 45%|β–ˆβ–ˆβ–ˆβ–ˆβ–Œ | 42/93 [00:17<00:24, 2.11it/s] 46%|β–ˆβ–ˆβ–ˆβ–ˆβ–Œ | 43/93 [00:17<00:24, 2.03it/s] 47%|β–ˆβ–ˆβ–ˆβ–ˆβ–‹ | 44/93 [00:18<00:23, 2.13it/s] 48%|β–ˆβ–ˆβ–ˆβ–ˆβ–Š | 45/93 [00:18<00:21, 2.25it/s] 49%|β–ˆβ–ˆβ–ˆβ–ˆβ–‰ | 46/93 [00:19<00:20, 2.31it/s] 51%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆ | 47/93 [00:19<00:21, 2.19it/s] 52%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ– | 48/93 [00:20<00:19, 2.26it/s] 53%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Ž | 49/93 [00:20<00:19, 2.30it/s] 54%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ– | 50/93 [00:20<00:18, 2.31it/s] 55%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ– | 51/93 [00:21<00:17, 2.42it/s] 56%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Œ | 52/93 [00:21<00:19, 2.15it/s] 57%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‹ | 53/93 [00:22<00:18, 2.14it/s] 58%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Š | 54/93 [00:22<00:18, 2.06it/s] 59%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‰ | 55/93 [00:23<00:17, 2.18it/s] 60%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ | 56/93 [00:23<00:16, 2.30it/s] 61%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ– | 57/93 [00:24<00:16, 2.20it/s] 62%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ– | 58/93 [00:24<00:15, 2.28it/s] 63%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Ž | 59/93 [00:24<00:15, 2.22it/s] 65%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ– | 60/93 [00:25<00:15, 2.13it/s] 66%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Œ | 61/93 [00:25<00:14, 2.27it/s] 67%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‹ | 62/93 [00:26<00:14, 2.14it/s] 68%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Š | 63/93 [00:26<00:13, 2.29it/s] 69%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‰ | 64/93 [00:28<00:26, 1.10it/s] 70%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‰ | 65/93 [00:29<00:20, 1.35it/s] 71%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ | 66/93 [00:29<00:17, 1.57it/s] 72%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ– | 67/93 [00:29<00:14, 1.77it/s] 73%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Ž | 68/93 [00:30<00:13, 1.92it/s] 74%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ– | 69/93 [00:30<00:12, 1.91it/s] 75%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Œ | 70/93 [00:31<00:11, 2.05it/s] 76%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‹ | 71/93 [00:31<00:10, 2.15it/s] 77%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‹ | 72/93 [00:32<00:10, 2.08it/s] 78%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Š | 73/93 [00:32<00:08, 2.27it/s] 80%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‰ | 74/93 [00:32<00:08, 2.22it/s] 81%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ | 75/93 [00:33<00:08, 2.21it/s] 82%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ– | 76/93 [00:33<00:07, 2.17it/s] 83%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Ž | 77/93 [00:34<00:07, 2.20it/s] 84%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ– | 78/93 [00:34<00:06, 2.23it/s] 85%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ– | 79/93 [00:35<00:07, 1.95it/s] 86%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Œ | 80/93 [00:35<00:06, 1.95it/s] 87%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‹ | 81/93 [00:36<00:05, 2.04it/s] 88%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Š | 82/93 [00:36<00:04, 2.22it/s] 89%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‰ | 83/93 [00:37<00:04, 2.22it/s] 90%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ | 84/93 [00:37<00:04, 2.20it/s] 91%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–| 85/93 [00:38<00:03, 2.24it/s] 92%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–| 86/93 [00:38<00:03, 2.31it/s] 94%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Ž| 87/93 [00:38<00:02, 2.23it/s] 95%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–| 88/93 [00:39<00:02, 2.24it/s] 96%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Œ| 89/93 [00:39<00:01, 2.19it/s] 97%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‹| 90/93 [00:40<00:01, 2.33it/s] 98%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Š| 91/93 [00:40<00:00, 2.16it/s] 99%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‰| 92/93 [00:41<00:00, 2.23it/s] 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 93/93 [00:41<00:00, 2.23it/s] 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 93/93 [00:41<00:00, 2.22it/s]
***** 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
0%| | 0/93 [00:00<?, ?it/s] 2%|▏ | 2/93 [00:00<00:22, 4.11it/s] 3%|β–Ž | 3/93 [00:00<00:30, 2.95it/s] 4%|▍ | 4/93 [00:01<00:33, 2.65it/s] 5%|β–Œ | 5/93 [00:01<00:38, 2.30it/s] 6%|β–‹ | 6/93 [00:02<00:36, 2.37it/s] 8%|β–Š | 7/93 [00:02<00:38, 2.23it/s] 9%|β–Š | 8/93 [00:03<00:37, 2.27it/s] 10%|β–‰ | 9/93 [00:03<00:37, 2.26it/s] 11%|β–ˆ | 10/93 [00:04<00:38, 2.17it/s] 12%|β–ˆβ– | 11/93 [00:04<00:37, 2.20it/s] 13%|β–ˆβ–Ž | 12/93 [00:05<00:36, 2.22it/s] 14%|β–ˆβ– | 13/93 [00:05<00:36, 2.19it/s] 15%|β–ˆβ–Œ | 14/93 [00:06<00:41, 1.89it/s] 16%|β–ˆβ–Œ | 15/93 [00:06<00:40, 1.91it/s] 17%|β–ˆβ–‹ | 16/93 [00:07<00:37, 2.03it/s] 18%|β–ˆβ–Š | 17/93 [00:07<00:39, 1.91it/s] 19%|β–ˆβ–‰ | 18/93 [00:08<00:37, 1.98it/s] 20%|β–ˆβ–ˆ | 19/93 [00:08<00:37, 1.98it/s] 22%|β–ˆβ–ˆβ– | 20/93 [00:09<00:36, 2.03it/s] 23%|β–ˆβ–ˆβ–Ž | 21/93 [00:09<00:35, 2.04it/s] 24%|β–ˆβ–ˆβ–Ž | 22/93 [00:10<00:35, 1.99it/s] 25%|β–ˆβ–ˆβ– | 23/93 [00:10<00:33, 2.06it/s] 26%|β–ˆβ–ˆβ–Œ | 24/93 [00:11<00:33, 2.09it/s] 27%|β–ˆβ–ˆβ–‹ | 25/93 [00:11<00:35, 1.92it/s] 28%|β–ˆβ–ˆβ–Š | 26/93 [00:12<00:36, 1.85it/s] 29%|β–ˆβ–ˆβ–‰ | 27/93 [00:12<00:32, 2.05it/s] 30%|β–ˆβ–ˆβ–ˆ | 28/93 [00:13<00:30, 2.13it/s] 31%|β–ˆβ–ˆβ–ˆ | 29/93 [00:13<00:30, 2.11it/s] 32%|β–ˆβ–ˆβ–ˆβ– | 30/93 [00:14<00:28, 2.21it/s] 33%|β–ˆβ–ˆβ–ˆβ–Ž | 31/93 [00:14<00:28, 2.19it/s] 34%|β–ˆβ–ˆβ–ˆβ– | 32/93 [00:14<00:28, 2.13it/s] 35%|β–ˆβ–ˆβ–ˆβ–Œ | 33/93 [00:15<00:28, 2.08it/s] 37%|β–ˆβ–ˆβ–ˆβ–‹ | 34/93 [00:15<00:28, 2.09it/s] 38%|β–ˆβ–ˆβ–ˆβ–Š | 35/93 [00:16<00:26, 2.20it/s] 39%|β–ˆβ–ˆβ–ˆβ–Š | 36/93 [00:16<00:25, 2.25it/s] 40%|β–ˆβ–ˆβ–ˆβ–‰ | 37/93 [00:17<00:23, 2.39it/s] 41%|β–ˆβ–ˆβ–ˆβ–ˆ | 38/93 [00:17<00:26, 2.06it/s] 42%|β–ˆβ–ˆβ–ˆβ–ˆβ– | 39/93 [00:18<00:24, 2.17it/s] 43%|β–ˆβ–ˆβ–ˆβ–ˆβ–Ž | 40/93 [00:18<00:24, 2.18it/s] 44%|β–ˆβ–ˆβ–ˆβ–ˆβ– | 41/93 [00:19<00:24, 2.10it/s] 45%|β–ˆβ–ˆβ–ˆβ–ˆβ–Œ | 42/93 [00:19<00:26, 1.89it/s] 46%|β–ˆβ–ˆβ–ˆβ–ˆβ–Œ | 43/93 [00:20<00:27, 1.82it/s] 47%|β–ˆβ–ˆβ–ˆβ–ˆβ–‹ | 44/93 [00:20<00:26, 1.86it/s] 48%|β–ˆβ–ˆβ–ˆβ–ˆβ–Š | 45/93 [00:21<00:23, 2.00it/s] 49%|β–ˆβ–ˆβ–ˆβ–ˆβ–‰ | 46/93 [00:21<00:23, 1.98it/s] 51%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆ | 47/93 [00:22<00:23, 1.99it/s] 52%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ– | 48/93 [00:22<00:21, 2.10it/s] 53%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Ž | 49/93 [00:23<00:20, 2.14it/s] 54%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ– | 50/93 [00:23<00:20, 2.12it/s] 55%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ– | 51/93 [00:24<00:20, 2.04it/s] 56%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Œ | 52/93 [00:24<00:19, 2.08it/s] 57%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‹ | 53/93 [00:25<00:19, 2.07it/s] 58%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Š | 54/93 [00:25<00:19, 2.03it/s] 59%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‰ | 55/93 [00:26<00:19, 1.95it/s] 60%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ | 56/93 [00:26<00:17, 2.15it/s] 61%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ– | 57/93 [00:26<00:15, 2.28it/s] 62%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ– | 58/93 [00:27<00:15, 2.27it/s] 63%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Ž | 59/93 [00:27<00:15, 2.18it/s] 65%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ– | 60/93 [00:28<00:15, 2.16it/s] 66%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Œ | 61/93 [00:28<00:13, 2.31it/s] 67%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‹ | 62/93 [00:29<00:14, 2.14it/s] 68%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Š | 63/93 [00:29<00:14, 2.06it/s] 69%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‰ | 64/93 [00:31<00:27, 1.04it/s] 70%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‰ | 65/93 [00:32<00:23, 1.21it/s] 71%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ | 66/93 [00:32<00:19, 1.39it/s] 72%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ– | 67/93 [00:33<00:16, 1.56it/s] 73%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Ž | 68/93 [00:33<00:16, 1.56it/s] 74%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ– | 69/93 [00:34<00:13, 1.74it/s] 75%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Œ | 70/93 [00:34<00:12, 1.88it/s] 76%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‹ | 71/93 [00:35<00:11, 1.96it/s] 77%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‹ | 72/93 [00:35<00:11, 1.90it/s] 78%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Š | 73/93 [00:36<00:10, 1.98it/s] 80%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‰ | 74/93 [00:36<00:09, 2.08it/s] 81%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ | 75/93 [00:37<00:07, 2.25it/s] 82%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ– | 76/93 [00:37<00:07, 2.16it/s] 83%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Ž | 77/93 [00:38<00:07, 2.17it/s] 84%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ– | 78/93 [00:38<00:07, 2.08it/s] 85%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ– | 79/93 [00:39<00:06, 2.12it/s] 86%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Œ | 80/93 [00:40<00:11, 1.11it/s] 87%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‹ | 81/93 [00:41<00:09, 1.29it/s] 88%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Š | 82/93 [00:41<00:07, 1.50it/s] 89%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‰ | 83/93 [00:42<00:06, 1.64it/s] 90%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ | 84/93 [00:42<00:05, 1.72it/s] 91%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–| 85/93 [00:43<00:04, 1.82it/s] 92%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–| 86/93 [00:43<00:03, 1.95it/s] 94%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Ž| 87/93 [00:44<00:03, 1.89it/s] 95%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–| 88/93 [00:44<00:02, 1.92it/s] 96%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Œ| 89/93 [00:45<00:02, 1.98it/s] 97%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‹| 90/93 [00:45<00:01, 2.10it/s] 98%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Š| 91/93 [00:46<00:00, 2.00it/s] 99%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‰| 92/93 [00:46<00:00, 2.07it/s] 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 93/93 [00:47<00:00, 2.07it/s] 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 93/93 [00:47<00:00, 1.96it/s]
***** 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
0%| | 0/93 [00:00<?, ?it/s] 2%|▏ | 2/93 [00:00<00:24, 3.77it/s] 3%|β–Ž | 3/93 [00:00<00:28, 3.12it/s] 4%|▍ | 4/93 [00:01<00:29, 3.00it/s] 5%|β–Œ | 5/93 [00:01<00:34, 2.52it/s] 6%|β–‹ | 6/93 [00:02<00:35, 2.42it/s] 8%|β–Š | 7/93 [00:02<00:39, 2.17it/s] 9%|β–Š | 8/93 [00:03<00:37, 2.25it/s] 10%|β–‰ | 9/93 [00:03<00:38, 2.18it/s] 11%|β–ˆ | 10/93 [00:04<00:38, 2.17it/s] 12%|β–ˆβ– | 11/93 [00:04<00:38, 2.15it/s] 13%|β–ˆβ–Ž | 12/93 [00:05<00:36, 2.20it/s] 14%|β–ˆβ– | 13/93 [00:05<00:36, 2.17it/s] 15%|β–ˆβ–Œ | 14/93 [00:06<00:40, 1.96it/s] 16%|β–ˆβ–Œ | 15/93 [00:06<00:38, 2.03it/s] 17%|β–ˆβ–‹ | 16/93 [00:07<00:36, 2.13it/s] 18%|β–ˆβ–Š | 17/93 [00:07<00:37, 2.04it/s] 19%|β–ˆβ–‰ | 18/93 [00:07<00:34, 2.18it/s] 20%|β–ˆβ–ˆ | 19/93 [00:08<00:34, 2.15it/s] 22%|β–ˆβ–ˆβ– | 20/93 [00:08<00:32, 2.24it/s] 23%|β–ˆβ–ˆβ–Ž | 21/93 [00:09<00:31, 2.30it/s] 24%|β–ˆβ–ˆβ–Ž | 22/93 [00:09<00:32, 2.17it/s] 25%|β–ˆβ–ˆβ– | 23/93 [00:10<00:32, 2.17it/s] 26%|β–ˆβ–ˆβ–Œ | 24/93 [00:10<00:30, 2.25it/s] 27%|β–ˆβ–ˆβ–‹ | 25/93 [00:11<00:31, 2.19it/s] 28%|β–ˆβ–ˆβ–Š | 26/93 [00:11<00:30, 2.21it/s] 29%|β–ˆβ–ˆβ–‰ | 27/93 [00:12<00:29, 2.24it/s] 30%|β–ˆβ–ˆβ–ˆ | 28/93 [00:12<00:28, 2.32it/s] 31%|β–ˆβ–ˆβ–ˆ | 29/93 [00:12<00:27, 2.31it/s] 32%|β–ˆβ–ˆβ–ˆβ– | 30/93 [00:13<00:28, 2.24it/s] 33%|β–ˆβ–ˆβ–ˆβ–Ž | 31/93 [00:13<00:27, 2.29it/s] 34%|β–ˆβ–ˆβ–ˆβ– | 32/93 [00:14<00:27, 2.26it/s] 35%|β–ˆβ–ˆβ–ˆβ–Œ | 33/93 [00:14<00:26, 2.23it/s] 37%|β–ˆβ–ˆβ–ˆβ–‹ | 34/93 [00:15<00:29, 2.02it/s] 38%|β–ˆβ–ˆβ–ˆβ–Š | 35/93 [00:15<00:27, 2.11it/s] 39%|β–ˆβ–ˆβ–ˆβ–Š | 36/93 [00:15<00:24, 2.33it/s] 40%|β–ˆβ–ˆβ–ˆβ–‰ | 37/93 [00:16<00:23, 2.39it/s] 41%|β–ˆβ–ˆβ–ˆβ–ˆ | 38/93 [00:16<00:25, 2.18it/s] 42%|β–ˆβ–ˆβ–ˆβ–ˆβ– | 39/93 [00:17<00:23, 2.25it/s] 43%|β–ˆβ–ˆβ–ˆβ–ˆβ–Ž | 40/93 [00:17<00:24, 2.19it/s] 44%|β–ˆβ–ˆβ–ˆβ–ˆβ– | 41/93 [00:18<00:26, 1.99it/s] 45%|β–ˆβ–ˆβ–ˆβ–ˆβ–Œ | 42/93 [00:19<00:26, 1.92it/s] 46%|β–ˆβ–ˆβ–ˆβ–ˆβ–Œ | 43/93 [00:19<00:27, 1.81it/s] 47%|β–ˆβ–ˆβ–ˆβ–ˆβ–‹ | 44/93 [00:20<00:25, 1.91it/s] 48%|β–ˆβ–ˆβ–ˆβ–ˆβ–Š | 45/93 [00:20<00:23, 2.07it/s] 49%|β–ˆβ–ˆβ–ˆβ–ˆβ–‰ | 46/93 [00:21<00:23, 2.01it/s] 51%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆ | 47/93 [00:21<00:24, 1.86it/s] 52%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ– | 48/93 [00:22<00:23, 1.91it/s] 53%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Ž | 49/93 [00:22<00:20, 2.14it/s] 54%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ– | 50/93 [00:23<00:21, 2.00it/s] 55%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ– | 51/93 [00:23<00:20, 2.02it/s] 56%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Œ | 52/93 [00:24<00:20, 1.99it/s] 57%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‹ | 53/93 [00:24<00:20, 1.99it/s] 58%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Š | 54/93 [00:25<00:20, 1.87it/s] 59%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‰ | 55/93 [00:25<00:20, 1.88it/s] 60%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ | 56/93 [00:26<00:17, 2.12it/s] 61%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ– | 57/93 [00:26<00:16, 2.15it/s] 62%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ– | 58/93 [00:26<00:15, 2.24it/s] 63%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Ž | 59/93 [00:27<00:15, 2.23it/s] 65%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ– | 60/93 [00:27<00:15, 2.11it/s] 66%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Œ | 61/93 [00:28<00:14, 2.29it/s] 67%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‹ | 62/93 [00:28<00:14, 2.19it/s] 68%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Š | 63/93 [00:29<00:13, 2.19it/s] 69%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‰ | 64/93 [00:31<00:27, 1.07it/s] 70%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‰ | 65/93 [00:31<00:22, 1.27it/s] 71%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ | 66/93 [00:32<00:20, 1.35it/s] 72%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ– | 67/93 [00:32<00:17, 1.52it/s] 73%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Ž | 68/93 [00:33<00:17, 1.47it/s] 74%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ– | 69/93 [00:34<00:15, 1.59it/s] 75%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Œ | 70/93 [00:34<00:14, 1.63it/s] 76%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‹ | 71/93 [00:35<00:12, 1.78it/s] 77%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‹ | 72/93 [00:35<00:11, 1.86it/s] 78%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Š | 73/93 [00:35<00:09, 2.02it/s] 80%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‰ | 74/93 [00:36<00:09, 2.00it/s] 81%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ | 75/93 [00:36<00:08, 2.09it/s] 82%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ– | 76/93 [00:37<00:08, 2.08it/s] 83%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Ž | 77/93 [00:37<00:07, 2.13it/s] 84%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ– | 78/93 [00:38<00:07, 2.12it/s] 85%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ– | 79/93 [00:38<00:06, 2.02it/s] 86%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Œ | 80/93 [00:39<00:06, 1.87it/s] 87%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‹ | 81/93 [00:39<00:06, 1.87it/s] 88%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Š | 82/93 [00:40<00:05, 2.03it/s] 89%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‰ | 83/93 [00:40<00:04, 2.04it/s] 90%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ | 84/93 [00:41<00:04, 1.94it/s] 91%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–| 85/93 [00:41<00:03, 2.03it/s] 92%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–| 86/93 [00:42<00:03, 2.15it/s] 94%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Ž| 87/93 [00:42<00:02, 2.11it/s] 95%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–| 88/93 [00:43<00:02, 2.05it/s] 96%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Œ| 89/93 [00:43<00:01, 2.00it/s] 97%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‹| 90/93 [00:44<00:01, 2.06it/s] 98%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Š| 91/93 [00:44<00:01, 2.00it/s] 99%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‰| 92/93 [00:45<00:00, 2.08it/s] 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 93/93 [00:45<00:00, 2.19it/s] 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 93/93 [00:45<00:00, 2.03it/s]
***** 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
0%| | 0/93 [00:00<?, ?it/s] 2%|▏ | 2/93 [00:00<00:14, 6.19it/s] 3%|β–Ž | 3/93 [00:00<00:22, 4.04it/s] 4%|▍ | 4/93 [00:01<00:24, 3.63it/s] 5%|β–Œ | 5/93 [00:01<00:32, 2.71it/s] 6%|β–‹ | 6/93 [00:01<00:32, 2.67it/s] 8%|β–Š | 7/93 [00:02<00:32, 2.64it/s] 9%|β–Š | 8/93 [00:02<00:32, 2.60it/s] 10%|β–‰ | 9/93 [00:03<00:32, 2.62it/s] 11%|β–ˆ | 10/93 [00:03<00:31, 2.62it/s] 12%|β–ˆβ– | 11/93 [00:03<00:33, 2.44it/s] 13%|β–ˆβ–Ž | 12/93 [00:04<00:33, 2.44it/s] 14%|β–ˆβ– | 13/93 [00:04<00:32, 2.45it/s] 15%|β–ˆβ–Œ | 14/93 [00:05<00:35, 2.26it/s] 16%|β–ˆβ–Œ | 15/93 [00:05<00:34, 2.26it/s] 17%|β–ˆβ–‹ | 16/93 [00:06<00:32, 2.37it/s] 18%|β–ˆβ–Š | 17/93 [00:06<00:33, 2.28it/s] 19%|β–ˆβ–‰ | 18/93 [00:06<00:31, 2.37it/s] 20%|β–ˆβ–ˆ | 19/93 [00:07<00:31, 2.34it/s] 22%|β–ˆβ–ˆβ– | 20/93 [00:07<00:29, 2.49it/s] 23%|β–ˆβ–ˆβ–Ž | 21/93 [00:08<00:28, 2.52it/s] 24%|β–ˆβ–ˆβ–Ž | 22/93 [00:08<00:30, 2.33it/s] 25%|β–ˆβ–ˆβ– | 23/93 [00:09<00:28, 2.42it/s] 26%|β–ˆβ–ˆβ–Œ | 24/93 [00:09<00:29, 2.36it/s] 27%|β–ˆβ–ˆβ–‹ | 25/93 [00:10<00:33, 2.05it/s] 28%|β–ˆβ–ˆβ–Š | 26/93 [00:10<00:33, 2.00it/s] 29%|β–ˆβ–ˆβ–‰ | 27/93 [00:11<00:30, 2.16it/s] 30%|β–ˆβ–ˆβ–ˆ | 28/93 [00:11<00:28, 2.31it/s] 31%|β–ˆβ–ˆβ–ˆ | 29/93 [00:11<00:26, 2.44it/s] 32%|β–ˆβ–ˆβ–ˆβ– | 30/93 [00:12<00:27, 2.32it/s] 33%|β–ˆβ–ˆβ–ˆβ–Ž | 31/93 [00:12<00:26, 2.37it/s] 34%|β–ˆβ–ˆβ–ˆβ– | 32/93 [00:12<00:24, 2.44it/s] 35%|β–ˆβ–ˆβ–ˆβ–Œ | 33/93 [00:13<00:24, 2.40it/s] 37%|β–ˆβ–ˆβ–ˆβ–‹ | 34/93 [00:13<00:24, 2.44it/s] 38%|β–ˆβ–ˆβ–ˆβ–Š | 35/93 [00:14<00:22, 2.55it/s] 39%|β–ˆβ–ˆβ–ˆβ–Š | 36/93 [00:14<00:20, 2.76it/s] 40%|β–ˆβ–ˆβ–ˆβ–‰ | 37/93 [00:14<00:19, 2.89it/s] 41%|β–ˆβ–ˆβ–ˆβ–ˆ | 38/93 [00:15<00:21, 2.57it/s] 42%|β–ˆβ–ˆβ–ˆβ–ˆβ– | 39/93 [00:15<00:20, 2.66it/s] 43%|β–ˆβ–ˆβ–ˆβ–ˆβ–Ž | 40/93 [00:16<00:20, 2.58it/s] 44%|β–ˆβ–ˆβ–ˆβ–ˆβ– | 41/93 [00:16<00:21, 2.42it/s] 45%|β–ˆβ–ˆβ–ˆβ–ˆβ–Œ | 42/93 [00:16<00:21, 2.41it/s] 46%|β–ˆβ–ˆβ–ˆβ–ˆβ–Œ | 43/93 [00:17<00:23, 2.13it/s] 47%|β–ˆβ–ˆβ–ˆβ–ˆβ–‹ | 44/93 [00:17<00:21, 2.26it/s] 48%|β–ˆβ–ˆβ–ˆβ–ˆβ–Š | 45/93 [00:18<00:20, 2.39it/s] 49%|β–ˆβ–ˆβ–ˆβ–ˆβ–‰ | 46/93 [00:18<00:20, 2.28it/s] 51%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆ | 47/93 [00:19<00:20, 2.23it/s] 52%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ– | 48/93 [00:19<00:19, 2.29it/s] 53%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Ž | 49/93 [00:19<00:18, 2.44it/s] 54%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ– | 50/93 [00:20<00:18, 2.32it/s] 55%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ– | 51/93 [00:20<00:18, 2.25it/s] 56%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Œ | 52/93 [00:21<00:19, 2.14it/s] 57%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‹ | 53/93 [00:21<00:18, 2.12it/s] 58%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Š | 54/93 [00:22<00:17, 2.21it/s] 59%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‰ | 55/93 [00:22<00:15, 2.38it/s] 60%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ | 56/93 [00:22<00:14, 2.57it/s] 61%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ– | 57/93 [00:23<00:14, 2.52it/s] 62%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ– | 58/93 [00:23<00:13, 2.61it/s] 63%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Ž | 59/93 [00:24<00:13, 2.58it/s] 65%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ– | 60/93 [00:24<00:13, 2.48it/s] 66%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Œ | 61/93 [00:24<00:12, 2.51it/s] 67%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‹ | 62/93 [00:25<00:13, 2.31it/s] 68%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Š | 63/93 [00:25<00:12, 2.44it/s] 69%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‰ | 64/93 [00:27<00:25, 1.12it/s] 70%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‰ | 65/93 [00:28<00:20, 1.34it/s] 71%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ | 66/93 [00:28<00:17, 1.57it/s] 72%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ– | 67/93 [00:29<00:14, 1.76it/s] 73%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Ž | 68/93 [00:29<00:14, 1.72it/s] 74%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ– | 69/93 [00:30<00:12, 1.90it/s] 75%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Œ | 70/93 [00:30<00:11, 2.04it/s] 76%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‹ | 71/93 [00:30<00:10, 2.09it/s] 77%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‹ | 72/93 [00:31<00:10, 2.09it/s] 78%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Š | 73/93 [00:31<00:08, 2.25it/s] 80%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‰ | 74/93 [00:32<00:08, 2.21it/s] 81%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ | 75/93 [00:32<00:07, 2.40it/s] 82%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ– | 76/93 [00:33<00:07, 2.36it/s] 83%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Ž | 77/93 [00:33<00:06, 2.50it/s] 84%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ– | 78/93 [00:33<00:05, 2.68it/s] 85%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ– | 79/93 [00:34<00:06, 2.25it/s] 86%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Œ | 80/93 [00:34<00:06, 2.01it/s] 87%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‹ | 81/93 [00:35<00:05, 2.18it/s] 88%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Š | 82/93 [00:35<00:04, 2.33it/s] 89%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‰ | 83/93 [00:36<00:04, 2.39it/s] 90%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ | 84/93 [00:36<00:04, 2.21it/s] 91%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–| 85/93 [00:37<00:03, 2.19it/s] 92%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–| 86/93 [00:37<00:03, 2.30it/s] 94%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Ž| 87/93 [00:37<00:02, 2.36it/s] 95%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–| 88/93 [00:38<00:02, 2.41it/s] 96%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Œ| 89/93 [00:38<00:01, 2.35it/s] 97%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‹| 90/93 [00:39<00:01, 2.42it/s] 98%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Š| 91/93 [00:39<00:00, 2.30it/s] 99%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‰| 92/93 [00:39<00:00, 2.29it/s] 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 93/93 [00:40<00:00, 2.45it/s] 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 93/93 [00:40<00:00, 2.30it/s]
***** 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
0%| | 0/93 [00:00<?, ?it/s] 2%|▏ | 2/93 [00:00<00:22, 4.13it/s] 3%|β–Ž | 3/93 [00:00<00:26, 3.35it/s] 4%|▍ | 4/93 [00:01<00:28, 3.15it/s] 5%|β–Œ | 5/93 [00:01<00:33, 2.60it/s] 6%|β–‹ | 6/93 [00:02<00:33, 2.60it/s] 8%|β–Š | 7/93 [00:02<00:32, 2.63it/s] 9%|β–Š | 8/93 [00:02<00:33, 2.57it/s] 10%|β–‰ | 9/93 [00:03<00:34, 2.45it/s] 11%|β–ˆ | 10/93 [00:03<00:33, 2.48it/s] 12%|β–ˆβ– | 11/93 [00:04<00:32, 2.49it/s] 13%|β–ˆβ–Ž | 12/93 [00:04<00:32, 2.46it/s] 14%|β–ˆβ– | 13/93 [00:04<00:33, 2.41it/s] 15%|β–ˆβ–Œ | 14/93 [00:05<00:35, 2.20it/s] 16%|β–ˆβ–Œ | 15/93 [00:05<00:35, 2.22it/s] 17%|β–ˆβ–‹ | 16/93 [00:06<00:35, 2.19it/s] 18%|β–ˆβ–Š | 17/93 [00:06<00:35, 2.16it/s] 19%|β–ˆβ–‰ | 18/93 [00:07<00:35, 2.11it/s] 20%|β–ˆβ–ˆ | 19/93 [00:07<00:34, 2.14it/s] 22%|β–ˆβ–ˆβ– | 20/93 [00:08<00:33, 2.19it/s] 23%|β–ˆβ–ˆβ–Ž | 21/93 [00:08<00:31, 2.28it/s] 24%|β–ˆβ–ˆβ–Ž | 22/93 [00:09<00:33, 2.14it/s] 25%|β–ˆβ–ˆβ– | 23/93 [00:09<00:31, 2.26it/s] 26%|β–ˆβ–ˆβ–Œ | 24/93 [00:09<00:29, 2.37it/s] 27%|β–ˆβ–ˆβ–‹ | 25/93 [00:10<00:29, 2.33it/s] 28%|β–ˆβ–ˆβ–Š | 26/93 [00:10<00:29, 2.28it/s] 29%|β–ˆβ–ˆβ–‰ | 27/93 [00:11<00:28, 2.33it/s] 30%|β–ˆβ–ˆβ–ˆ | 28/93 [00:11<00:27, 2.37it/s] 31%|β–ˆβ–ˆβ–ˆ | 29/93 [00:12<00:26, 2.41it/s] 32%|β–ˆβ–ˆβ–ˆβ– | 30/93 [00:12<00:27, 2.30it/s] 33%|β–ˆβ–ˆβ–ˆβ–Ž | 31/93 [00:13<00:29, 2.09it/s] 34%|β–ˆβ–ˆβ–ˆβ– | 32/93 [00:13<00:29, 2.04it/s] 35%|β–ˆβ–ˆβ–ˆβ–Œ | 33/93 [00:14<00:29, 2.01it/s] 37%|β–ˆβ–ˆβ–ˆβ–‹ | 34/93 [00:14<00:28, 2.09it/s] 38%|β–ˆβ–ˆβ–ˆβ–Š | 35/93 [00:15<00:27, 2.14it/s] 39%|β–ˆβ–ˆβ–ˆβ–Š | 36/93 [00:15<00:24, 2.31it/s] 40%|β–ˆβ–ˆβ–ˆβ–‰ | 37/93 [00:15<00:23, 2.41it/s] 41%|β–ˆβ–ˆβ–ˆβ–ˆ | 38/93 [00:16<00:24, 2.26it/s] 42%|β–ˆβ–ˆβ–ˆβ–ˆβ– | 39/93 [00:16<00:24, 2.21it/s] 43%|β–ˆβ–ˆβ–ˆβ–ˆβ–Ž | 40/93 [00:17<00:23, 2.23it/s] 44%|β–ˆβ–ˆβ–ˆβ–ˆβ– | 41/93 [00:17<00:23, 2.24it/s] 45%|β–ˆβ–ˆβ–ˆβ–ˆβ–Œ | 42/93 [00:18<00:24, 2.08it/s] 46%|β–ˆβ–ˆβ–ˆβ–ˆβ–Œ | 43/93 [00:18<00:26, 1.91it/s] 47%|β–ˆβ–ˆβ–ˆβ–ˆβ–‹ | 44/93 [00:19<00:23, 2.06it/s] 48%|β–ˆβ–ˆβ–ˆβ–ˆβ–Š | 45/93 [00:19<00:22, 2.17it/s] 49%|β–ˆβ–ˆβ–ˆβ–ˆβ–‰ | 46/93 [00:20<00:22, 2.06it/s] 51%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆ | 47/93 [00:20<00:22, 2.03it/s] 52%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ– | 48/93 [00:21<00:21, 2.13it/s] 53%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Ž | 49/93 [00:21<00:19, 2.22it/s] 54%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ– | 50/93 [00:22<00:19, 2.18it/s] 55%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ– | 51/93 [00:22<00:19, 2.17it/s] 56%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Œ | 52/93 [00:22<00:18, 2.19it/s] 57%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‹ | 53/93 [00:23<00:19, 2.06it/s] 58%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Š | 54/93 [00:23<00:18, 2.09it/s] 59%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‰ | 55/93 [00:24<00:18, 2.08it/s] 60%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ | 56/93 [00:24<00:17, 2.13it/s] 61%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ– | 57/93 [00:25<00:17, 2.08it/s] 62%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ– | 58/93 [00:25<00:15, 2.21it/s] 63%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Ž | 59/93 [00:26<00:15, 2.19it/s] 65%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ– | 60/93 [00:26<00:15, 2.15it/s] 66%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Œ | 61/93 [00:27<00:13, 2.29it/s] 67%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‹ | 62/93 [00:27<00:14, 2.13it/s] 68%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Š | 63/93 [00:28<00:13, 2.16it/s] 69%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‰ | 64/93 [00:30<00:26, 1.08it/s] 70%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‰ | 65/93 [00:30<00:21, 1.29it/s] 71%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ | 66/93 [00:30<00:18, 1.49it/s] 72%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ– | 67/93 [00:31<00:16, 1.60it/s] 73%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Ž | 68/93 [00:31<00:14, 1.73it/s] 74%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ– | 69/93 [00:32<00:12, 1.85it/s] 75%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Œ | 70/93 [00:32<00:12, 1.88it/s] 76%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‹ | 71/93 [00:33<00:10, 2.02it/s] 77%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‹ | 72/93 [00:33<00:10, 1.97it/s] 78%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Š | 73/93 [00:34<00:09, 2.12it/s] 80%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‰ | 74/93 [00:34<00:09, 2.01it/s] 81%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ | 75/93 [00:35<00:08, 2.13it/s] 82%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ– | 76/93 [00:35<00:08, 2.11it/s] 83%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Ž | 77/93 [00:36<00:07, 2.17it/s] 84%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ– | 78/93 [00:36<00:07, 2.11it/s] 85%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ– | 79/93 [00:37<00:07, 1.87it/s] 86%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Œ | 80/93 [00:37<00:07, 1.74it/s] 87%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‹ | 81/93 [00:38<00:06, 1.87it/s] 88%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Š | 82/93 [00:38<00:05, 2.07it/s] 89%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‰ | 83/93 [00:39<00:04, 2.15it/s] 90%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ | 84/93 [00:39<00:04, 1.99it/s] 91%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–| 85/93 [00:40<00:03, 2.10it/s] 92%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–| 86/93 [00:40<00:03, 2.19it/s] 94%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Ž| 87/93 [00:41<00:02, 2.14it/s] 95%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–| 88/93 [00:41<00:02, 2.22it/s] 96%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Œ| 89/93 [00:41<00:01, 2.19it/s] 97%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‹| 90/93 [00:42<00:01, 2.26it/s] 98%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Š| 91/93 [00:42<00:00, 2.08it/s] 99%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‰| 92/93 [00:43<00:00, 2.13it/s] 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 93/93 [00:43<00:00, 2.17it/s] 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 93/93 [00:44<00:00, 2.11it/s]
***** 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
0%| | 0/93 [00:00<?, ?it/s] 2%|▏ | 2/93 [00:00<00:20, 4.36it/s] 3%|β–Ž | 3/93 [00:00<00:25, 3.54it/s] 4%|▍ | 4/93 [00:01<00:29, 3.05it/s] 5%|β–Œ | 5/93 [00:01<00:32, 2.67it/s] 6%|β–‹ | 6/93 [00:02<00:32, 2.64it/s] 8%|β–Š | 7/93 [00:02<00:32, 2.63it/s] 9%|β–Š | 8/93 [00:02<00:33, 2.53it/s] 10%|β–‰ | 9/93 [00:03<00:34, 2.42it/s] 11%|β–ˆ | 10/93 [00:03<00:33, 2.50it/s] 12%|β–ˆβ– | 11/93 [00:04<00:35, 2.31it/s] 13%|β–ˆβ–Ž | 12/93 [00:04<00:33, 2.44it/s] 14%|β–ˆβ– | 13/93 [00:04<00:32, 2.46it/s] 15%|β–ˆβ–Œ | 14/93 [00:05<00:35, 2.24it/s] 16%|β–ˆβ–Œ | 15/93 [00:05<00:33, 2.33it/s] 17%|β–ˆβ–‹ | 16/93 [00:06<00:31, 2.45it/s] 18%|β–ˆβ–Š | 17/93 [00:06<00:33, 2.29it/s] 19%|β–ˆβ–‰ | 18/93 [00:07<00:31, 2.40it/s] 20%|β–ˆβ–ˆ | 19/93 [00:07<00:30, 2.40it/s] 22%|β–ˆβ–ˆβ– | 20/93 [00:07<00:30, 2.40it/s] 23%|β–ˆβ–ˆβ–Ž | 21/93 [00:08<00:28, 2.55it/s] 24%|β–ˆβ–ˆβ–Ž | 22/93 [00:08<00:29, 2.39it/s] 25%|β–ˆβ–ˆβ– | 23/93 [00:09<00:28, 2.47it/s] 26%|β–ˆβ–ˆβ–Œ | 24/93 [00:09<00:26, 2.57it/s] 27%|β–ˆβ–ˆβ–‹ | 25/93 [00:10<00:30, 2.25it/s] 28%|β–ˆβ–ˆβ–Š | 26/93 [00:10<00:27, 2.42it/s] 29%|β–ˆβ–ˆβ–‰ | 27/93 [00:10<00:26, 2.47it/s] 30%|β–ˆβ–ˆβ–ˆ | 28/93 [00:11<00:25, 2.56it/s] 31%|β–ˆβ–ˆβ–ˆ | 29/93 [00:11<00:24, 2.56it/s] 32%|β–ˆβ–ˆβ–ˆβ– | 30/93 [00:12<00:29, 2.16it/s] 33%|β–ˆβ–ˆβ–ˆβ–Ž | 31/93 [00:12<00:28, 2.17it/s] 34%|β–ˆβ–ˆβ–ˆβ– | 32/93 [00:13<00:26, 2.26it/s] 35%|β–ˆβ–ˆβ–ˆβ–Œ | 33/93 [00:13<00:27, 2.21it/s] 37%|β–ˆβ–ˆβ–ˆβ–‹ | 34/93 [00:13<00:26, 2.19it/s] 38%|β–ˆβ–ˆβ–ˆβ–Š | 35/93 [00:14<00:25, 2.26it/s] 39%|β–ˆβ–ˆβ–ˆβ–Š | 36/93 [00:14<00:23, 2.46it/s] 40%|β–ˆβ–ˆβ–ˆβ–‰ | 37/93 [00:14<00:20, 2.76it/s] 41%|β–ˆβ–ˆβ–ˆβ–ˆ | 38/93 [00:15<00:21, 2.54it/s] 42%|β–ˆβ–ˆβ–ˆβ–ˆβ– | 39/93 [00:15<00:20, 2.62it/s] 43%|β–ˆβ–ˆβ–ˆβ–ˆβ–Ž | 40/93 [00:16<00:20, 2.63it/s] 44%|β–ˆβ–ˆβ–ˆβ–ˆβ– | 41/93 [00:16<00:20, 2.51it/s] 45%|β–ˆβ–ˆβ–ˆβ–ˆβ–Œ | 42/93 [00:17<00:21, 2.34it/s] 46%|β–ˆβ–ˆβ–ˆβ–ˆβ–Œ | 43/93 [00:17<00:21, 2.31it/s] 47%|β–ˆβ–ˆβ–ˆβ–ˆβ–‹ | 44/93 [00:17<00:20, 2.40it/s] 48%|β–ˆβ–ˆβ–ˆβ–ˆβ–Š | 45/93 [00:18<00:19, 2.41it/s] 49%|β–ˆβ–ˆβ–ˆβ–ˆβ–‰ | 46/93 [00:18<00:19, 2.36it/s] 51%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆ | 47/93 [00:19<00:21, 2.17it/s] 52%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ– | 48/93 [00:19<00:19, 2.36it/s] 53%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Ž | 49/93 [00:19<00:17, 2.54it/s] 54%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ– | 50/93 [00:20<00:17, 2.50it/s] 55%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ– | 51/93 [00:20<00:16, 2.52it/s] 56%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Œ | 52/93 [00:21<00:16, 2.44it/s] 57%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‹ | 53/93 [00:21<00:17, 2.33it/s] 58%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Š | 54/93 [00:22<00:17, 2.21it/s] 59%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‰ | 55/93 [00:22<00:17, 2.19it/s] 60%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ | 56/93 [00:22<00:15, 2.40it/s] 61%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ– | 57/93 [00:23<00:15, 2.36it/s] 62%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ– | 58/93 [00:23<00:13, 2.54it/s] 63%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Ž | 59/93 [00:24<00:12, 2.69it/s] 65%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ– | 60/93 [00:24<00:12, 2.65it/s] 66%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Œ | 61/93 [00:24<00:12, 2.64it/s] 67%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‹ | 62/93 [00:25<00:12, 2.42it/s] 68%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Š | 63/93 [00:25<00:12, 2.50it/s] 69%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‰ | 64/93 [00:27<00:25, 1.12it/s] 70%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‰ | 65/93 [00:28<00:20, 1.35it/s] 71%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ | 66/93 [00:28<00:17, 1.57it/s] 72%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ– | 67/93 [00:28<00:14, 1.77it/s] 73%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Ž | 68/93 [00:29<00:13, 1.88it/s] 74%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ– | 69/93 [00:29<00:11, 2.12it/s] 75%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Œ | 70/93 [00:30<00:09, 2.34it/s] 76%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‹ | 71/93 [00:30<00:09, 2.40it/s] 77%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‹ | 72/93 [00:30<00:09, 2.25it/s] 78%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Š | 73/93 [00:31<00:08, 2.46it/s] 80%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‰ | 74/93 [00:31<00:08, 2.25it/s] 81%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ | 75/93 [00:32<00:07, 2.42it/s] 82%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ– | 76/93 [00:32<00:07, 2.35it/s] 83%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Ž | 77/93 [00:32<00:06, 2.45it/s] 84%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ– | 78/93 [00:33<00:05, 2.58it/s] 85%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ– | 79/93 [00:33<00:06, 2.12it/s] 86%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Œ | 80/93 [00:34<00:06, 1.98it/s] 87%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‹ | 81/93 [00:34<00:05, 2.18it/s] 88%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Š | 82/93 [00:35<00:04, 2.35it/s] 89%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‰ | 83/93 [00:35<00:04, 2.35it/s] 90%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ | 84/93 [00:36<00:04, 2.19it/s] 91%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–| 85/93 [00:36<00:03, 2.25it/s] 92%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–| 86/93 [00:37<00:03, 2.30it/s] 94%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Ž| 87/93 [00:37<00:02, 2.28it/s] 95%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–| 88/93 [00:37<00:02, 2.28it/s] 96%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Œ| 89/93 [00:38<00:01, 2.22it/s] 97%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‹| 90/93 [00:38<00:01, 2.25it/s] 98%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Š| 91/93 [00:39<00:00, 2.10it/s] 99%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‰| 92/93 [00:39<00:00, 2.18it/s] 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 93/93 [00:40<00:00, 2.29it/s] 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 93/93 [00:40<00:00, 2.29it/s]
***** 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
0%| | 0/93 [00:00<?, ?it/s] 2%|▏ | 2/93 [00:00<00:18, 4.79it/s] 3%|β–Ž | 3/93 [00:00<00:24, 3.73it/s] 4%|▍ | 4/93 [00:01<00:26, 3.39it/s] 5%|β–Œ | 5/93 [00:01<00:29, 2.95it/s] 6%|β–‹ | 6/93 [00:01<00:30, 2.83it/s] 8%|β–Š | 7/93 [00:02<00:31, 2.70it/s] 9%|β–Š | 8/93 [00:02<00:32, 2.65it/s] 10%|β–‰ | 9/93 [00:03<00:32, 2.58it/s] 11%|β–ˆ | 10/93 [00:03<00:33, 2.49it/s] 12%|β–ˆβ– | 11/93 [00:03<00:32, 2.53it/s] 13%|β–ˆβ–Ž | 12/93 [00:04<00:32, 2.52it/s] 14%|β–ˆβ– | 13/93 [00:04<00:31, 2.52it/s] 15%|β–ˆβ–Œ | 14/93 [00:05<00:36, 2.19it/s] 16%|β–ˆβ–Œ | 15/93 [00:05<00:35, 2.22it/s] 17%|β–ˆβ–‹ | 16/93 [00:06<00:32, 2.35it/s] 18%|β–ˆβ–Š | 17/93 [00:06<00:33, 2.26it/s] 19%|β–ˆβ–‰ | 18/93 [00:06<00:31, 2.35it/s] 20%|β–ˆβ–ˆ | 19/93 [00:07<00:31, 2.38it/s] 22%|β–ˆβ–ˆβ– | 20/93 [00:07<00:29, 2.44it/s] 23%|β–ˆβ–ˆβ–Ž | 21/93 [00:08<00:28, 2.50it/s] 24%|β–ˆβ–ˆβ–Ž | 22/93 [00:08<00:31, 2.28it/s] 25%|β–ˆβ–ˆβ– | 23/93 [00:09<00:30, 2.33it/s] 26%|β–ˆβ–ˆβ–Œ | 24/93 [00:09<00:28, 2.43it/s] 27%|β–ˆβ–ˆβ–‹ | 25/93 [00:10<00:30, 2.22it/s] 28%|β–ˆβ–ˆβ–Š | 26/93 [00:10<00:30, 2.21it/s] 29%|β–ˆβ–ˆβ–‰ | 27/93 [00:10<00:28, 2.32it/s] 30%|β–ˆβ–ˆβ–ˆ | 28/93 [00:11<00:27, 2.40it/s] 31%|β–ˆβ–ˆβ–ˆ | 29/93 [00:11<00:25, 2.48it/s] 32%|β–ˆβ–ˆβ–ˆβ– | 30/93 [00:12<00:25, 2.45it/s] 33%|β–ˆβ–ˆβ–ˆβ–Ž | 31/93 [00:12<00:26, 2.35it/s] 34%|β–ˆβ–ˆβ–ˆβ– | 32/93 [00:12<00:25, 2.42it/s] 35%|β–ˆβ–ˆβ–ˆβ–Œ | 33/93 [00:13<00:24, 2.47it/s] 37%|β–ˆβ–ˆβ–ˆβ–‹ | 34/93 [00:13<00:25, 2.34it/s] 38%|β–ˆβ–ˆβ–ˆβ–Š | 35/93 [00:14<00:24, 2.39it/s] 39%|β–ˆβ–ˆβ–ˆβ–Š | 36/93 [00:14<00:22, 2.54it/s] 40%|β–ˆβ–ˆβ–ˆβ–‰ | 37/93 [00:14<00:20, 2.76it/s] 41%|β–ˆβ–ˆβ–ˆβ–ˆ | 38/93 [00:15<00:21, 2.56it/s] 42%|β–ˆβ–ˆβ–ˆβ–ˆβ– | 39/93 [00:15<00:21, 2.52it/s] 43%|β–ˆβ–ˆβ–ˆβ–ˆβ–Ž | 40/93 [00:16<00:22, 2.36it/s] 44%|β–ˆβ–ˆβ–ˆβ–ˆβ– | 41/93 [00:16<00:22, 2.30it/s] 45%|β–ˆβ–ˆβ–ˆβ–ˆβ–Œ | 42/93 [00:17<00:21, 2.32it/s] 46%|β–ˆβ–ˆβ–ˆβ–ˆβ–Œ | 43/93 [00:17<00:23, 2.12it/s] 47%|β–ˆβ–ˆβ–ˆβ–ˆβ–‹ | 44/93 [00:17<00:21, 2.27it/s] 48%|β–ˆβ–ˆβ–ˆβ–ˆβ–Š | 45/93 [00:18<00:21, 2.27it/s] 49%|β–ˆβ–ˆβ–ˆβ–ˆβ–‰ | 46/93 [00:18<00:20, 2.28it/s] 51%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆ | 47/93 [00:19<00:24, 1.85it/s] 52%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ– | 48/93 [00:19<00:21, 2.06it/s] 53%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Ž | 49/93 [00:20<00:19, 2.24it/s] 54%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ– | 50/93 [00:20<00:18, 2.27it/s] 55%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ– | 51/93 [00:21<00:19, 2.20it/s] 56%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Œ | 52/93 [00:21<00:18, 2.17it/s] 57%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‹ | 53/93 [00:22<00:19, 2.07it/s] 58%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Š | 54/93 [00:22<00:18, 2.06it/s] 59%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‰ | 55/93 [00:23<00:17, 2.12it/s] 60%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ | 56/93 [00:23<00:15, 2.35it/s] 61%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ– | 57/93 [00:23<00:15, 2.37it/s] 62%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ– | 58/93 [00:24<00:13, 2.52it/s] 63%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Ž | 59/93 [00:24<00:14, 2.40it/s] 65%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ– | 60/93 [00:25<00:14, 2.36it/s] 66%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Œ | 61/93 [00:25<00:13, 2.44it/s] 67%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‹ | 62/93 [00:25<00:13, 2.31it/s] 68%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Š | 63/93 [00:26<00:12, 2.31it/s] 69%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‰ | 64/93 [00:28<00:26, 1.10it/s] 70%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‰ | 65/93 [00:28<00:21, 1.33it/s] 71%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ | 66/93 [00:29<00:17, 1.56it/s] 72%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ– | 67/93 [00:29<00:14, 1.76it/s] 73%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Ž | 68/93 [00:30<00:14, 1.76it/s] 74%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ– | 69/93 [00:30<00:13, 1.84it/s] 75%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Œ | 70/93 [00:31<00:11, 1.92it/s] 76%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‹ | 71/93 [00:31<00:10, 2.05it/s] 77%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‹ | 72/93 [00:32<00:10, 2.06it/s] 78%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Š | 73/93 [00:32<00:08, 2.26it/s] 80%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‰ | 74/93 [00:32<00:09, 2.06it/s] 81%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ | 75/93 [00:33<00:08, 2.22it/s] 82%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ– | 76/93 [00:33<00:07, 2.15it/s] 83%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Ž | 77/93 [00:34<00:07, 2.11it/s] 84%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ– | 78/93 [00:34<00:07, 2.02it/s] 85%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ– | 79/93 [00:35<00:07, 1.98it/s] 86%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Œ | 80/93 [00:35<00:06, 1.95it/s] 87%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‹ | 81/93 [00:36<00:06, 1.86it/s] 88%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Š | 82/93 [00:36<00:05, 2.08it/s] 89%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‰ | 83/93 [00:37<00:04, 2.23it/s] 90%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ | 84/93 [00:37<00:04, 2.17it/s] 91%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–| 85/93 [00:38<00:03, 2.16it/s] 92%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–| 86/93 [00:38<00:03, 2.25it/s] 94%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Ž| 87/93 [00:39<00:02, 2.25it/s] 95%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–| 88/93 [00:39<00:02, 2.15it/s] 96%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Œ| 89/93 [00:40<00:01, 2.12it/s] 97%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‹| 90/93 [00:40<00:01, 2.18it/s] 98%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Š| 91/93 [00:41<00:00, 2.05it/s] 99%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‰| 92/93 [00:41<00:00, 2.15it/s] 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 93/93 [00:41<00:00, 2.32it/s] 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 93/93 [00:41<00:00, 2.21it/s]
***** 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
0%| | 0/93 [00:00<?, ?it/s] 2%|▏ | 2/93 [00:00<00:17, 5.33it/s] 3%|β–Ž | 3/93 [00:00<00:23, 3.85it/s] 4%|▍ | 4/93 [00:01<00:24, 3.70it/s] 5%|β–Œ | 5/93 [00:01<00:28, 3.10it/s] 6%|β–‹ | 6/93 [00:01<00:29, 2.96it/s] 8%|β–Š | 7/93 [00:02<00:30, 2.80it/s] 9%|β–Š | 8/93 [00:02<00:29, 2.87it/s] 10%|β–‰ | 9/93 [00:03<00:32, 2.55it/s] 11%|β–ˆ | 10/93 [00:03<00:33, 2.47it/s] 12%|β–ˆβ– | 11/93 [00:03<00:33, 2.48it/s] 13%|β–ˆβ–Ž | 12/93 [00:04<00:31, 2.55it/s] 14%|β–ˆβ– | 13/93 [00:04<00:32, 2.49it/s] 15%|β–ˆβ–Œ | 14/93 [00:05<00:35, 2.23it/s] 16%|β–ˆβ–Œ | 15/93 [00:05<00:36, 2.12it/s] 17%|β–ˆβ–‹ | 16/93 [00:06<00:34, 2.25it/s] 18%|β–ˆβ–Š | 17/93 [00:06<00:33, 2.30it/s] 19%|β–ˆβ–‰ | 18/93 [00:06<00:31, 2.39it/s] 20%|β–ˆβ–ˆ | 19/93 [00:07<00:31, 2.37it/s] 22%|β–ˆβ–ˆβ– | 20/93 [00:07<00:28, 2.53it/s] 23%|β–ˆβ–ˆβ–Ž | 21/93 [00:08<00:27, 2.59it/s] 24%|β–ˆβ–ˆβ–Ž | 22/93 [00:08<00:29, 2.41it/s] 25%|β–ˆβ–ˆβ– | 23/93 [00:08<00:28, 2.47it/s] 26%|β–ˆβ–ˆβ–Œ | 24/93 [00:09<00:28, 2.43it/s] 27%|β–ˆβ–ˆβ–‹ | 25/93 [00:09<00:27, 2.49it/s] 28%|β–ˆβ–ˆβ–Š | 26/93 [00:10<00:27, 2.42it/s] 29%|β–ˆβ–ˆβ–‰ | 27/93 [00:10<00:26, 2.45it/s] 30%|β–ˆβ–ˆβ–ˆ | 28/93 [00:10<00:24, 2.65it/s] 31%|β–ˆβ–ˆβ–ˆ | 29/93 [00:11<00:23, 2.75it/s] 32%|β–ˆβ–ˆβ–ˆβ– | 30/93 [00:11<00:23, 2.70it/s] 33%|β–ˆβ–ˆβ–ˆβ–Ž | 31/93 [00:11<00:23, 2.59it/s] 34%|β–ˆβ–ˆβ–ˆβ– | 32/93 [00:12<00:23, 2.63it/s] 35%|β–ˆβ–ˆβ–ˆβ–Œ | 33/93 [00:12<00:23, 2.56it/s] 37%|β–ˆβ–ˆβ–ˆβ–‹ | 34/93 [00:13<00:23, 2.54it/s] 38%|β–ˆβ–ˆβ–ˆβ–Š | 35/93 [00:13<00:22, 2.61it/s] 39%|β–ˆβ–ˆβ–ˆβ–Š | 36/93 [00:13<00:20, 2.79it/s] 40%|β–ˆβ–ˆβ–ˆβ–‰ | 37/93 [00:14<00:18, 3.07it/s] 41%|β–ˆβ–ˆβ–ˆβ–ˆ | 38/93 [00:14<00:20, 2.71it/s] 42%|β–ˆβ–ˆβ–ˆβ–ˆβ– | 39/93 [00:14<00:19, 2.74it/s] 43%|β–ˆβ–ˆβ–ˆβ–ˆβ–Ž | 40/93 [00:15<00:19, 2.67it/s] 44%|β–ˆβ–ˆβ–ˆβ–ˆβ– | 41/93 [00:15<00:20, 2.58it/s] 45%|β–ˆβ–ˆβ–ˆβ–ˆβ–Œ | 42/93 [00:16<00:20, 2.48it/s] 46%|β–ˆβ–ˆβ–ˆβ–ˆβ–Œ | 43/93 [00:16<00:22, 2.24it/s] 47%|β–ˆβ–ˆβ–ˆβ–ˆβ–‹ | 44/93 [00:17<00:20, 2.34it/s] 48%|β–ˆβ–ˆβ–ˆβ–ˆβ–Š | 45/93 [00:17<00:19, 2.49it/s] 49%|β–ˆβ–ˆβ–ˆβ–ˆβ–‰ | 46/93 [00:17<00:19, 2.45it/s] 51%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆ | 47/93 [00:18<00:19, 2.41it/s] 52%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ– | 48/93 [00:18<00:18, 2.41it/s] 53%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Ž | 49/93 [00:19<00:17, 2.58it/s] 54%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ– | 50/93 [00:19<00:18, 2.32it/s] 55%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ– | 51/93 [00:19<00:17, 2.37it/s] 56%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Œ | 52/93 [00:20<00:17, 2.35it/s] 57%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‹ | 53/93 [00:20<00:18, 2.17it/s] 58%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Š | 54/93 [00:21<00:18, 2.10it/s] 59%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‰ | 55/93 [00:21<00:17, 2.22it/s] 60%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ | 56/93 [00:22<00:15, 2.41it/s] 61%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ– | 57/93 [00:22<00:14, 2.44it/s] 62%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ– | 58/93 [00:22<00:13, 2.60it/s] 63%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Ž | 59/93 [00:23<00:12, 2.72it/s] 65%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ– | 60/93 [00:23<00:13, 2.52it/s] 66%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Œ | 61/93 [00:24<00:12, 2.56it/s] 67%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‹ | 62/93 [00:24<00:13, 2.36it/s] 68%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Š | 63/93 [00:25<00:13, 2.30it/s] 69%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‰ | 64/93 [00:27<00:26, 1.11it/s] 70%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‰ | 65/93 [00:27<00:20, 1.36it/s] 71%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ | 66/93 [00:27<00:16, 1.59it/s] 72%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ– | 67/93 [00:28<00:14, 1.78it/s] 73%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Ž | 68/93 [00:28<00:13, 1.81it/s] 74%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ– | 69/93 [00:29<00:12, 1.96it/s] 75%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Œ | 70/93 [00:29<00:11, 2.09it/s] 76%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‹ | 71/93 [00:29<00:09, 2.20it/s] 77%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‹ | 72/93 [00:30<00:09, 2.22it/s] 78%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Š | 73/93 [00:30<00:08, 2.40it/s] 80%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‰ | 74/93 [00:31<00:08, 2.33it/s] 81%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ | 75/93 [00:31<00:07, 2.37it/s] 82%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ– | 76/93 [00:31<00:07, 2.39it/s] 83%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Ž | 77/93 [00:32<00:06, 2.52it/s] 84%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ– | 78/93 [00:32<00:05, 2.57it/s] 85%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ– | 79/93 [00:33<00:06, 2.19it/s] 86%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Œ | 80/93 [00:33<00:06, 2.01it/s] 87%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‹ | 81/93 [00:34<00:05, 2.05it/s] 88%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Š | 82/93 [00:34<00:04, 2.23it/s] 89%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‰ | 83/93 [00:35<00:04, 2.33it/s] 90%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ | 84/93 [00:35<00:03, 2.25it/s] 91%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–| 85/93 [00:35<00:03, 2.27it/s] 92%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–| 86/93 [00:36<00:02, 2.36it/s] 94%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Ž| 87/93 [00:36<00:02, 2.32it/s] 95%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–| 88/93 [00:37<00:02, 2.29it/s] 96%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Œ| 89/93 [00:37<00:01, 2.33it/s] 97%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‹| 90/93 [00:38<00:01, 2.40it/s] 98%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Š| 91/93 [00:38<00:00, 2.26it/s] 99%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‰| 92/93 [00:38<00:00, 2.32it/s] 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 93/93 [00:39<00:00, 2.48it/s] 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 93/93 [00:39<00:00, 2.35it/s]
***** 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
0%| | 0/93 [00:00<?, ?it/s] 2%|▏ | 2/93 [00:00<00:31, 2.86it/s] 3%|β–Ž | 3/93 [00:01<00:36, 2.44it/s] 4%|▍ | 4/93 [00:01<00:38, 2.33it/s] 5%|β–Œ | 5/93 [00:02<00:42, 2.05it/s] 6%|β–‹ | 6/93 [00:02<00:42, 2.06it/s] 8%|β–Š | 7/93 [00:03<00:43, 1.97it/s] 9%|β–Š | 8/93 [00:03<00:41, 2.03it/s] 10%|β–‰ | 9/93 [00:04<00:40, 2.07it/s] 11%|β–ˆ | 10/93 [00:04<00:41, 2.00it/s] 12%|β–ˆβ– | 11/93 [00:05<00:43, 1.89it/s] 13%|β–ˆβ–Ž | 12/93 [00:05<00:43, 1.85it/s] 14%|β–ˆβ– | 13/93 [00:06<00:41, 1.94it/s] 15%|β–ˆβ–Œ | 14/93 [00:07<00:45, 1.72it/s] 16%|β–ˆβ–Œ | 15/93 [00:07<00:44, 1.74it/s] 17%|β–ˆβ–‹ | 16/93 [00:08<00:44, 1.73it/s] 18%|β–ˆβ–Š | 17/93 [00:08<00:45, 1.69it/s] 19%|β–ˆβ–‰ | 18/93 [00:09<00:42, 1.78it/s] 20%|β–ˆβ–ˆ | 19/93 [00:09<00:42, 1.75it/s] 22%|β–ˆβ–ˆβ– | 20/93 [00:10<00:40, 1.80it/s] 23%|β–ˆβ–ˆβ–Ž | 21/93 [00:11<00:39, 1.82it/s] 24%|β–ˆβ–ˆβ–Ž | 22/93 [00:11<00:38, 1.84it/s] 25%|β–ˆβ–ˆβ– | 23/93 [00:12<00:37, 1.87it/s] 26%|β–ˆβ–ˆβ–Œ | 24/93 [00:12<00:38, 1.82it/s] 27%|β–ˆβ–ˆβ–‹ | 25/93 [00:14<00:54, 1.25it/s] 28%|β–ˆβ–ˆβ–Š | 26/93 [00:14<00:48, 1.38it/s] 29%|β–ˆβ–ˆβ–‰ | 27/93 [00:15<00:42, 1.55it/s] 30%|β–ˆβ–ˆβ–ˆ | 28/93 [00:15<00:37, 1.73it/s] 31%|β–ˆβ–ˆβ–ˆ | 29/93 [00:15<00:34, 1.84it/s] 32%|β–ˆβ–ˆβ–ˆβ– | 30/93 [00:16<00:33, 1.86it/s] 33%|β–ˆβ–ˆβ–ˆβ–Ž | 31/93 [00:17<00:33, 1.83it/s] 34%|β–ˆβ–ˆβ–ˆβ– | 32/93 [00:17<00:33, 1.82it/s] 35%|β–ˆβ–ˆβ–ˆβ–Œ | 33/93 [00:18<00:33, 1.78it/s] 37%|β–ˆβ–ˆβ–ˆβ–‹ | 34/93 [00:18<00:36, 1.62it/s] 38%|β–ˆβ–ˆβ–ˆβ–Š | 35/93 [00:19<00:33, 1.75it/s] 39%|β–ˆβ–ˆβ–ˆβ–Š | 36/93 [00:19<00:33, 1.72it/s] 40%|β–ˆβ–ˆβ–ˆβ–‰ | 37/93 [00:20<00:31, 1.80it/s] 41%|β–ˆβ–ˆβ–ˆβ–ˆ | 38/93 [00:21<00:35, 1.55it/s] 42%|β–ˆβ–ˆβ–ˆβ–ˆβ– | 39/93 [00:21<00:32, 1.64it/s] 43%|β–ˆβ–ˆβ–ˆβ–ˆβ–Ž | 40/93 [00:22<00:32, 1.64it/s] 44%|β–ˆβ–ˆβ–ˆβ–ˆβ– | 41/93 [00:23<00:33, 1.55it/s] 45%|β–ˆβ–ˆβ–ˆβ–ˆβ–Œ | 42/93 [00:23<00:33, 1.54it/s] 46%|β–ˆβ–ˆβ–ˆβ–ˆβ–Œ | 43/93 [00:24<00:33, 1.49it/s] 47%|β–ˆβ–ˆβ–ˆβ–ˆβ–‹ | 44/93 [00:25<00:32, 1.52it/s] 48%|β–ˆβ–ˆβ–ˆβ–ˆβ–Š | 45/93 [00:25<00:30, 1.59it/s] 49%|β–ˆβ–ˆβ–ˆβ–ˆβ–‰ | 46/93 [00:26<00:30, 1.55it/s] 51%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆ | 47/93 [00:27<00:28, 1.61it/s] 52%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ– | 48/93 [00:27<00:27, 1.65it/s] 53%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Ž | 49/93 [00:28<00:25, 1.72it/s] 54%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ– | 50/93 [00:28<00:25, 1.71it/s] 55%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ– | 51/93 [00:29<00:24, 1.75it/s] 56%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Œ | 52/93 [00:29<00:23, 1.75it/s] 57%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‹ | 53/93 [00:30<00:23, 1.69it/s] 58%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Š | 54/93 [00:31<00:24, 1.57it/s] 59%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‰ | 55/93 [00:31<00:23, 1.65it/s] 60%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ | 56/93 [00:32<00:20, 1.81it/s] 61%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ– | 57/93 [00:32<00:18, 1.90it/s] 62%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ– | 58/93 [00:33<00:18, 1.90it/s] 63%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Ž | 59/93 [00:33<00:17, 1.92it/s] 65%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ– | 60/93 [00:34<00:16, 1.95it/s] 66%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Œ | 61/93 [00:34<00:15, 2.06it/s] 67%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‹ | 62/93 [00:35<00:18, 1.72it/s] 68%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Š | 63/93 [00:36<00:17, 1.67it/s] 69%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‰ | 64/93 [00:38<00:30, 1.05s/it] 70%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‰ | 65/93 [00:38<00:25, 1.10it/s] 71%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ | 66/93 [00:39<00:21, 1.25it/s] 72%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ– | 67/93 [00:39<00:19, 1.35it/s] 73%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Ž | 68/93 [00:40<00:18, 1.32it/s] 74%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ– | 69/93 [00:41<00:16, 1.42it/s] 75%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Œ | 70/93 [00:41<00:15, 1.51it/s] 76%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‹ | 71/93 [00:42<00:14, 1.51it/s] 77%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‹ | 72/93 [00:43<00:14, 1.50it/s] 78%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Š | 73/93 [00:43<00:12, 1.63it/s] 80%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‰ | 74/93 [00:44<00:11, 1.70it/s] 81%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ | 75/93 [00:44<00:10, 1.74it/s] 82%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ– | 76/93 [00:46<00:16, 1.02it/s] 83%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Ž | 77/93 [00:47<00:13, 1.16it/s] 84%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ– | 78/93 [00:47<00:11, 1.31it/s] 85%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ– | 79/93 [00:48<00:10, 1.38it/s] 86%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Œ | 80/93 [00:50<00:14, 1.10s/it] 87%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‹ | 81/93 [00:51<00:11, 1.01it/s] 88%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Š | 82/93 [00:51<00:09, 1.20it/s] 89%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‰ | 83/93 [00:52<00:07, 1.31it/s] 90%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ | 84/93 [00:52<00:06, 1.41it/s] 91%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–| 85/93 [00:53<00:05, 1.47it/s] 92%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–| 86/93 [00:53<00:04, 1.61it/s] 94%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Ž| 87/93 [00:54<00:03, 1.53it/s] 95%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–| 88/93 [00:55<00:03, 1.61it/s] 96%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Œ| 89/93 [00:55<00:02, 1.71it/s] 97%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‹| 90/93 [00:56<00:01, 1.73it/s] 98%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Š| 91/93 [00:56<00:01, 1.67it/s] 99%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‰| 92/93 [00:57<00:00, 1.73it/s] 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 93/93 [00:57<00:00, 1.82it/s] 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 93/93 [00:58<00:00, 1.60it/s]
***** 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
0%| | 0/93 [00:00<?, ?it/s] 2%|▏ | 2/93 [00:00<00:16, 5.41it/s] 3%|β–Ž | 3/93 [00:00<00:24, 3.74it/s] 4%|▍ | 4/93 [00:01<00:25, 3.47it/s] 5%|β–Œ | 5/93 [00:01<00:30, 2.85it/s] 6%|β–‹ | 6/93 [00:01<00:30, 2.87it/s] 8%|β–Š | 7/93 [00:02<00:30, 2.81it/s] 9%|β–Š | 8/93 [00:02<00:30, 2.79it/s] 10%|β–‰ | 9/93 [00:03<00:33, 2.49it/s] 11%|β–ˆ | 10/93 [00:03<00:33, 2.49it/s] 12%|β–ˆβ– | 11/93 [00:03<00:33, 2.42it/s] 13%|β–ˆβ–Ž | 12/93 [00:04<00:32, 2.51it/s] 14%|β–ˆβ– | 13/93 [00:04<00:32, 2.45it/s] 15%|β–ˆβ–Œ | 14/93 [00:05<00:36, 2.18it/s] 16%|β–ˆβ–Œ | 15/93 [00:05<00:36, 2.16it/s] 17%|β–ˆβ–‹ | 16/93 [00:06<00:34, 2.22it/s] 18%|β–ˆβ–Š | 17/93 [00:06<00:33, 2.25it/s] 19%|β–ˆβ–‰ | 18/93 [00:07<00:33, 2.22it/s] 20%|β–ˆβ–ˆ | 19/93 [00:07<00:33, 2.21it/s] 22%|β–ˆβ–ˆβ– | 20/93 [00:07<00:30, 2.37it/s] 23%|β–ˆβ–ˆβ–Ž | 21/93 [00:08<00:29, 2.45it/s] 24%|β–ˆβ–ˆβ–Ž | 22/93 [00:08<00:29, 2.41it/s] 25%|β–ˆβ–ˆβ– | 23/93 [00:09<00:29, 2.41it/s] 26%|β–ˆβ–ˆβ–Œ | 24/93 [00:09<00:29, 2.34it/s] 27%|β–ˆβ–ˆβ–‹ | 25/93 [00:09<00:26, 2.54it/s] 28%|β–ˆβ–ˆβ–Š | 26/93 [00:10<00:26, 2.57it/s] 29%|β–ˆβ–ˆβ–‰ | 27/93 [00:10<00:24, 2.65it/s] 30%|β–ˆβ–ˆβ–ˆ | 28/93 [00:10<00:23, 2.74it/s] 31%|β–ˆβ–ˆβ–ˆ | 29/93 [00:11<00:24, 2.63it/s] 32%|β–ˆβ–ˆβ–ˆβ– | 30/93 [00:11<00:24, 2.61it/s] 33%|β–ˆβ–ˆβ–ˆβ–Ž | 31/93 [00:12<00:24, 2.54it/s] 34%|β–ˆβ–ˆβ–ˆβ– | 32/93 [00:12<00:24, 2.52it/s] 35%|β–ˆβ–ˆβ–ˆβ–Œ | 33/93 [00:13<00:26, 2.31it/s] 37%|β–ˆβ–ˆβ–ˆβ–‹ | 34/93 [00:13<00:24, 2.41it/s] 38%|β–ˆβ–ˆβ–ˆβ–Š | 35/93 [00:13<00:23, 2.43it/s] 39%|β–ˆβ–ˆβ–ˆβ–Š | 36/93 [00:14<00:22, 2.58it/s] 40%|β–ˆβ–ˆβ–ˆβ–‰ | 37/93 [00:14<00:20, 2.70it/s] 41%|β–ˆβ–ˆβ–ˆβ–ˆ | 38/93 [00:14<00:21, 2.60it/s] 42%|β–ˆβ–ˆβ–ˆβ–ˆβ– | 39/93 [00:15<00:19, 2.80it/s] 43%|β–ˆβ–ˆβ–ˆβ–ˆβ–Ž | 40/93 [00:15<00:20, 2.62it/s] 44%|β–ˆβ–ˆβ–ˆβ–ˆβ– | 41/93 [00:16<00:20, 2.54it/s] 45%|β–ˆβ–ˆβ–ˆβ–ˆβ–Œ | 42/93 [00:16<00:20, 2.44it/s] 46%|β–ˆβ–ˆβ–ˆβ–ˆβ–Œ | 43/93 [00:17<00:22, 2.20it/s] 47%|β–ˆβ–ˆβ–ˆβ–ˆβ–‹ | 44/93 [00:17<00:20, 2.38it/s] 48%|β–ˆβ–ˆβ–ˆβ–ˆβ–Š | 45/93 [00:17<00:18, 2.55it/s] 49%|β–ˆβ–ˆβ–ˆβ–ˆβ–‰ | 46/93 [00:18<00:18, 2.53it/s] 51%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆ | 47/93 [00:18<00:18, 2.43it/s] 52%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ– | 48/93 [00:18<00:17, 2.59it/s] 53%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Ž | 49/93 [00:19<00:16, 2.74it/s] 54%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ– | 50/93 [00:19<00:17, 2.50it/s] 55%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ– | 51/93 [00:20<00:17, 2.38it/s] 56%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Œ | 52/93 [00:20<00:18, 2.23it/s] 57%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‹ | 53/93 [00:21<00:20, 1.98it/s] 58%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Š | 54/93 [00:21<00:19, 2.03it/s] 59%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‰ | 55/93 [00:22<00:17, 2.17it/s] 60%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ | 56/93 [00:22<00:16, 2.26it/s] 61%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ– | 57/93 [00:23<00:16, 2.12it/s] 62%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ– | 58/93 [00:23<00:15, 2.28it/s] 63%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Ž | 59/93 [00:23<00:14, 2.33it/s] 65%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ– | 60/93 [00:24<00:14, 2.32it/s] 66%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Œ | 61/93 [00:24<00:12, 2.48it/s] 67%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‹ | 62/93 [00:25<00:13, 2.37it/s] 68%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Š | 63/93 [00:25<00:12, 2.40it/s] 69%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‰ | 64/93 [00:27<00:25, 1.13it/s] 70%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‰ | 65/93 [00:28<00:21, 1.31it/s] 71%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ | 66/93 [00:28<00:18, 1.48it/s] 72%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ– | 67/93 [00:28<00:14, 1.75it/s] 73%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Ž | 68/93 [00:29<00:13, 1.82it/s] 74%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ– | 69/93 [00:29<00:12, 2.00it/s] 75%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Œ | 70/93 [00:30<00:10, 2.19it/s] 76%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‹ | 71/93 [00:30<00:09, 2.30it/s] 77%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‹ | 72/93 [00:31<00:09, 2.16it/s] 78%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Š | 73/93 [00:31<00:08, 2.35it/s] 80%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‰ | 74/93 [00:31<00:08, 2.28it/s] 81%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ | 75/93 [00:32<00:07, 2.39it/s] 82%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ– | 76/93 [00:32<00:07, 2.33it/s] 83%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Ž | 77/93 [00:33<00:06, 2.32it/s] 84%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ– | 78/93 [00:33<00:06, 2.30it/s] 85%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ– | 79/93 [00:34<00:06, 2.03it/s] 86%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Œ | 80/93 [00:34<00:06, 2.01it/s] 87%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‹ | 81/93 [00:35<00:05, 2.04it/s] 88%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Š | 82/93 [00:35<00:04, 2.23it/s] 89%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‰ | 83/93 [00:35<00:04, 2.22it/s] 90%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ | 84/93 [00:36<00:04, 2.16it/s] 91%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–| 85/93 [00:36<00:03, 2.29it/s] 92%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–| 86/93 [00:37<00:02, 2.35it/s] 94%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Ž| 87/93 [00:37<00:02, 2.41it/s] 95%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–| 88/93 [00:38<00:02, 2.42it/s] 96%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Œ| 89/93 [00:38<00:01, 2.36it/s] 97%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‹| 90/93 [00:38<00:01, 2.39it/s] 98%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Š| 91/93 [00:39<00:00, 2.25it/s] 99%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‰| 92/93 [00:39<00:00, 2.28it/s] 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 93/93 [00:40<00:00, 2.31it/s] 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 93/93 [00:40<00:00, 2.30it/s]
***** 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
0%| | 0/93 [00:00<?, ?it/s] 2%|▏ | 2/93 [00:00<00:14, 6.13it/s] 3%|β–Ž | 3/93 [00:00<00:21, 4.23it/s] 4%|▍ | 4/93 [00:01<00:24, 3.56it/s] 5%|β–Œ | 5/93 [00:01<00:30, 2.87it/s] 6%|β–‹ | 6/93 [00:01<00:29, 2.96it/s] 8%|β–Š | 7/93 [00:02<00:29, 2.87it/s] 9%|β–Š | 8/93 [00:02<00:29, 2.89it/s] 10%|β–‰ | 9/93 [00:02<00:29, 2.87it/s] 11%|β–ˆ | 10/93 [00:03<00:29, 2.80it/s] 12%|β–ˆβ– | 11/93 [00:03<00:29, 2.75it/s] 13%|β–ˆβ–Ž | 12/93 [00:03<00:28, 2.80it/s] 14%|β–ˆβ– | 13/93 [00:04<00:29, 2.74it/s] 15%|β–ˆβ–Œ | 14/93 [00:04<00:30, 2.58it/s] 16%|β–ˆβ–Œ | 15/93 [00:05<00:30, 2.60it/s] 17%|β–ˆβ–‹ | 16/93 [00:05<00:28, 2.66it/s] 18%|β–ˆβ–Š | 17/93 [00:06<00:31, 2.45it/s] 19%|β–ˆβ–‰ | 18/93 [00:06<00:29, 2.56it/s] 20%|β–ˆβ–ˆ | 19/93 [00:06<00:29, 2.53it/s] 22%|β–ˆβ–ˆβ– | 20/93 [00:07<00:28, 2.59it/s] 23%|β–ˆβ–ˆβ–Ž | 21/93 [00:07<00:26, 2.71it/s] 24%|β–ˆβ–ˆβ–Ž | 22/93 [00:07<00:28, 2.45it/s] 25%|β–ˆβ–ˆβ– | 23/93 [00:08<00:28, 2.43it/s] 26%|β–ˆβ–ˆβ–Œ | 24/93 [00:08<00:27, 2.52it/s] 27%|β–ˆβ–ˆβ–‹ | 25/93 [00:09<00:27, 2.50it/s] 28%|β–ˆβ–ˆβ–Š | 26/93 [00:09<00:27, 2.46it/s] 29%|β–ˆβ–ˆβ–‰ | 27/93 [00:09<00:25, 2.60it/s] 30%|β–ˆβ–ˆβ–ˆ | 28/93 [00:10<00:23, 2.73it/s] 31%|β–ˆβ–ˆβ–ˆ | 29/93 [00:10<00:23, 2.76it/s] 32%|β–ˆβ–ˆβ–ˆβ– | 30/93 [00:11<00:23, 2.64it/s] 33%|β–ˆβ–ˆβ–ˆβ–Ž | 31/93 [00:11<00:28, 2.21it/s] 34%|β–ˆβ–ˆβ–ˆβ– | 32/93 [00:11<00:25, 2.42it/s] 35%|β–ˆβ–ˆβ–ˆβ–Œ | 33/93 [00:12<00:24, 2.48it/s] 37%|β–ˆβ–ˆβ–ˆβ–‹ | 34/93 [00:12<00:22, 2.63it/s] 38%|β–ˆβ–ˆβ–ˆβ–Š | 35/93 [00:13<00:21, 2.68it/s] 39%|β–ˆβ–ˆβ–ˆβ–Š | 36/93 [00:13<00:19, 2.87it/s] 40%|β–ˆβ–ˆβ–ˆβ–‰ | 37/93 [00:13<00:18, 2.95it/s] 41%|β–ˆβ–ˆβ–ˆβ–ˆ | 38/93 [00:13<00:19, 2.87it/s] 42%|β–ˆβ–ˆβ–ˆβ–ˆβ– | 39/93 [00:14<00:17, 3.02it/s] 43%|β–ˆβ–ˆβ–ˆβ–ˆβ–Ž | 40/93 [00:14<00:17, 2.96it/s] 44%|β–ˆβ–ˆβ–ˆβ–ˆβ– | 41/93 [00:14<00:17, 2.99it/s] 45%|β–ˆβ–ˆβ–ˆβ–ˆβ–Œ | 42/93 [00:15<00:18, 2.69it/s] 46%|β–ˆβ–ˆβ–ˆβ–ˆβ–Œ | 43/93 [00:15<00:19, 2.58it/s] 47%|β–ˆβ–ˆβ–ˆβ–ˆβ–‹ | 44/93 [00:16<00:19, 2.55it/s] 48%|β–ˆβ–ˆβ–ˆβ–ˆβ–Š | 45/93 [00:16<00:17, 2.71it/s] 49%|β–ˆβ–ˆβ–ˆβ–ˆβ–‰ | 46/93 [00:16<00:16, 2.80it/s] 51%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆ | 47/93 [00:17<00:17, 2.63it/s] 52%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ– | 48/93 [00:17<00:16, 2.75it/s] 53%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Ž | 49/93 [00:17<00:15, 2.91it/s] 54%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ– | 50/93 [00:18<00:15, 2.85it/s] 55%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ– | 51/93 [00:18<00:15, 2.79it/s] 56%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Œ | 52/93 [00:19<00:15, 2.65it/s] 57%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‹ | 53/93 [00:19<00:15, 2.60it/s] 58%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Š | 54/93 [00:19<00:14, 2.62it/s] 59%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‰ | 55/93 [00:20<00:13, 2.73it/s] 60%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ | 56/93 [00:20<00:13, 2.83it/s] 61%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ– | 57/93 [00:20<00:12, 2.92it/s] 62%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ– | 58/93 [00:21<00:11, 2.94it/s] 63%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Ž | 59/93 [00:21<00:11, 3.00it/s] 65%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ– | 60/93 [00:21<00:10, 3.05it/s] 66%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Œ | 61/93 [00:22<00:10, 3.09it/s] 67%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‹ | 62/93 [00:22<00:10, 2.82it/s] 68%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Š | 63/93 [00:22<00:10, 2.87it/s] 69%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‰ | 64/93 [00:23<00:12, 2.36it/s] 70%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‰ | 65/93 [00:23<00:11, 2.51it/s] 71%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ | 66/93 [00:24<00:11, 2.42it/s] 72%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ– | 67/93 [00:24<00:10, 2.47it/s] 73%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Ž | 68/93 [00:25<00:10, 2.48it/s] 74%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ– | 69/93 [00:25<00:09, 2.47it/s] 75%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Œ | 70/93 [00:25<00:09, 2.49it/s] 76%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‹ | 71/93 [00:26<00:08, 2.67it/s] 77%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‹ | 72/93 [00:26<00:08, 2.55it/s] 78%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Š | 73/93 [00:26<00:07, 2.74it/s] 80%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‰ | 74/93 [00:27<00:07, 2.57it/s] 81%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ | 75/93 [00:27<00:06, 2.79it/s] 82%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ– | 76/93 [00:28<00:06, 2.55it/s] 83%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Ž | 77/93 [00:28<00:06, 2.66it/s] 84%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ– | 78/93 [00:28<00:05, 2.70it/s] 85%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ– | 79/93 [00:29<00:06, 2.27it/s] 86%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Œ | 80/93 [00:29<00:05, 2.20it/s] 87%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‹ | 81/93 [00:30<00:05, 2.32it/s] 88%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Š | 82/93 [00:30<00:04, 2.46it/s] 89%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‰ | 83/93 [00:31<00:03, 2.52it/s] 90%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ | 84/93 [00:31<00:03, 2.34it/s] 91%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–| 85/93 [00:31<00:03, 2.46it/s] 92%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–| 86/93 [00:32<00:02, 2.60it/s] 94%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Ž| 87/93 [00:32<00:02, 2.67it/s] 95%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–| 88/93 [00:32<00:01, 2.74it/s] 96%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Œ| 89/93 [00:33<00:01, 2.65it/s] 97%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‹| 90/93 [00:33<00:01, 2.62it/s] 98%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Š| 91/93 [00:34<00:00, 2.39it/s] 99%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‰| 92/93 [00:34<00:00, 2.37it/s] 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 93/93 [00:34<00:00, 2.50it/s] 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 93/93 [00:35<00:00, 2.64it/s]
***** 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