|
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). |
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06/10/2024 22:36:58 - WARNING - __main__ - Process rank: 0, device: cuda:0, n_gpu: 1, distributed training: False, 16-bits training: False |
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06/10/2024 22:36:58 - INFO - __main__ - Training/evaluation parameters Seq2SeqTrainingArguments( |
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_n_gpu=1, |
|
adafactor=False, |
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adam_beta1=0.9, |
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adam_beta2=0.999, |
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adam_epsilon=1e-08, |
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auto_find_batch_size=False, |
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bf16=False, |
|
bf16_full_eval=False, |
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data_seed=None, |
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dataloader_drop_last=False, |
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dataloader_num_workers=0, |
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dataloader_persistent_workers=False, |
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dataloader_pin_memory=True, |
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ddp_backend=None, |
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ddp_broadcast_buffers=None, |
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ddp_bucket_cap_mb=None, |
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ddp_find_unused_parameters=None, |
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ddp_timeout=1800, |
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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}, |
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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, |
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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>, |
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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, |
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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, |
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logging_first_step=False, |
|
logging_nan_inf_filter=True, |
|
logging_steps=500, |
|
logging_strategy=steps, |
|
lr_scheduler_kwargs={}, |
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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. |
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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 |
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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 |
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[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 |
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[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 |
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} |
|
|
|
[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 |
|
} |
|
|
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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 |
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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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Running tokenizer on prediction dataset: 100%|ββββββββββ| 2974/2974 [00:00<00:00, 23379.87 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 |
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Running tokenizer on prediction dataset: 100%|ββββββββββ| 2974/2974 [00:00<00:00, 24733.67 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 |
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Running tokenizer on prediction dataset: 100%|ββββββββββ| 2974/2974 [00:00<00:00, 11672.89 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 |
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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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Running tokenizer on prediction dataset: 100%|ββββββββββ| 2974/2974 [00:00<00:00, 23271.39 examples/s]
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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-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-8571e8bc453b7162.arrow |
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Running tokenizer on prediction dataset: 100%|ββββββββββ| 2974/2974 [00:00<00:00, 23459.63 examples/s]
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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-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-c74febb30a64318a.arrow |
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Running tokenizer on prediction dataset: 100%|ββββββββββ| 2974/2974 [00:00<00:00, 11319.17 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 |
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Running tokenizer on prediction dataset: 100%|ββββββββββ| 2974/2974 [00:00<00:00, 24163.00 examples/s]
Running tokenizer on prediction dataset: 100%|ββββββββββ| 2974/2974 [00:00<00:00, 20604.87 examples/s] |
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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 |
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Running tokenizer on prediction dataset: 100%|ββββββββββ| 2974/2974 [00:00<00:00, 20147.34 examples/s]
Running tokenizer on prediction dataset: 100%|ββββββββββ| 2974/2974 [00:00<00:00, 12138.36 examples/s] |
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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 |
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Running tokenizer on prediction dataset: 100%|ββββββββββ| 2974/2974 [00:00<00:00, 25152.92 examples/s]
Running tokenizer on prediction dataset: 100%|ββββββββββ| 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%|ββββββββββ| 2974/2974 [00:00<00:00, 26368.84 examples/s]
Running tokenizer on prediction dataset: 100%|ββββββββββ| 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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100%|ββββββββββ| 93/93 [00:42<00:00, 2.18it/s]
100%|ββββββββββ| 93/93 [00:43<00:00, 2.14it/s] |
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***** predict_test_en_US metrics ***** |
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predict_ex_match_acc = 0.7317 |
|
predict_ex_match_acc_stderr = 0.0081 |
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predict_intent_acc = 0.8894 |
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predict_intent_acc_stderr = 0.0058 |
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predict_loss = 0.1316 |
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predict_runtime = 0:00:44.50 |
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predict_samples = 2974 |
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predict_samples_per_second = 66.819 |
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predict_slot_micro_f1 = 0.8224 |
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predict_slot_micro_f1_stderr = 0.0027 |
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predict_steps_per_second = 2.089 |
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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 ***** |
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[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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76%|ββββββββ | 71/93 [00:37<00:12, 1.80it/s]
77%|ββββββββ | 72/93 [00:37<00:11, 1.86it/s]
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84%|βββββββββ | 78/93 [00:40<00:06, 2.29it/s]
85%|βββββββββ | 79/93 [00:40<00:07, 1.95it/s]
86%|βββββββββ | 80/93 [00:41<00:07, 1.86it/s]
87%|βββββββββ | 81/93 [00:42<00:06, 1.93it/s]
88%|βββββββββ | 82/93 [00:42<00:05, 2.11it/s]
89%|βββββββββ | 83/93 [00:42<00:04, 2.06it/s]
90%|βββββββββ | 84/93 [00:43<00:04, 1.88it/s]
91%|ββββββββββ| 85/93 [00:44<00:04, 1.99it/s]
92%|ββββββββββ| 86/93 [00:44<00:03, 2.09it/s]
94%|ββββββββββ| 87/93 [00:44<00:02, 2.01it/s]
95%|ββββββββββ| 88/93 [00:45<00:02, 2.00it/s]
96%|ββββββββββ| 89/93 [00:45<00:01, 2.04it/s]
97%|ββββββββββ| 90/93 [00:46<00:01, 2.09it/s]
98%|ββββββββββ| 91/93 [00:46<00:01, 2.00it/s]
99%|ββββββββββ| 92/93 [00:47<00:00, 2.03it/s]
100%|ββββββββββ| 93/93 [00:47<00:00, 2.13it/s]
100%|ββββββββββ| 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%|β | 2/93 [00:00<00:20, 4.47it/s]
3%|β | 3/93 [00:00<00:24, 3.64it/s]
4%|β | 4/93 [00:01<00:26, 3.31it/s]
5%|β | 5/93 [00:01<00:30, 2.91it/s]
6%|β | 6/93 [00:01<00:31, 2.74it/s]
8%|β | 7/93 [00:02<00:31, 2.69it/s]
9%|β | 8/93 [00:02<00:31, 2.66it/s]
10%|β | 9/93 [00:03<00:33, 2.54it/s]
11%|β | 10/93 [00:03<00:32, 2.53it/s]
12%|ββ | 11/93 [00:03<00:32, 2.55it/s]
13%|ββ | 12/93 [00:04<00:31, 2.56it/s]
14%|ββ | 13/93 [00:04<00:32, 2.49it/s]
15%|ββ | 14/93 [00:05<00:36, 2.14it/s]
16%|ββ | 15/93 [00:05<00:36, 2.16it/s]
17%|ββ | 16/93 [00:06<00:32, 2.34it/s]
18%|ββ | 17/93 [00:06<00:34, 2.22it/s]
19%|ββ | 18/93 [00:07<00:32, 2.29it/s]
20%|ββ | 19/93 [00:07<00:32, 2.29it/s]
22%|βββ | 20/93 [00:07<00:30, 2.38it/s]
23%|βββ | 21/93 [00:08<00:29, 2.46it/s]
24%|βββ | 22/93 [00:08<00:30, 2.29it/s]
25%|βββ | 23/93 [00:09<00:31, 2.26it/s]
26%|βββ | 24/93 [00:09<00:29, 2.35it/s]
27%|βββ | 25/93 [00:10<00:30, 2.25it/s]
28%|βββ | 26/93 [00:10<00:30, 2.22it/s]
29%|βββ | 27/93 [00:10<00:28, 2.33it/s]
30%|βββ | 28/93 [00:11<00:26, 2.47it/s]
31%|βββ | 29/93 [00:11<00:25, 2.51it/s]
32%|ββββ | 30/93 [00:12<00:25, 2.49it/s]
33%|ββββ | 31/93 [00:12<00:25, 2.40it/s]
34%|ββββ | 32/93 [00:12<00:25, 2.39it/s]
35%|ββββ | 33/93 [00:13<00:27, 2.22it/s]
37%|ββββ | 34/93 [00:13<00:25, 2.27it/s]
38%|ββββ | 35/93 [00:14<00:24, 2.32it/s]
39%|ββββ | 36/93 [00:14<00:22, 2.48it/s]
40%|ββββ | 37/93 [00:14<00:21, 2.64it/s]
41%|ββββ | 38/93 [00:15<00:22, 2.49it/s]
42%|βββββ | 39/93 [00:15<00:21, 2.49it/s]
43%|βββββ | 40/93 [00:16<00:21, 2.45it/s]
44%|βββββ | 41/93 [00:16<00:22, 2.33it/s]
45%|βββββ | 42/93 [00:17<00:24, 2.11it/s]
46%|βββββ | 43/93 [00:17<00:24, 2.03it/s]
47%|βββββ | 44/93 [00:18<00:23, 2.13it/s]
48%|βββββ | 45/93 [00:18<00:21, 2.25it/s]
49%|βββββ | 46/93 [00:19<00:20, 2.31it/s]
51%|βββββ | 47/93 [00:19<00:21, 2.19it/s]
52%|ββββββ | 48/93 [00:20<00:19, 2.26it/s]
53%|ββββββ | 49/93 [00:20<00:19, 2.30it/s]
54%|ββββββ | 50/93 [00:20<00:18, 2.31it/s]
55%|ββββββ | 51/93 [00:21<00:17, 2.42it/s]
56%|ββββββ | 52/93 [00:21<00:19, 2.15it/s]
57%|ββββββ | 53/93 [00:22<00:18, 2.14it/s]
58%|ββββββ | 54/93 [00:22<00:18, 2.06it/s]
59%|ββββββ | 55/93 [00:23<00:17, 2.18it/s]
60%|ββββββ | 56/93 [00:23<00:16, 2.30it/s]
61%|βββββββ | 57/93 [00:24<00:16, 2.20it/s]
62%|βββββββ | 58/93 [00:24<00:15, 2.28it/s]
63%|βββββββ | 59/93 [00:24<00:15, 2.22it/s]
65%|βββββββ | 60/93 [00:25<00:15, 2.13it/s]
66%|βββββββ | 61/93 [00:25<00:14, 2.27it/s]
67%|βββββββ | 62/93 [00:26<00:14, 2.14it/s]
68%|βββββββ | 63/93 [00:26<00:13, 2.29it/s]
69%|βββββββ | 64/93 [00:28<00:26, 1.10it/s]
70%|βββββββ | 65/93 [00:29<00:20, 1.35it/s]
71%|βββββββ | 66/93 [00:29<00:17, 1.57it/s]
72%|ββββββββ | 67/93 [00:29<00:14, 1.77it/s]
73%|ββββββββ | 68/93 [00:30<00:13, 1.92it/s]
74%|ββββββββ | 69/93 [00:30<00:12, 1.91it/s]
75%|ββββββββ | 70/93 [00:31<00:11, 2.05it/s]
76%|ββββββββ | 71/93 [00:31<00:10, 2.15it/s]
77%|ββββββββ | 72/93 [00:32<00:10, 2.08it/s]
78%|ββββββββ | 73/93 [00:32<00:08, 2.27it/s]
80%|ββββββββ | 74/93 [00:32<00:08, 2.22it/s]
81%|ββββββββ | 75/93 [00:33<00:08, 2.21it/s]
82%|βββββββββ | 76/93 [00:33<00:07, 2.17it/s]
83%|βββββββββ | 77/93 [00:34<00:07, 2.20it/s]
84%|βββββββββ | 78/93 [00:34<00:06, 2.23it/s]
85%|βββββββββ | 79/93 [00:35<00:07, 1.95it/s]
86%|βββββββββ | 80/93 [00:35<00:06, 1.95it/s]
87%|βββββββββ | 81/93 [00:36<00:05, 2.04it/s]
88%|βββββββββ | 82/93 [00:36<00:04, 2.22it/s]
89%|βββββββββ | 83/93 [00:37<00:04, 2.22it/s]
90%|βββββββββ | 84/93 [00:37<00:04, 2.20it/s]
91%|ββββββββββ| 85/93 [00:38<00:03, 2.24it/s]
92%|ββββββββββ| 86/93 [00:38<00:03, 2.31it/s]
94%|ββββββββββ| 87/93 [00:38<00:02, 2.23it/s]
95%|ββββββββββ| 88/93 [00:39<00:02, 2.24it/s]
96%|ββββββββββ| 89/93 [00:39<00:01, 2.19it/s]
97%|ββββββββββ| 90/93 [00:40<00:01, 2.33it/s]
98%|ββββββββββ| 91/93 [00:40<00:00, 2.16it/s]
99%|ββββββββββ| 92/93 [00:41<00:00, 2.23it/s]
100%|ββββββββββ| 93/93 [00:41<00:00, 2.23it/s]
100%|ββββββββββ| 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%|β | 2/93 [00:00<00:22, 4.11it/s]
3%|β | 3/93 [00:00<00:30, 2.95it/s]
4%|β | 4/93 [00:01<00:33, 2.65it/s]
5%|β | 5/93 [00:01<00:38, 2.30it/s]
6%|β | 6/93 [00:02<00:36, 2.37it/s]
8%|β | 7/93 [00:02<00:38, 2.23it/s]
9%|β | 8/93 [00:03<00:37, 2.27it/s]
10%|β | 9/93 [00:03<00:37, 2.26it/s]
11%|β | 10/93 [00:04<00:38, 2.17it/s]
12%|ββ | 11/93 [00:04<00:37, 2.20it/s]
13%|ββ | 12/93 [00:05<00:36, 2.22it/s]
14%|ββ | 13/93 [00:05<00:36, 2.19it/s]
15%|ββ | 14/93 [00:06<00:41, 1.89it/s]
16%|ββ | 15/93 [00:06<00:40, 1.91it/s]
17%|ββ | 16/93 [00:07<00:37, 2.03it/s]
18%|ββ | 17/93 [00:07<00:39, 1.91it/s]
19%|ββ | 18/93 [00:08<00:37, 1.98it/s]
20%|ββ | 19/93 [00:08<00:37, 1.98it/s]
22%|βββ | 20/93 [00:09<00:36, 2.03it/s]
23%|βββ | 21/93 [00:09<00:35, 2.04it/s]
24%|βββ | 22/93 [00:10<00:35, 1.99it/s]
25%|βββ | 23/93 [00:10<00:33, 2.06it/s]
26%|βββ | 24/93 [00:11<00:33, 2.09it/s]
27%|βββ | 25/93 [00:11<00:35, 1.92it/s]
28%|βββ | 26/93 [00:12<00:36, 1.85it/s]
29%|βββ | 27/93 [00:12<00:32, 2.05it/s]
30%|βββ | 28/93 [00:13<00:30, 2.13it/s]
31%|βββ | 29/93 [00:13<00:30, 2.11it/s]
32%|ββββ | 30/93 [00:14<00:28, 2.21it/s]
33%|ββββ | 31/93 [00:14<00:28, 2.19it/s]
34%|ββββ | 32/93 [00:14<00:28, 2.13it/s]
35%|ββββ | 33/93 [00:15<00:28, 2.08it/s]
37%|ββββ | 34/93 [00:15<00:28, 2.09it/s]
38%|ββββ | 35/93 [00:16<00:26, 2.20it/s]
39%|ββββ | 36/93 [00:16<00:25, 2.25it/s]
40%|ββββ | 37/93 [00:17<00:23, 2.39it/s]
41%|ββββ | 38/93 [00:17<00:26, 2.06it/s]
42%|βββββ | 39/93 [00:18<00:24, 2.17it/s]
43%|βββββ | 40/93 [00:18<00:24, 2.18it/s]
44%|βββββ | 41/93 [00:19<00:24, 2.10it/s]
45%|βββββ | 42/93 [00:19<00:26, 1.89it/s]
46%|βββββ | 43/93 [00:20<00:27, 1.82it/s]
47%|βββββ | 44/93 [00:20<00:26, 1.86it/s]
48%|βββββ | 45/93 [00:21<00:23, 2.00it/s]
49%|βββββ | 46/93 [00:21<00:23, 1.98it/s]
51%|βββββ | 47/93 [00:22<00:23, 1.99it/s]
52%|ββββββ | 48/93 [00:22<00:21, 2.10it/s]
53%|ββββββ | 49/93 [00:23<00:20, 2.14it/s]
54%|ββββββ | 50/93 [00:23<00:20, 2.12it/s]
55%|ββββββ | 51/93 [00:24<00:20, 2.04it/s]
56%|ββββββ | 52/93 [00:24<00:19, 2.08it/s]
57%|ββββββ | 53/93 [00:25<00:19, 2.07it/s]
58%|ββββββ | 54/93 [00:25<00:19, 2.03it/s]
59%|ββββββ | 55/93 [00:26<00:19, 1.95it/s]
60%|ββββββ | 56/93 [00:26<00:17, 2.15it/s]
61%|βββββββ | 57/93 [00:26<00:15, 2.28it/s]
62%|βββββββ | 58/93 [00:27<00:15, 2.27it/s]
63%|βββββββ | 59/93 [00:27<00:15, 2.18it/s]
65%|βββββββ | 60/93 [00:28<00:15, 2.16it/s]
66%|βββββββ | 61/93 [00:28<00:13, 2.31it/s]
67%|βββββββ | 62/93 [00:29<00:14, 2.14it/s]
68%|βββββββ | 63/93 [00:29<00:14, 2.06it/s]
69%|βββββββ | 64/93 [00:31<00:27, 1.04it/s]
70%|βββββββ | 65/93 [00:32<00:23, 1.21it/s]
71%|βββββββ | 66/93 [00:32<00:19, 1.39it/s]
72%|ββββββββ | 67/93 [00:33<00:16, 1.56it/s]
73%|ββββββββ | 68/93 [00:33<00:16, 1.56it/s]
74%|ββββββββ | 69/93 [00:34<00:13, 1.74it/s]
75%|ββββββββ | 70/93 [00:34<00:12, 1.88it/s]
76%|ββββββββ | 71/93 [00:35<00:11, 1.96it/s]
77%|ββββββββ | 72/93 [00:35<00:11, 1.90it/s]
78%|ββββββββ | 73/93 [00:36<00:10, 1.98it/s]
80%|ββββββββ | 74/93 [00:36<00:09, 2.08it/s]
81%|ββββββββ | 75/93 [00:37<00:07, 2.25it/s]
82%|βββββββββ | 76/93 [00:37<00:07, 2.16it/s]
83%|βββββββββ | 77/93 [00:38<00:07, 2.17it/s]
84%|βββββββββ | 78/93 [00:38<00:07, 2.08it/s]
85%|βββββββββ | 79/93 [00:39<00:06, 2.12it/s]
86%|βββββββββ | 80/93 [00:40<00:11, 1.11it/s]
87%|βββββββββ | 81/93 [00:41<00:09, 1.29it/s]
88%|βββββββββ | 82/93 [00:41<00:07, 1.50it/s]
89%|βββββββββ | 83/93 [00:42<00:06, 1.64it/s]
90%|βββββββββ | 84/93 [00:42<00:05, 1.72it/s]
91%|ββββββββββ| 85/93 [00:43<00:04, 1.82it/s]
92%|ββββββββββ| 86/93 [00:43<00:03, 1.95it/s]
94%|ββββββββββ| 87/93 [00:44<00:03, 1.89it/s]
95%|ββββββββββ| 88/93 [00:44<00:02, 1.92it/s]
96%|ββββββββββ| 89/93 [00:45<00:02, 1.98it/s]
97%|ββββββββββ| 90/93 [00:45<00:01, 2.10it/s]
98%|ββββββββββ| 91/93 [00:46<00:00, 2.00it/s]
99%|ββββββββββ| 92/93 [00:46<00:00, 2.07it/s]
100%|ββββββββββ| 93/93 [00:47<00:00, 2.07it/s]
100%|ββββββββββ| 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%|β | 2/93 [00:00<00:24, 3.77it/s]
3%|β | 3/93 [00:00<00:28, 3.12it/s]
4%|β | 4/93 [00:01<00:29, 3.00it/s]
5%|β | 5/93 [00:01<00:34, 2.52it/s]
6%|β | 6/93 [00:02<00:35, 2.42it/s]
8%|β | 7/93 [00:02<00:39, 2.17it/s]
9%|β | 8/93 [00:03<00:37, 2.25it/s]
10%|β | 9/93 [00:03<00:38, 2.18it/s]
11%|β | 10/93 [00:04<00:38, 2.17it/s]
12%|ββ | 11/93 [00:04<00:38, 2.15it/s]
13%|ββ | 12/93 [00:05<00:36, 2.20it/s]
14%|ββ | 13/93 [00:05<00:36, 2.17it/s]
15%|ββ | 14/93 [00:06<00:40, 1.96it/s]
16%|ββ | 15/93 [00:06<00:38, 2.03it/s]
17%|ββ | 16/93 [00:07<00:36, 2.13it/s]
18%|ββ | 17/93 [00:07<00:37, 2.04it/s]
19%|ββ | 18/93 [00:07<00:34, 2.18it/s]
20%|ββ | 19/93 [00:08<00:34, 2.15it/s]
22%|βββ | 20/93 [00:08<00:32, 2.24it/s]
23%|βββ | 21/93 [00:09<00:31, 2.30it/s]
24%|βββ | 22/93 [00:09<00:32, 2.17it/s]
25%|βββ | 23/93 [00:10<00:32, 2.17it/s]
26%|βββ | 24/93 [00:10<00:30, 2.25it/s]
27%|βββ | 25/93 [00:11<00:31, 2.19it/s]
28%|βββ | 26/93 [00:11<00:30, 2.21it/s]
29%|βββ | 27/93 [00:12<00:29, 2.24it/s]
30%|βββ | 28/93 [00:12<00:28, 2.32it/s]
31%|βββ | 29/93 [00:12<00:27, 2.31it/s]
32%|ββββ | 30/93 [00:13<00:28, 2.24it/s]
33%|ββββ | 31/93 [00:13<00:27, 2.29it/s]
34%|ββββ | 32/93 [00:14<00:27, 2.26it/s]
35%|ββββ | 33/93 [00:14<00:26, 2.23it/s]
37%|ββββ | 34/93 [00:15<00:29, 2.02it/s]
38%|ββββ | 35/93 [00:15<00:27, 2.11it/s]
39%|ββββ | 36/93 [00:15<00:24, 2.33it/s]
40%|ββββ | 37/93 [00:16<00:23, 2.39it/s]
41%|ββββ | 38/93 [00:16<00:25, 2.18it/s]
42%|βββββ | 39/93 [00:17<00:23, 2.25it/s]
43%|βββββ | 40/93 [00:17<00:24, 2.19it/s]
44%|βββββ | 41/93 [00:18<00:26, 1.99it/s]
45%|βββββ | 42/93 [00:19<00:26, 1.92it/s]
46%|βββββ | 43/93 [00:19<00:27, 1.81it/s]
47%|βββββ | 44/93 [00:20<00:25, 1.91it/s]
48%|βββββ | 45/93 [00:20<00:23, 2.07it/s]
49%|βββββ | 46/93 [00:21<00:23, 2.01it/s]
51%|βββββ | 47/93 [00:21<00:24, 1.86it/s]
52%|ββββββ | 48/93 [00:22<00:23, 1.91it/s]
53%|ββββββ | 49/93 [00:22<00:20, 2.14it/s]
54%|ββββββ | 50/93 [00:23<00:21, 2.00it/s]
55%|ββββββ | 51/93 [00:23<00:20, 2.02it/s]
56%|ββββββ | 52/93 [00:24<00:20, 1.99it/s]
57%|ββββββ | 53/93 [00:24<00:20, 1.99it/s]
58%|ββββββ | 54/93 [00:25<00:20, 1.87it/s]
59%|ββββββ | 55/93 [00:25<00:20, 1.88it/s]
60%|ββββββ | 56/93 [00:26<00:17, 2.12it/s]
61%|βββββββ | 57/93 [00:26<00:16, 2.15it/s]
62%|βββββββ | 58/93 [00:26<00:15, 2.24it/s]
63%|βββββββ | 59/93 [00:27<00:15, 2.23it/s]
65%|βββββββ | 60/93 [00:27<00:15, 2.11it/s]
66%|βββββββ | 61/93 [00:28<00:14, 2.29it/s]
67%|βββββββ | 62/93 [00:28<00:14, 2.19it/s]
68%|βββββββ | 63/93 [00:29<00:13, 2.19it/s]
69%|βββββββ | 64/93 [00:31<00:27, 1.07it/s]
70%|βββββββ | 65/93 [00:31<00:22, 1.27it/s]
71%|βββββββ | 66/93 [00:32<00:20, 1.35it/s]
72%|ββββββββ | 67/93 [00:32<00:17, 1.52it/s]
73%|ββββββββ | 68/93 [00:33<00:17, 1.47it/s]
74%|ββββββββ | 69/93 [00:34<00:15, 1.59it/s]
75%|ββββββββ | 70/93 [00:34<00:14, 1.63it/s]
76%|ββββββββ | 71/93 [00:35<00:12, 1.78it/s]
77%|ββββββββ | 72/93 [00:35<00:11, 1.86it/s]
78%|ββββββββ | 73/93 [00:35<00:09, 2.02it/s]
80%|ββββββββ | 74/93 [00:36<00:09, 2.00it/s]
81%|ββββββββ | 75/93 [00:36<00:08, 2.09it/s]
82%|βββββββββ | 76/93 [00:37<00:08, 2.08it/s]
83%|βββββββββ | 77/93 [00:37<00:07, 2.13it/s]
84%|βββββββββ | 78/93 [00:38<00:07, 2.12it/s]
85%|βββββββββ | 79/93 [00:38<00:06, 2.02it/s]
86%|βββββββββ | 80/93 [00:39<00:06, 1.87it/s]
87%|βββββββββ | 81/93 [00:39<00:06, 1.87it/s]
88%|βββββββββ | 82/93 [00:40<00:05, 2.03it/s]
89%|βββββββββ | 83/93 [00:40<00:04, 2.04it/s]
90%|βββββββββ | 84/93 [00:41<00:04, 1.94it/s]
91%|ββββββββββ| 85/93 [00:41<00:03, 2.03it/s]
92%|ββββββββββ| 86/93 [00:42<00:03, 2.15it/s]
94%|ββββββββββ| 87/93 [00:42<00:02, 2.11it/s]
95%|ββββββββββ| 88/93 [00:43<00:02, 2.05it/s]
96%|ββββββββββ| 89/93 [00:43<00:01, 2.00it/s]
97%|ββββββββββ| 90/93 [00:44<00:01, 2.06it/s]
98%|ββββββββββ| 91/93 [00:44<00:01, 2.00it/s]
99%|ββββββββββ| 92/93 [00:45<00:00, 2.08it/s]
100%|ββββββββββ| 93/93 [00:45<00:00, 2.19it/s]
100%|ββββββββββ| 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%|β | 2/93 [00:00<00:14, 6.19it/s]
3%|β | 3/93 [00:00<00:22, 4.04it/s]
4%|β | 4/93 [00:01<00:24, 3.63it/s]
5%|β | 5/93 [00:01<00:32, 2.71it/s]
6%|β | 6/93 [00:01<00:32, 2.67it/s]
8%|β | 7/93 [00:02<00:32, 2.64it/s]
9%|β | 8/93 [00:02<00:32, 2.60it/s]
10%|β | 9/93 [00:03<00:32, 2.62it/s]
11%|β | 10/93 [00:03<00:31, 2.62it/s]
12%|ββ | 11/93 [00:03<00:33, 2.44it/s]
13%|ββ | 12/93 [00:04<00:33, 2.44it/s]
14%|ββ | 13/93 [00:04<00:32, 2.45it/s]
15%|ββ | 14/93 [00:05<00:35, 2.26it/s]
16%|ββ | 15/93 [00:05<00:34, 2.26it/s]
17%|ββ | 16/93 [00:06<00:32, 2.37it/s]
18%|ββ | 17/93 [00:06<00:33, 2.28it/s]
19%|ββ | 18/93 [00:06<00:31, 2.37it/s]
20%|ββ | 19/93 [00:07<00:31, 2.34it/s]
22%|βββ | 20/93 [00:07<00:29, 2.49it/s]
23%|βββ | 21/93 [00:08<00:28, 2.52it/s]
24%|βββ | 22/93 [00:08<00:30, 2.33it/s]
25%|βββ | 23/93 [00:09<00:28, 2.42it/s]
26%|βββ | 24/93 [00:09<00:29, 2.36it/s]
27%|βββ | 25/93 [00:10<00:33, 2.05it/s]
28%|βββ | 26/93 [00:10<00:33, 2.00it/s]
29%|βββ | 27/93 [00:11<00:30, 2.16it/s]
30%|βββ | 28/93 [00:11<00:28, 2.31it/s]
31%|βββ | 29/93 [00:11<00:26, 2.44it/s]
32%|ββββ | 30/93 [00:12<00:27, 2.32it/s]
33%|ββββ | 31/93 [00:12<00:26, 2.37it/s]
34%|ββββ | 32/93 [00:12<00:24, 2.44it/s]
35%|ββββ | 33/93 [00:13<00:24, 2.40it/s]
37%|ββββ | 34/93 [00:13<00:24, 2.44it/s]
38%|ββββ | 35/93 [00:14<00:22, 2.55it/s]
39%|ββββ | 36/93 [00:14<00:20, 2.76it/s]
40%|ββββ | 37/93 [00:14<00:19, 2.89it/s]
41%|ββββ | 38/93 [00:15<00:21, 2.57it/s]
42%|βββββ | 39/93 [00:15<00:20, 2.66it/s]
43%|βββββ | 40/93 [00:16<00:20, 2.58it/s]
44%|βββββ | 41/93 [00:16<00:21, 2.42it/s]
45%|βββββ | 42/93 [00:16<00:21, 2.41it/s]
46%|βββββ | 43/93 [00:17<00:23, 2.13it/s]
47%|βββββ | 44/93 [00:17<00:21, 2.26it/s]
48%|βββββ | 45/93 [00:18<00:20, 2.39it/s]
49%|βββββ | 46/93 [00:18<00:20, 2.28it/s]
51%|βββββ | 47/93 [00:19<00:20, 2.23it/s]
52%|ββββββ | 48/93 [00:19<00:19, 2.29it/s]
53%|ββββββ | 49/93 [00:19<00:18, 2.44it/s]
54%|ββββββ | 50/93 [00:20<00:18, 2.32it/s]
55%|ββββββ | 51/93 [00:20<00:18, 2.25it/s]
56%|ββββββ | 52/93 [00:21<00:19, 2.14it/s]
57%|ββββββ | 53/93 [00:21<00:18, 2.12it/s]
58%|ββββββ | 54/93 [00:22<00:17, 2.21it/s]
59%|ββββββ | 55/93 [00:22<00:15, 2.38it/s]
60%|ββββββ | 56/93 [00:22<00:14, 2.57it/s]
61%|βββββββ | 57/93 [00:23<00:14, 2.52it/s]
62%|βββββββ | 58/93 [00:23<00:13, 2.61it/s]
63%|βββββββ | 59/93 [00:24<00:13, 2.58it/s]
65%|βββββββ | 60/93 [00:24<00:13, 2.48it/s]
66%|βββββββ | 61/93 [00:24<00:12, 2.51it/s]
67%|βββββββ | 62/93 [00:25<00:13, 2.31it/s]
68%|βββββββ | 63/93 [00:25<00:12, 2.44it/s]
69%|βββββββ | 64/93 [00:27<00:25, 1.12it/s]
70%|βββββββ | 65/93 [00:28<00:20, 1.34it/s]
71%|βββββββ | 66/93 [00:28<00:17, 1.57it/s]
72%|ββββββββ | 67/93 [00:29<00:14, 1.76it/s]
73%|ββββββββ | 68/93 [00:29<00:14, 1.72it/s]
74%|ββββββββ | 69/93 [00:30<00:12, 1.90it/s]
75%|ββββββββ | 70/93 [00:30<00:11, 2.04it/s]
76%|ββββββββ | 71/93 [00:30<00:10, 2.09it/s]
77%|ββββββββ | 72/93 [00:31<00:10, 2.09it/s]
78%|ββββββββ | 73/93 [00:31<00:08, 2.25it/s]
80%|ββββββββ | 74/93 [00:32<00:08, 2.21it/s]
81%|ββββββββ | 75/93 [00:32<00:07, 2.40it/s]
82%|βββββββββ | 76/93 [00:33<00:07, 2.36it/s]
83%|βββββββββ | 77/93 [00:33<00:06, 2.50it/s]
84%|βββββββββ | 78/93 [00:33<00:05, 2.68it/s]
85%|βββββββββ | 79/93 [00:34<00:06, 2.25it/s]
86%|βββββββββ | 80/93 [00:34<00:06, 2.01it/s]
87%|βββββββββ | 81/93 [00:35<00:05, 2.18it/s]
88%|βββββββββ | 82/93 [00:35<00:04, 2.33it/s]
89%|βββββββββ | 83/93 [00:36<00:04, 2.39it/s]
90%|βββββββββ | 84/93 [00:36<00:04, 2.21it/s]
91%|ββββββββββ| 85/93 [00:37<00:03, 2.19it/s]
92%|ββββββββββ| 86/93 [00:37<00:03, 2.30it/s]
94%|ββββββββββ| 87/93 [00:37<00:02, 2.36it/s]
95%|ββββββββββ| 88/93 [00:38<00:02, 2.41it/s]
96%|ββββββββββ| 89/93 [00:38<00:01, 2.35it/s]
97%|ββββββββββ| 90/93 [00:39<00:01, 2.42it/s]
98%|ββββββββββ| 91/93 [00:39<00:00, 2.30it/s]
99%|ββββββββββ| 92/93 [00:39<00:00, 2.29it/s]
100%|ββββββββββ| 93/93 [00:40<00:00, 2.45it/s]
100%|ββββββββββ| 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%|β | 2/93 [00:00<00:22, 4.13it/s]
3%|β | 3/93 [00:00<00:26, 3.35it/s]
4%|β | 4/93 [00:01<00:28, 3.15it/s]
5%|β | 5/93 [00:01<00:33, 2.60it/s]
6%|β | 6/93 [00:02<00:33, 2.60it/s]
8%|β | 7/93 [00:02<00:32, 2.63it/s]
9%|β | 8/93 [00:02<00:33, 2.57it/s]
10%|β | 9/93 [00:03<00:34, 2.45it/s]
11%|β | 10/93 [00:03<00:33, 2.48it/s]
12%|ββ | 11/93 [00:04<00:32, 2.49it/s]
13%|ββ | 12/93 [00:04<00:32, 2.46it/s]
14%|ββ | 13/93 [00:04<00:33, 2.41it/s]
15%|ββ | 14/93 [00:05<00:35, 2.20it/s]
16%|ββ | 15/93 [00:05<00:35, 2.22it/s]
17%|ββ | 16/93 [00:06<00:35, 2.19it/s]
18%|ββ | 17/93 [00:06<00:35, 2.16it/s]
19%|ββ | 18/93 [00:07<00:35, 2.11it/s]
20%|ββ | 19/93 [00:07<00:34, 2.14it/s]
22%|βββ | 20/93 [00:08<00:33, 2.19it/s]
23%|βββ | 21/93 [00:08<00:31, 2.28it/s]
24%|βββ | 22/93 [00:09<00:33, 2.14it/s]
25%|βββ | 23/93 [00:09<00:31, 2.26it/s]
26%|βββ | 24/93 [00:09<00:29, 2.37it/s]
27%|βββ | 25/93 [00:10<00:29, 2.33it/s]
28%|βββ | 26/93 [00:10<00:29, 2.28it/s]
29%|βββ | 27/93 [00:11<00:28, 2.33it/s]
30%|βββ | 28/93 [00:11<00:27, 2.37it/s]
31%|βββ | 29/93 [00:12<00:26, 2.41it/s]
32%|ββββ | 30/93 [00:12<00:27, 2.30it/s]
33%|ββββ | 31/93 [00:13<00:29, 2.09it/s]
34%|ββββ | 32/93 [00:13<00:29, 2.04it/s]
35%|ββββ | 33/93 [00:14<00:29, 2.01it/s]
37%|ββββ | 34/93 [00:14<00:28, 2.09it/s]
38%|ββββ | 35/93 [00:15<00:27, 2.14it/s]
39%|ββββ | 36/93 [00:15<00:24, 2.31it/s]
40%|ββββ | 37/93 [00:15<00:23, 2.41it/s]
41%|ββββ | 38/93 [00:16<00:24, 2.26it/s]
42%|βββββ | 39/93 [00:16<00:24, 2.21it/s]
43%|βββββ | 40/93 [00:17<00:23, 2.23it/s]
44%|βββββ | 41/93 [00:17<00:23, 2.24it/s]
45%|βββββ | 42/93 [00:18<00:24, 2.08it/s]
46%|βββββ | 43/93 [00:18<00:26, 1.91it/s]
47%|βββββ | 44/93 [00:19<00:23, 2.06it/s]
48%|βββββ | 45/93 [00:19<00:22, 2.17it/s]
49%|βββββ | 46/93 [00:20<00:22, 2.06it/s]
51%|βββββ | 47/93 [00:20<00:22, 2.03it/s]
52%|ββββββ | 48/93 [00:21<00:21, 2.13it/s]
53%|ββββββ | 49/93 [00:21<00:19, 2.22it/s]
54%|ββββββ | 50/93 [00:22<00:19, 2.18it/s]
55%|ββββββ | 51/93 [00:22<00:19, 2.17it/s]
56%|ββββββ | 52/93 [00:22<00:18, 2.19it/s]
57%|ββββββ | 53/93 [00:23<00:19, 2.06it/s]
58%|ββββββ | 54/93 [00:23<00:18, 2.09it/s]
59%|ββββββ | 55/93 [00:24<00:18, 2.08it/s]
60%|ββββββ | 56/93 [00:24<00:17, 2.13it/s]
61%|βββββββ | 57/93 [00:25<00:17, 2.08it/s]
62%|βββββββ | 58/93 [00:25<00:15, 2.21it/s]
63%|βββββββ | 59/93 [00:26<00:15, 2.19it/s]
65%|βββββββ | 60/93 [00:26<00:15, 2.15it/s]
66%|βββββββ | 61/93 [00:27<00:13, 2.29it/s]
67%|βββββββ | 62/93 [00:27<00:14, 2.13it/s]
68%|βββββββ | 63/93 [00:28<00:13, 2.16it/s]
69%|βββββββ | 64/93 [00:30<00:26, 1.08it/s]
70%|βββββββ | 65/93 [00:30<00:21, 1.29it/s]
71%|βββββββ | 66/93 [00:30<00:18, 1.49it/s]
72%|ββββββββ | 67/93 [00:31<00:16, 1.60it/s]
73%|ββββββββ | 68/93 [00:31<00:14, 1.73it/s]
74%|ββββββββ | 69/93 [00:32<00:12, 1.85it/s]
75%|ββββββββ | 70/93 [00:32<00:12, 1.88it/s]
76%|ββββββββ | 71/93 [00:33<00:10, 2.02it/s]
77%|ββββββββ | 72/93 [00:33<00:10, 1.97it/s]
78%|ββββββββ | 73/93 [00:34<00:09, 2.12it/s]
80%|ββββββββ | 74/93 [00:34<00:09, 2.01it/s]
81%|ββββββββ | 75/93 [00:35<00:08, 2.13it/s]
82%|βββββββββ | 76/93 [00:35<00:08, 2.11it/s]
83%|βββββββββ | 77/93 [00:36<00:07, 2.17it/s]
84%|βββββββββ | 78/93 [00:36<00:07, 2.11it/s]
85%|βββββββββ | 79/93 [00:37<00:07, 1.87it/s]
86%|βββββββββ | 80/93 [00:37<00:07, 1.74it/s]
87%|βββββββββ | 81/93 [00:38<00:06, 1.87it/s]
88%|βββββββββ | 82/93 [00:38<00:05, 2.07it/s]
89%|βββββββββ | 83/93 [00:39<00:04, 2.15it/s]
90%|βββββββββ | 84/93 [00:39<00:04, 1.99it/s]
91%|ββββββββββ| 85/93 [00:40<00:03, 2.10it/s]
92%|ββββββββββ| 86/93 [00:40<00:03, 2.19it/s]
94%|ββββββββββ| 87/93 [00:41<00:02, 2.14it/s]
95%|ββββββββββ| 88/93 [00:41<00:02, 2.22it/s]
96%|ββββββββββ| 89/93 [00:41<00:01, 2.19it/s]
97%|ββββββββββ| 90/93 [00:42<00:01, 2.26it/s]
98%|ββββββββββ| 91/93 [00:42<00:00, 2.08it/s]
99%|ββββββββββ| 92/93 [00:43<00:00, 2.13it/s]
100%|ββββββββββ| 93/93 [00:43<00:00, 2.17it/s]
100%|ββββββββββ| 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%|β | 2/93 [00:00<00:20, 4.36it/s]
3%|β | 3/93 [00:00<00:25, 3.54it/s]
4%|β | 4/93 [00:01<00:29, 3.05it/s]
5%|β | 5/93 [00:01<00:32, 2.67it/s]
6%|β | 6/93 [00:02<00:32, 2.64it/s]
8%|β | 7/93 [00:02<00:32, 2.63it/s]
9%|β | 8/93 [00:02<00:33, 2.53it/s]
10%|β | 9/93 [00:03<00:34, 2.42it/s]
11%|β | 10/93 [00:03<00:33, 2.50it/s]
12%|ββ | 11/93 [00:04<00:35, 2.31it/s]
13%|ββ | 12/93 [00:04<00:33, 2.44it/s]
14%|ββ | 13/93 [00:04<00:32, 2.46it/s]
15%|ββ | 14/93 [00:05<00:35, 2.24it/s]
16%|ββ | 15/93 [00:05<00:33, 2.33it/s]
17%|ββ | 16/93 [00:06<00:31, 2.45it/s]
18%|ββ | 17/93 [00:06<00:33, 2.29it/s]
19%|ββ | 18/93 [00:07<00:31, 2.40it/s]
20%|ββ | 19/93 [00:07<00:30, 2.40it/s]
22%|βββ | 20/93 [00:07<00:30, 2.40it/s]
23%|βββ | 21/93 [00:08<00:28, 2.55it/s]
24%|βββ | 22/93 [00:08<00:29, 2.39it/s]
25%|βββ | 23/93 [00:09<00:28, 2.47it/s]
26%|βββ | 24/93 [00:09<00:26, 2.57it/s]
27%|βββ | 25/93 [00:10<00:30, 2.25it/s]
28%|βββ | 26/93 [00:10<00:27, 2.42it/s]
29%|βββ | 27/93 [00:10<00:26, 2.47it/s]
30%|βββ | 28/93 [00:11<00:25, 2.56it/s]
31%|βββ | 29/93 [00:11<00:24, 2.56it/s]
32%|ββββ | 30/93 [00:12<00:29, 2.16it/s]
33%|ββββ | 31/93 [00:12<00:28, 2.17it/s]
34%|ββββ | 32/93 [00:13<00:26, 2.26it/s]
35%|ββββ | 33/93 [00:13<00:27, 2.21it/s]
37%|ββββ | 34/93 [00:13<00:26, 2.19it/s]
38%|ββββ | 35/93 [00:14<00:25, 2.26it/s]
39%|ββββ | 36/93 [00:14<00:23, 2.46it/s]
40%|ββββ | 37/93 [00:14<00:20, 2.76it/s]
41%|ββββ | 38/93 [00:15<00:21, 2.54it/s]
42%|βββββ | 39/93 [00:15<00:20, 2.62it/s]
43%|βββββ | 40/93 [00:16<00:20, 2.63it/s]
44%|βββββ | 41/93 [00:16<00:20, 2.51it/s]
45%|βββββ | 42/93 [00:17<00:21, 2.34it/s]
46%|βββββ | 43/93 [00:17<00:21, 2.31it/s]
47%|βββββ | 44/93 [00:17<00:20, 2.40it/s]
48%|βββββ | 45/93 [00:18<00:19, 2.41it/s]
49%|βββββ | 46/93 [00:18<00:19, 2.36it/s]
51%|βββββ | 47/93 [00:19<00:21, 2.17it/s]
52%|ββββββ | 48/93 [00:19<00:19, 2.36it/s]
53%|ββββββ | 49/93 [00:19<00:17, 2.54it/s]
54%|ββββββ | 50/93 [00:20<00:17, 2.50it/s]
55%|ββββββ | 51/93 [00:20<00:16, 2.52it/s]
56%|ββββββ | 52/93 [00:21<00:16, 2.44it/s]
57%|ββββββ | 53/93 [00:21<00:17, 2.33it/s]
58%|ββββββ | 54/93 [00:22<00:17, 2.21it/s]
59%|ββββββ | 55/93 [00:22<00:17, 2.19it/s]
60%|ββββββ | 56/93 [00:22<00:15, 2.40it/s]
61%|βββββββ | 57/93 [00:23<00:15, 2.36it/s]
62%|βββββββ | 58/93 [00:23<00:13, 2.54it/s]
63%|βββββββ | 59/93 [00:24<00:12, 2.69it/s]
65%|βββββββ | 60/93 [00:24<00:12, 2.65it/s]
66%|βββββββ | 61/93 [00:24<00:12, 2.64it/s]
67%|βββββββ | 62/93 [00:25<00:12, 2.42it/s]
68%|βββββββ | 63/93 [00:25<00:12, 2.50it/s]
69%|βββββββ | 64/93 [00:27<00:25, 1.12it/s]
70%|βββββββ | 65/93 [00:28<00:20, 1.35it/s]
71%|βββββββ | 66/93 [00:28<00:17, 1.57it/s]
72%|ββββββββ | 67/93 [00:28<00:14, 1.77it/s]
73%|ββββββββ | 68/93 [00:29<00:13, 1.88it/s]
74%|ββββββββ | 69/93 [00:29<00:11, 2.12it/s]
75%|ββββββββ | 70/93 [00:30<00:09, 2.34it/s]
76%|ββββββββ | 71/93 [00:30<00:09, 2.40it/s]
77%|ββββββββ | 72/93 [00:30<00:09, 2.25it/s]
78%|ββββββββ | 73/93 [00:31<00:08, 2.46it/s]
80%|ββββββββ | 74/93 [00:31<00:08, 2.25it/s]
81%|ββββββββ | 75/93 [00:32<00:07, 2.42it/s]
82%|βββββββββ | 76/93 [00:32<00:07, 2.35it/s]
83%|βββββββββ | 77/93 [00:32<00:06, 2.45it/s]
84%|βββββββββ | 78/93 [00:33<00:05, 2.58it/s]
85%|βββββββββ | 79/93 [00:33<00:06, 2.12it/s]
86%|βββββββββ | 80/93 [00:34<00:06, 1.98it/s]
87%|βββββββββ | 81/93 [00:34<00:05, 2.18it/s]
88%|βββββββββ | 82/93 [00:35<00:04, 2.35it/s]
89%|βββββββββ | 83/93 [00:35<00:04, 2.35it/s]
90%|βββββββββ | 84/93 [00:36<00:04, 2.19it/s]
91%|ββββββββββ| 85/93 [00:36<00:03, 2.25it/s]
92%|ββββββββββ| 86/93 [00:37<00:03, 2.30it/s]
94%|ββββββββββ| 87/93 [00:37<00:02, 2.28it/s]
95%|ββββββββββ| 88/93 [00:37<00:02, 2.28it/s]
96%|ββββββββββ| 89/93 [00:38<00:01, 2.22it/s]
97%|ββββββββββ| 90/93 [00:38<00:01, 2.25it/s]
98%|ββββββββββ| 91/93 [00:39<00:00, 2.10it/s]
99%|ββββββββββ| 92/93 [00:39<00:00, 2.18it/s]
100%|ββββββββββ| 93/93 [00:40<00:00, 2.29it/s]
100%|ββββββββββ| 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%|β | 2/93 [00:00<00:18, 4.79it/s]
3%|β | 3/93 [00:00<00:24, 3.73it/s]
4%|β | 4/93 [00:01<00:26, 3.39it/s]
5%|β | 5/93 [00:01<00:29, 2.95it/s]
6%|β | 6/93 [00:01<00:30, 2.83it/s]
8%|β | 7/93 [00:02<00:31, 2.70it/s]
9%|β | 8/93 [00:02<00:32, 2.65it/s]
10%|β | 9/93 [00:03<00:32, 2.58it/s]
11%|β | 10/93 [00:03<00:33, 2.49it/s]
12%|ββ | 11/93 [00:03<00:32, 2.53it/s]
13%|ββ | 12/93 [00:04<00:32, 2.52it/s]
14%|ββ | 13/93 [00:04<00:31, 2.52it/s]
15%|ββ | 14/93 [00:05<00:36, 2.19it/s]
16%|ββ | 15/93 [00:05<00:35, 2.22it/s]
17%|ββ | 16/93 [00:06<00:32, 2.35it/s]
18%|ββ | 17/93 [00:06<00:33, 2.26it/s]
19%|ββ | 18/93 [00:06<00:31, 2.35it/s]
20%|ββ | 19/93 [00:07<00:31, 2.38it/s]
22%|βββ | 20/93 [00:07<00:29, 2.44it/s]
23%|βββ | 21/93 [00:08<00:28, 2.50it/s]
24%|βββ | 22/93 [00:08<00:31, 2.28it/s]
25%|βββ | 23/93 [00:09<00:30, 2.33it/s]
26%|βββ | 24/93 [00:09<00:28, 2.43it/s]
27%|βββ | 25/93 [00:10<00:30, 2.22it/s]
28%|βββ | 26/93 [00:10<00:30, 2.21it/s]
29%|βββ | 27/93 [00:10<00:28, 2.32it/s]
30%|βββ | 28/93 [00:11<00:27, 2.40it/s]
31%|βββ | 29/93 [00:11<00:25, 2.48it/s]
32%|ββββ | 30/93 [00:12<00:25, 2.45it/s]
33%|ββββ | 31/93 [00:12<00:26, 2.35it/s]
34%|ββββ | 32/93 [00:12<00:25, 2.42it/s]
35%|ββββ | 33/93 [00:13<00:24, 2.47it/s]
37%|ββββ | 34/93 [00:13<00:25, 2.34it/s]
38%|ββββ | 35/93 [00:14<00:24, 2.39it/s]
39%|ββββ | 36/93 [00:14<00:22, 2.54it/s]
40%|ββββ | 37/93 [00:14<00:20, 2.76it/s]
41%|ββββ | 38/93 [00:15<00:21, 2.56it/s]
42%|βββββ | 39/93 [00:15<00:21, 2.52it/s]
43%|βββββ | 40/93 [00:16<00:22, 2.36it/s]
44%|βββββ | 41/93 [00:16<00:22, 2.30it/s]
45%|βββββ | 42/93 [00:17<00:21, 2.32it/s]
46%|βββββ | 43/93 [00:17<00:23, 2.12it/s]
47%|βββββ | 44/93 [00:17<00:21, 2.27it/s]
48%|βββββ | 45/93 [00:18<00:21, 2.27it/s]
49%|βββββ | 46/93 [00:18<00:20, 2.28it/s]
51%|βββββ | 47/93 [00:19<00:24, 1.85it/s]
52%|ββββββ | 48/93 [00:19<00:21, 2.06it/s]
53%|ββββββ | 49/93 [00:20<00:19, 2.24it/s]
54%|ββββββ | 50/93 [00:20<00:18, 2.27it/s]
55%|ββββββ | 51/93 [00:21<00:19, 2.20it/s]
56%|ββββββ | 52/93 [00:21<00:18, 2.17it/s]
57%|ββββββ | 53/93 [00:22<00:19, 2.07it/s]
58%|ββββββ | 54/93 [00:22<00:18, 2.06it/s]
59%|ββββββ | 55/93 [00:23<00:17, 2.12it/s]
60%|ββββββ | 56/93 [00:23<00:15, 2.35it/s]
61%|βββββββ | 57/93 [00:23<00:15, 2.37it/s]
62%|βββββββ | 58/93 [00:24<00:13, 2.52it/s]
63%|βββββββ | 59/93 [00:24<00:14, 2.40it/s]
65%|βββββββ | 60/93 [00:25<00:14, 2.36it/s]
66%|βββββββ | 61/93 [00:25<00:13, 2.44it/s]
67%|βββββββ | 62/93 [00:25<00:13, 2.31it/s]
68%|βββββββ | 63/93 [00:26<00:12, 2.31it/s]
69%|βββββββ | 64/93 [00:28<00:26, 1.10it/s]
70%|βββββββ | 65/93 [00:28<00:21, 1.33it/s]
71%|βββββββ | 66/93 [00:29<00:17, 1.56it/s]
72%|ββββββββ | 67/93 [00:29<00:14, 1.76it/s]
73%|ββββββββ | 68/93 [00:30<00:14, 1.76it/s]
74%|ββββββββ | 69/93 [00:30<00:13, 1.84it/s]
75%|ββββββββ | 70/93 [00:31<00:11, 1.92it/s]
76%|ββββββββ | 71/93 [00:31<00:10, 2.05it/s]
77%|ββββββββ | 72/93 [00:32<00:10, 2.06it/s]
78%|ββββββββ | 73/93 [00:32<00:08, 2.26it/s]
80%|ββββββββ | 74/93 [00:32<00:09, 2.06it/s]
81%|ββββββββ | 75/93 [00:33<00:08, 2.22it/s]
82%|βββββββββ | 76/93 [00:33<00:07, 2.15it/s]
83%|βββββββββ | 77/93 [00:34<00:07, 2.11it/s]
84%|βββββββββ | 78/93 [00:34<00:07, 2.02it/s]
85%|βββββββββ | 79/93 [00:35<00:07, 1.98it/s]
86%|βββββββββ | 80/93 [00:35<00:06, 1.95it/s]
87%|βββββββββ | 81/93 [00:36<00:06, 1.86it/s]
88%|βββββββββ | 82/93 [00:36<00:05, 2.08it/s]
89%|βββββββββ | 83/93 [00:37<00:04, 2.23it/s]
90%|βββββββββ | 84/93 [00:37<00:04, 2.17it/s]
91%|ββββββββββ| 85/93 [00:38<00:03, 2.16it/s]
92%|ββββββββββ| 86/93 [00:38<00:03, 2.25it/s]
94%|ββββββββββ| 87/93 [00:39<00:02, 2.25it/s]
95%|ββββββββββ| 88/93 [00:39<00:02, 2.15it/s]
96%|ββββββββββ| 89/93 [00:40<00:01, 2.12it/s]
97%|ββββββββββ| 90/93 [00:40<00:01, 2.18it/s]
98%|ββββββββββ| 91/93 [00:41<00:00, 2.05it/s]
99%|ββββββββββ| 92/93 [00:41<00:00, 2.15it/s]
100%|ββββββββββ| 93/93 [00:41<00:00, 2.32it/s]
100%|ββββββββββ| 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%|β | 2/93 [00:00<00:17, 5.33it/s]
3%|β | 3/93 [00:00<00:23, 3.85it/s]
4%|β | 4/93 [00:01<00:24, 3.70it/s]
5%|β | 5/93 [00:01<00:28, 3.10it/s]
6%|β | 6/93 [00:01<00:29, 2.96it/s]
8%|β | 7/93 [00:02<00:30, 2.80it/s]
9%|β | 8/93 [00:02<00:29, 2.87it/s]
10%|β | 9/93 [00:03<00:32, 2.55it/s]
11%|β | 10/93 [00:03<00:33, 2.47it/s]
12%|ββ | 11/93 [00:03<00:33, 2.48it/s]
13%|ββ | 12/93 [00:04<00:31, 2.55it/s]
14%|ββ | 13/93 [00:04<00:32, 2.49it/s]
15%|ββ | 14/93 [00:05<00:35, 2.23it/s]
16%|ββ | 15/93 [00:05<00:36, 2.12it/s]
17%|ββ | 16/93 [00:06<00:34, 2.25it/s]
18%|ββ | 17/93 [00:06<00:33, 2.30it/s]
19%|ββ | 18/93 [00:06<00:31, 2.39it/s]
20%|ββ | 19/93 [00:07<00:31, 2.37it/s]
22%|βββ | 20/93 [00:07<00:28, 2.53it/s]
23%|βββ | 21/93 [00:08<00:27, 2.59it/s]
24%|βββ | 22/93 [00:08<00:29, 2.41it/s]
25%|βββ | 23/93 [00:08<00:28, 2.47it/s]
26%|βββ | 24/93 [00:09<00:28, 2.43it/s]
27%|βββ | 25/93 [00:09<00:27, 2.49it/s]
28%|βββ | 26/93 [00:10<00:27, 2.42it/s]
29%|βββ | 27/93 [00:10<00:26, 2.45it/s]
30%|βββ | 28/93 [00:10<00:24, 2.65it/s]
31%|βββ | 29/93 [00:11<00:23, 2.75it/s]
32%|ββββ | 30/93 [00:11<00:23, 2.70it/s]
33%|ββββ | 31/93 [00:11<00:23, 2.59it/s]
34%|ββββ | 32/93 [00:12<00:23, 2.63it/s]
35%|ββββ | 33/93 [00:12<00:23, 2.56it/s]
37%|ββββ | 34/93 [00:13<00:23, 2.54it/s]
38%|ββββ | 35/93 [00:13<00:22, 2.61it/s]
39%|ββββ | 36/93 [00:13<00:20, 2.79it/s]
40%|ββββ | 37/93 [00:14<00:18, 3.07it/s]
41%|ββββ | 38/93 [00:14<00:20, 2.71it/s]
42%|βββββ | 39/93 [00:14<00:19, 2.74it/s]
43%|βββββ | 40/93 [00:15<00:19, 2.67it/s]
44%|βββββ | 41/93 [00:15<00:20, 2.58it/s]
45%|βββββ | 42/93 [00:16<00:20, 2.48it/s]
46%|βββββ | 43/93 [00:16<00:22, 2.24it/s]
47%|βββββ | 44/93 [00:17<00:20, 2.34it/s]
48%|βββββ | 45/93 [00:17<00:19, 2.49it/s]
49%|βββββ | 46/93 [00:17<00:19, 2.45it/s]
51%|βββββ | 47/93 [00:18<00:19, 2.41it/s]
52%|ββββββ | 48/93 [00:18<00:18, 2.41it/s]
53%|ββββββ | 49/93 [00:19<00:17, 2.58it/s]
54%|ββββββ | 50/93 [00:19<00:18, 2.32it/s]
55%|ββββββ | 51/93 [00:19<00:17, 2.37it/s]
56%|ββββββ | 52/93 [00:20<00:17, 2.35it/s]
57%|ββββββ | 53/93 [00:20<00:18, 2.17it/s]
58%|ββββββ | 54/93 [00:21<00:18, 2.10it/s]
59%|ββββββ | 55/93 [00:21<00:17, 2.22it/s]
60%|ββββββ | 56/93 [00:22<00:15, 2.41it/s]
61%|βββββββ | 57/93 [00:22<00:14, 2.44it/s]
62%|βββββββ | 58/93 [00:22<00:13, 2.60it/s]
63%|βββββββ | 59/93 [00:23<00:12, 2.72it/s]
65%|βββββββ | 60/93 [00:23<00:13, 2.52it/s]
66%|βββββββ | 61/93 [00:24<00:12, 2.56it/s]
67%|βββββββ | 62/93 [00:24<00:13, 2.36it/s]
68%|βββββββ | 63/93 [00:25<00:13, 2.30it/s]
69%|βββββββ | 64/93 [00:27<00:26, 1.11it/s]
70%|βββββββ | 65/93 [00:27<00:20, 1.36it/s]
71%|βββββββ | 66/93 [00:27<00:16, 1.59it/s]
72%|ββββββββ | 67/93 [00:28<00:14, 1.78it/s]
73%|ββββββββ | 68/93 [00:28<00:13, 1.81it/s]
74%|ββββββββ | 69/93 [00:29<00:12, 1.96it/s]
75%|ββββββββ | 70/93 [00:29<00:11, 2.09it/s]
76%|ββββββββ | 71/93 [00:29<00:09, 2.20it/s]
77%|ββββββββ | 72/93 [00:30<00:09, 2.22it/s]
78%|ββββββββ | 73/93 [00:30<00:08, 2.40it/s]
80%|ββββββββ | 74/93 [00:31<00:08, 2.33it/s]
81%|ββββββββ | 75/93 [00:31<00:07, 2.37it/s]
82%|βββββββββ | 76/93 [00:31<00:07, 2.39it/s]
83%|βββββββββ | 77/93 [00:32<00:06, 2.52it/s]
84%|βββββββββ | 78/93 [00:32<00:05, 2.57it/s]
85%|βββββββββ | 79/93 [00:33<00:06, 2.19it/s]
86%|βββββββββ | 80/93 [00:33<00:06, 2.01it/s]
87%|βββββββββ | 81/93 [00:34<00:05, 2.05it/s]
88%|βββββββββ | 82/93 [00:34<00:04, 2.23it/s]
89%|βββββββββ | 83/93 [00:35<00:04, 2.33it/s]
90%|βββββββββ | 84/93 [00:35<00:03, 2.25it/s]
91%|ββββββββββ| 85/93 [00:35<00:03, 2.27it/s]
92%|ββββββββββ| 86/93 [00:36<00:02, 2.36it/s]
94%|ββββββββββ| 87/93 [00:36<00:02, 2.32it/s]
95%|ββββββββββ| 88/93 [00:37<00:02, 2.29it/s]
96%|ββββββββββ| 89/93 [00:37<00:01, 2.33it/s]
97%|ββββββββββ| 90/93 [00:38<00:01, 2.40it/s]
98%|ββββββββββ| 91/93 [00:38<00:00, 2.26it/s]
99%|ββββββββββ| 92/93 [00:38<00:00, 2.32it/s]
100%|ββββββββββ| 93/93 [00:39<00:00, 2.48it/s]
100%|ββββββββββ| 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%|β | 2/93 [00:00<00:31, 2.86it/s]
3%|β | 3/93 [00:01<00:36, 2.44it/s]
4%|β | 4/93 [00:01<00:38, 2.33it/s]
5%|β | 5/93 [00:02<00:42, 2.05it/s]
6%|β | 6/93 [00:02<00:42, 2.06it/s]
8%|β | 7/93 [00:03<00:43, 1.97it/s]
9%|β | 8/93 [00:03<00:41, 2.03it/s]
10%|β | 9/93 [00:04<00:40, 2.07it/s]
11%|β | 10/93 [00:04<00:41, 2.00it/s]
12%|ββ | 11/93 [00:05<00:43, 1.89it/s]
13%|ββ | 12/93 [00:05<00:43, 1.85it/s]
14%|ββ | 13/93 [00:06<00:41, 1.94it/s]
15%|ββ | 14/93 [00:07<00:45, 1.72it/s]
16%|ββ | 15/93 [00:07<00:44, 1.74it/s]
17%|ββ | 16/93 [00:08<00:44, 1.73it/s]
18%|ββ | 17/93 [00:08<00:45, 1.69it/s]
19%|ββ | 18/93 [00:09<00:42, 1.78it/s]
20%|ββ | 19/93 [00:09<00:42, 1.75it/s]
22%|βββ | 20/93 [00:10<00:40, 1.80it/s]
23%|βββ | 21/93 [00:11<00:39, 1.82it/s]
24%|βββ | 22/93 [00:11<00:38, 1.84it/s]
25%|βββ | 23/93 [00:12<00:37, 1.87it/s]
26%|βββ | 24/93 [00:12<00:38, 1.82it/s]
27%|βββ | 25/93 [00:14<00:54, 1.25it/s]
28%|βββ | 26/93 [00:14<00:48, 1.38it/s]
29%|βββ | 27/93 [00:15<00:42, 1.55it/s]
30%|βββ | 28/93 [00:15<00:37, 1.73it/s]
31%|βββ | 29/93 [00:15<00:34, 1.84it/s]
32%|ββββ | 30/93 [00:16<00:33, 1.86it/s]
33%|ββββ | 31/93 [00:17<00:33, 1.83it/s]
34%|ββββ | 32/93 [00:17<00:33, 1.82it/s]
35%|ββββ | 33/93 [00:18<00:33, 1.78it/s]
37%|ββββ | 34/93 [00:18<00:36, 1.62it/s]
38%|ββββ | 35/93 [00:19<00:33, 1.75it/s]
39%|ββββ | 36/93 [00:19<00:33, 1.72it/s]
40%|ββββ | 37/93 [00:20<00:31, 1.80it/s]
41%|ββββ | 38/93 [00:21<00:35, 1.55it/s]
42%|βββββ | 39/93 [00:21<00:32, 1.64it/s]
43%|βββββ | 40/93 [00:22<00:32, 1.64it/s]
44%|βββββ | 41/93 [00:23<00:33, 1.55it/s]
45%|βββββ | 42/93 [00:23<00:33, 1.54it/s]
46%|βββββ | 43/93 [00:24<00:33, 1.49it/s]
47%|βββββ | 44/93 [00:25<00:32, 1.52it/s]
48%|βββββ | 45/93 [00:25<00:30, 1.59it/s]
49%|βββββ | 46/93 [00:26<00:30, 1.55it/s]
51%|βββββ | 47/93 [00:27<00:28, 1.61it/s]
52%|ββββββ | 48/93 [00:27<00:27, 1.65it/s]
53%|ββββββ | 49/93 [00:28<00:25, 1.72it/s]
54%|ββββββ | 50/93 [00:28<00:25, 1.71it/s]
55%|ββββββ | 51/93 [00:29<00:24, 1.75it/s]
56%|ββββββ | 52/93 [00:29<00:23, 1.75it/s]
57%|ββββββ | 53/93 [00:30<00:23, 1.69it/s]
58%|ββββββ | 54/93 [00:31<00:24, 1.57it/s]
59%|ββββββ | 55/93 [00:31<00:23, 1.65it/s]
60%|ββββββ | 56/93 [00:32<00:20, 1.81it/s]
61%|βββββββ | 57/93 [00:32<00:18, 1.90it/s]
62%|βββββββ | 58/93 [00:33<00:18, 1.90it/s]
63%|βββββββ | 59/93 [00:33<00:17, 1.92it/s]
65%|βββββββ | 60/93 [00:34<00:16, 1.95it/s]
66%|βββββββ | 61/93 [00:34<00:15, 2.06it/s]
67%|βββββββ | 62/93 [00:35<00:18, 1.72it/s]
68%|βββββββ | 63/93 [00:36<00:17, 1.67it/s]
69%|βββββββ | 64/93 [00:38<00:30, 1.05s/it]
70%|βββββββ | 65/93 [00:38<00:25, 1.10it/s]
71%|βββββββ | 66/93 [00:39<00:21, 1.25it/s]
72%|ββββββββ | 67/93 [00:39<00:19, 1.35it/s]
73%|ββββββββ | 68/93 [00:40<00:18, 1.32it/s]
74%|ββββββββ | 69/93 [00:41<00:16, 1.42it/s]
75%|ββββββββ | 70/93 [00:41<00:15, 1.51it/s]
76%|ββββββββ | 71/93 [00:42<00:14, 1.51it/s]
77%|ββββββββ | 72/93 [00:43<00:14, 1.50it/s]
78%|ββββββββ | 73/93 [00:43<00:12, 1.63it/s]
80%|ββββββββ | 74/93 [00:44<00:11, 1.70it/s]
81%|ββββββββ | 75/93 [00:44<00:10, 1.74it/s]
82%|βββββββββ | 76/93 [00:46<00:16, 1.02it/s]
83%|βββββββββ | 77/93 [00:47<00:13, 1.16it/s]
84%|βββββββββ | 78/93 [00:47<00:11, 1.31it/s]
85%|βββββββββ | 79/93 [00:48<00:10, 1.38it/s]
86%|βββββββββ | 80/93 [00:50<00:14, 1.10s/it]
87%|βββββββββ | 81/93 [00:51<00:11, 1.01it/s]
88%|βββββββββ | 82/93 [00:51<00:09, 1.20it/s]
89%|βββββββββ | 83/93 [00:52<00:07, 1.31it/s]
90%|βββββββββ | 84/93 [00:52<00:06, 1.41it/s]
91%|ββββββββββ| 85/93 [00:53<00:05, 1.47it/s]
92%|ββββββββββ| 86/93 [00:53<00:04, 1.61it/s]
94%|ββββββββββ| 87/93 [00:54<00:03, 1.53it/s]
95%|ββββββββββ| 88/93 [00:55<00:03, 1.61it/s]
96%|ββββββββββ| 89/93 [00:55<00:02, 1.71it/s]
97%|ββββββββββ| 90/93 [00:56<00:01, 1.73it/s]
98%|ββββββββββ| 91/93 [00:56<00:01, 1.67it/s]
99%|ββββββββββ| 92/93 [00:57<00:00, 1.73it/s]
100%|ββββββββββ| 93/93 [00:57<00:00, 1.82it/s]
100%|ββββββββββ| 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%|β | 2/93 [00:00<00:16, 5.41it/s]
3%|β | 3/93 [00:00<00:24, 3.74it/s]
4%|β | 4/93 [00:01<00:25, 3.47it/s]
5%|β | 5/93 [00:01<00:30, 2.85it/s]
6%|β | 6/93 [00:01<00:30, 2.87it/s]
8%|β | 7/93 [00:02<00:30, 2.81it/s]
9%|β | 8/93 [00:02<00:30, 2.79it/s]
10%|β | 9/93 [00:03<00:33, 2.49it/s]
11%|β | 10/93 [00:03<00:33, 2.49it/s]
12%|ββ | 11/93 [00:03<00:33, 2.42it/s]
13%|ββ | 12/93 [00:04<00:32, 2.51it/s]
14%|ββ | 13/93 [00:04<00:32, 2.45it/s]
15%|ββ | 14/93 [00:05<00:36, 2.18it/s]
16%|ββ | 15/93 [00:05<00:36, 2.16it/s]
17%|ββ | 16/93 [00:06<00:34, 2.22it/s]
18%|ββ | 17/93 [00:06<00:33, 2.25it/s]
19%|ββ | 18/93 [00:07<00:33, 2.22it/s]
20%|ββ | 19/93 [00:07<00:33, 2.21it/s]
22%|βββ | 20/93 [00:07<00:30, 2.37it/s]
23%|βββ | 21/93 [00:08<00:29, 2.45it/s]
24%|βββ | 22/93 [00:08<00:29, 2.41it/s]
25%|βββ | 23/93 [00:09<00:29, 2.41it/s]
26%|βββ | 24/93 [00:09<00:29, 2.34it/s]
27%|βββ | 25/93 [00:09<00:26, 2.54it/s]
28%|βββ | 26/93 [00:10<00:26, 2.57it/s]
29%|βββ | 27/93 [00:10<00:24, 2.65it/s]
30%|βββ | 28/93 [00:10<00:23, 2.74it/s]
31%|βββ | 29/93 [00:11<00:24, 2.63it/s]
32%|ββββ | 30/93 [00:11<00:24, 2.61it/s]
33%|ββββ | 31/93 [00:12<00:24, 2.54it/s]
34%|ββββ | 32/93 [00:12<00:24, 2.52it/s]
35%|ββββ | 33/93 [00:13<00:26, 2.31it/s]
37%|ββββ | 34/93 [00:13<00:24, 2.41it/s]
38%|ββββ | 35/93 [00:13<00:23, 2.43it/s]
39%|ββββ | 36/93 [00:14<00:22, 2.58it/s]
40%|ββββ | 37/93 [00:14<00:20, 2.70it/s]
41%|ββββ | 38/93 [00:14<00:21, 2.60it/s]
42%|βββββ | 39/93 [00:15<00:19, 2.80it/s]
43%|βββββ | 40/93 [00:15<00:20, 2.62it/s]
44%|βββββ | 41/93 [00:16<00:20, 2.54it/s]
45%|βββββ | 42/93 [00:16<00:20, 2.44it/s]
46%|βββββ | 43/93 [00:17<00:22, 2.20it/s]
47%|βββββ | 44/93 [00:17<00:20, 2.38it/s]
48%|βββββ | 45/93 [00:17<00:18, 2.55it/s]
49%|βββββ | 46/93 [00:18<00:18, 2.53it/s]
51%|βββββ | 47/93 [00:18<00:18, 2.43it/s]
52%|ββββββ | 48/93 [00:18<00:17, 2.59it/s]
53%|ββββββ | 49/93 [00:19<00:16, 2.74it/s]
54%|ββββββ | 50/93 [00:19<00:17, 2.50it/s]
55%|ββββββ | 51/93 [00:20<00:17, 2.38it/s]
56%|ββββββ | 52/93 [00:20<00:18, 2.23it/s]
57%|ββββββ | 53/93 [00:21<00:20, 1.98it/s]
58%|ββββββ | 54/93 [00:21<00:19, 2.03it/s]
59%|ββββββ | 55/93 [00:22<00:17, 2.17it/s]
60%|ββββββ | 56/93 [00:22<00:16, 2.26it/s]
61%|βββββββ | 57/93 [00:23<00:16, 2.12it/s]
62%|βββββββ | 58/93 [00:23<00:15, 2.28it/s]
63%|βββββββ | 59/93 [00:23<00:14, 2.33it/s]
65%|βββββββ | 60/93 [00:24<00:14, 2.32it/s]
66%|βββββββ | 61/93 [00:24<00:12, 2.48it/s]
67%|βββββββ | 62/93 [00:25<00:13, 2.37it/s]
68%|βββββββ | 63/93 [00:25<00:12, 2.40it/s]
69%|βββββββ | 64/93 [00:27<00:25, 1.13it/s]
70%|βββββββ | 65/93 [00:28<00:21, 1.31it/s]
71%|βββββββ | 66/93 [00:28<00:18, 1.48it/s]
72%|ββββββββ | 67/93 [00:28<00:14, 1.75it/s]
73%|ββββββββ | 68/93 [00:29<00:13, 1.82it/s]
74%|ββββββββ | 69/93 [00:29<00:12, 2.00it/s]
75%|ββββββββ | 70/93 [00:30<00:10, 2.19it/s]
76%|ββββββββ | 71/93 [00:30<00:09, 2.30it/s]
77%|ββββββββ | 72/93 [00:31<00:09, 2.16it/s]
78%|ββββββββ | 73/93 [00:31<00:08, 2.35it/s]
80%|ββββββββ | 74/93 [00:31<00:08, 2.28it/s]
81%|ββββββββ | 75/93 [00:32<00:07, 2.39it/s]
82%|βββββββββ | 76/93 [00:32<00:07, 2.33it/s]
83%|βββββββββ | 77/93 [00:33<00:06, 2.32it/s]
84%|βββββββββ | 78/93 [00:33<00:06, 2.30it/s]
85%|βββββββββ | 79/93 [00:34<00:06, 2.03it/s]
86%|βββββββββ | 80/93 [00:34<00:06, 2.01it/s]
87%|βββββββββ | 81/93 [00:35<00:05, 2.04it/s]
88%|βββββββββ | 82/93 [00:35<00:04, 2.23it/s]
89%|βββββββββ | 83/93 [00:35<00:04, 2.22it/s]
90%|βββββββββ | 84/93 [00:36<00:04, 2.16it/s]
91%|ββββββββββ| 85/93 [00:36<00:03, 2.29it/s]
92%|ββββββββββ| 86/93 [00:37<00:02, 2.35it/s]
94%|ββββββββββ| 87/93 [00:37<00:02, 2.41it/s]
95%|ββββββββββ| 88/93 [00:38<00:02, 2.42it/s]
96%|ββββββββββ| 89/93 [00:38<00:01, 2.36it/s]
97%|ββββββββββ| 90/93 [00:38<00:01, 2.39it/s]
98%|ββββββββββ| 91/93 [00:39<00:00, 2.25it/s]
99%|ββββββββββ| 92/93 [00:39<00:00, 2.28it/s]
100%|ββββββββββ| 93/93 [00:40<00:00, 2.31it/s]
100%|ββββββββββ| 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 ***** |
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[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%|β | 2/93 [00:00<00:14, 6.13it/s]
3%|β | 3/93 [00:00<00:21, 4.23it/s]
4%|β | 4/93 [00:01<00:24, 3.56it/s]
5%|β | 5/93 [00:01<00:30, 2.87it/s]
6%|β | 6/93 [00:01<00:29, 2.96it/s]
8%|β | 7/93 [00:02<00:29, 2.87it/s]
9%|β | 8/93 [00:02<00:29, 2.89it/s]
10%|β | 9/93 [00:02<00:29, 2.87it/s]
11%|β | 10/93 [00:03<00:29, 2.80it/s]
12%|ββ | 11/93 [00:03<00:29, 2.75it/s]
13%|ββ | 12/93 [00:03<00:28, 2.80it/s]
14%|ββ | 13/93 [00:04<00:29, 2.74it/s]
15%|ββ | 14/93 [00:04<00:30, 2.58it/s]
16%|ββ | 15/93 [00:05<00:30, 2.60it/s]
17%|ββ | 16/93 [00:05<00:28, 2.66it/s]
18%|ββ | 17/93 [00:06<00:31, 2.45it/s]
19%|ββ | 18/93 [00:06<00:29, 2.56it/s]
20%|ββ | 19/93 [00:06<00:29, 2.53it/s]
22%|βββ | 20/93 [00:07<00:28, 2.59it/s]
23%|βββ | 21/93 [00:07<00:26, 2.71it/s]
24%|βββ | 22/93 [00:07<00:28, 2.45it/s]
25%|βββ | 23/93 [00:08<00:28, 2.43it/s]
26%|βββ | 24/93 [00:08<00:27, 2.52it/s]
27%|βββ | 25/93 [00:09<00:27, 2.50it/s]
28%|βββ | 26/93 [00:09<00:27, 2.46it/s]
29%|βββ | 27/93 [00:09<00:25, 2.60it/s]
30%|βββ | 28/93 [00:10<00:23, 2.73it/s]
31%|βββ | 29/93 [00:10<00:23, 2.76it/s]
32%|ββββ | 30/93 [00:11<00:23, 2.64it/s]
33%|ββββ | 31/93 [00:11<00:28, 2.21it/s]
34%|ββββ | 32/93 [00:11<00:25, 2.42it/s]
35%|ββββ | 33/93 [00:12<00:24, 2.48it/s]
37%|ββββ | 34/93 [00:12<00:22, 2.63it/s]
38%|ββββ | 35/93 [00:13<00:21, 2.68it/s]
39%|ββββ | 36/93 [00:13<00:19, 2.87it/s]
40%|ββββ | 37/93 [00:13<00:18, 2.95it/s]
41%|ββββ | 38/93 [00:13<00:19, 2.87it/s]
42%|βββββ | 39/93 [00:14<00:17, 3.02it/s]
43%|βββββ | 40/93 [00:14<00:17, 2.96it/s]
44%|βββββ | 41/93 [00:14<00:17, 2.99it/s]
45%|βββββ | 42/93 [00:15<00:18, 2.69it/s]
46%|βββββ | 43/93 [00:15<00:19, 2.58it/s]
47%|βββββ | 44/93 [00:16<00:19, 2.55it/s]
48%|βββββ | 45/93 [00:16<00:17, 2.71it/s]
49%|βββββ | 46/93 [00:16<00:16, 2.80it/s]
51%|βββββ | 47/93 [00:17<00:17, 2.63it/s]
52%|ββββββ | 48/93 [00:17<00:16, 2.75it/s]
53%|ββββββ | 49/93 [00:17<00:15, 2.91it/s]
54%|ββββββ | 50/93 [00:18<00:15, 2.85it/s]
55%|ββββββ | 51/93 [00:18<00:15, 2.79it/s]
56%|ββββββ | 52/93 [00:19<00:15, 2.65it/s]
57%|ββββββ | 53/93 [00:19<00:15, 2.60it/s]
58%|ββββββ | 54/93 [00:19<00:14, 2.62it/s]
59%|ββββββ | 55/93 [00:20<00:13, 2.73it/s]
60%|ββββββ | 56/93 [00:20<00:13, 2.83it/s]
61%|βββββββ | 57/93 [00:20<00:12, 2.92it/s]
62%|βββββββ | 58/93 [00:21<00:11, 2.94it/s]
63%|βββββββ | 59/93 [00:21<00:11, 3.00it/s]
65%|βββββββ | 60/93 [00:21<00:10, 3.05it/s]
66%|βββββββ | 61/93 [00:22<00:10, 3.09it/s]
67%|βββββββ | 62/93 [00:22<00:10, 2.82it/s]
68%|βββββββ | 63/93 [00:22<00:10, 2.87it/s]
69%|βββββββ | 64/93 [00:23<00:12, 2.36it/s]
70%|βββββββ | 65/93 [00:23<00:11, 2.51it/s]
71%|βββββββ | 66/93 [00:24<00:11, 2.42it/s]
72%|ββββββββ | 67/93 [00:24<00:10, 2.47it/s]
73%|ββββββββ | 68/93 [00:25<00:10, 2.48it/s]
74%|ββββββββ | 69/93 [00:25<00:09, 2.47it/s]
75%|ββββββββ | 70/93 [00:25<00:09, 2.49it/s]
76%|ββββββββ | 71/93 [00:26<00:08, 2.67it/s]
77%|ββββββββ | 72/93 [00:26<00:08, 2.55it/s]
78%|ββββββββ | 73/93 [00:26<00:07, 2.74it/s]
80%|ββββββββ | 74/93 [00:27<00:07, 2.57it/s]
81%|ββββββββ | 75/93 [00:27<00:06, 2.79it/s]
82%|βββββββββ | 76/93 [00:28<00:06, 2.55it/s]
83%|βββββββββ | 77/93 [00:28<00:06, 2.66it/s]
84%|βββββββββ | 78/93 [00:28<00:05, 2.70it/s]
85%|βββββββββ | 79/93 [00:29<00:06, 2.27it/s]
86%|βββββββββ | 80/93 [00:29<00:05, 2.20it/s]
87%|βββββββββ | 81/93 [00:30<00:05, 2.32it/s]
88%|βββββββββ | 82/93 [00:30<00:04, 2.46it/s]
89%|βββββββββ | 83/93 [00:31<00:03, 2.52it/s]
90%|βββββββββ | 84/93 [00:31<00:03, 2.34it/s]
91%|ββββββββββ| 85/93 [00:31<00:03, 2.46it/s]
92%|ββββββββββ| 86/93 [00:32<00:02, 2.60it/s]
94%|ββββββββββ| 87/93 [00:32<00:02, 2.67it/s]
95%|ββββββββββ| 88/93 [00:32<00:01, 2.74it/s]
96%|ββββββββββ| 89/93 [00:33<00:01, 2.65it/s]
97%|ββββββββββ| 90/93 [00:33<00:01, 2.62it/s]
98%|ββββββββββ| 91/93 [00:34<00:00, 2.39it/s]
99%|ββββββββββ| 92/93 [00:34<00:00, 2.37it/s]
100%|ββββββββββ| 93/93 [00:34<00:00, 2.50it/s]
100%|ββββββββββ| 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 |
|
|