| 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). | |
| 02/05/2024 17:21:05 - WARNING - __main__ - Process rank: 0, device: cuda:0, n_gpu: 1, distributed training: False, 16-bits training: False | |
| 02/05/2024 17:21:05 - 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=True, | |
| 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/cascaded_SLU/runs/Feb05_17-21-05_chasma-02, | |
| 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/cascaded_SLU, | |
| 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=[], | |
| 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/cascaded_SLU, | |
| 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/speech_massive_cascaded/f36c9e4210ec02a91ee05c9fa785d90aec211ba2025363c65b643c68e109b293 | |
| 02/05/2024 17:21:05 - INFO - datasets.info - Loading Dataset Infos from /beegfs/scratch/user/blee/hugging-face/models/modules/datasets_modules/datasets/speech_massive_cascaded/f36c9e4210ec02a91ee05c9fa785d90aec211ba2025363c65b643c68e109b293 | |
| Overwrite dataset info from restored data version if exists. | |
| 02/05/2024 17:21:05 - INFO - datasets.builder - Overwrite dataset info from restored data version if exists. | |
| Loading Dataset info from /beegfs/scratch/user/blee/hugging-face/models/datasets/speech_massive_cascaded/multilingual-test/1.0.0/f36c9e4210ec02a91ee05c9fa785d90aec211ba2025363c65b643c68e109b293 | |
| 02/05/2024 17:21:05 - INFO - datasets.info - Loading Dataset info from /beegfs/scratch/user/blee/hugging-face/models/datasets/speech_massive_cascaded/multilingual-test/1.0.0/f36c9e4210ec02a91ee05c9fa785d90aec211ba2025363c65b643c68e109b293 | |
| Found cached dataset speech_massive_cascaded (/beegfs/scratch/user/blee/hugging-face/models/datasets/speech_massive_cascaded/multilingual-test/1.0.0/f36c9e4210ec02a91ee05c9fa785d90aec211ba2025363c65b643c68e109b293) | |
| 02/05/2024 17:21:05 - INFO - datasets.builder - Found cached dataset speech_massive_cascaded (/beegfs/scratch/user/blee/hugging-face/models/datasets/speech_massive_cascaded/multilingual-test/1.0.0/f36c9e4210ec02a91ee05c9fa785d90aec211ba2025363c65b643c68e109b293) | |
| Loading Dataset info from /beegfs/scratch/user/blee/hugging-face/models/datasets/speech_massive_cascaded/multilingual-test/1.0.0/f36c9e4210ec02a91ee05c9fa785d90aec211ba2025363c65b643c68e109b293 | |
| 02/05/2024 17:21:05 - INFO - datasets.info - Loading Dataset info from /beegfs/scratch/user/blee/hugging-face/models/datasets/speech_massive_cascaded/multilingual-test/1.0.0/f36c9e4210ec02a91ee05c9fa785d90aec211ba2025363c65b643c68e109b293 | |
| [INFO|configuration_utils.py:737] 2024-02-05 17:21:05,923 >> 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] 2024-02-05 17:21:05,933 >> 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] 2024-02-05 17:21:05,935 >> loading file spiece.model | |
| [INFO|tokenization_utils_base.py:2024] 2024-02-05 17:21:05,935 >> loading file tokenizer.json | |
| [INFO|tokenization_utils_base.py:2024] 2024-02-05 17:21:05,935 >> loading file added_tokens.json | |
| [INFO|tokenization_utils_base.py:2024] 2024-02-05 17:21:05,936 >> loading file special_tokens_map.json | |
| [INFO|tokenization_utils_base.py:2024] 2024-02-05 17:21:05,936 >> loading file tokenizer_config.json | |
| [INFO|modeling_utils.py:3373] 2024-02-05 17:21:06,419 >> 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] 2024-02-05 17:21:06,574 >> Generate config GenerationConfig { | |
| "decoder_start_token_id": 0, | |
| "eos_token_id": 1, | |
| "pad_token_id": 0 | |
| } | |
| [INFO|modeling_utils.py:4224] 2024-02-05 17:21:11,310 >> All model checkpoint weights were used when initializing MT5ForConditionalGeneration. | |
| [INFO|modeling_utils.py:4232] 2024-02-05 17:21:11,310 >> 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] 2024-02-05 17:21:11,318 >> 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] 2024-02-05 17:21:11,318 >> Generate config GenerationConfig { | |
| "decoder_start_token_id": 0, | |
| "eos_token_id": 1, | |
| "pad_token_id": 0 | |
| } | |
| Loading cached processed dataset at /beegfs/scratch/user/blee/hugging-face/models/datasets/speech_massive_cascaded/multilingual-test/1.0.0/f36c9e4210ec02a91ee05c9fa785d90aec211ba2025363c65b643c68e109b293/cache-a28263cfb71413f6.arrow | |
| 02/05/2024 17:21:11 - INFO - datasets.arrow_dataset - Loading cached processed dataset at /beegfs/scratch/user/blee/hugging-face/models/datasets/speech_massive_cascaded/multilingual-test/1.0.0/f36c9e4210ec02a91ee05c9fa785d90aec211ba2025363c65b643c68e109b293/cache-a28263cfb71413f6.arrow | |
| Loading cached processed dataset at /beegfs/scratch/user/blee/hugging-face/models/datasets/speech_massive_cascaded/multilingual-test/1.0.0/f36c9e4210ec02a91ee05c9fa785d90aec211ba2025363c65b643c68e109b293/cache-0f6b9ba1cc4e5fb1.arrow | |
| 02/05/2024 17:21:11 - INFO - datasets.arrow_dataset - Loading cached processed dataset at /beegfs/scratch/user/blee/hugging-face/models/datasets/speech_massive_cascaded/multilingual-test/1.0.0/f36c9e4210ec02a91ee05c9fa785d90aec211ba2025363c65b643c68e109b293/cache-0f6b9ba1cc4e5fb1.arrow | |
| Loading cached processed dataset at /beegfs/scratch/user/blee/hugging-face/models/datasets/speech_massive_cascaded/multilingual-test/1.0.0/f36c9e4210ec02a91ee05c9fa785d90aec211ba2025363c65b643c68e109b293/cache-cd10216b4341f1a2.arrow | |
| 02/05/2024 17:21:11 - INFO - datasets.arrow_dataset - Loading cached processed dataset at /beegfs/scratch/user/blee/hugging-face/models/datasets/speech_massive_cascaded/multilingual-test/1.0.0/f36c9e4210ec02a91ee05c9fa785d90aec211ba2025363c65b643c68e109b293/cache-cd10216b4341f1a2.arrow | |
| Loading cached processed dataset at /beegfs/scratch/user/blee/hugging-face/models/datasets/speech_massive_cascaded/multilingual-test/1.0.0/f36c9e4210ec02a91ee05c9fa785d90aec211ba2025363c65b643c68e109b293/cache-cf09cb968cef4c56.arrow | |
| 02/05/2024 17:21:11 - INFO - datasets.arrow_dataset - Loading cached processed dataset at /beegfs/scratch/user/blee/hugging-face/models/datasets/speech_massive_cascaded/multilingual-test/1.0.0/f36c9e4210ec02a91ee05c9fa785d90aec211ba2025363c65b643c68e109b293/cache-cf09cb968cef4c56.arrow | |
| Loading cached processed dataset at /beegfs/scratch/user/blee/hugging-face/models/datasets/speech_massive_cascaded/multilingual-test/1.0.0/f36c9e4210ec02a91ee05c9fa785d90aec211ba2025363c65b643c68e109b293/cache-b372c4d6e9ad447f.arrow | |
| 02/05/2024 17:21:11 - INFO - datasets.arrow_dataset - Loading cached processed dataset at /beegfs/scratch/user/blee/hugging-face/models/datasets/speech_massive_cascaded/multilingual-test/1.0.0/f36c9e4210ec02a91ee05c9fa785d90aec211ba2025363c65b643c68e109b293/cache-b372c4d6e9ad447f.arrow | |
| Loading cached processed dataset at /beegfs/scratch/user/blee/hugging-face/models/datasets/speech_massive_cascaded/multilingual-test/1.0.0/f36c9e4210ec02a91ee05c9fa785d90aec211ba2025363c65b643c68e109b293/cache-5e85733ab0d7983c.arrow | |
| 02/05/2024 17:21:11 - INFO - datasets.arrow_dataset - Loading cached processed dataset at /beegfs/scratch/user/blee/hugging-face/models/datasets/speech_massive_cascaded/multilingual-test/1.0.0/f36c9e4210ec02a91ee05c9fa785d90aec211ba2025363c65b643c68e109b293/cache-5e85733ab0d7983c.arrow | |
| Loading cached processed dataset at /beegfs/scratch/user/blee/hugging-face/models/datasets/speech_massive_cascaded/multilingual-test/1.0.0/f36c9e4210ec02a91ee05c9fa785d90aec211ba2025363c65b643c68e109b293/cache-3498127f38d0e88c.arrow | |
| 02/05/2024 17:21:11 - INFO - datasets.arrow_dataset - Loading cached processed dataset at /beegfs/scratch/user/blee/hugging-face/models/datasets/speech_massive_cascaded/multilingual-test/1.0.0/f36c9e4210ec02a91ee05c9fa785d90aec211ba2025363c65b643c68e109b293/cache-3498127f38d0e88c.arrow | |
| Loading cached processed dataset at /beegfs/scratch/user/blee/hugging-face/models/datasets/speech_massive_cascaded/multilingual-test/1.0.0/f36c9e4210ec02a91ee05c9fa785d90aec211ba2025363c65b643c68e109b293/cache-5031b1ae09c119f0.arrow | |
| 02/05/2024 17:21:11 - INFO - datasets.arrow_dataset - Loading cached processed dataset at /beegfs/scratch/user/blee/hugging-face/models/datasets/speech_massive_cascaded/multilingual-test/1.0.0/f36c9e4210ec02a91ee05c9fa785d90aec211ba2025363c65b643c68e109b293/cache-5031b1ae09c119f0.arrow | |
| Loading cached processed dataset at /beegfs/scratch/user/blee/hugging-face/models/datasets/speech_massive_cascaded/multilingual-test/1.0.0/f36c9e4210ec02a91ee05c9fa785d90aec211ba2025363c65b643c68e109b293/cache-afad46b8cc76fbde.arrow | |
| 02/05/2024 17:21:11 - INFO - datasets.arrow_dataset - Loading cached processed dataset at /beegfs/scratch/user/blee/hugging-face/models/datasets/speech_massive_cascaded/multilingual-test/1.0.0/f36c9e4210ec02a91ee05c9fa785d90aec211ba2025363c65b643c68e109b293/cache-afad46b8cc76fbde.arrow | |
| Loading cached processed dataset at /beegfs/scratch/user/blee/hugging-face/models/datasets/speech_massive_cascaded/multilingual-test/1.0.0/f36c9e4210ec02a91ee05c9fa785d90aec211ba2025363c65b643c68e109b293/cache-44bbdcb1f95b0505.arrow | |
| 02/05/2024 17:21:11 - INFO - datasets.arrow_dataset - Loading cached processed dataset at /beegfs/scratch/user/blee/hugging-face/models/datasets/speech_massive_cascaded/multilingual-test/1.0.0/f36c9e4210ec02a91ee05c9fa785d90aec211ba2025363c65b643c68e109b293/cache-44bbdcb1f95b0505.arrow | |
| Loading cached processed dataset at /beegfs/scratch/user/blee/hugging-face/models/datasets/speech_massive_cascaded/multilingual-test/1.0.0/f36c9e4210ec02a91ee05c9fa785d90aec211ba2025363c65b643c68e109b293/cache-649defd19aa00c44.arrow | |
| 02/05/2024 17:21:11 - INFO - datasets.arrow_dataset - Loading cached processed dataset at /beegfs/scratch/user/blee/hugging-face/models/datasets/speech_massive_cascaded/multilingual-test/1.0.0/f36c9e4210ec02a91ee05c9fa785d90aec211ba2025363c65b643c68e109b293/cache-649defd19aa00c44.arrow | |
| Loading cached processed dataset at /beegfs/scratch/user/blee/hugging-face/models/datasets/speech_massive_cascaded/multilingual-test/1.0.0/f36c9e4210ec02a91ee05c9fa785d90aec211ba2025363c65b643c68e109b293/cache-bc58b34f0f2e6d55.arrow | |
| 02/05/2024 17:21:11 - INFO - datasets.arrow_dataset - Loading cached processed dataset at /beegfs/scratch/user/blee/hugging-face/models/datasets/speech_massive_cascaded/multilingual-test/1.0.0/f36c9e4210ec02a91ee05c9fa785d90aec211ba2025363c65b643c68e109b293/cache-bc58b34f0f2e6d55.arrow | |
| 02/05/2024 17:21:28 - 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. | |
| 02/05/2024 17:21:28 - INFO - __main__ - *** Predict *** | |
| 02/05/2024 17:21:28 - INFO - __main__ - *** test_ar_SA *** | |
| [INFO|trainer.py:718] 2024-02-05 17:21:28,574 >> The following columns in the test set don't have a corresponding argument in `MT5ForConditionalGeneration.forward` and have been ignored: intent_str, id, annot_utt. If intent_str, id, annot_utt are not expected by `MT5ForConditionalGeneration.forward`, you can safely ignore this message. | |
| [INFO|trainer.py:3199] 2024-02-05 17:21:28,584 >> ***** Running Prediction ***** | |
| [INFO|trainer.py:3201] 2024-02-05 17:21:28,584 >> Num examples = 2974 | |
| [INFO|trainer.py:3204] 2024-02-05 17:21:28,584 >> Batch size = 32 | |
| [WARNING|logging.py:314] 2024-02-05 17:21:28,589 >> 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. | |
| 0%| | 0/93 [00:00<?, ?it/s] 2%|▏ | 2/93 [00:00<00:18, 5.03it/s] 3%|▎ | 3/93 [00:00<00:23, 3.91it/s] 4%|▍ | 4/93 [00:01<00:29, 2.97it/s] 5%|▌ | 5/93 [00:01<00:35, 2.50it/s] 6%|▋ | 6/93 [00:02<00:33, 2.59it/s] 8%|▊ | 7/93 [00:02<00:35, 2.41it/s] 9%|▊ | 8/93 [00:02<00:33, 2.53it/s] 10%|▉ | 9/93 [00:03<00:33, 2.49it/s] 11%|█ | 10/93 [00:03<00:32, 2.54it/s] 12%|█▏ | 11/93 [00:04<00:32, 2.49it/s] 13%|█▎ | 12/93 [00:04<00:31, 2.57it/s] 14%|█▍ | 13/93 [00:04<00:30, 2.62it/s] 15%|█▌ | 14/93 [00:05<00:31, 2.54it/s] 16%|█▌ | 15/93 [00:05<00:32, 2.42it/s] 17%|█▋ | 16/93 [00:06<00:34, 2.21it/s] 18%|█▊ | 17/93 [00:06<00:34, 2.18it/s] 19%|█▉ | 18/93 [00:07<00:33, 2.26it/s] 20%|██ | 19/93 [00:07<00:31, 2.39it/s] 22%|██▏ | 20/93 [00:07<00:29, 2.45it/s] 23%|██▎ | 21/93 [00:08<00:28, 2.50it/s] 24%|██▎ | 22/93 [00:08<00:32, 2.20it/s] 25%|██▍ | 23/93 [00:09<00:31, 2.25it/s] 26%|██▌ | 24/93 [00:09<00:31, 2.21it/s] 27%|██▋ | 25/93 [00:10<00:29, 2.27it/s] 28%|██▊ | 26/93 [00:10<00:30, 2.21it/s] 29%|██▉ | 27/93 [00:11<00:29, 2.21it/s] 30%|███ | 28/93 [00:13<01:01, 1.06it/s] 31%|███ | 29/93 [00:13<00:50, 1.27it/s] 32%|███▏ | 30/93 [00:13<00:41, 1.50it/s] 33%|███▎ | 31/93 [00:14<00:37, 1.66it/s] 34%|███▍ | 32/93 [00:14<00:33, 1.79it/s] 35%|███▌ | 33/93 [00:15<00:31, 1.92it/s] 37%|███▋ | 34/93 [00:15<00:28, 2.11it/s] 38%|███▊ | 35/93 [00:16<00:24, 2.32it/s] 39%|███▊ | 36/93 [00:16<00:24, 2.35it/s] 40%|███▉ | 37/93 [00:16<00:24, 2.29it/s] 41%|████ | 38/93 [00:17<00:25, 2.15it/s] 42%|████▏ | 39/93 [00:17<00:25, 2.16it/s] 43%|████▎ | 40/93 [00:18<00:23, 2.28it/s] 44%|████▍ | 41/93 [00:18<00:22, 2.29it/s] 45%|████▌ | 42/93 [00:19<00:21, 2.36it/s] 46%|████▌ | 43/93 [00:19<00:20, 2.49it/s] 47%|████▋ | 44/93 [00:19<00:18, 2.64it/s] 48%|████▊ | 45/93 [00:20<00:18, 2.55it/s] 49%|████▉ | 46/93 [00:20<00:19, 2.43it/s] 51%|█████ | 47/93 [00:21<00:19, 2.30it/s] 52%|█████▏ | 48/93 [00:21<00:20, 2.18it/s] 53%|█████▎ | 49/93 [00:22<00:19, 2.30it/s] 54%|█████▍ | 50/93 [00:22<00:18, 2.34it/s] 55%|█████▍ | 51/93 [00:23<00:20, 2.03it/s] 56%|█████▌ | 52/93 [00:23<00:19, 2.13it/s] 57%|█████▋ | 53/93 [00:23<00:17, 2.26it/s] 58%|█████▊ | 54/93 [00:24<00:17, 2.23it/s] 59%|█████▉ | 55/93 [00:24<00:17, 2.22it/s] 60%|██████ | 56/93 [00:25<00:16, 2.30it/s] 61%|██████▏ | 57/93 [00:25<00:15, 2.37it/s] 62%|██████▏ | 58/93 [00:26<00:15, 2.33it/s] 63%|██████▎ | 59/93 [00:26<00:13, 2.49it/s] 65%|██████▍ | 60/93 [00:26<00:12, 2.65it/s] 66%|██████▌ | 61/93 [00:27<00:13, 2.41it/s] 67%|██████▋ | 62/93 [00:27<00:12, 2.48it/s] 68%|██████▊ | 63/93 [00:27<00:11, 2.65it/s] 69%|██████▉ | 64/93 [00:28<00:10, 2.68it/s] 70%|██████▉ | 65/93 [00:28<00:10, 2.64it/s] 71%|███████ | 66/93 [00:29<00:10, 2.58it/s] 72%|███████▏ | 67/93 [00:29<00:10, 2.59it/s] 73%|███████▎ | 68/93 [00:29<00:09, 2.60it/s] 74%|███████▍ | 69/93 [00:30<00:09, 2.43it/s] 75%|███████▌ | 70/93 [00:30<00:09, 2.37it/s] 76%|███████▋ | 71/93 [00:31<00:09, 2.24it/s] 77%|███████▋ | 72/93 [00:31<00:09, 2.21it/s] 78%|███████▊ | 73/93 [00:32<00:08, 2.32it/s] 80%|███████▉ | 74/93 [00:32<00:08, 2.32it/s] 81%|████████ | 75/93 [00:32<00:07, 2.36it/s] 82%|████████▏ | 76/93 [00:33<00:06, 2.46it/s] 83%|████████▎ | 77/93 [00:33<00:06, 2.57it/s] 84%|████████▍ | 78/93 [00:34<00:05, 2.61it/s] 85%|████████▍ | 79/93 [00:34<00:05, 2.46it/s] 86%|████████▌ | 80/93 [00:34<00:05, 2.39it/s] 87%|████████▋ | 81/93 [00:35<00:05, 2.35it/s] 88%|████████▊ | 82/93 [00:35<00:04, 2.50it/s] 89%|████████▉ | 83/93 [00:36<00:03, 2.54it/s] 90%|█████████ | 84/93 [00:36<00:03, 2.57it/s] 91%|█████████▏| 85/93 [00:36<00:03, 2.30it/s] 92%|█████████▏| 86/93 [00:37<00:03, 2.16it/s] 94%|█████████▎| 87/93 [00:37<00:02, 2.27it/s] 95%|█████████▍| 88/93 [00:38<00:02, 2.43it/s] 96%|█████████▌| 89/93 [00:38<00:01, 2.39it/s] 97%|█████████▋| 90/93 [00:39<00:01, 2.32it/s] 98%|█████████▊| 91/93 [00:39<00:00, 2.13it/s] 99%|█████████▉| 92/93 [00:40<00:00, 2.09it/s] 100%|██████████| 93/93 [00:40<00:00, 2.25it/s] 100%|██████████| 93/93 [00:40<00:00, 2.28it/s] | |
| ***** predict_test_ar_SA metrics ***** | |
| predict_ex_match_acc = 0.4455 | |
| predict_ex_match_acc_stderr = 0.0091 | |
| predict_intent_acc = 0.7061 | |
| predict_intent_acc_stderr = 0.0084 | |
| predict_loss = 0.5896 | |
| predict_runtime = 0:00:41.80 | |
| predict_samples = 2974 | |
| predict_samples_per_second = 71.142 | |
| predict_slot_micro_f1 = 0.5994 | |
| predict_slot_micro_f1_stderr = 0.0039 | |
| predict_steps_per_second = 2.225 | |
| 02/05/2024 17:22:10 - INFO - __main__ - *** test_de_DE *** | |
| [INFO|trainer.py:718] 2024-02-05 17:22:10,606 >> The following columns in the test set don't have a corresponding argument in `MT5ForConditionalGeneration.forward` and have been ignored: intent_str, id, annot_utt. If intent_str, id, annot_utt are not expected by `MT5ForConditionalGeneration.forward`, you can safely ignore this message. | |
| [INFO|trainer.py:3199] 2024-02-05 17:22:10,609 >> ***** Running Prediction ***** | |
| [INFO|trainer.py:3201] 2024-02-05 17:22:10,610 >> Num examples = 2974 | |
| [INFO|trainer.py:3204] 2024-02-05 17:22:10,610 >> Batch size = 32 | |
| 0%| | 0/93 [00:00<?, ?it/s] 2%|▏ | 2/93 [00:00<00:18, 4.88it/s] 3%|▎ | 3/93 [00:00<00:23, 3.78it/s] 4%|▍ | 4/93 [00:01<00:31, 2.80it/s] 5%|▌ | 5/93 [00:01<00:31, 2.77it/s] 6%|▋ | 6/93 [00:01<00:30, 2.84it/s] 8%|▊ | 7/93 [00:02<00:32, 2.63it/s] 9%|▊ | 8/93 [00:02<00:32, 2.64it/s] 10%|▉ | 9/93 [00:03<00:33, 2.52it/s] 11%|█ | 10/93 [00:03<00:30, 2.68it/s] 12%|█▏ | 11/93 [00:03<00:31, 2.64it/s] 13%|█▎ | 12/93 [00:04<00:32, 2.51it/s] 14%|█▍ | 13/93 [00:04<00:30, 2.61it/s] 15%|█▌ | 14/93 [00:05<00:30, 2.62it/s] 16%|█▌ | 15/93 [00:05<00:33, 2.33it/s] 17%|█▋ | 16/93 [00:06<00:35, 2.19it/s] 18%|█▊ | 17/93 [00:06<00:35, 2.16it/s] 19%|█▉ | 18/93 [00:07<00:33, 2.25it/s] 20%|██ | 19/93 [00:07<00:33, 2.24it/s] 22%|██▏ | 20/93 [00:07<00:32, 2.28it/s] 23%|██▎ | 21/93 [00:08<00:31, 2.32it/s] 24%|██▎ | 22/93 [00:08<00:34, 2.06it/s] 25%|██▍ | 23/93 [00:09<00:34, 2.04it/s] 26%|██▌ | 24/93 [00:09<00:32, 2.14it/s] 27%|██▋ | 25/93 [00:10<00:30, 2.20it/s] 28%|██▊ | 26/93 [00:10<00:29, 2.24it/s] 29%|██▉ | 27/93 [00:11<00:28, 2.34it/s] 30%|███ | 28/93 [00:13<01:00, 1.07it/s] 31%|███ | 29/93 [00:13<00:48, 1.31it/s] 32%|███▏ | 30/93 [00:14<00:42, 1.48it/s] 33%|███▎ | 31/93 [00:14<00:36, 1.72it/s] 34%|███▍ | 32/93 [00:14<00:33, 1.83it/s] 35%|███▌ | 33/93 [00:15<00:32, 1.83it/s] 37%|███▋ | 34/93 [00:15<00:29, 2.00it/s] 38%|███▊ | 35/93 [00:16<00:28, 2.00it/s] 39%|███▊ | 36/93 [00:16<00:26, 2.11it/s] 40%|███▉ | 37/93 [00:17<00:25, 2.18it/s] 41%|████ | 38/93 [00:17<00:24, 2.24it/s] 42%|████▏ | 39/93 [00:18<00:25, 2.15it/s] 43%|████▎ | 40/93 [00:18<00:23, 2.25it/s] 44%|████▍ | 41/93 [00:18<00:22, 2.35it/s] 45%|████▌ | 42/93 [00:19<00:21, 2.34it/s] 46%|████▌ | 43/93 [00:19<00:21, 2.37it/s] 47%|████▋ | 44/93 [00:20<00:19, 2.46it/s] 48%|████▊ | 45/93 [00:20<00:19, 2.42it/s] 49%|████▉ | 46/93 [00:20<00:18, 2.51it/s] 51%|█████ | 47/93 [00:21<00:17, 2.61it/s] 52%|█████▏ | 48/93 [00:21<00:17, 2.52it/s] 53%|█████▎ | 49/93 [00:22<00:17, 2.49it/s] 54%|█████▍ | 50/93 [00:22<00:18, 2.26it/s] 55%|█████▍ | 51/93 [00:23<00:18, 2.23it/s] 56%|█████▌ | 52/93 [00:23<00:20, 1.98it/s] 57%|█████▋ | 53/93 [00:24<00:19, 2.09it/s] 58%|█████▊ | 54/93 [00:24<00:17, 2.20it/s] 59%|█████▉ | 55/93 [00:24<00:16, 2.32it/s] 60%|██████ | 56/93 [00:25<00:15, 2.45it/s] 61%|██████▏ | 57/93 [00:25<00:15, 2.37it/s] 62%|██████▏ | 58/93 [00:26<00:15, 2.30it/s] 63%|██████▎ | 59/93 [00:26<00:14, 2.36it/s] 65%|██████▍ | 60/93 [00:26<00:13, 2.40it/s] 66%|██████▌ | 61/93 [00:27<00:14, 2.23it/s] 67%|██████▋ | 62/93 [00:27<00:13, 2.22it/s] 68%|██████▊ | 63/93 [00:28<00:13, 2.27it/s] 69%|██████▉ | 64/93 [00:28<00:13, 2.15it/s] 70%|██████▉ | 65/93 [00:29<00:11, 2.35it/s] 71%|███████ | 66/93 [00:29<00:11, 2.40it/s] 72%|███████▏ | 67/93 [00:29<00:10, 2.44it/s] 73%|███████▎ | 68/93 [00:30<00:10, 2.40it/s] 74%|███████▍ | 69/93 [00:30<00:10, 2.23it/s] 75%|███████▌ | 70/93 [00:31<00:10, 2.15it/s] 76%|███████▋ | 71/93 [00:31<00:10, 2.10it/s] 77%|███████▋ | 72/93 [00:32<00:10, 2.00it/s] 78%|███████▊ | 73/93 [00:32<00:09, 2.09it/s] 80%|███████▉ | 74/93 [00:33<00:08, 2.12it/s] 81%|████████ | 75/93 [00:33<00:08, 2.12it/s] 82%|████████▏ | 76/93 [00:34<00:08, 2.11it/s] 83%|████████▎ | 77/93 [00:34<00:07, 2.17it/s] 84%|████████▍ | 78/93 [00:35<00:08, 1.76it/s] 85%|████████▍ | 79/93 [00:36<00:07, 1.88it/s] 86%|████████▌ | 80/93 [00:36<00:06, 1.97it/s] 87%|████████▋ | 81/93 [00:36<00:05, 2.13it/s] 88%|████████▊ | 82/93 [00:37<00:05, 2.19it/s] 89%|████████▉ | 83/93 [00:37<00:04, 2.28it/s] 90%|█████████ | 84/93 [00:38<00:03, 2.32it/s] 91%|█████████▏| 85/93 [00:38<00:03, 2.36it/s] 92%|█████████▏| 86/93 [00:38<00:02, 2.36it/s] 94%|█████████▎| 87/93 [00:39<00:02, 2.36it/s] 95%|█████████▍| 88/93 [00:39<00:02, 2.45it/s] 96%|█████████▌| 89/93 [00:40<00:01, 2.41it/s] 97%|█████████▋| 90/93 [00:40<00:01, 2.46it/s] 98%|█████████▊| 91/93 [00:41<00:00, 2.23it/s] 99%|█████████▉| 92/93 [00:41<00:00, 2.34it/s] 100%|██████████| 93/93 [00:41<00:00, 2.43it/s] 100%|██████████| 93/93 [00:42<00:00, 2.20it/s] | |
| ***** predict_test_de_DE metrics ***** | |
| predict_ex_match_acc = 0.5662 | |
| predict_ex_match_acc_stderr = 0.0091 | |
| predict_intent_acc = 0.8366 | |
| predict_intent_acc_stderr = 0.0068 | |
| predict_loss = 0.4835 | |
| predict_runtime = 0:00:42.68 | |
| predict_samples = 2974 | |
| predict_samples_per_second = 69.67 | |
| predict_slot_micro_f1 = 0.6886 | |
| predict_slot_micro_f1_stderr = 0.0033 | |
| predict_steps_per_second = 2.179 | |
| 02/05/2024 17:22:53 - INFO - __main__ - *** test_es_ES *** | |
| [INFO|trainer.py:718] 2024-02-05 17:22:53,532 >> The following columns in the test set don't have a corresponding argument in `MT5ForConditionalGeneration.forward` and have been ignored: intent_str, id, annot_utt. If intent_str, id, annot_utt are not expected by `MT5ForConditionalGeneration.forward`, you can safely ignore this message. | |
| [INFO|trainer.py:3199] 2024-02-05 17:22:53,534 >> ***** Running Prediction ***** | |
| [INFO|trainer.py:3201] 2024-02-05 17:22:53,534 >> Num examples = 2974 | |
| [INFO|trainer.py:3204] 2024-02-05 17:22:53,535 >> Batch size = 32 | |
| 0%| | 0/93 [00:00<?, ?it/s] 2%|▏ | 2/93 [00:00<00:19, 4.60it/s] 3%|▎ | 3/93 [00:00<00:28, 3.15it/s] 4%|▍ | 4/93 [00:01<00:35, 2.48it/s] 5%|▌ | 5/93 [00:01<00:38, 2.30it/s] 6%|▋ | 6/93 [00:02<00:37, 2.32it/s] 8%|▊ | 7/93 [00:02<00:37, 2.28it/s] 9%|▊ | 8/93 [00:03<00:35, 2.38it/s] 10%|▉ | 9/93 [00:03<00:35, 2.37it/s] 11%|█ | 10/93 [00:04<00:34, 2.40it/s] 12%|█▏ | 11/93 [00:04<00:35, 2.30it/s] 13%|█▎ | 12/93 [00:05<00:37, 2.15it/s] 14%|█▍ | 13/93 [00:05<00:36, 2.17it/s] 15%|█▌ | 14/93 [00:05<00:36, 2.19it/s] 16%|█▌ | 15/93 [00:06<00:38, 2.03it/s] 17%|█▋ | 16/93 [00:07<00:39, 1.95it/s] 18%|█▊ | 17/93 [00:07<00:37, 2.02it/s] 19%|█▉ | 18/93 [00:07<00:35, 2.10it/s] 20%|██ | 19/93 [00:08<00:35, 2.10it/s] 22%|██▏ | 20/93 [00:08<00:33, 2.19it/s] 23%|██▎ | 21/93 [00:09<00:32, 2.20it/s] 24%|██▎ | 22/93 [00:10<00:37, 1.87it/s] 25%|██▍ | 23/93 [00:10<00:37, 1.87it/s] 26%|██▌ | 24/93 [00:11<00:35, 1.93it/s] 27%|██▋ | 25/93 [00:11<00:33, 2.03it/s] 28%|██▊ | 26/93 [00:11<00:32, 2.06it/s] 29%|██▉ | 27/93 [00:12<00:31, 2.08it/s] 30%|███ | 28/93 [00:14<01:03, 1.03it/s] 31%|███ | 29/93 [00:14<00:52, 1.22it/s] 32%|███▏ | 30/93 [00:15<00:45, 1.39it/s] 33%|███▎ | 31/93 [00:15<00:38, 1.61it/s] 34%|███▍ | 32/93 [00:16<00:37, 1.63it/s] 35%|███▌ | 33/93 [00:17<00:39, 1.53it/s] 37%|███▋ | 34/93 [00:17<00:34, 1.70it/s] 38%|███▊ | 35/93 [00:18<00:32, 1.80it/s] 39%|███▊ | 36/93 [00:18<00:31, 1.82it/s] 40%|███▉ | 37/93 [00:19<00:32, 1.71it/s] 41%|████ | 38/93 [00:19<00:32, 1.72it/s] 42%|████▏ | 39/93 [00:20<00:29, 1.81it/s] 43%|████▎ | 40/93 [00:20<00:28, 1.88it/s] 44%|████▍ | 41/93 [00:21<00:25, 2.01it/s] 45%|████▌ | 42/93 [00:21<00:24, 2.07it/s] 46%|████▌ | 43/93 [00:22<00:22, 2.19it/s] 47%|████▋ | 44/93 [00:22<00:21, 2.29it/s] 48%|████▊ | 45/93 [00:23<00:21, 2.19it/s] 49%|████▉ | 46/93 [00:23<00:23, 2.00it/s] 51%|█████ | 47/93 [00:23<00:21, 2.16it/s] 52%|█████▏ | 48/93 [00:24<00:24, 1.84it/s] 53%|█████▎ | 49/93 [00:25<00:21, 2.00it/s] 54%|█████▍ | 50/93 [00:25<00:21, 1.97it/s] 55%|█████▍ | 51/93 [00:26<00:20, 2.07it/s] 56%|█████▌ | 52/93 [00:26<00:22, 1.80it/s] 57%|█████▋ | 53/93 [00:27<00:20, 1.95it/s] 58%|█████▊ | 54/93 [00:27<00:19, 2.02it/s] 59%|█████▉ | 55/93 [00:28<00:17, 2.11it/s] 60%|██████ | 56/93 [00:28<00:17, 2.13it/s] 61%|██████▏ | 57/93 [00:29<00:18, 2.00it/s] 62%|██████▏ | 58/93 [00:29<00:17, 2.06it/s] 63%|██████▎ | 59/93 [00:30<00:17, 1.97it/s] 65%|██████▍ | 60/93 [00:30<00:15, 2.13it/s] 66%|██████▌ | 61/93 [00:31<00:15, 2.08it/s] 67%|██████▋ | 62/93 [00:31<00:15, 2.00it/s] 68%|██████▊ | 63/93 [00:31<00:13, 2.16it/s] 69%|██████▉ | 64/93 [00:32<00:12, 2.27it/s] 70%|██████▉ | 65/93 [00:32<00:12, 2.32it/s] 71%|███████ | 66/93 [00:33<00:11, 2.39it/s] 72%|███████▏ | 67/93 [00:33<00:13, 1.94it/s] 73%|███████▎ | 68/93 [00:34<00:13, 1.87it/s] 74%|███████▍ | 69/93 [00:34<00:12, 1.87it/s] 75%|███████▌ | 70/93 [00:35<00:12, 1.89it/s] 76%|███████▋ | 71/93 [00:36<00:12, 1.81it/s] 77%|███████▋ | 72/93 [00:36<00:10, 1.93it/s] 78%|███████▊ | 73/93 [00:36<00:09, 2.05it/s] 80%|███████▉ | 74/93 [00:37<00:09, 2.01it/s] 81%|████████ | 75/93 [00:38<00:09, 1.95it/s] 82%|████████▏ | 76/93 [00:38<00:09, 1.84it/s] 83%|████████▎ | 77/93 [00:39<00:08, 1.81it/s] 84%|████████▍ | 78/93 [00:41<00:14, 1.00it/s] 85%|████████▍ | 79/93 [00:41<00:11, 1.21it/s] 86%|████████▌ | 80/93 [00:42<00:09, 1.34it/s] 87%|████████▋ | 81/93 [00:42<00:07, 1.51it/s] 88%|████████▊ | 82/93 [00:43<00:06, 1.67it/s] 89%|████████▉ | 83/93 [00:43<00:05, 1.82it/s] 90%|█████████ | 84/93 [00:44<00:04, 1.89it/s] 91%|█████████▏| 85/93 [00:44<00:04, 1.91it/s] 92%|█████████▏| 86/93 [00:45<00:03, 1.90it/s] 94%|█████████▎| 87/93 [00:45<00:03, 2.00it/s] 95%|█████████▍| 88/93 [00:45<00:02, 2.16it/s] 96%|█████████▌| 89/93 [00:46<00:01, 2.05it/s] 97%|█████████▋| 90/93 [00:46<00:01, 2.14it/s] 98%|█████████▊| 91/93 [00:47<00:00, 2.02it/s] 99%|█████████▉| 92/93 [00:47<00:00, 2.02it/s] 100%|██████████| 93/93 [00:48<00:00, 2.20it/s] 100%|██████████| 93/93 [00:48<00:00, 1.91it/s] | |
| ***** predict_test_es_ES metrics ***** | |
| predict_ex_match_acc = 0.6009 | |
| predict_ex_match_acc_stderr = 0.009 | |
| predict_intent_acc = 0.8521 | |
| predict_intent_acc_stderr = 0.0065 | |
| predict_loss = 0.2917 | |
| predict_runtime = 0:00:49.03 | |
| predict_samples = 2974 | |
| predict_samples_per_second = 60.649 | |
| predict_slot_micro_f1 = 0.7136 | |
| predict_slot_micro_f1_stderr = 0.0031 | |
| predict_steps_per_second = 1.897 | |
| 02/05/2024 17:23:42 - INFO - __main__ - *** test_fr_FR *** | |
| [INFO|trainer.py:718] 2024-02-05 17:23:42,811 >> The following columns in the test set don't have a corresponding argument in `MT5ForConditionalGeneration.forward` and have been ignored: intent_str, id, annot_utt. If intent_str, id, annot_utt are not expected by `MT5ForConditionalGeneration.forward`, you can safely ignore this message. | |
| [INFO|trainer.py:3199] 2024-02-05 17:23:42,813 >> ***** Running Prediction ***** | |
| [INFO|trainer.py:3201] 2024-02-05 17:23:42,814 >> Num examples = 2974 | |
| [INFO|trainer.py:3204] 2024-02-05 17:23:42,814 >> Batch size = 32 | |
| 0%| | 0/93 [00:00<?, ?it/s] 2%|▏ | 2/93 [00:00<00:19, 4.61it/s] 3%|▎ | 3/93 [00:00<00:29, 3.00it/s] 4%|▍ | 4/93 [00:01<00:36, 2.47it/s] 5%|▌ | 5/93 [00:02<00:40, 2.18it/s] 6%|▋ | 6/93 [00:02<00:39, 2.19it/s] 8%|▊ | 7/93 [00:02<00:40, 2.12it/s] 9%|▊ | 8/93 [00:03<00:38, 2.18it/s] 10%|▉ | 9/93 [00:03<00:38, 2.18it/s] 11%|█ | 10/93 [00:04<00:37, 2.24it/s] 12%|█▏ | 11/93 [00:04<00:38, 2.11it/s] 13%|█▎ | 12/93 [00:05<00:38, 2.08it/s] 14%|█▍ | 13/93 [00:05<00:38, 2.05it/s] 15%|█▌ | 14/93 [00:06<00:39, 2.01it/s] 16%|█▌ | 15/93 [00:06<00:40, 1.91it/s] 17%|█▋ | 16/93 [00:07<00:41, 1.85it/s] 18%|█▊ | 17/93 [00:08<00:42, 1.78it/s] 19%|█▉ | 18/93 [00:08<00:39, 1.90it/s] 20%|██ | 19/93 [00:09<00:37, 1.99it/s] 22%|██▏ | 20/93 [00:09<00:36, 1.98it/s] 23%|██▎ | 21/93 [00:09<00:34, 2.07it/s] 24%|██▎ | 22/93 [00:10<00:37, 1.91it/s] 25%|██▍ | 23/93 [00:11<00:37, 1.84it/s] 26%|██▌ | 24/93 [00:11<00:38, 1.80it/s] 27%|██▋ | 25/93 [00:12<00:37, 1.83it/s] 28%|██▊ | 26/93 [00:12<00:34, 1.92it/s] 29%|██▉ | 27/93 [00:13<00:33, 1.96it/s] 30%|███ | 28/93 [00:15<01:05, 1.00s/it] 31%|███ | 29/93 [00:15<00:53, 1.20it/s] 32%|███▏ | 30/93 [00:16<00:45, 1.37it/s] 33%|███▎ | 31/93 [00:16<00:39, 1.57it/s] 34%|███▍ | 32/93 [00:17<00:36, 1.67it/s] 35%|███▌ | 33/93 [00:17<00:33, 1.80it/s] 37%|███▋ | 34/93 [00:18<00:30, 1.95it/s] 38%|███▊ | 35/93 [00:18<00:31, 1.83it/s] 39%|███▊ | 36/93 [00:19<00:30, 1.86it/s] 40%|███▉ | 37/93 [00:19<00:30, 1.85it/s] 41%|████ | 38/93 [00:20<00:30, 1.80it/s] 42%|████▏ | 39/93 [00:20<00:29, 1.84it/s] 43%|████▎ | 40/93 [00:21<00:28, 1.87it/s] 44%|████▍ | 41/93 [00:21<00:27, 1.90it/s] 45%|████▌ | 42/93 [00:22<00:26, 1.96it/s] 46%|████▌ | 43/93 [00:22<00:25, 1.94it/s] 47%|████▋ | 44/93 [00:23<00:23, 2.06it/s] 48%|████▊ | 45/93 [00:23<00:23, 2.05it/s] 49%|████▉ | 46/93 [00:24<00:22, 2.11it/s] 51%|█████ | 47/93 [00:24<00:21, 2.13it/s] 52%|█████▏ | 48/93 [00:25<00:23, 1.88it/s] 53%|█████▎ | 49/93 [00:25<00:23, 1.86it/s] 54%|█████▍ | 50/93 [00:26<00:23, 1.87it/s] 55%|█████▍ | 51/93 [00:26<00:21, 1.93it/s] 56%|█████▌ | 52/93 [00:27<00:24, 1.66it/s] 57%|█████▋ | 53/93 [00:28<00:21, 1.83it/s] 58%|█████▊ | 54/93 [00:28<00:20, 1.92it/s] 59%|█████▉ | 55/93 [00:29<00:19, 1.94it/s] 60%|██████ | 56/93 [00:29<00:18, 1.96it/s] 61%|██████▏ | 57/93 [00:30<00:17, 2.00it/s] 62%|██████▏ | 58/93 [00:30<00:17, 1.99it/s] 63%|██████▎ | 59/93 [00:31<00:16, 2.03it/s] 65%|██████▍ | 60/93 [00:31<00:15, 2.13it/s] 66%|██████▌ | 61/93 [00:32<00:16, 1.98it/s] 67%|██████▋ | 62/93 [00:32<00:15, 1.97it/s] 68%|██████▊ | 63/93 [00:32<00:14, 2.10it/s] 69%|██████▉ | 64/93 [00:33<00:13, 2.21it/s] 70%|██████▉ | 65/93 [00:33<00:12, 2.28it/s] 71%|███████ | 66/93 [00:34<00:11, 2.27it/s] 72%|███████▏ | 67/93 [00:34<00:11, 2.22it/s] 73%|███████▎ | 68/93 [00:35<00:12, 1.93it/s] 74%|███████▍ | 69/93 [00:35<00:12, 1.89it/s] 75%|███████▌ | 70/93 [00:36<00:12, 1.85it/s] 76%|███████▋ | 71/93 [00:38<00:21, 1.01it/s] 77%|███████▋ | 72/93 [00:39<00:17, 1.17it/s] 78%|███████▊ | 73/93 [00:39<00:14, 1.38it/s] 80%|███████▉ | 74/93 [00:40<00:12, 1.51it/s] 81%|████████ | 75/93 [00:40<00:11, 1.56it/s] 82%|████████▏ | 76/93 [00:41<00:10, 1.59it/s] 83%|████████▎ | 77/93 [00:41<00:09, 1.67it/s] 84%|████████▍ | 78/93 [00:42<00:09, 1.64it/s] 85%|████████▍ | 79/93 [00:42<00:07, 1.76it/s] 86%|████████▌ | 80/93 [00:43<00:07, 1.80it/s] 87%|████████▋ | 81/93 [00:43<00:06, 1.90it/s] 88%|████████▊ | 82/93 [00:44<00:05, 1.99it/s] 89%|████████▉ | 83/93 [00:44<00:04, 2.11it/s] 90%|█████████ | 84/93 [00:45<00:04, 2.13it/s] 91%|█████████▏| 85/93 [00:45<00:03, 2.04it/s] 92%|█████████▏| 86/93 [00:46<00:03, 2.07it/s] 94%|█████████▎| 87/93 [00:46<00:02, 2.12it/s] 95%|█████████▍| 88/93 [00:47<00:02, 2.19it/s] 96%|█████████▌| 89/93 [00:47<00:01, 2.10it/s] 97%|█████████▋| 90/93 [00:48<00:01, 2.12it/s] 98%|█████████▊| 91/93 [00:48<00:01, 1.95it/s] 99%|█████████▉| 92/93 [00:49<00:00, 1.85it/s] 100%|██████████| 93/93 [00:49<00:00, 2.04it/s] 100%|██████████| 93/93 [00:49<00:00, 1.87it/s] | |
| ***** predict_test_fr_FR metrics ***** | |
| predict_ex_match_acc = 0.459 | |
| predict_ex_match_acc_stderr = 0.0091 | |
| predict_intent_acc = 0.8366 | |
| predict_intent_acc_stderr = 0.0068 | |
| predict_loss = 0.675 | |
| predict_runtime = 0:00:50.38 | |
| predict_samples = 2974 | |
| predict_samples_per_second = 59.028 | |
| predict_slot_micro_f1 = 0.5132 | |
| predict_slot_micro_f1_stderr = 0.0034 | |
| predict_steps_per_second = 1.846 | |
| 02/05/2024 17:24:33 - INFO - __main__ - *** test_hu_HU *** | |
| [INFO|trainer.py:718] 2024-02-05 17:24:33,448 >> The following columns in the test set don't have a corresponding argument in `MT5ForConditionalGeneration.forward` and have been ignored: intent_str, id, annot_utt. If intent_str, id, annot_utt are not expected by `MT5ForConditionalGeneration.forward`, you can safely ignore this message. | |
| [INFO|trainer.py:3199] 2024-02-05 17:24:33,451 >> ***** Running Prediction ***** | |
| [INFO|trainer.py:3201] 2024-02-05 17:24:33,451 >> Num examples = 2974 | |
| [INFO|trainer.py:3204] 2024-02-05 17:24:33,451 >> Batch size = 32 | |
| 0%| | 0/93 [00:00<?, ?it/s] 2%|▏ | 2/93 [00:00<00:15, 5.73it/s] 3%|▎ | 3/93 [00:00<00:22, 4.04it/s] 4%|▍ | 4/93 [00:01<00:33, 2.66it/s] 5%|▌ | 5/93 [00:01<00:33, 2.64it/s] 6%|▋ | 6/93 [00:02<00:31, 2.76it/s] 8%|▊ | 7/93 [00:02<00:32, 2.64it/s] 9%|▊ | 8/93 [00:02<00:31, 2.68it/s] 10%|▉ | 9/93 [00:03<00:31, 2.66it/s] 11%|█ | 10/93 [00:03<00:31, 2.60it/s] 12%|█▏ | 11/93 [00:03<00:30, 2.65it/s] 13%|█▎ | 12/93 [00:04<00:31, 2.56it/s] 14%|█▍ | 13/93 [00:04<00:32, 2.45it/s] 15%|█▌ | 14/93 [00:05<00:32, 2.42it/s] 16%|█▌ | 15/93 [00:05<00:31, 2.49it/s] 17%|█▋ | 16/93 [00:06<00:35, 2.15it/s] 18%|█▊ | 17/93 [00:06<00:35, 2.14it/s] 19%|█▉ | 18/93 [00:07<00:33, 2.21it/s] 20%|██ | 19/93 [00:07<00:31, 2.37it/s] 22%|██▏ | 20/93 [00:07<00:29, 2.48it/s] 23%|██▎ | 21/93 [00:08<00:29, 2.47it/s] 24%|██▎ | 22/93 [00:08<00:28, 2.48it/s] 25%|██▍ | 23/93 [00:09<00:29, 2.35it/s] 26%|██▌ | 24/93 [00:09<00:30, 2.26it/s] 27%|██▋ | 25/93 [00:10<00:29, 2.27it/s] 28%|██▊ | 26/93 [00:10<00:28, 2.37it/s] 29%|██▉ | 27/93 [00:10<00:26, 2.46it/s] 30%|███ | 28/93 [00:12<00:59, 1.08it/s] 31%|███ | 29/93 [00:13<00:48, 1.32it/s] 32%|███▏ | 30/93 [00:13<00:43, 1.46it/s] 33%|███▎ | 31/93 [00:14<00:36, 1.70it/s] 34%|███▍ | 32/93 [00:14<00:34, 1.75it/s] 35%|███▌ | 33/93 [00:15<00:33, 1.80it/s] 37%|███▋ | 34/93 [00:15<00:30, 1.92it/s] 38%|███▊ | 35/93 [00:16<00:28, 2.05it/s] 39%|███▊ | 36/93 [00:16<00:26, 2.13it/s] 40%|███▉ | 37/93 [00:16<00:25, 2.20it/s] 41%|████ | 38/93 [00:17<00:25, 2.16it/s] 42%|████▏ | 39/93 [00:17<00:25, 2.13it/s] 43%|████▎ | 40/93 [00:18<00:23, 2.23it/s] 44%|████▍ | 41/93 [00:18<00:22, 2.31it/s] 45%|████▌ | 42/93 [00:19<00:21, 2.39it/s] 46%|████▌ | 43/93 [00:19<00:20, 2.47it/s] 47%|████▋ | 44/93 [00:19<00:21, 2.24it/s] 48%|████▊ | 45/93 [00:20<00:21, 2.24it/s] 49%|████▉ | 46/93 [00:20<00:21, 2.20it/s] 51%|█████ | 47/93 [00:21<00:19, 2.32it/s] 52%|█████▏ | 48/93 [00:21<00:19, 2.33it/s] 53%|█████▎ | 49/93 [00:22<00:18, 2.44it/s] 54%|█████▍ | 50/93 [00:22<00:17, 2.48it/s] 55%|█████▍ | 51/93 [00:22<00:16, 2.50it/s] 56%|█████▌ | 52/93 [00:23<00:16, 2.46it/s] 57%|█████▋ | 53/93 [00:23<00:16, 2.40it/s] 58%|█████▊ | 54/93 [00:24<00:16, 2.34it/s] 59%|█████▉ | 55/93 [00:24<00:15, 2.42it/s] 60%|██████ | 56/93 [00:24<00:14, 2.51it/s] 61%|██████▏ | 57/93 [00:25<00:14, 2.45it/s] 62%|██████▏ | 58/93 [00:25<00:13, 2.53it/s] 63%|██████▎ | 59/93 [00:26<00:13, 2.61it/s] 65%|██████▍ | 60/93 [00:26<00:12, 2.66it/s] 66%|██████▌ | 61/93 [00:26<00:13, 2.32it/s] 67%|██████▋ | 62/93 [00:27<00:12, 2.39it/s] 68%|██████▊ | 63/93 [00:27<00:12, 2.44it/s] 69%|██████▉ | 64/93 [00:28<00:13, 2.15it/s] 70%|██████▉ | 65/93 [00:28<00:12, 2.33it/s] 71%|███████ | 66/93 [00:29<00:11, 2.37it/s] 72%|███████▏ | 67/93 [00:29<00:10, 2.44it/s] 73%|███████▎ | 68/93 [00:29<00:11, 2.24it/s] 74%|███████▍ | 69/93 [00:30<00:11, 2.11it/s] 75%|███████▌ | 70/93 [00:30<00:10, 2.19it/s] 76%|███████▋ | 71/93 [00:31<00:11, 1.97it/s] 77%|███████▋ | 72/93 [00:31<00:09, 2.10it/s] 78%|███████▊ | 73/93 [00:32<00:09, 2.21it/s] 80%|███████▉ | 74/93 [00:32<00:08, 2.24it/s] 81%|████████ | 75/93 [00:33<00:08, 2.21it/s] 82%|████████▏ | 76/93 [00:33<00:07, 2.28it/s] 83%|████████▎ | 77/93 [00:34<00:06, 2.29it/s] 84%|████████▍ | 78/93 [00:34<00:06, 2.24it/s] 85%|████████▍ | 79/93 [00:35<00:06, 2.15it/s] 86%|████████▌ | 80/93 [00:35<00:06, 2.11it/s] 87%|████████▋ | 81/93 [00:35<00:05, 2.26it/s] 88%|████████▊ | 82/93 [00:36<00:04, 2.20it/s] 89%|████████▉ | 83/93 [00:36<00:04, 2.20it/s] 90%|█████████ | 84/93 [00:37<00:03, 2.30it/s] 91%|█████████▏| 85/93 [00:37<00:03, 2.18it/s] 92%|█████████▏| 86/93 [00:38<00:03, 1.97it/s] 94%|█████████▎| 87/93 [00:38<00:02, 2.07it/s] 95%|█████████▍| 88/93 [00:39<00:02, 2.24it/s] 96%|█████████▌| 89/93 [00:39<00:01, 2.16it/s] 97%|█████████▋| 90/93 [00:40<00:01, 2.28it/s] 98%|█████████▊| 91/93 [00:40<00:00, 2.15it/s] 99%|█████████▉| 92/93 [00:41<00:00, 2.16it/s] 100%|██████████| 93/93 [00:41<00:00, 2.37it/s] 100%|██████████| 93/93 [00:41<00:00, 2.23it/s] | |
| ***** predict_test_hu_HU metrics ***** | |
| predict_ex_match_acc = 0.273 | |
| predict_ex_match_acc_stderr = 0.0082 | |
| predict_intent_acc = 0.5454 | |
| predict_intent_acc_stderr = 0.0091 | |
| predict_loss = 0.7601 | |
| predict_runtime = 0:00:42.04 | |
| predict_samples = 2974 | |
| predict_samples_per_second = 70.741 | |
| predict_slot_micro_f1 = 0.5011 | |
| predict_slot_micro_f1_stderr = 0.0038 | |
| predict_steps_per_second = 2.212 | |
| 02/05/2024 17:25:15 - INFO - __main__ - *** test_ko_KR *** | |
| [INFO|trainer.py:718] 2024-02-05 17:25:15,739 >> The following columns in the test set don't have a corresponding argument in `MT5ForConditionalGeneration.forward` and have been ignored: intent_str, id, annot_utt. If intent_str, id, annot_utt are not expected by `MT5ForConditionalGeneration.forward`, you can safely ignore this message. | |
| [INFO|trainer.py:3199] 2024-02-05 17:25:15,741 >> ***** Running Prediction ***** | |
| [INFO|trainer.py:3201] 2024-02-05 17:25:15,742 >> Num examples = 2974 | |
| [INFO|trainer.py:3204] 2024-02-05 17:25:15,742 >> Batch size = 32 | |
| 0%| | 0/93 [00:00<?, ?it/s] 2%|▏ | 2/93 [00:00<00:18, 4.96it/s] 3%|▎ | 3/93 [00:00<00:21, 4.19it/s] 4%|▍ | 4/93 [00:01<00:25, 3.42it/s] 5%|▌ | 5/93 [00:01<00:26, 3.33it/s] 6%|▋ | 6/93 [00:01<00:25, 3.36it/s] 8%|▊ | 7/93 [00:02<00:26, 3.19it/s] 9%|▊ | 8/93 [00:02<00:27, 3.12it/s] 10%|▉ | 9/93 [00:02<00:27, 3.05it/s] 11%|█ | 10/93 [00:03<00:27, 3.04it/s] 12%|█▏ | 11/93 [00:03<00:27, 3.01it/s] 13%|█▎ | 12/93 [00:03<00:28, 2.83it/s] 14%|█▍ | 13/93 [00:04<00:29, 2.70it/s] 15%|█▌ | 14/93 [00:04<00:28, 2.79it/s] 16%|█▌ | 15/93 [00:04<00:28, 2.70it/s] 17%|█▋ | 16/93 [00:05<00:28, 2.75it/s] 18%|█▊ | 17/93 [00:05<00:27, 2.76it/s] 19%|█▉ | 18/93 [00:06<00:28, 2.68it/s] 20%|██ | 19/93 [00:06<00:27, 2.70it/s] 22%|██▏ | 20/93 [00:06<00:27, 2.67it/s] 23%|██▎ | 21/93 [00:07<00:26, 2.75it/s] 24%|██▎ | 22/93 [00:07<00:25, 2.75it/s] 25%|██▍ | 23/93 [00:07<00:25, 2.71it/s] 26%|██▌ | 24/93 [00:08<00:24, 2.83it/s] 27%|██▋ | 25/93 [00:08<00:24, 2.75it/s] 28%|██▊ | 26/93 [00:08<00:25, 2.63it/s] 29%|██▉ | 27/93 [00:09<00:24, 2.73it/s] 30%|███ | 28/93 [00:09<00:24, 2.62it/s] 31%|███ | 29/93 [00:10<00:23, 2.72it/s] 32%|███▏ | 30/93 [00:10<00:23, 2.72it/s] 33%|███▎ | 31/93 [00:10<00:22, 2.78it/s] 34%|███▍ | 32/93 [00:11<00:23, 2.63it/s] 35%|███▌ | 33/93 [00:11<00:22, 2.66it/s] 37%|███▋ | 34/93 [00:11<00:21, 2.76it/s] 38%|███▊ | 35/93 [00:12<00:23, 2.45it/s] 39%|███▊ | 36/93 [00:12<00:22, 2.58it/s] 40%|███▉ | 37/93 [00:13<00:21, 2.66it/s] 41%|████ | 38/93 [00:13<00:21, 2.60it/s] 42%|████▏ | 39/93 [00:13<00:21, 2.55it/s] 43%|████▎ | 40/93 [00:14<00:20, 2.59it/s] 44%|████▍ | 41/93 [00:14<00:19, 2.66it/s] 45%|████▌ | 42/93 [00:14<00:18, 2.75it/s] 46%|████▌ | 43/93 [00:15<00:18, 2.68it/s] 47%|████▋ | 44/93 [00:15<00:18, 2.71it/s] 48%|████▊ | 45/93 [00:16<00:17, 2.79it/s] 49%|████▉ | 46/93 [00:16<00:16, 2.87it/s] 51%|█████ | 47/93 [00:16<00:16, 2.86it/s] 52%|█████▏ | 48/93 [00:17<00:16, 2.77it/s] 53%|█████▎ | 49/93 [00:17<00:15, 2.87it/s] 54%|█████▍ | 50/93 [00:17<00:15, 2.80it/s] 55%|█████▍ | 51/93 [00:18<00:14, 2.82it/s] 56%|█████▌ | 52/93 [00:18<00:15, 2.68it/s] 57%|█████▋ | 53/93 [00:18<00:15, 2.62it/s] 58%|█████▊ | 54/93 [00:19<00:14, 2.71it/s] 59%|█████▉ | 55/93 [00:19<00:13, 2.80it/s] 60%|██████ | 56/93 [00:20<00:13, 2.75it/s] 61%|██████▏ | 57/93 [00:20<00:13, 2.65it/s] 62%|██████▏ | 58/93 [00:20<00:12, 2.71it/s] 63%|██████▎ | 59/93 [00:21<00:12, 2.74it/s] 65%|██████▍ | 60/93 [00:21<00:11, 2.81it/s] 66%|██████▌ | 61/93 [00:21<00:11, 2.67it/s] 67%|██████▋ | 62/93 [00:22<00:11, 2.76it/s] 68%|██████▊ | 63/93 [00:22<00:10, 2.88it/s] 69%|██████▉ | 64/93 [00:22<00:10, 2.85it/s] 70%|██████▉ | 65/93 [00:23<00:09, 2.88it/s] 71%|███████ | 66/93 [00:23<00:09, 2.96it/s] 72%|███████▏ | 67/93 [00:24<00:09, 2.68it/s] 73%|███████▎ | 68/93 [00:24<00:09, 2.63it/s] 74%|███████▍ | 69/93 [00:24<00:08, 2.81it/s] 75%|███████▌ | 70/93 [00:25<00:08, 2.73it/s] 76%|███████▋ | 71/93 [00:25<00:08, 2.59it/s] 77%|███████▋ | 72/93 [00:25<00:08, 2.58it/s] 78%|███████▊ | 73/93 [00:26<00:07, 2.68it/s] 80%|███████▉ | 74/93 [00:26<00:07, 2.53it/s] 81%|████████ | 75/93 [00:27<00:07, 2.51it/s] 82%|████████▏ | 76/93 [00:27<00:06, 2.58it/s] 83%|████████▎ | 77/93 [00:27<00:05, 2.68it/s] 84%|████████▍ | 78/93 [00:28<00:05, 2.63it/s] 85%|████████▍ | 79/93 [00:28<00:05, 2.65it/s] 86%|████████▌ | 80/93 [00:28<00:04, 2.73it/s] 87%|████████▋ | 81/93 [00:29<00:04, 2.79it/s] 88%|████████▊ | 82/93 [00:29<00:04, 2.67it/s] 89%|████████▉ | 83/93 [00:30<00:03, 2.66it/s] 90%|█████████ | 84/93 [00:30<00:03, 2.72it/s] 91%|█████████▏| 85/93 [00:30<00:03, 2.61it/s] 92%|█████████▏| 86/93 [00:31<00:02, 2.61it/s] 94%|█████████▎| 87/93 [00:31<00:02, 2.77it/s] 95%|█████████▍| 88/93 [00:31<00:01, 2.84it/s] 96%|█████████▌| 89/93 [00:32<00:01, 2.82it/s] 97%|█████████▋| 90/93 [00:32<00:01, 2.74it/s] 98%|█████████▊| 91/93 [00:32<00:00, 2.75it/s] 99%|█████████▉| 92/93 [00:33<00:00, 2.74it/s] 100%|██████████| 93/93 [00:33<00:00, 2.73it/s] 100%|██████████| 93/93 [00:33<00:00, 2.74it/s] | |
| ***** predict_test_ko_KR metrics ***** | |
| predict_ex_match_acc = 0.0333 | |
| predict_ex_match_acc_stderr = 0.0033 | |
| predict_intent_acc = 0.0568 | |
| predict_intent_acc_stderr = 0.0042 | |
| predict_loss = 1.5014 | |
| predict_runtime = 0:00:34.26 | |
| predict_samples = 2974 | |
| predict_samples_per_second = 86.783 | |
| predict_slot_micro_f1 = 0.1925 | |
| predict_slot_micro_f1_stderr = 0.0033 | |
| predict_steps_per_second = 2.714 | |
| 02/05/2024 17:25:50 - INFO - __main__ - *** test_nl_NL *** | |
| [INFO|trainer.py:718] 2024-02-05 17:25:50,247 >> The following columns in the test set don't have a corresponding argument in `MT5ForConditionalGeneration.forward` and have been ignored: intent_str, id, annot_utt. If intent_str, id, annot_utt are not expected by `MT5ForConditionalGeneration.forward`, you can safely ignore this message. | |
| [INFO|trainer.py:3199] 2024-02-05 17:25:50,249 >> ***** Running Prediction ***** | |
| [INFO|trainer.py:3201] 2024-02-05 17:25:50,250 >> Num examples = 2974 | |
| [INFO|trainer.py:3204] 2024-02-05 17:25:50,250 >> Batch size = 32 | |
| 0%| | 0/93 [00:00<?, ?it/s] 2%|▏ | 2/93 [00:00<00:20, 4.52it/s] 3%|▎ | 3/93 [00:00<00:31, 2.82it/s] 4%|▍ | 4/93 [00:01<00:33, 2.64it/s] 5%|▌ | 5/93 [00:01<00:33, 2.63it/s] 6%|▋ | 6/93 [00:02<00:33, 2.56it/s] 8%|▊ | 7/93 [00:02<00:35, 2.41it/s] 9%|▊ | 8/93 [00:03<00:35, 2.39it/s] 10%|▉ | 9/93 [00:03<00:36, 2.32it/s] 11%|█ | 10/93 [00:04<00:36, 2.25it/s] 12%|█▏ | 11/93 [00:04<00:37, 2.21it/s] 13%|█▎ | 12/93 [00:04<00:37, 2.17it/s] 14%|█▍ | 13/93 [00:05<00:36, 2.18it/s] 15%|█▌ | 14/93 [00:05<00:35, 2.25it/s] 16%|█▌ | 15/93 [00:06<00:39, 1.99it/s] 17%|█▋ | 16/93 [00:07<00:40, 1.91it/s] 18%|█▊ | 17/93 [00:07<00:40, 1.88it/s] 19%|█▉ | 18/93 [00:08<00:37, 1.97it/s] 20%|██ | 19/93 [00:08<00:36, 2.05it/s] 22%|██▏ | 20/93 [00:08<00:34, 2.10it/s] 23%|██▎ | 21/93 [00:09<00:32, 2.19it/s] 24%|██▎ | 22/93 [00:09<00:36, 1.96it/s] 25%|██▍ | 23/93 [00:10<00:34, 2.02it/s] 26%|██▌ | 24/93 [00:10<00:34, 2.00it/s] 27%|██▋ | 25/93 [00:11<00:33, 2.04it/s] 28%|██▊ | 26/93 [00:11<00:31, 2.10it/s] 29%|██▉ | 27/93 [00:12<00:30, 2.18it/s] 30%|███ | 28/93 [00:14<01:01, 1.05it/s] 31%|███ | 29/93 [00:14<00:49, 1.29it/s] 32%|███▏ | 30/93 [00:15<00:42, 1.47it/s] 33%|███▎ | 31/93 [00:15<00:37, 1.64it/s] 34%|███▍ | 32/93 [00:16<00:35, 1.74it/s] 35%|███▌ | 33/93 [00:16<00:34, 1.74it/s] 37%|███▋ | 34/93 [00:17<00:30, 1.93it/s] 38%|███▊ | 35/93 [00:17<00:29, 1.95it/s] 39%|███▊ | 36/93 [00:18<00:27, 2.04it/s] 40%|███▉ | 37/93 [00:18<00:26, 2.08it/s] 41%|████ | 38/93 [00:19<00:30, 1.78it/s] 42%|████▏ | 39/93 [00:19<00:30, 1.78it/s] 43%|████▎ | 40/93 [00:20<00:27, 1.93it/s] 44%|████▍ | 41/93 [00:20<00:24, 2.09it/s] 45%|████▌ | 42/93 [00:21<00:23, 2.16it/s] 46%|████▌ | 43/93 [00:21<00:22, 2.27it/s] 47%|████▋ | 44/93 [00:21<00:21, 2.32it/s] 48%|████▊ | 45/93 [00:22<00:21, 2.28it/s] 49%|████▉ | 46/93 [00:22<00:20, 2.29it/s] 51%|█████ | 47/93 [00:23<00:19, 2.39it/s] 52%|█████▏ | 48/93 [00:23<00:18, 2.37it/s] 53%|█████▎ | 49/93 [00:23<00:18, 2.38it/s] 54%|█████▍ | 50/93 [00:24<00:17, 2.41it/s] 55%|█████▍ | 51/93 [00:24<00:19, 2.18it/s] 56%|█████▌ | 52/93 [00:25<00:19, 2.10it/s] 57%|█████▋ | 53/93 [00:25<00:19, 2.07it/s] 58%|█████▊ | 54/93 [00:26<00:18, 2.14it/s] 59%|█████▉ | 55/93 [00:26<00:16, 2.30it/s] 60%|██████ | 56/93 [00:27<00:15, 2.36it/s] 61%|██████▏ | 57/93 [00:27<00:15, 2.33it/s] 62%|██████▏ | 58/93 [00:28<00:15, 2.25it/s] 63%|██████▎ | 59/93 [00:28<00:14, 2.27it/s] 65%|██████▍ | 60/93 [00:28<00:14, 2.34it/s] 66%|██████▌ | 61/93 [00:29<00:14, 2.15it/s] 67%|██████▋ | 62/93 [00:29<00:14, 2.14it/s] 68%|██████▊ | 63/93 [00:30<00:13, 2.27it/s] 69%|██████▉ | 64/93 [00:30<00:13, 2.15it/s] 70%|██████▉ | 65/93 [00:31<00:12, 2.32it/s] 71%|███████ | 66/93 [00:31<00:11, 2.33it/s] 72%|███████▏ | 67/93 [00:31<00:10, 2.36it/s] 73%|███████▎ | 68/93 [00:32<00:11, 2.27it/s] 74%|███████▍ | 69/93 [00:33<00:11, 2.10it/s] 75%|███████▌ | 70/93 [00:33<00:10, 2.13it/s] 76%|███████▋ | 71/93 [00:34<00:11, 1.93it/s] 77%|███████▋ | 72/93 [00:34<00:10, 1.91it/s] 78%|███████▊ | 73/93 [00:35<00:09, 2.04it/s] 80%|███████▉ | 74/93 [00:35<00:09, 1.97it/s] 81%|████████ | 75/93 [00:36<00:09, 1.96it/s] 82%|████████▏ | 76/93 [00:36<00:08, 2.11it/s] 83%|████████▎ | 77/93 [00:36<00:07, 2.14it/s] 84%|████████▍ | 78/93 [00:37<00:07, 1.98it/s] 85%|████████▍ | 79/93 [00:38<00:07, 1.95it/s] 86%|████████▌ | 80/93 [00:38<00:06, 2.01it/s] 87%|████████▋ | 81/93 [00:38<00:05, 2.12it/s] 88%|████████▊ | 82/93 [00:39<00:05, 2.17it/s] 89%|████████▉ | 83/93 [00:39<00:04, 2.14it/s] 90%|█████████ | 84/93 [00:40<00:04, 2.19it/s] 91%|█████████▏| 85/93 [00:40<00:03, 2.26it/s] 92%|█████████▏| 86/93 [00:41<00:03, 2.13it/s] 94%|█████████▎| 87/93 [00:41<00:02, 2.21it/s] 95%|█████████▍| 88/93 [00:42<00:02, 2.30it/s] 96%|█████████▌| 89/93 [00:42<00:01, 2.17it/s] 97%|█████████▋| 90/93 [00:43<00:01, 2.13it/s] 98%|█████████▊| 91/93 [00:43<00:00, 2.04it/s] 99%|█████████▉| 92/93 [00:44<00:00, 2.08it/s] 100%|██████████| 93/93 [00:44<00:00, 2.16it/s] 100%|██████████| 93/93 [00:44<00:00, 2.08it/s] | |
| ***** predict_test_nl_NL metrics ***** | |
| predict_ex_match_acc = 0.5928 | |
| predict_ex_match_acc_stderr = 0.009 | |
| predict_intent_acc = 0.8578 | |
| predict_intent_acc_stderr = 0.0064 | |
| predict_loss = 0.473 | |
| predict_runtime = 0:00:45.28 | |
| predict_samples = 2974 | |
| predict_samples_per_second = 65.673 | |
| predict_slot_micro_f1 = 0.6919 | |
| predict_slot_micro_f1_stderr = 0.0032 | |
| predict_steps_per_second = 2.054 | |
| 02/05/2024 17:26:35 - INFO - __main__ - *** test_pl_PL *** | |
| [INFO|trainer.py:718] 2024-02-05 17:26:35,766 >> The following columns in the test set don't have a corresponding argument in `MT5ForConditionalGeneration.forward` and have been ignored: intent_str, id, annot_utt. If intent_str, id, annot_utt are not expected by `MT5ForConditionalGeneration.forward`, you can safely ignore this message. | |
| [INFO|trainer.py:3199] 2024-02-05 17:26:35,768 >> ***** Running Prediction ***** | |
| [INFO|trainer.py:3201] 2024-02-05 17:26:35,769 >> Num examples = 2974 | |
| [INFO|trainer.py:3204] 2024-02-05 17:26:35,769 >> Batch size = 32 | |
| 0%| | 0/93 [00:00<?, ?it/s] 2%|▏ | 2/93 [00:00<00:16, 5.51it/s] 3%|▎ | 3/93 [00:00<00:25, 3.49it/s] 4%|▍ | 4/93 [00:01<00:30, 2.94it/s] 5%|▌ | 5/93 [00:01<00:32, 2.73it/s] 6%|▋ | 6/93 [00:02<00:32, 2.68it/s] 8%|▊ | 7/93 [00:02<00:33, 2.55it/s] 9%|▊ | 8/93 [00:03<00:38, 2.22it/s] 10%|▉ | 9/93 [00:03<00:39, 2.10it/s] 11%|█ | 10/93 [00:03<00:37, 2.21it/s] 12%|█▏ | 11/93 [00:04<00:38, 2.14it/s] 13%|█▎ | 12/93 [00:04<00:34, 2.32it/s] 14%|█▍ | 13/93 [00:05<00:33, 2.36it/s] 15%|█▌ | 14/93 [00:05<00:32, 2.45it/s] 16%|█▌ | 15/93 [00:06<00:33, 2.36it/s] 17%|█▋ | 16/93 [00:06<00:32, 2.35it/s] 18%|█▊ | 17/93 [00:06<00:32, 2.35it/s] 19%|█▉ | 18/93 [00:07<00:32, 2.34it/s] 20%|██ | 19/93 [00:07<00:32, 2.30it/s] 22%|██▏ | 20/93 [00:08<00:33, 2.15it/s] 23%|██▎ | 21/93 [00:08<00:32, 2.22it/s] 24%|██▎ | 22/93 [00:09<00:30, 2.32it/s] 25%|██▍ | 23/93 [00:09<00:29, 2.37it/s] 26%|██▌ | 24/93 [00:09<00:28, 2.43it/s] 27%|██▋ | 25/93 [00:10<00:27, 2.48it/s] 28%|██▊ | 26/93 [00:10<00:26, 2.50it/s] 29%|██▉ | 27/93 [00:11<00:26, 2.51it/s] 30%|███ | 28/93 [00:11<00:26, 2.41it/s] 31%|███ | 29/93 [00:12<00:27, 2.30it/s] 32%|███▏ | 30/93 [00:12<00:26, 2.39it/s] 33%|███▎ | 31/93 [00:12<00:25, 2.40it/s] 34%|███▍ | 32/93 [00:13<00:24, 2.45it/s] 35%|███▌ | 33/93 [00:13<00:24, 2.41it/s] 37%|███▋ | 34/93 [00:13<00:23, 2.56it/s] 38%|███▊ | 35/93 [00:14<00:23, 2.47it/s] 39%|███▊ | 36/93 [00:14<00:25, 2.25it/s] 40%|███▉ | 37/93 [00:15<00:26, 2.12it/s] 41%|████ | 38/93 [00:15<00:25, 2.15it/s] 42%|████▏ | 39/93 [00:16<00:24, 2.21it/s] 43%|████▎ | 40/93 [00:16<00:24, 2.13it/s] 44%|████▍ | 41/93 [00:18<00:50, 1.04it/s] 45%|████▌ | 42/93 [00:19<00:41, 1.22it/s] 46%|████▌ | 43/93 [00:19<00:33, 1.48it/s] 47%|████▋ | 44/93 [00:20<00:28, 1.72it/s] 48%|████▊ | 45/93 [00:20<00:26, 1.81it/s] 49%|████▉ | 46/93 [00:21<00:24, 1.92it/s] 51%|█████ | 47/93 [00:21<00:21, 2.10it/s] 52%|█████▏ | 48/93 [00:21<00:20, 2.20it/s] 53%|█████▎ | 49/93 [00:22<00:20, 2.20it/s] 54%|█████▍ | 50/93 [00:22<00:18, 2.26it/s] 55%|█████▍ | 51/93 [00:23<00:18, 2.28it/s] 56%|█████▌ | 52/93 [00:23<00:17, 2.35it/s] 57%|█████▋ | 53/93 [00:24<00:17, 2.32it/s] 58%|█████▊ | 54/93 [00:24<00:16, 2.41it/s] 59%|█████▉ | 55/93 [00:24<00:15, 2.45it/s] 60%|██████ | 56/93 [00:25<00:16, 2.27it/s] 61%|██████▏ | 57/93 [00:25<00:15, 2.28it/s] 62%|██████▏ | 58/93 [00:26<00:14, 2.33it/s] 63%|██████▎ | 59/93 [00:26<00:14, 2.40it/s] 65%|██████▍ | 60/93 [00:26<00:14, 2.35it/s] 66%|██████▌ | 61/93 [00:27<00:14, 2.24it/s] 67%|██████▋ | 62/93 [00:27<00:13, 2.25it/s] 68%|██████▊ | 63/93 [00:28<00:12, 2.34it/s] 69%|██████▉ | 64/93 [00:28<00:11, 2.48it/s] 70%|██████▉ | 65/93 [00:29<00:12, 2.33it/s] 71%|███████ | 66/93 [00:29<00:12, 2.17it/s] 72%|███████▏ | 67/93 [00:30<00:11, 2.23it/s] 73%|███████▎ | 68/93 [00:30<00:10, 2.32it/s] 74%|███████▍ | 69/93 [00:30<00:10, 2.24it/s] 75%|███████▌ | 70/93 [00:33<00:21, 1.07it/s] 76%|███████▋ | 71/93 [00:33<00:17, 1.25it/s] 77%|███████▋ | 72/93 [00:33<00:14, 1.44it/s] 78%|███████▊ | 73/93 [00:34<00:12, 1.66it/s] 80%|███████▉ | 74/93 [00:34<00:10, 1.89it/s] 81%|████████ | 75/93 [00:35<00:08, 2.05it/s] 82%|████████▏ | 76/93 [00:35<00:08, 2.07it/s] 83%|████████▎ | 77/93 [00:36<00:08, 1.91it/s] 84%|████████▍ | 78/93 [00:36<00:07, 2.05it/s] 85%|████████▍ | 79/93 [00:37<00:06, 2.09it/s] 86%|████████▌ | 80/93 [00:37<00:06, 2.12it/s] 87%|████████▋ | 81/93 [00:38<00:06, 1.98it/s] 88%|████████▊ | 82/93 [00:38<00:05, 2.11it/s] 89%|████████▉ | 83/93 [00:38<00:04, 2.22it/s] 90%|█████████ | 84/93 [00:39<00:03, 2.44it/s] 91%|█████████▏| 85/93 [00:39<00:03, 2.36it/s] 92%|█████████▏| 86/93 [00:40<00:03, 2.32it/s] 94%|█████████▎| 87/93 [00:40<00:02, 2.25it/s] 95%|█████████▍| 88/93 [00:41<00:02, 2.30it/s] 96%|█████████▌| 89/93 [00:41<00:01, 2.38it/s] 97%|█████████▋| 90/93 [00:41<00:01, 2.22it/s] 98%|█████████▊| 91/93 [00:42<00:00, 2.14it/s] 99%|█████████▉| 92/93 [00:42<00:00, 2.13it/s] 100%|██████████| 93/93 [00:43<00:00, 2.27it/s] 100%|██████████| 93/93 [00:43<00:00, 2.13it/s] | |
| ***** predict_test_pl_PL metrics ***** | |
| predict_ex_match_acc = 0.5128 | |
| predict_ex_match_acc_stderr = 0.0092 | |
| predict_intent_acc = 0.7993 | |
| predict_intent_acc_stderr = 0.0073 | |
| predict_loss = 0.3891 | |
| predict_runtime = 0:00:44.14 | |
| predict_samples = 2974 | |
| predict_samples_per_second = 67.369 | |
| predict_slot_micro_f1 = 0.6636 | |
| predict_slot_micro_f1_stderr = 0.0036 | |
| predict_steps_per_second = 2.107 | |
| 02/05/2024 17:27:20 - INFO - __main__ - *** test_pt_PT *** | |
| [INFO|trainer.py:718] 2024-02-05 17:27:20,151 >> The following columns in the test set don't have a corresponding argument in `MT5ForConditionalGeneration.forward` and have been ignored: intent_str, id, annot_utt. If intent_str, id, annot_utt are not expected by `MT5ForConditionalGeneration.forward`, you can safely ignore this message. | |
| [INFO|trainer.py:3199] 2024-02-05 17:27:20,153 >> ***** Running Prediction ***** | |
| [INFO|trainer.py:3201] 2024-02-05 17:27:20,153 >> Num examples = 2974 | |
| [INFO|trainer.py:3204] 2024-02-05 17:27:20,153 >> Batch size = 32 | |
| 0%| | 0/93 [00:00<?, ?it/s] 2%|▏ | 2/93 [00:00<00:18, 4.85it/s] 3%|▎ | 3/93 [00:00<00:31, 2.85it/s] 4%|▍ | 4/93 [00:01<00:33, 2.66it/s] 5%|▌ | 5/93 [00:01<00:35, 2.47it/s] 6%|▋ | 6/93 [00:02<00:42, 2.07it/s] 8%|▊ | 7/93 [00:03<00:44, 1.92it/s] 9%|▊ | 8/93 [00:03<00:44, 1.93it/s] 10%|▉ | 9/93 [00:04<00:42, 1.97it/s] 11%|█ | 10/93 [00:04<00:41, 1.98it/s] 12%|█▏ | 11/93 [00:05<00:44, 1.83it/s] 13%|█▎ | 12/93 [00:05<00:40, 1.99it/s] 14%|█▍ | 13/93 [00:06<00:39, 2.00it/s] 15%|█▌ | 14/93 [00:06<00:37, 2.13it/s] 16%|█▌ | 15/93 [00:07<00:37, 2.05it/s] 17%|█▋ | 16/93 [00:07<00:38, 2.01it/s] 18%|█▊ | 17/93 [00:07<00:36, 2.09it/s] 19%|█▉ | 18/93 [00:08<00:34, 2.16it/s] 20%|██ | 19/93 [00:08<00:35, 2.09it/s] 22%|██▏ | 20/93 [00:09<00:36, 1.99it/s] 23%|██▎ | 21/93 [00:09<00:34, 2.06it/s] 24%|██▎ | 22/93 [00:10<00:33, 2.15it/s] 25%|██▍ | 23/93 [00:10<00:32, 2.16it/s] 26%|██▌ | 24/93 [00:11<00:31, 2.17it/s] 27%|██▋ | 25/93 [00:11<00:31, 2.17it/s] 28%|██▊ | 26/93 [00:12<00:29, 2.26it/s] 29%|██▉ | 27/93 [00:12<00:33, 1.97it/s] 30%|███ | 28/93 [00:13<00:34, 1.89it/s] 31%|███ | 29/93 [00:13<00:32, 1.96it/s] 32%|███▏ | 30/93 [00:14<00:29, 2.14it/s] 33%|███▎ | 31/93 [00:14<00:27, 2.22it/s] 34%|███▍ | 32/93 [00:15<00:27, 2.19it/s] 35%|███▌ | 33/93 [00:15<00:29, 2.02it/s] 37%|███▋ | 34/93 [00:16<00:30, 1.91it/s] 38%|███▊ | 35/93 [00:18<00:58, 1.01s/it] 39%|███▊ | 36/93 [00:18<00:49, 1.16it/s] 40%|███▉ | 37/93 [00:19<00:41, 1.36it/s] 41%|████ | 38/93 [00:19<00:36, 1.51it/s] 42%|████▏ | 39/93 [00:20<00:32, 1.66it/s] 43%|████▎ | 40/93 [00:20<00:30, 1.72it/s] 44%|████▍ | 41/93 [00:21<00:29, 1.79it/s] 45%|████▌ | 42/93 [00:21<00:28, 1.81it/s] 46%|████▌ | 43/93 [00:22<00:26, 1.89it/s] 47%|████▋ | 44/93 [00:22<00:26, 1.84it/s] 48%|████▊ | 45/93 [00:23<00:26, 1.82it/s] 49%|████▉ | 46/93 [00:24<00:26, 1.77it/s] 51%|█████ | 47/93 [00:24<00:23, 1.94it/s] 52%|█████▏ | 48/93 [00:24<00:22, 2.03it/s] 53%|█████▎ | 49/93 [00:25<00:21, 2.05it/s] 54%|█████▍ | 50/93 [00:25<00:20, 2.05it/s] 55%|█████▍ | 51/93 [00:26<00:20, 2.02it/s] 56%|█████▌ | 52/93 [00:27<00:24, 1.68it/s] 57%|█████▋ | 53/93 [00:27<00:25, 1.58it/s] 58%|█████▊ | 54/93 [00:28<00:23, 1.65it/s] 59%|█████▉ | 55/93 [00:28<00:20, 1.82it/s] 60%|██████ | 56/93 [00:29<00:19, 1.87it/s] 61%|██████▏ | 57/93 [00:29<00:18, 1.96it/s] 62%|██████▏ | 58/93 [00:30<00:17, 1.95it/s] 63%|██████▎ | 59/93 [00:30<00:16, 2.07it/s] 65%|██████▍ | 60/93 [00:31<00:15, 2.14it/s] 66%|██████▌ | 61/93 [00:31<00:16, 1.92it/s] 67%|██████▋ | 62/93 [00:32<00:15, 1.95it/s] 68%|██████▊ | 63/93 [00:33<00:16, 1.84it/s] 69%|██████▉ | 64/93 [00:33<00:15, 1.92it/s] 70%|██████▉ | 65/93 [00:33<00:13, 2.02it/s] 71%|███████ | 66/93 [00:34<00:13, 2.07it/s] 72%|███████▏ | 67/93 [00:34<00:12, 2.04it/s] 73%|███████▎ | 68/93 [00:35<00:12, 2.05it/s] 74%|███████▍ | 69/93 [00:35<00:11, 2.11it/s] 75%|███████▌ | 70/93 [00:36<00:11, 2.09it/s] 76%|███████▋ | 71/93 [00:36<00:10, 2.12it/s] 77%|███████▋ | 72/93 [00:37<00:09, 2.21it/s] 78%|███████▊ | 73/93 [00:37<00:09, 2.22it/s] 80%|███████▉ | 74/93 [00:38<00:08, 2.18it/s] 81%|████████ | 75/93 [00:38<00:07, 2.27it/s] 82%|████████▏ | 76/93 [00:38<00:07, 2.14it/s] 83%|████████▎ | 77/93 [00:39<00:07, 2.13it/s] 84%|████████▍ | 78/93 [00:40<00:07, 1.97it/s] 85%|████████▍ | 79/93 [00:40<00:07, 1.98it/s] 86%|████████▌ | 80/93 [00:41<00:06, 2.02it/s] 87%|████████▋ | 81/93 [00:41<00:05, 2.10it/s] 88%|████████▊ | 82/93 [00:42<00:05, 1.95it/s] 89%|████████▉ | 83/93 [00:42<00:04, 2.02it/s] 90%|█████████ | 84/93 [00:42<00:04, 2.08it/s] 91%|█████████▏| 85/93 [00:43<00:03, 2.20it/s] 92%|█████████▏| 86/93 [00:43<00:02, 2.34it/s] 94%|█████████▎| 87/93 [00:44<00:02, 2.25it/s] 95%|█████████▍| 88/93 [00:44<00:02, 2.21it/s] 96%|█████████▌| 89/93 [00:45<00:01, 2.18it/s] 97%|█████████▋| 90/93 [00:45<00:01, 2.03it/s] 98%|█████████▊| 91/93 [00:46<00:00, 2.04it/s] 99%|█████████▉| 92/93 [00:46<00:00, 2.03it/s] 100%|██████████| 93/93 [00:47<00:00, 2.16it/s] 100%|██████████| 93/93 [00:47<00:00, 1.96it/s] | |
| ***** predict_test_pt_PT metrics ***** | |
| predict_ex_match_acc = 0.498 | |
| predict_ex_match_acc_stderr = 0.0092 | |
| predict_intent_acc = 0.8268 | |
| predict_intent_acc_stderr = 0.0069 | |
| predict_loss = 0.5365 | |
| predict_runtime = 0:00:47.90 | |
| predict_samples = 2974 | |
| predict_samples_per_second = 62.079 | |
| predict_slot_micro_f1 = 0.5705 | |
| predict_slot_micro_f1_stderr = 0.0034 | |
| predict_steps_per_second = 1.941 | |
| 02/05/2024 17:28:08 - INFO - __main__ - *** test_ru_RU *** | |
| [INFO|trainer.py:718] 2024-02-05 17:28:08,307 >> The following columns in the test set don't have a corresponding argument in `MT5ForConditionalGeneration.forward` and have been ignored: intent_str, id, annot_utt. If intent_str, id, annot_utt are not expected by `MT5ForConditionalGeneration.forward`, you can safely ignore this message. | |
| [INFO|trainer.py:3199] 2024-02-05 17:28:08,309 >> ***** Running Prediction ***** | |
| [INFO|trainer.py:3201] 2024-02-05 17:28:08,310 >> Num examples = 2974 | |
| [INFO|trainer.py:3204] 2024-02-05 17:28:08,310 >> Batch size = 32 | |
| 0%| | 0/93 [00:00<?, ?it/s] 2%|▏ | 2/93 [00:00<00:17, 5.13it/s] 3%|▎ | 3/93 [00:00<00:25, 3.52it/s] 4%|▍ | 4/93 [00:01<00:32, 2.74it/s] 5%|▌ | 5/93 [00:01<00:32, 2.73it/s] 6%|▋ | 6/93 [00:02<00:31, 2.78it/s] 8%|▊ | 7/93 [00:02<00:35, 2.44it/s] 9%|▊ | 8/93 [00:02<00:33, 2.50it/s] 10%|▉ | 9/93 [00:03<00:32, 2.57it/s] 11%|█ | 10/93 [00:03<00:32, 2.59it/s] 12%|█▏ | 11/93 [00:04<00:34, 2.40it/s] 13%|█▎ | 12/93 [00:04<00:34, 2.36it/s] 14%|█▍ | 13/93 [00:04<00:32, 2.48it/s] 15%|█▌ | 14/93 [00:05<00:31, 2.50it/s] 16%|█▌ | 15/93 [00:05<00:34, 2.26it/s] 17%|█▋ | 16/93 [00:06<00:37, 2.07it/s] 18%|█▊ | 17/93 [00:06<00:37, 2.01it/s] 19%|█▉ | 18/93 [00:07<00:35, 2.12it/s] 20%|██ | 19/93 [00:07<00:32, 2.25it/s] 22%|██▏ | 20/93 [00:08<00:31, 2.31it/s] 23%|██▎ | 21/93 [00:08<00:29, 2.45it/s] 24%|██▎ | 22/93 [00:08<00:28, 2.49it/s] 25%|██▍ | 23/93 [00:09<00:29, 2.34it/s] 26%|██▌ | 24/93 [00:09<00:28, 2.42it/s] 27%|██▋ | 25/93 [00:10<00:30, 2.24it/s] 28%|██▊ | 26/93 [00:10<00:28, 2.31it/s] 29%|██▉ | 27/93 [00:11<00:29, 2.22it/s] 30%|███ | 28/93 [00:13<01:01, 1.05it/s] 31%|███ | 29/93 [00:13<00:49, 1.28it/s] 32%|███▏ | 30/93 [00:14<00:42, 1.49it/s] 33%|███▎ | 31/93 [00:14<00:36, 1.68it/s] 34%|███▍ | 32/93 [00:15<00:38, 1.59it/s] 35%|███▌ | 33/93 [00:15<00:33, 1.77it/s] 37%|███▋ | 34/93 [00:16<00:32, 1.84it/s] 38%|███▊ | 35/93 [00:16<00:30, 1.90it/s] 39%|███▊ | 36/93 [00:17<00:27, 2.04it/s] 40%|███▉ | 37/93 [00:17<00:28, 2.00it/s] 41%|████ | 38/93 [00:18<00:28, 1.95it/s] 42%|████▏ | 39/93 [00:18<00:27, 1.95it/s] 43%|████▎ | 40/93 [00:19<00:25, 2.10it/s] 44%|████▍ | 41/93 [00:19<00:23, 2.18it/s] 45%|████▌ | 42/93 [00:19<00:21, 2.34it/s] 46%|████▌ | 43/93 [00:20<00:19, 2.50it/s] 47%|████▋ | 44/93 [00:20<00:19, 2.52it/s] 48%|████▊ | 45/93 [00:20<00:19, 2.48it/s] 49%|████▉ | 46/93 [00:21<00:19, 2.44it/s] 51%|█████ | 47/93 [00:21<00:18, 2.55it/s] 52%|█████▏ | 48/93 [00:22<00:20, 2.23it/s] 53%|█████▎ | 49/93 [00:22<00:18, 2.39it/s] 54%|█████▍ | 50/93 [00:23<00:18, 2.26it/s] 55%|█████▍ | 51/93 [00:23<00:20, 2.09it/s] 56%|█████▌ | 52/93 [00:24<00:21, 1.93it/s] 57%|█████▋ | 53/93 [00:24<00:19, 2.09it/s] 58%|█████▊ | 54/93 [00:25<00:18, 2.14it/s] 59%|█████▉ | 55/93 [00:25<00:16, 2.30it/s] 60%|██████ | 56/93 [00:25<00:16, 2.31it/s] 61%|██████▏ | 57/93 [00:26<00:15, 2.36it/s] 62%|██████▏ | 58/93 [00:26<00:15, 2.27it/s] 63%|██████▎ | 59/93 [00:27<00:14, 2.31it/s] 65%|██████▍ | 60/93 [00:27<00:13, 2.42it/s] 66%|██████▌ | 61/93 [00:28<00:13, 2.31it/s] 67%|██████▋ | 62/93 [00:28<00:13, 2.34it/s] 68%|██████▊ | 63/93 [00:28<00:12, 2.41it/s] 69%|██████▉ | 64/93 [00:29<00:12, 2.40it/s] 70%|██████▉ | 65/93 [00:29<00:11, 2.45it/s] 71%|███████ | 66/93 [00:30<00:11, 2.42it/s] 72%|███████▏ | 67/93 [00:30<00:10, 2.37it/s] 73%|███████▎ | 68/93 [00:31<00:10, 2.27it/s] 74%|███████▍ | 69/93 [00:31<00:10, 2.19it/s] 75%|███████▌ | 70/93 [00:31<00:10, 2.17it/s] 76%|███████▋ | 71/93 [00:32<00:10, 2.04it/s] 77%|███████▋ | 72/93 [00:32<00:09, 2.16it/s] 78%|███████▊ | 73/93 [00:33<00:08, 2.31it/s] 80%|███████▉ | 74/93 [00:33<00:08, 2.28it/s] 81%|████████ | 75/93 [00:34<00:08, 2.23it/s] 82%|████████▏ | 76/93 [00:34<00:07, 2.19it/s] 83%|████████▎ | 77/93 [00:35<00:07, 2.14it/s] 84%|████████▍ | 78/93 [00:35<00:07, 1.99it/s] 85%|████████▍ | 79/93 [00:36<00:07, 1.96it/s] 86%|████████▌ | 80/93 [00:36<00:06, 1.97it/s] 87%|████████▋ | 81/93 [00:37<00:05, 2.15it/s] 88%|████████▊ | 82/93 [00:37<00:04, 2.24it/s] 89%|████████▉ | 83/93 [00:38<00:04, 2.21it/s] 90%|█████████ | 84/93 [00:38<00:03, 2.30it/s] 91%|█████████▏| 85/93 [00:38<00:03, 2.31it/s] 92%|█████████▏| 86/93 [00:39<00:03, 2.22it/s] 94%|█████████▎| 87/93 [00:39<00:02, 2.23it/s] 95%|█████████▍| 88/93 [00:40<00:02, 2.27it/s] 96%|█████████▌| 89/93 [00:40<00:01, 2.27it/s] 97%|█████████▋| 90/93 [00:41<00:01, 2.28it/s] 98%|█████████▊| 91/93 [00:41<00:00, 2.08it/s] 99%|█████████▉| 92/93 [00:42<00:00, 2.12it/s] 100%|██████████| 93/93 [00:42<00:00, 2.29it/s] 100%|██████████| 93/93 [00:42<00:00, 2.18it/s] | |
| ***** predict_test_ru_RU metrics ***** | |
| predict_ex_match_acc = 0.6113 | |
| predict_ex_match_acc_stderr = 0.0089 | |
| predict_intent_acc = 0.8557 | |
| predict_intent_acc_stderr = 0.0064 | |
| predict_loss = 0.3015 | |
| predict_runtime = 0:00:43.16 | |
| predict_samples = 2974 | |
| predict_samples_per_second = 68.898 | |
| predict_slot_micro_f1 = 0.7306 | |
| predict_slot_micro_f1_stderr = 0.0034 | |
| predict_steps_per_second = 2.154 | |
| 02/05/2024 17:28:51 - INFO - __main__ - *** test_tr_TR *** | |
| [INFO|trainer.py:718] 2024-02-05 17:28:51,715 >> The following columns in the test set don't have a corresponding argument in `MT5ForConditionalGeneration.forward` and have been ignored: intent_str, id, annot_utt. If intent_str, id, annot_utt are not expected by `MT5ForConditionalGeneration.forward`, you can safely ignore this message. | |
| [INFO|trainer.py:3199] 2024-02-05 17:28:51,718 >> ***** Running Prediction ***** | |
| [INFO|trainer.py:3201] 2024-02-05 17:28:51,718 >> Num examples = 2974 | |
| [INFO|trainer.py:3204] 2024-02-05 17:28:51,718 >> Batch size = 32 | |
| 0%| | 0/93 [00:00<?, ?it/s] 2%|▏ | 2/93 [00:00<00:18, 4.87it/s] 3%|▎ | 3/93 [00:00<00:27, 3.28it/s] 4%|▍ | 4/93 [00:01<00:30, 2.90it/s] 5%|▌ | 5/93 [00:01<00:32, 2.72it/s] 6%|▋ | 6/93 [00:02<00:30, 2.81it/s] 8%|▊ | 7/93 [00:02<00:33, 2.54it/s] 9%|▊ | 8/93 [00:02<00:32, 2.65it/s] 10%|▉ | 9/93 [00:03<00:36, 2.33it/s] 11%|█ | 10/93 [00:03<00:34, 2.39it/s] 12%|█▏ | 11/93 [00:04<00:35, 2.30it/s] 13%|█▎ | 12/93 [00:04<00:33, 2.40it/s] 14%|█▍ | 13/93 [00:04<00:32, 2.48it/s] 15%|█▌ | 14/93 [00:05<00:32, 2.44it/s] 16%|█▌ | 15/93 [00:05<00:35, 2.20it/s] 17%|█▋ | 16/93 [00:06<00:38, 2.01it/s] 18%|█▊ | 17/93 [00:07<00:39, 1.93it/s] 19%|█▉ | 18/93 [00:07<00:38, 1.92it/s] 20%|██ | 19/93 [00:07<00:34, 2.14it/s] 22%|██▏ | 20/93 [00:08<00:32, 2.26it/s] 23%|██▎ | 21/93 [00:08<00:31, 2.29it/s] 24%|██▎ | 22/93 [00:09<00:34, 2.03it/s] 25%|██▍ | 23/93 [00:09<00:33, 2.08it/s] 26%|██▌ | 24/93 [00:10<00:30, 2.26it/s] 27%|██▋ | 25/93 [00:10<00:29, 2.30it/s] 28%|██▊ | 26/93 [00:11<00:27, 2.42it/s] 29%|██▉ | 27/93 [00:11<00:26, 2.47it/s] 30%|███ | 28/93 [00:13<00:59, 1.10it/s] 31%|███ | 29/93 [00:13<00:48, 1.33it/s] 32%|███▏ | 30/93 [00:14<00:41, 1.52it/s] 33%|███▎ | 31/93 [00:14<00:34, 1.79it/s] 34%|███▍ | 32/93 [00:15<00:31, 1.92it/s] 35%|███▌ | 33/93 [00:15<00:29, 2.06it/s] 37%|███▋ | 34/93 [00:15<00:26, 2.25it/s] 38%|███▊ | 35/93 [00:16<00:25, 2.23it/s] 39%|███▊ | 36/93 [00:16<00:24, 2.29it/s] 40%|███▉ | 37/93 [00:17<00:24, 2.24it/s] 41%|████ | 38/93 [00:17<00:26, 2.10it/s] 42%|████▏ | 39/93 [00:18<00:25, 2.15it/s] 43%|████▎ | 40/93 [00:18<00:23, 2.25it/s] 44%|████▍ | 41/93 [00:18<00:22, 2.32it/s] 45%|████▌ | 42/93 [00:19<00:22, 2.31it/s] 46%|████▌ | 43/93 [00:19<00:20, 2.39it/s] 47%|████▋ | 44/93 [00:20<00:20, 2.35it/s] 48%|████▊ | 45/93 [00:20<00:21, 2.25it/s] 49%|████▉ | 46/93 [00:21<00:22, 2.11it/s] 51%|█████ | 47/93 [00:21<00:20, 2.29it/s] 52%|█████▏ | 48/93 [00:21<00:19, 2.33it/s] 53%|█████▎ | 49/93 [00:22<00:18, 2.36it/s] 54%|█████▍ | 50/93 [00:22<00:17, 2.49it/s] 55%|█████▍ | 51/93 [00:23<00:17, 2.42it/s] 56%|█████▌ | 52/93 [00:23<00:18, 2.16it/s] 57%|█████▋ | 53/93 [00:24<00:18, 2.17it/s] 58%|█████▊ | 54/93 [00:24<00:17, 2.25it/s] 59%|█████▉ | 55/93 [00:25<00:15, 2.38it/s] 60%|██████ | 56/93 [00:25<00:14, 2.48it/s] 61%|██████▏ | 57/93 [00:25<00:14, 2.46it/s] 62%|██████▏ | 58/93 [00:26<00:13, 2.51it/s] 63%|██████▎ | 59/93 [00:26<00:12, 2.62it/s] 65%|██████▍ | 60/93 [00:26<00:13, 2.45it/s] 66%|██████▌ | 61/93 [00:27<00:14, 2.16it/s] 67%|██████▋ | 62/93 [00:27<00:13, 2.24it/s] 68%|██████▊ | 63/93 [00:28<00:12, 2.35it/s] 69%|██████▉ | 64/93 [00:28<00:12, 2.37it/s] 70%|██████▉ | 65/93 [00:29<00:11, 2.42it/s] 71%|███████ | 66/93 [00:29<00:11, 2.40it/s] 72%|███████▏ | 67/93 [00:29<00:10, 2.42it/s] 73%|███████▎ | 68/93 [00:30<00:10, 2.28it/s] 74%|███████▍ | 69/93 [00:31<00:11, 2.16it/s] 75%|███████▌ | 70/93 [00:31<00:10, 2.22it/s] 76%|███████▋ | 71/93 [00:32<00:10, 2.00it/s] 77%|███████▋ | 72/93 [00:32<00:09, 2.16it/s] 78%|███████▊ | 73/93 [00:32<00:08, 2.24it/s] 80%|███████▉ | 74/93 [00:33<00:08, 2.23it/s] 81%|████████ | 75/93 [00:33<00:08, 2.19it/s] 82%|████████▏ | 76/93 [00:34<00:07, 2.21it/s] 83%|████████▎ | 77/93 [00:34<00:06, 2.34it/s] 84%|████████▍ | 78/93 [00:34<00:06, 2.44it/s] 85%|████████▍ | 79/93 [00:35<00:05, 2.35it/s] 86%|████████▌ | 80/93 [00:35<00:05, 2.22it/s] 87%|████████▋ | 81/93 [00:36<00:05, 2.33it/s] 88%|████████▊ | 82/93 [00:36<00:04, 2.33it/s] 89%|████████▉ | 83/93 [00:37<00:04, 2.19it/s] 90%|█████████ | 84/93 [00:37<00:04, 2.24it/s] 91%|█████████▏| 85/93 [00:38<00:03, 2.29it/s] 92%|█████████▏| 86/93 [00:38<00:03, 2.32it/s] 94%|█████████▎| 87/93 [00:38<00:02, 2.41it/s] 95%|█████████▍| 88/93 [00:39<00:02, 2.45it/s] 96%|█████████▌| 89/93 [00:39<00:01, 2.33it/s] 97%|█████████▋| 90/93 [00:40<00:01, 2.33it/s] 98%|█████████▊| 91/93 [00:40<00:00, 2.21it/s] 99%|█████████▉| 92/93 [00:41<00:00, 2.21it/s] 100%|██████████| 93/93 [00:41<00:00, 2.41it/s] 100%|██████████| 93/93 [00:41<00:00, 2.23it/s] | |
| ***** predict_test_tr_TR metrics ***** | |
| predict_ex_match_acc = 0.4445 | |
| predict_ex_match_acc_stderr = 0.0091 | |
| predict_intent_acc = 0.7579 | |
| predict_intent_acc_stderr = 0.0079 | |
| predict_loss = 0.614 | |
| predict_runtime = 0:00:42.08 | |
| predict_samples = 2974 | |
| predict_samples_per_second = 70.668 | |
| predict_slot_micro_f1 = 0.6036 | |
| predict_slot_micro_f1_stderr = 0.0038 | |
| predict_steps_per_second = 2.21 | |
| 02/05/2024 17:29:34 - INFO - __main__ - *** test_vi_VN *** | |
| [INFO|trainer.py:718] 2024-02-05 17:29:34,025 >> The following columns in the test set don't have a corresponding argument in `MT5ForConditionalGeneration.forward` and have been ignored: intent_str, id, annot_utt. If intent_str, id, annot_utt are not expected by `MT5ForConditionalGeneration.forward`, you can safely ignore this message. | |
| [INFO|trainer.py:3199] 2024-02-05 17:29:34,027 >> ***** Running Prediction ***** | |
| [INFO|trainer.py:3201] 2024-02-05 17:29:34,028 >> Num examples = 2974 | |
| [INFO|trainer.py:3204] 2024-02-05 17:29:34,028 >> Batch size = 32 | |
| 0%| | 0/93 [00:00<?, ?it/s] 2%|▏ | 2/93 [00:00<00:21, 4.24it/s] 3%|▎ | 3/93 [00:00<00:28, 3.19it/s] 4%|▍ | 4/93 [00:01<00:36, 2.42it/s] 5%|▌ | 5/93 [00:02<00:41, 2.14it/s] 6%|▋ | 6/93 [00:02<00:41, 2.11it/s] 8%|▊ | 7/93 [00:03<00:42, 2.01it/s] 9%|▊ | 8/93 [00:03<00:43, 1.96it/s] 10%|▉ | 9/93 [00:04<00:40, 2.06it/s] 11%|█ | 10/93 [00:04<00:44, 1.88it/s] 12%|█▏ | 11/93 [00:05<00:43, 1.87it/s] 13%|█▎ | 12/93 [00:05<00:44, 1.83it/s] 14%|█▍ | 13/93 [00:06<00:47, 1.69it/s] 15%|█▌ | 14/93 [00:07<00:46, 1.70it/s] 16%|█▌ | 15/93 [00:07<00:49, 1.58it/s] 17%|█▋ | 16/93 [00:08<00:52, 1.48it/s] 18%|█▊ | 17/93 [00:09<00:50, 1.51it/s] 19%|█▉ | 18/93 [00:10<00:53, 1.40it/s] 20%|██ | 19/93 [00:10<00:49, 1.51it/s] 22%|██▏ | 20/93 [00:11<00:44, 1.63it/s] 23%|██▎ | 21/93 [00:11<00:42, 1.69it/s] 24%|██▎ | 22/93 [00:12<00:44, 1.60it/s] 25%|██▍ | 23/93 [00:12<00:42, 1.63it/s] 26%|██▌ | 24/93 [00:13<00:39, 1.73it/s] 27%|██▋ | 25/93 [00:14<00:40, 1.66it/s] 28%|██▊ | 26/93 [00:14<00:38, 1.74it/s] 29%|██▉ | 27/93 [00:15<00:45, 1.46it/s] 30%|███ | 28/93 [00:17<01:13, 1.13s/it] 31%|███ | 29/93 [00:18<00:58, 1.09it/s] 32%|███▏ | 30/93 [00:18<00:54, 1.15it/s] 33%|███▎ | 31/93 [00:19<00:47, 1.31it/s] 34%|███▍ | 32/93 [00:20<00:43, 1.39it/s] 35%|███▌ | 33/93 [00:20<00:40, 1.48it/s] 37%|███▋ | 34/93 [00:21<00:37, 1.56it/s] 38%|███▊ | 35/93 [00:21<00:36, 1.60it/s] 39%|███▊ | 36/93 [00:22<00:39, 1.45it/s] 40%|███▉ | 37/93 [00:23<00:36, 1.53it/s] 41%|████ | 38/93 [00:23<00:35, 1.54it/s] 42%|████▏ | 39/93 [00:25<00:57, 1.07s/it] 43%|████▎ | 40/93 [00:26<00:50, 1.06it/s] 44%|████▍ | 41/93 [00:27<00:43, 1.20it/s] 45%|████▌ | 42/93 [00:27<00:38, 1.33it/s] 46%|████▌ | 43/93 [00:28<00:35, 1.42it/s] 47%|████▋ | 44/93 [00:28<00:30, 1.61it/s] 48%|████▊ | 45/93 [00:29<00:30, 1.56it/s] 49%|████▉ | 46/93 [00:30<00:30, 1.52it/s] 51%|█████ | 47/93 [00:30<00:33, 1.37it/s] 52%|█████▏ | 48/93 [00:31<00:34, 1.31it/s] 53%|█████▎ | 49/93 [00:32<00:29, 1.47it/s] 54%|█████▍ | 50/93 [00:32<00:26, 1.60it/s] 55%|█████▍ | 51/93 [00:34<00:44, 1.07s/it] 56%|█████▌ | 52/93 [00:35<00:39, 1.03it/s] 57%|█████▋ | 53/93 [00:36<00:33, 1.21it/s] 58%|█████▊ | 54/93 [00:36<00:29, 1.32it/s] 59%|█████▉ | 55/93 [00:37<00:26, 1.44it/s] 60%|██████ | 56/93 [00:37<00:22, 1.61it/s] 61%|██████▏ | 57/93 [00:38<00:21, 1.71it/s] 62%|██████▏ | 58/93 [00:38<00:21, 1.64it/s] 63%|██████▎ | 59/93 [00:39<00:22, 1.53it/s] 65%|██████▍ | 60/93 [00:40<00:19, 1.68it/s] 66%|██████▌ | 61/93 [00:40<00:19, 1.61it/s] 67%|██████▋ | 62/93 [00:41<00:19, 1.61it/s] 68%|██████▊ | 63/93 [00:42<00:20, 1.47it/s] 69%|██████▉ | 64/93 [00:42<00:18, 1.61it/s] 70%|██████▉ | 65/93 [00:43<00:17, 1.63it/s] 71%|███████ | 66/93 [00:43<00:17, 1.55it/s] 72%|███████▏ | 67/93 [00:44<00:16, 1.61it/s] 73%|███████▎ | 68/93 [00:45<00:14, 1.72it/s] 74%|███████▍ | 69/93 [00:45<00:14, 1.66it/s] 75%|███████▌ | 70/93 [00:46<00:13, 1.76it/s] 76%|███████▋ | 71/93 [00:48<00:22, 1.01s/it] 77%|███████▋ | 72/93 [00:48<00:18, 1.14it/s] 78%|███████▊ | 73/93 [00:49<00:15, 1.30it/s] 80%|███████▉ | 74/93 [00:49<00:13, 1.41it/s] 81%|████████ | 75/93 [00:51<00:19, 1.11s/it] 82%|████████▏ | 76/93 [00:52<00:15, 1.07it/s] 83%|████████▎ | 77/93 [00:53<00:13, 1.19it/s] 84%|████████▍ | 78/93 [00:53<00:11, 1.32it/s] 85%|████████▍ | 79/93 [00:54<00:09, 1.43it/s] 86%|████████▌ | 80/93 [00:54<00:08, 1.49it/s] 87%|████████▋ | 81/93 [00:55<00:07, 1.53it/s] 88%|████████▊ | 82/93 [00:55<00:06, 1.65it/s] 89%|████████▉ | 83/93 [00:56<00:05, 1.69it/s] 90%|█████████ | 84/93 [00:56<00:05, 1.79it/s] 91%|█████████▏| 85/93 [00:57<00:04, 1.63it/s] 92%|█████████▏| 86/93 [00:58<00:04, 1.60it/s] 94%|█████████▎| 87/93 [00:58<00:03, 1.70it/s] 95%|█████████▍| 88/93 [00:59<00:02, 1.79it/s] 96%|█████████▌| 89/93 [00:59<00:02, 1.78it/s] 97%|█████████▋| 90/93 [01:00<00:01, 1.81it/s] 98%|█████████▊| 91/93 [01:01<00:01, 1.68it/s] 99%|█████████▉| 92/93 [01:01<00:00, 1.74it/s] 100%|██████████| 93/93 [01:02<00:00, 1.77it/s] 100%|██████████| 93/93 [01:02<00:00, 1.49it/s] | |
| ***** predict_test_vi_VN metrics ***** | |
| predict_ex_match_acc = 0.1416 | |
| predict_ex_match_acc_stderr = 0.0064 | |
| predict_intent_acc = 0.3584 | |
| predict_intent_acc_stderr = 0.0088 | |
| predict_loss = 0.6596 | |
| predict_runtime = 0:01:03.04 | |
| predict_samples = 2974 | |
| predict_samples_per_second = 47.174 | |
| predict_slot_micro_f1 = 0.3368 | |
| predict_slot_micro_f1_stderr = 0.0029 | |
| predict_steps_per_second = 1.475 | |