| 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 22:54:59 - WARNING - __main__ - Process rank: 0, device: cuda:0, n_gpu: 1, distributed training: False, 16-bits training: False | |
| 02/05/2024 22:54:59 - 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_22-54-59_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 22:54:59 - 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 22:54:59 - 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 22:54:59 - 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 22:54:59 - 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 22:54:59 - 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 22:54:59,933 >> 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 22:54:59,942 >> 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 22:54:59,944 >> loading file spiece.model | |
| [INFO|tokenization_utils_base.py:2024] 2024-02-05 22:54:59,945 >> loading file tokenizer.json | |
| [INFO|tokenization_utils_base.py:2024] 2024-02-05 22:54:59,945 >> loading file added_tokens.json | |
| [INFO|tokenization_utils_base.py:2024] 2024-02-05 22:54:59,945 >> loading file special_tokens_map.json | |
| [INFO|tokenization_utils_base.py:2024] 2024-02-05 22:54:59,945 >> loading file tokenizer_config.json | |
| [INFO|modeling_utils.py:3373] 2024-02-05 22:55:00,407 >> 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 22:55:00,566 >> Generate config GenerationConfig { | |
| "decoder_start_token_id": 0, | |
| "eos_token_id": 1, | |
| "pad_token_id": 0 | |
| } | |
| [INFO|modeling_utils.py:4224] 2024-02-05 22:55:05,619 >> All model checkpoint weights were used when initializing MT5ForConditionalGeneration. | |
| [INFO|modeling_utils.py:4232] 2024-02-05 22:55:05,620 >> 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 22:55:05,627 >> 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 22:55:05,628 >> Generate config GenerationConfig { | |
| "decoder_start_token_id": 0, | |
| "eos_token_id": 1, | |
| "pad_token_id": 0 | |
| } | |
| Running tokenizer on prediction dataset: 0%| | 0/2974 [00:00<?, ? examples/s]Caching processed dataset at /beegfs/scratch/user/blee/hugging-face/models/datasets/speech_massive_cascaded/multilingual-test/1.0.0/f36c9e4210ec02a91ee05c9fa785d90aec211ba2025363c65b643c68e109b293/cache-a28263cfb71413f6.arrow | |
| 02/05/2024 22:55:05 - INFO - datasets.arrow_dataset - Caching processed dataset at /beegfs/scratch/user/blee/hugging-face/models/datasets/speech_massive_cascaded/multilingual-test/1.0.0/f36c9e4210ec02a91ee05c9fa785d90aec211ba2025363c65b643c68e109b293/cache-a28263cfb71413f6.arrow | |
| Running tokenizer on prediction dataset: 100%|██████████| 2974/2974 [00:00<00:00, 9722.44 examples/s] Running tokenizer on prediction dataset: 100%|██████████| 2974/2974 [00:00<00:00, 9509.58 examples/s] | |
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| 02/05/2024 22:55:06 - INFO - datasets.arrow_dataset - Caching processed dataset at /beegfs/scratch/user/blee/hugging-face/models/datasets/speech_massive_cascaded/multilingual-test/1.0.0/f36c9e4210ec02a91ee05c9fa785d90aec211ba2025363c65b643c68e109b293/cache-0f6b9ba1cc4e5fb1.arrow | |
| Running tokenizer on prediction dataset: 100%|██████████| 2974/2974 [00:00<00:00, 19076.56 examples/s] Running tokenizer on prediction dataset: 100%|██████████| 2974/2974 [00:00<00:00, 18274.25 examples/s] | |
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| 02/05/2024 22:55:06 - INFO - datasets.arrow_dataset - Caching processed dataset at /beegfs/scratch/user/blee/hugging-face/models/datasets/speech_massive_cascaded/multilingual-test/1.0.0/f36c9e4210ec02a91ee05c9fa785d90aec211ba2025363c65b643c68e109b293/cache-cd10216b4341f1a2.arrow | |
| Running tokenizer on prediction dataset: 67%|██████▋ | 2000/2974 [00:00<00:00, 17978.35 examples/s] Running tokenizer on prediction dataset: 100%|██████████| 2974/2974 [00:00<00:00, 17438.54 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/speech_massive_cascaded/multilingual-test/1.0.0/f36c9e4210ec02a91ee05c9fa785d90aec211ba2025363c65b643c68e109b293/cache-cf09cb968cef4c56.arrow | |
| 02/05/2024 22:55:06 - INFO - datasets.arrow_dataset - Caching processed dataset at /beegfs/scratch/user/blee/hugging-face/models/datasets/speech_massive_cascaded/multilingual-test/1.0.0/f36c9e4210ec02a91ee05c9fa785d90aec211ba2025363c65b643c68e109b293/cache-cf09cb968cef4c56.arrow | |
| Running tokenizer on prediction dataset: 67%|██████▋ | 2000/2974 [00:00<00:00, 17167.19 examples/s] Running tokenizer on prediction dataset: 100%|██████████| 2974/2974 [00:00<00:00, 16973.43 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/speech_massive_cascaded/multilingual-test/1.0.0/f36c9e4210ec02a91ee05c9fa785d90aec211ba2025363c65b643c68e109b293/cache-b372c4d6e9ad447f.arrow | |
| 02/05/2024 22:55:06 - INFO - datasets.arrow_dataset - Caching processed dataset at /beegfs/scratch/user/blee/hugging-face/models/datasets/speech_massive_cascaded/multilingual-test/1.0.0/f36c9e4210ec02a91ee05c9fa785d90aec211ba2025363c65b643c68e109b293/cache-b372c4d6e9ad447f.arrow | |
| Running tokenizer on prediction dataset: 67%|██████▋ | 2000/2974 [00:00<00:00, 18156.53 examples/s] Running tokenizer on prediction dataset: 100%|██████████| 2974/2974 [00:00<00:00, 9564.04 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/speech_massive_cascaded/multilingual-test/1.0.0/f36c9e4210ec02a91ee05c9fa785d90aec211ba2025363c65b643c68e109b293/cache-5e85733ab0d7983c.arrow | |
| 02/05/2024 22:55:07 - INFO - datasets.arrow_dataset - Caching processed dataset at /beegfs/scratch/user/blee/hugging-face/models/datasets/speech_massive_cascaded/multilingual-test/1.0.0/f36c9e4210ec02a91ee05c9fa785d90aec211ba2025363c65b643c68e109b293/cache-5e85733ab0d7983c.arrow | |
| Running tokenizer on prediction dataset: 100%|██████████| 2974/2974 [00:00<00:00, 21103.51 examples/s] Running tokenizer on prediction dataset: 100%|██████████| 2974/2974 [00:00<00:00, 20206.18 examples/s] | |
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| 02/05/2024 22:55:07 - INFO - datasets.arrow_dataset - Caching processed dataset at /beegfs/scratch/user/blee/hugging-face/models/datasets/speech_massive_cascaded/multilingual-test/1.0.0/f36c9e4210ec02a91ee05c9fa785d90aec211ba2025363c65b643c68e109b293/cache-3498127f38d0e88c.arrow | |
| Running tokenizer on prediction dataset: 67%|██████▋ | 2000/2974 [00:00<00:00, 17313.98 examples/s] Running tokenizer on prediction dataset: 100%|██████████| 2974/2974 [00:00<00:00, 17062.70 examples/s] | |
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| 02/05/2024 22:55:07 - INFO - datasets.arrow_dataset - Caching processed dataset at /beegfs/scratch/user/blee/hugging-face/models/datasets/speech_massive_cascaded/multilingual-test/1.0.0/f36c9e4210ec02a91ee05c9fa785d90aec211ba2025363c65b643c68e109b293/cache-5031b1ae09c119f0.arrow | |
| Running tokenizer on prediction dataset: 67%|██████▋ | 2000/2974 [00:00<00:00, 18263.66 examples/s] Running tokenizer on prediction dataset: 100%|██████████| 2974/2974 [00:00<00:00, 17891.90 examples/s] | |
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| 02/05/2024 22:55:07 - INFO - datasets.arrow_dataset - Caching processed dataset at /beegfs/scratch/user/blee/hugging-face/models/datasets/speech_massive_cascaded/multilingual-test/1.0.0/f36c9e4210ec02a91ee05c9fa785d90aec211ba2025363c65b643c68e109b293/cache-afad46b8cc76fbde.arrow | |
| Running tokenizer on prediction dataset: 67%|██████▋ | 2000/2974 [00:00<00:00, 17239.79 examples/s] Running tokenizer on prediction dataset: 100%|██████████| 2974/2974 [00:00<00:00, 16795.72 examples/s] | |
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| 02/05/2024 22:55:08 - INFO - datasets.arrow_dataset - Caching processed dataset at /beegfs/scratch/user/blee/hugging-face/models/datasets/speech_massive_cascaded/multilingual-test/1.0.0/f36c9e4210ec02a91ee05c9fa785d90aec211ba2025363c65b643c68e109b293/cache-44bbdcb1f95b0505.arrow | |
| Running tokenizer on prediction dataset: 34%|███▎ | 1000/2974 [00:00<00:00, 4587.00 examples/s] Running tokenizer on prediction dataset: 100%|██████████| 2974/2974 [00:00<00:00, 10501.79 examples/s] Running tokenizer on prediction dataset: 100%|██████████| 2974/2974 [00:00<00:00, 9148.27 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/speech_massive_cascaded/multilingual-test/1.0.0/f36c9e4210ec02a91ee05c9fa785d90aec211ba2025363c65b643c68e109b293/cache-649defd19aa00c44.arrow | |
| 02/05/2024 22:55:08 - INFO - datasets.arrow_dataset - Caching processed dataset at /beegfs/scratch/user/blee/hugging-face/models/datasets/speech_massive_cascaded/multilingual-test/1.0.0/f36c9e4210ec02a91ee05c9fa785d90aec211ba2025363c65b643c68e109b293/cache-649defd19aa00c44.arrow | |
| Running tokenizer on prediction dataset: 100%|██████████| 2974/2974 [00:00<00:00, 20318.81 examples/s] Running tokenizer on prediction dataset: 100%|██████████| 2974/2974 [00:00<00:00, 19383.07 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/speech_massive_cascaded/multilingual-test/1.0.0/f36c9e4210ec02a91ee05c9fa785d90aec211ba2025363c65b643c68e109b293/cache-bc58b34f0f2e6d55.arrow | |
| 02/05/2024 22:55:08 - INFO - datasets.arrow_dataset - Caching processed dataset at /beegfs/scratch/user/blee/hugging-face/models/datasets/speech_massive_cascaded/multilingual-test/1.0.0/f36c9e4210ec02a91ee05c9fa785d90aec211ba2025363c65b643c68e109b293/cache-bc58b34f0f2e6d55.arrow | |
| Running tokenizer on prediction dataset: 67%|██████▋ | 2000/2974 [00:00<00:00, 17147.99 examples/s] Running tokenizer on prediction dataset: 100%|██████████| 2974/2974 [00:00<00:00, 16740.65 examples/s] | |
| 02/05/2024 22:55:21 - 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 22:55:21 - INFO - __main__ - *** Predict *** | |
| 02/05/2024 22:55:21 - INFO - __main__ - *** test_ar_SA *** | |
| [INFO|trainer.py:718] 2024-02-05 22:55:21,494 >> The following columns in the test set don't have a corresponding argument in `MT5ForConditionalGeneration.forward` and have been ignored: id, intent_str, annot_utt. If id, intent_str, annot_utt are not expected by `MT5ForConditionalGeneration.forward`, you can safely ignore this message. | |
| [INFO|trainer.py:3199] 2024-02-05 22:55:21,504 >> ***** Running Prediction ***** | |
| [INFO|trainer.py:3201] 2024-02-05 22:55:21,504 >> Num examples = 2974 | |
| [INFO|trainer.py:3204] 2024-02-05 22:55:21,505 >> Batch size = 32 | |
| [WARNING|logging.py:314] 2024-02-05 22:55:21,509 >> You're using a T5TokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding. | |
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| ***** predict_test_ar_SA metrics ***** | |
| predict_ex_match_acc = 0.4526 | |
| predict_ex_match_acc_stderr = 0.0091 | |
| predict_intent_acc = 0.7135 | |
| predict_intent_acc_stderr = 0.0083 | |
| predict_loss = 0.5892 | |
| predict_runtime = 0:00:41.35 | |
| predict_samples = 2974 | |
| predict_samples_per_second = 71.922 | |
| predict_slot_micro_f1 = 0.6007 | |
| predict_slot_micro_f1_stderr = 0.0039 | |
| predict_steps_per_second = 2.249 | |
| 02/05/2024 22:56:03 - INFO - __main__ - *** test_de_DE *** | |
| [INFO|trainer.py:718] 2024-02-05 22:56:03,080 >> The following columns in the test set don't have a corresponding argument in `MT5ForConditionalGeneration.forward` and have been ignored: id, intent_str, annot_utt. If id, intent_str, annot_utt are not expected by `MT5ForConditionalGeneration.forward`, you can safely ignore this message. | |
| [INFO|trainer.py:3199] 2024-02-05 22:56:03,083 >> ***** Running Prediction ***** | |
| [INFO|trainer.py:3201] 2024-02-05 22:56:03,083 >> Num examples = 2974 | |
| [INFO|trainer.py:3204] 2024-02-05 22:56:03,083 >> Batch size = 32 | |
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| ***** predict_test_de_DE metrics ***** | |
| predict_ex_match_acc = 0.6019 | |
| predict_ex_match_acc_stderr = 0.009 | |
| predict_intent_acc = 0.8507 | |
| predict_intent_acc_stderr = 0.0065 | |
| predict_loss = 0.4208 | |
| predict_runtime = 0:00:41.93 | |
| predict_samples = 2974 | |
| predict_samples_per_second = 70.912 | |
| predict_slot_micro_f1 = 0.7291 | |
| predict_slot_micro_f1_stderr = 0.0032 | |
| predict_steps_per_second = 2.217 | |
| 02/05/2024 22:56:45 - INFO - __main__ - *** test_es_ES *** | |
| [INFO|trainer.py:718] 2024-02-05 22:56:45,260 >> The following columns in the test set don't have a corresponding argument in `MT5ForConditionalGeneration.forward` and have been ignored: id, intent_str, annot_utt. If id, intent_str, annot_utt are not expected by `MT5ForConditionalGeneration.forward`, you can safely ignore this message. | |
| [INFO|trainer.py:3199] 2024-02-05 22:56:45,263 >> ***** Running Prediction ***** | |
| [INFO|trainer.py:3201] 2024-02-05 22:56:45,263 >> Num examples = 2974 | |
| [INFO|trainer.py:3204] 2024-02-05 22:56:45,263 >> Batch size = 32 | |
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| ***** predict_test_es_ES metrics ***** | |
| predict_ex_match_acc = 0.6231 | |
| predict_ex_match_acc_stderr = 0.0089 | |
| predict_intent_acc = 0.8591 | |
| predict_intent_acc_stderr = 0.0064 | |
| predict_loss = 0.2681 | |
| predict_runtime = 0:00:48.92 | |
| predict_samples = 2974 | |
| predict_samples_per_second = 60.782 | |
| predict_slot_micro_f1 = 0.7314 | |
| predict_slot_micro_f1_stderr = 0.003 | |
| predict_steps_per_second = 1.901 | |
| 02/05/2024 22:57:34 - INFO - __main__ - *** test_fr_FR *** | |
| [INFO|trainer.py:718] 2024-02-05 22:57:34,438 >> The following columns in the test set don't have a corresponding argument in `MT5ForConditionalGeneration.forward` and have been ignored: id, intent_str, annot_utt. If id, intent_str, annot_utt are not expected by `MT5ForConditionalGeneration.forward`, you can safely ignore this message. | |
| [INFO|trainer.py:3199] 2024-02-05 22:57:34,440 >> ***** Running Prediction ***** | |
| [INFO|trainer.py:3201] 2024-02-05 22:57:34,441 >> Num examples = 2974 | |
| [INFO|trainer.py:3204] 2024-02-05 22:57:34,441 >> Batch size = 32 | |
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| ***** predict_test_fr_FR metrics ***** | |
| predict_ex_match_acc = 0.466 | |
| predict_ex_match_acc_stderr = 0.0091 | |
| predict_intent_acc = 0.847 | |
| predict_intent_acc_stderr = 0.0066 | |
| predict_loss = 0.6841 | |
| predict_runtime = 0:00:49.51 | |
| predict_samples = 2974 | |
| predict_samples_per_second = 60.061 | |
| predict_slot_micro_f1 = 0.5181 | |
| predict_slot_micro_f1_stderr = 0.0034 | |
| predict_steps_per_second = 1.878 | |
| 02/05/2024 22:58:24 - INFO - __main__ - *** test_hu_HU *** | |
| [INFO|trainer.py:718] 2024-02-05 22:58:24,215 >> The following columns in the test set don't have a corresponding argument in `MT5ForConditionalGeneration.forward` and have been ignored: id, intent_str, annot_utt. If id, intent_str, annot_utt are not expected by `MT5ForConditionalGeneration.forward`, you can safely ignore this message. | |
| [INFO|trainer.py:3199] 2024-02-05 22:58:24,218 >> ***** Running Prediction ***** | |
| [INFO|trainer.py:3201] 2024-02-05 22:58:24,219 >> Num examples = 2974 | |
| [INFO|trainer.py:3204] 2024-02-05 22:58:24,219 >> Batch size = 32 | |
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| ***** predict_test_hu_HU metrics ***** | |
| predict_ex_match_acc = 0.534 | |
| predict_ex_match_acc_stderr = 0.0091 | |
| predict_intent_acc = 0.8184 | |
| predict_intent_acc_stderr = 0.0071 | |
| predict_loss = 0.5463 | |
| predict_runtime = 0:00:41.23 | |
| predict_samples = 2974 | |
| predict_samples_per_second = 72.121 | |
| predict_slot_micro_f1 = 0.6581 | |
| predict_slot_micro_f1_stderr = 0.0036 | |
| predict_steps_per_second = 2.255 | |
| 02/05/2024 22:59:05 - INFO - __main__ - *** test_ko_KR *** | |
| [INFO|trainer.py:718] 2024-02-05 22:59:05,706 >> The following columns in the test set don't have a corresponding argument in `MT5ForConditionalGeneration.forward` and have been ignored: id, intent_str, annot_utt. If id, intent_str, annot_utt are not expected by `MT5ForConditionalGeneration.forward`, you can safely ignore this message. | |
| [INFO|trainer.py:3199] 2024-02-05 22:59:05,709 >> ***** Running Prediction ***** | |
| [INFO|trainer.py:3201] 2024-02-05 22:59:05,709 >> Num examples = 2974 | |
| [INFO|trainer.py:3204] 2024-02-05 22:59:05,710 >> Batch size = 32 | |
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| ***** predict_test_ko_KR metrics ***** | |
| predict_ex_match_acc = 0.5562 | |
| predict_ex_match_acc_stderr = 0.0091 | |
| predict_intent_acc = 0.8305 | |
| predict_intent_acc_stderr = 0.0069 | |
| predict_loss = 0.8564 | |
| predict_runtime = 0:00:35.72 | |
| predict_samples = 2974 | |
| predict_samples_per_second = 83.241 | |
| predict_slot_micro_f1 = 0.654 | |
| predict_slot_micro_f1_stderr = 0.004 | |
| predict_steps_per_second = 2.603 | |
| 02/05/2024 22:59:41 - INFO - __main__ - *** test_nl_NL *** | |
| [INFO|trainer.py:718] 2024-02-05 22:59:41,670 >> The following columns in the test set don't have a corresponding argument in `MT5ForConditionalGeneration.forward` and have been ignored: id, intent_str, annot_utt. If id, intent_str, annot_utt are not expected by `MT5ForConditionalGeneration.forward`, you can safely ignore this message. | |
| [INFO|trainer.py:3199] 2024-02-05 22:59:41,672 >> ***** Running Prediction ***** | |
| [INFO|trainer.py:3201] 2024-02-05 22:59:41,673 >> Num examples = 2974 | |
| [INFO|trainer.py:3204] 2024-02-05 22:59:41,673 >> Batch size = 32 | |
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| ***** predict_test_nl_NL metrics ***** | |
| predict_ex_match_acc = 0.5941 | |
| predict_ex_match_acc_stderr = 0.009 | |
| predict_intent_acc = 0.8584 | |
| predict_intent_acc_stderr = 0.0064 | |
| predict_loss = 0.4583 | |
| predict_runtime = 0:00:45.08 | |
| predict_samples = 2974 | |
| predict_samples_per_second = 65.959 | |
| predict_slot_micro_f1 = 0.6931 | |
| predict_slot_micro_f1_stderr = 0.0032 | |
| predict_steps_per_second = 2.063 | |
| 02/05/2024 23:00:26 - INFO - __main__ - *** test_pl_PL *** | |
| [INFO|trainer.py:718] 2024-02-05 23:00:26,995 >> The following columns in the test set don't have a corresponding argument in `MT5ForConditionalGeneration.forward` and have been ignored: id, intent_str, annot_utt. If id, intent_str, annot_utt are not expected by `MT5ForConditionalGeneration.forward`, you can safely ignore this message. | |
| [INFO|trainer.py:3199] 2024-02-05 23:00:26,998 >> ***** Running Prediction ***** | |
| [INFO|trainer.py:3201] 2024-02-05 23:00:26,998 >> Num examples = 2974 | |
| [INFO|trainer.py:3204] 2024-02-05 23:00:26,998 >> Batch size = 32 | |
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| ***** predict_test_pl_PL metrics ***** | |
| predict_ex_match_acc = 0.5901 | |
| predict_ex_match_acc_stderr = 0.009 | |
| predict_intent_acc = 0.8521 | |
| predict_intent_acc_stderr = 0.0065 | |
| predict_loss = 0.3363 | |
| predict_runtime = 0:00:43.71 | |
| predict_samples = 2974 | |
| predict_samples_per_second = 68.026 | |
| predict_slot_micro_f1 = 0.7076 | |
| predict_slot_micro_f1_stderr = 0.0035 | |
| predict_steps_per_second = 2.127 | |
| 02/05/2024 23:01:10 - INFO - __main__ - *** test_pt_PT *** | |
| [INFO|trainer.py:718] 2024-02-05 23:01:10,962 >> The following columns in the test set don't have a corresponding argument in `MT5ForConditionalGeneration.forward` and have been ignored: id, intent_str, annot_utt. If id, intent_str, annot_utt are not expected by `MT5ForConditionalGeneration.forward`, you can safely ignore this message. | |
| [INFO|trainer.py:3199] 2024-02-05 23:01:10,964 >> ***** Running Prediction ***** | |
| [INFO|trainer.py:3201] 2024-02-05 23:01:10,965 >> Num examples = 2974 | |
| [INFO|trainer.py:3204] 2024-02-05 23:01:10,965 >> Batch size = 32 | |
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| ***** predict_test_pt_PT metrics ***** | |
| predict_ex_match_acc = 0.5723 | |
| predict_ex_match_acc_stderr = 0.0091 | |
| predict_intent_acc = 0.8514 | |
| predict_intent_acc_stderr = 0.0065 | |
| predict_loss = 0.4385 | |
| predict_runtime = 0:00:47.86 | |
| predict_samples = 2974 | |
| predict_samples_per_second = 62.134 | |
| predict_slot_micro_f1 = 0.6632 | |
| predict_slot_micro_f1_stderr = 0.0033 | |
| predict_steps_per_second = 1.943 | |
| 02/05/2024 23:01:59 - INFO - __main__ - *** test_ru_RU *** | |
| [INFO|trainer.py:718] 2024-02-05 23:01:59,083 >> The following columns in the test set don't have a corresponding argument in `MT5ForConditionalGeneration.forward` and have been ignored: id, intent_str, annot_utt. If id, intent_str, annot_utt are not expected by `MT5ForConditionalGeneration.forward`, you can safely ignore this message. | |
| [INFO|trainer.py:3199] 2024-02-05 23:01:59,085 >> ***** Running Prediction ***** | |
| [INFO|trainer.py:3201] 2024-02-05 23:01:59,086 >> Num examples = 2974 | |
| [INFO|trainer.py:3204] 2024-02-05 23:01:59,086 >> Batch size = 32 | |
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| ***** predict_test_ru_RU metrics ***** | |
| predict_ex_match_acc = 0.6513 | |
| predict_ex_match_acc_stderr = 0.0087 | |
| predict_intent_acc = 0.8652 | |
| predict_intent_acc_stderr = 0.0063 | |
| predict_loss = 0.2806 | |
| predict_runtime = 0:00:42.98 | |
| predict_samples = 2974 | |
| predict_samples_per_second = 69.18 | |
| predict_slot_micro_f1 = 0.7584 | |
| predict_slot_micro_f1_stderr = 0.0033 | |
| predict_steps_per_second = 2.163 | |
| 02/05/2024 23:02:42 - INFO - __main__ - *** test_tr_TR *** | |
| [INFO|trainer.py:718] 2024-02-05 23:02:42,317 >> The following columns in the test set don't have a corresponding argument in `MT5ForConditionalGeneration.forward` and have been ignored: id, intent_str, annot_utt. If id, intent_str, annot_utt are not expected by `MT5ForConditionalGeneration.forward`, you can safely ignore this message. | |
| [INFO|trainer.py:3199] 2024-02-05 23:02:42,320 >> ***** Running Prediction ***** | |
| [INFO|trainer.py:3201] 2024-02-05 23:02:42,320 >> Num examples = 2974 | |
| [INFO|trainer.py:3204] 2024-02-05 23:02:42,320 >> Batch size = 32 | |
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| ***** predict_test_tr_TR metrics ***** | |
| predict_ex_match_acc = 0.5521 | |
| predict_ex_match_acc_stderr = 0.0091 | |
| predict_intent_acc = 0.8285 | |
| predict_intent_acc_stderr = 0.0069 | |
| predict_loss = 0.5882 | |
| predict_runtime = 0:00:43.26 | |
| predict_samples = 2974 | |
| predict_samples_per_second = 68.745 | |
| predict_slot_micro_f1 = 0.6555 | |
| predict_slot_micro_f1_stderr = 0.0037 | |
| predict_steps_per_second = 2.15 | |
| 02/05/2024 23:03:25 - INFO - __main__ - *** test_vi_VN *** | |
| [INFO|trainer.py:718] 2024-02-05 23:03:25,808 >> The following columns in the test set don't have a corresponding argument in `MT5ForConditionalGeneration.forward` and have been ignored: id, intent_str, annot_utt. If id, intent_str, annot_utt are not expected by `MT5ForConditionalGeneration.forward`, you can safely ignore this message. | |
| [INFO|trainer.py:3199] 2024-02-05 23:03:25,811 >> ***** Running Prediction ***** | |
| [INFO|trainer.py:3201] 2024-02-05 23:03:25,811 >> Num examples = 2974 | |
| [INFO|trainer.py:3204] 2024-02-05 23:03:25,811 >> Batch size = 32 | |
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| ***** predict_test_vi_VN metrics ***** | |
| predict_ex_match_acc = 0.5215 | |
| predict_ex_match_acc_stderr = 0.0092 | |
| predict_intent_acc = 0.7774 | |
| predict_intent_acc_stderr = 0.0076 | |
| predict_loss = 0.325 | |
| predict_runtime = 0:01:02.07 | |
| predict_samples = 2974 | |
| predict_samples_per_second = 47.909 | |
| predict_slot_micro_f1 = 0.6561 | |
| predict_slot_micro_f1_stderr = 0.0029 | |
| predict_steps_per_second = 1.498 | |