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2024-09-09 12:14:35.494661: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2024-09-09 12:14:35.513016: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:485] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
2024-09-09 12:14:35.535014: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:8454] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
2024-09-09 12:14:35.541769: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1452] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
2024-09-09 12:14:35.557993: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
2024-09-09 12:14:36.793402: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
/usr/local/lib/python3.10/dist-packages/transformers/training_args.py:1525: FutureWarning: `evaluation_strategy` is deprecated and will be removed in version 4.46 of 🤗 Transformers. Use `eval_strategy` instead
warnings.warn(
09/09/2024 12:14:38 - WARNING - __main__ - Process rank: 0, device: cuda:0, n_gpu: 1distributed training: True, 16-bits training: False
09/09/2024 12:14:38 - INFO - __main__ - Training/evaluation parameters TrainingArguments(
_n_gpu=1,
accelerator_config={'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None, 'use_configured_state': False},
adafactor=False,
adam_beta1=0.9,
adam_beta2=0.999,
adam_epsilon=1e-08,
auto_find_batch_size=False,
batch_eval_metrics=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,
dataloader_prefetch_factor=None,
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=True,
do_predict=True,
do_train=True,
eval_accumulation_steps=None,
eval_delay=0,
eval_do_concat_batches=True,
eval_on_start=False,
eval_steps=None,
eval_strategy=epoch,
eval_use_gather_object=False,
evaluation_strategy=epoch,
fp16=False,
fp16_backend=auto,
fp16_full_eval=False,
fp16_opt_level=O1,
fsdp=[],
fsdp_config={'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False},
fsdp_min_num_params=0,
fsdp_transformer_layer_cls_to_wrap=None,
full_determinism=False,
gradient_accumulation_steps=2,
gradient_checkpointing=False,
gradient_checkpointing_kwargs=None,
greater_is_better=True,
group_by_length=False,
half_precision_backend=auto,
hub_always_push=False,
hub_model_id=None,
hub_private_repo=False,
hub_strategy=every_save,
hub_token=<HUB_TOKEN>,
ignore_data_skip=False,
include_inputs_for_metrics=False,
include_num_input_tokens_seen=False,
include_tokens_per_second=False,
jit_mode_eval=False,
label_names=None,
label_smoothing_factor=0.0,
learning_rate=5e-05,
length_column_name=length,
load_best_model_at_end=True,
local_rank=0,
log_level=passive,
log_level_replica=warning,
log_on_each_node=True,
logging_dir=/content/dissertation/scripts/ner/output/tb,
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=f1,
mp_parameters=,
neftune_noise_alpha=None,
no_cuda=False,
num_train_epochs=10.0,
optim=adamw_torch,
optim_args=None,
optim_target_modules=None,
output_dir=/content/dissertation/scripts/ner/output,
overwrite_output_dir=True,
past_index=-1,
per_device_eval_batch_size=8,
per_device_train_batch_size=32,
prediction_loss_only=False,
push_to_hub=True,
push_to_hub_model_id=None,
push_to_hub_organization=None,
push_to_hub_token=<PUSH_TO_HUB_TOKEN>,
ray_scope=last,
remove_unused_columns=True,
report_to=['tensorboard'],
restore_callback_states_from_checkpoint=False,
resume_from_checkpoint=None,
run_name=/content/dissertation/scripts/ner/output,
save_on_each_node=False,
save_only_model=False,
save_safetensors=True,
save_steps=500,
save_strategy=epoch,
save_total_limit=None,
seed=42,
skip_memory_metrics=True,
split_batches=None,
tf32=None,
torch_compile=False,
torch_compile_backend=None,
torch_compile_mode=None,
torch_empty_cache_steps=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,
)
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[INFO|configuration_utils.py:733] 2024-09-09 12:14:50,533 >> loading configuration file config.json from cache at /root/.cache/huggingface/hub/models--PlanTL-GOB-ES--bsc-bio-ehr-es/snapshots/1e543adb2d21f19d85a89305eebdbd64ab656b99/config.json
[INFO|configuration_utils.py:800] 2024-09-09 12:14:50,537 >> Model config RobertaConfig {
"_name_or_path": "PlanTL-GOB-ES/bsc-bio-ehr-es",
"architectures": [
"RobertaForMaskedLM"
],
"attention_probs_dropout_prob": 0.1,
"bos_token_id": 0,
"classifier_dropout": null,
"eos_token_id": 2,
"finetuning_task": "ner",
"gradient_checkpointing": false,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.1,
"hidden_size": 768,
"id2label": {
"0": "O",
"1": "B-SINTOMA",
"2": "I-SINTOMA"
},
"initializer_range": 0.02,
"intermediate_size": 3072,
"label2id": {
"B-SINTOMA": 1,
"I-SINTOMA": 2,
"O": 0
},
"layer_norm_eps": 1e-05,
"max_position_embeddings": 514,
"model_type": "roberta",
"num_attention_heads": 12,
"num_hidden_layers": 12,
"pad_token_id": 1,
"position_embedding_type": "absolute",
"transformers_version": "4.44.2",
"type_vocab_size": 1,
"use_cache": true,
"vocab_size": 50262
}
[INFO|configuration_utils.py:733] 2024-09-09 12:14:50,787 >> loading configuration file config.json from cache at /root/.cache/huggingface/hub/models--PlanTL-GOB-ES--bsc-bio-ehr-es/snapshots/1e543adb2d21f19d85a89305eebdbd64ab656b99/config.json
[INFO|configuration_utils.py:800] 2024-09-09 12:14:50,788 >> Model config RobertaConfig {
"_name_or_path": "PlanTL-GOB-ES/bsc-bio-ehr-es",
"architectures": [
"RobertaForMaskedLM"
],
"attention_probs_dropout_prob": 0.1,
"bos_token_id": 0,
"classifier_dropout": null,
"eos_token_id": 2,
"gradient_checkpointing": false,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.1,
"hidden_size": 768,
"initializer_range": 0.02,
"intermediate_size": 3072,
"layer_norm_eps": 1e-05,
"max_position_embeddings": 514,
"model_type": "roberta",
"num_attention_heads": 12,
"num_hidden_layers": 12,
"pad_token_id": 1,
"position_embedding_type": "absolute",
"transformers_version": "4.44.2",
"type_vocab_size": 1,
"use_cache": true,
"vocab_size": 50262
}
[INFO|tokenization_utils_base.py:2269] 2024-09-09 12:14:50,800 >> loading file vocab.json from cache at /root/.cache/huggingface/hub/models--PlanTL-GOB-ES--bsc-bio-ehr-es/snapshots/1e543adb2d21f19d85a89305eebdbd64ab656b99/vocab.json
[INFO|tokenization_utils_base.py:2269] 2024-09-09 12:14:50,801 >> loading file merges.txt from cache at /root/.cache/huggingface/hub/models--PlanTL-GOB-ES--bsc-bio-ehr-es/snapshots/1e543adb2d21f19d85a89305eebdbd64ab656b99/merges.txt
[INFO|tokenization_utils_base.py:2269] 2024-09-09 12:14:50,801 >> loading file tokenizer.json from cache at None
[INFO|tokenization_utils_base.py:2269] 2024-09-09 12:14:50,801 >> loading file added_tokens.json from cache at None
[INFO|tokenization_utils_base.py:2269] 2024-09-09 12:14:50,801 >> loading file special_tokens_map.json from cache at /root/.cache/huggingface/hub/models--PlanTL-GOB-ES--bsc-bio-ehr-es/snapshots/1e543adb2d21f19d85a89305eebdbd64ab656b99/special_tokens_map.json
[INFO|tokenization_utils_base.py:2269] 2024-09-09 12:14:50,801 >> loading file tokenizer_config.json from cache at /root/.cache/huggingface/hub/models--PlanTL-GOB-ES--bsc-bio-ehr-es/snapshots/1e543adb2d21f19d85a89305eebdbd64ab656b99/tokenizer_config.json
[INFO|configuration_utils.py:733] 2024-09-09 12:14:50,801 >> loading configuration file config.json from cache at /root/.cache/huggingface/hub/models--PlanTL-GOB-ES--bsc-bio-ehr-es/snapshots/1e543adb2d21f19d85a89305eebdbd64ab656b99/config.json
[INFO|configuration_utils.py:800] 2024-09-09 12:14:50,802 >> Model config RobertaConfig {
"_name_or_path": "PlanTL-GOB-ES/bsc-bio-ehr-es",
"architectures": [
"RobertaForMaskedLM"
],
"attention_probs_dropout_prob": 0.1,
"bos_token_id": 0,
"classifier_dropout": null,
"eos_token_id": 2,
"gradient_checkpointing": false,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.1,
"hidden_size": 768,
"initializer_range": 0.02,
"intermediate_size": 3072,
"layer_norm_eps": 1e-05,
"max_position_embeddings": 514,
"model_type": "roberta",
"num_attention_heads": 12,
"num_hidden_layers": 12,
"pad_token_id": 1,
"position_embedding_type": "absolute",
"transformers_version": "4.44.2",
"type_vocab_size": 1,
"use_cache": true,
"vocab_size": 50262
}
/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:1601: FutureWarning: `clean_up_tokenization_spaces` was not set. It will be set to `True` by default. This behavior will be depracted in transformers v4.45, and will be then set to `False` by default. For more details check this issue: https://github.com/huggingface/transformers/issues/31884
warnings.warn(
[INFO|configuration_utils.py:733] 2024-09-09 12:14:50,882 >> loading configuration file config.json from cache at /root/.cache/huggingface/hub/models--PlanTL-GOB-ES--bsc-bio-ehr-es/snapshots/1e543adb2d21f19d85a89305eebdbd64ab656b99/config.json
[INFO|configuration_utils.py:800] 2024-09-09 12:14:50,883 >> Model config RobertaConfig {
"_name_or_path": "PlanTL-GOB-ES/bsc-bio-ehr-es",
"architectures": [
"RobertaForMaskedLM"
],
"attention_probs_dropout_prob": 0.1,
"bos_token_id": 0,
"classifier_dropout": null,
"eos_token_id": 2,
"gradient_checkpointing": false,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.1,
"hidden_size": 768,
"initializer_range": 0.02,
"intermediate_size": 3072,
"layer_norm_eps": 1e-05,
"max_position_embeddings": 514,
"model_type": "roberta",
"num_attention_heads": 12,
"num_hidden_layers": 12,
"pad_token_id": 1,
"position_embedding_type": "absolute",
"transformers_version": "4.44.2",
"type_vocab_size": 1,
"use_cache": true,
"vocab_size": 50262
}
[INFO|modeling_utils.py:3678] 2024-09-09 12:14:51,213 >> loading weights file pytorch_model.bin from cache at /root/.cache/huggingface/hub/models--PlanTL-GOB-ES--bsc-bio-ehr-es/snapshots/1e543adb2d21f19d85a89305eebdbd64ab656b99/pytorch_model.bin
[INFO|modeling_utils.py:4497] 2024-09-09 12:14:51,293 >> Some weights of the model checkpoint at PlanTL-GOB-ES/bsc-bio-ehr-es were not used when initializing RobertaForTokenClassification: ['lm_head.bias', 'lm_head.decoder.bias', 'lm_head.decoder.weight', 'lm_head.dense.bias', 'lm_head.dense.weight', 'lm_head.layer_norm.bias', 'lm_head.layer_norm.weight']
- This IS expected if you are initializing RobertaForTokenClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).
- This IS NOT expected if you are initializing RobertaForTokenClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).
[WARNING|modeling_utils.py:4509] 2024-09-09 12:14:51,293 >> Some weights of RobertaForTokenClassification were not initialized from the model checkpoint at PlanTL-GOB-ES/bsc-bio-ehr-es and are newly initialized: ['classifier.bias', 'classifier.weight']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
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/content/dissertation/scripts/ner/run_ner_train.py:397: FutureWarning: load_metric is deprecated and will be removed in the next major version of datasets. Use 'evaluate.load' instead, from the new library 🤗 Evaluate: https://huggingface.co/docs/evaluate
metric = load_metric("seqeval", trust_remote_code=True)
[INFO|trainer.py:811] 2024-09-09 12:14:55,082 >> The following columns in the training set don't have a corresponding argument in `RobertaForTokenClassification.forward` and have been ignored: ner_tags, id, tokens. If ner_tags, id, tokens are not expected by `RobertaForTokenClassification.forward`, you can safely ignore this message.
[INFO|trainer.py:2134] 2024-09-09 12:14:55,636 >> ***** Running training *****
[INFO|trainer.py:2135] 2024-09-09 12:14:55,636 >> Num examples = 10,936
[INFO|trainer.py:2136] 2024-09-09 12:14:55,636 >> Num Epochs = 10
[INFO|trainer.py:2137] 2024-09-09 12:14:55,636 >> Instantaneous batch size per device = 32
[INFO|trainer.py:2140] 2024-09-09 12:14:55,636 >> Total train batch size (w. parallel, distributed & accumulation) = 64
[INFO|trainer.py:2141] 2024-09-09 12:14:55,636 >> Gradient Accumulation steps = 2
[INFO|trainer.py:2142] 2024-09-09 12:14:55,636 >> Total optimization steps = 1,710
[INFO|trainer.py:2143] 2024-09-09 12:14:55,637 >> Number of trainable parameters = 124,055,043
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10%|█ | 171/1710 [01:19<11:08, 2.30it/s][INFO|trainer.py:811] 2024-09-09 12:16:15,508 >> The following columns in the evaluation set don't have a corresponding argument in `RobertaForTokenClassification.forward` and have been ignored: ner_tags, id, tokens. If ner_tags, id, tokens are not expected by `RobertaForTokenClassification.forward`, you can safely ignore this message.
[INFO|trainer.py:3819] 2024-09-09 12:16:15,510 >>
***** Running Evaluation *****
[INFO|trainer.py:3821] 2024-09-09 12:16:15,510 >> Num examples = 2519
[INFO|trainer.py:3824] 2024-09-09 12:16:15,510 >> Batch size = 8
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[A
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[A[INFO|trainer.py:3503] 2024-09-09 12:16:21,499 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-171
[INFO|configuration_utils.py:472] 2024-09-09 12:16:21,501 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-171/config.json
[INFO|modeling_utils.py:2799] 2024-09-09 12:16:22,527 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-171/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-09 12:16:22,528 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-171/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 12:16:22,529 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-171/special_tokens_map.json
[INFO|tokenization_utils_base.py:2684] 2024-09-09 12:16:25,565 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 12:16:25,565 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json
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20%|██ | 342/1710 [02:49<11:08, 2.05it/s][INFO|trainer.py:811] 2024-09-09 12:17:44,889 >> The following columns in the evaluation set don't have a corresponding argument in `RobertaForTokenClassification.forward` and have been ignored: ner_tags, id, tokens. If ner_tags, id, tokens are not expected by `RobertaForTokenClassification.forward`, you can safely ignore this message.
[INFO|trainer.py:3819] 2024-09-09 12:17:44,891 >>
***** Running Evaluation *****
[INFO|trainer.py:3821] 2024-09-09 12:17:44,891 >> Num examples = 2519
[INFO|trainer.py:3824] 2024-09-09 12:17:44,891 >> Batch size = 8
{'eval_loss': 0.15023551881313324, 'eval_precision': 0.5421052631578948, 'eval_recall': 0.6765188834154351, 'eval_f1': 0.6018991964937911, 'eval_accuracy': 0.9458275851005807, 'eval_runtime': 5.988, 'eval_samples_per_second': 420.673, 'eval_steps_per_second': 52.605, 'epoch': 1.0}
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[A[INFO|trainer.py:3503] 2024-09-09 12:17:50,795 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-342
[INFO|configuration_utils.py:472] 2024-09-09 12:17:50,796 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-342/config.json
[INFO|modeling_utils.py:2799] 2024-09-09 12:17:51,803 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-342/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-09 12:17:51,804 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-342/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 12:17:51,804 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-342/special_tokens_map.json
[INFO|tokenization_utils_base.py:2684] 2024-09-09 12:17:54,812 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 12:17:54,812 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json
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30%|███ | 513/1710 [04:18<11:39, 1.71it/s][INFO|trainer.py:811] 2024-09-09 12:19:13,918 >> The following columns in the evaluation set don't have a corresponding argument in `RobertaForTokenClassification.forward` and have been ignored: ner_tags, id, tokens. If ner_tags, id, tokens are not expected by `RobertaForTokenClassification.forward`, you can safely ignore this message.
[INFO|trainer.py:3819] 2024-09-09 12:19:13,920 >>
***** Running Evaluation *****
[INFO|trainer.py:3821] 2024-09-09 12:19:13,920 >> Num examples = 2519
[INFO|trainer.py:3824] 2024-09-09 12:19:13,920 >> Batch size = 8
{'eval_loss': 0.15393643081188202, 'eval_precision': 0.5957753240518483, 'eval_recall': 0.6792556102900931, 'eval_f1': 0.6347826086956522, 'eval_accuracy': 0.9468221630466168, 'eval_runtime': 5.9023, 'eval_samples_per_second': 426.784, 'eval_steps_per_second': 53.369, 'epoch': 2.0}
{'loss': 0.1273, 'grad_norm': 1.020290732383728, 'learning_rate': 3.538011695906433e-05, 'epoch': 2.92}
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[A[INFO|trainer.py:3503] 2024-09-09 12:19:19,842 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-513
[INFO|configuration_utils.py:472] 2024-09-09 12:19:19,843 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-513/config.json
[INFO|modeling_utils.py:2799] 2024-09-09 12:19:20,869 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-513/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-09 12:19:20,870 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-513/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 12:19:20,871 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-513/special_tokens_map.json
[INFO|tokenization_utils_base.py:2684] 2024-09-09 12:19:25,899 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 12:19:25,900 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json
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40%|████ | 684/1710 [05:48<07:29, 2.28it/s][INFO|trainer.py:811] 2024-09-09 12:20:44,622 >> The following columns in the evaluation set don't have a corresponding argument in `RobertaForTokenClassification.forward` and have been ignored: ner_tags, id, tokens. If ner_tags, id, tokens are not expected by `RobertaForTokenClassification.forward`, you can safely ignore this message.
[INFO|trainer.py:3819] 2024-09-09 12:20:44,625 >>
***** Running Evaluation *****
[INFO|trainer.py:3821] 2024-09-09 12:20:44,625 >> Num examples = 2519
[INFO|trainer.py:3824] 2024-09-09 12:20:44,625 >> Batch size = 8
{'eval_loss': 0.1837530881166458, 'eval_precision': 0.6325831702544031, 'eval_recall': 0.7077175697865353, 'eval_f1': 0.6680444329630587, 'eval_accuracy': 0.9468382046263916, 'eval_runtime': 5.9204, 'eval_samples_per_second': 425.477, 'eval_steps_per_second': 53.206, 'epoch': 3.0}
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[A
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[A[INFO|trainer.py:3503] 2024-09-09 12:20:50,545 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-684
[INFO|configuration_utils.py:472] 2024-09-09 12:20:50,547 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-684/config.json
[INFO|modeling_utils.py:2799] 2024-09-09 12:20:51,556 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-684/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-09 12:20:51,557 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-684/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 12:20:51,558 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-684/special_tokens_map.json
[INFO|tokenization_utils_base.py:2684] 2024-09-09 12:20:54,643 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 12:20:54,643 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json
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50%|█████ | 855/1710 [07:17<07:02, 2.03it/s][INFO|trainer.py:811] 2024-09-09 12:22:13,597 >> The following columns in the evaluation set don't have a corresponding argument in `RobertaForTokenClassification.forward` and have been ignored: ner_tags, id, tokens. If ner_tags, id, tokens are not expected by `RobertaForTokenClassification.forward`, you can safely ignore this message.
[INFO|trainer.py:3819] 2024-09-09 12:22:13,600 >>
***** Running Evaluation *****
[INFO|trainer.py:3821] 2024-09-09 12:22:13,600 >> Num examples = 2519
[INFO|trainer.py:3824] 2024-09-09 12:22:13,600 >> Batch size = 8
{'eval_loss': 0.20180054008960724, 'eval_precision': 0.6321671525753159, 'eval_recall': 0.7120963327859879, 'eval_f1': 0.6697554697554697, 'eval_accuracy': 0.9466296640893195, 'eval_runtime': 5.9193, 'eval_samples_per_second': 425.559, 'eval_steps_per_second': 53.216, 'epoch': 4.0}
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[A[INFO|trainer.py:3503] 2024-09-09 12:22:19,502 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-855
[INFO|configuration_utils.py:472] 2024-09-09 12:22:19,503 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-855/config.json
[INFO|modeling_utils.py:2799] 2024-09-09 12:22:20,520 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-855/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-09 12:22:20,521 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-855/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 12:22:20,521 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-855/special_tokens_map.json
[INFO|tokenization_utils_base.py:2684] 2024-09-09 12:22:23,617 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 12:22:23,618 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json
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60%|██████ | 1026/1710 [08:47<04:55, 2.32it/s][INFO|trainer.py:811] 2024-09-09 12:23:43,183 >> The following columns in the evaluation set don't have a corresponding argument in `RobertaForTokenClassification.forward` and have been ignored: ner_tags, id, tokens. If ner_tags, id, tokens are not expected by `RobertaForTokenClassification.forward`, you can safely ignore this message.
[INFO|trainer.py:3819] 2024-09-09 12:23:43,185 >>
***** Running Evaluation *****
[INFO|trainer.py:3821] 2024-09-09 12:23:43,185 >> Num examples = 2519
[INFO|trainer.py:3824] 2024-09-09 12:23:43,185 >> Batch size = 8
{'eval_loss': 0.21526136994361877, 'eval_precision': 0.6441176470588236, 'eval_recall': 0.7192118226600985, 'eval_f1': 0.6795965865011637, 'eval_accuracy': 0.9465013314511213, 'eval_runtime': 5.9003, 'eval_samples_per_second': 426.928, 'eval_steps_per_second': 53.387, 'epoch': 5.0}
{'loss': 0.0234, 'grad_norm': 1.644018530845642, 'learning_rate': 2.0760233918128656e-05, 'epoch': 5.85}
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[A[INFO|trainer.py:3503] 2024-09-09 12:23:49,149 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-1026
[INFO|configuration_utils.py:472] 2024-09-09 12:23:49,150 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-1026/config.json
[INFO|modeling_utils.py:2799] 2024-09-09 12:23:50,155 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-1026/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-09 12:23:50,156 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-1026/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 12:23:50,156 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-1026/special_tokens_map.json
[INFO|tokenization_utils_base.py:2684] 2024-09-09 12:23:53,206 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 12:23:53,207 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json
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[INFO|trainer.py:3819] 2024-09-09 12:25:11,391 >>
***** Running Evaluation *****
[INFO|trainer.py:3821] 2024-09-09 12:25:11,392 >> Num examples = 2519
[INFO|trainer.py:3824] 2024-09-09 12:25:11,392 >> Batch size = 8
{'eval_loss': 0.24975360929965973, 'eval_precision': 0.6461383139828369, 'eval_recall': 0.7006020799124247, 'eval_f1': 0.6722689075630252, 'eval_accuracy': 0.9469825788443645, 'eval_runtime': 5.9627, 'eval_samples_per_second': 422.461, 'eval_steps_per_second': 52.829, 'epoch': 6.0}
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[A[INFO|trainer.py:3503] 2024-09-09 12:25:17,290 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-1197
[INFO|configuration_utils.py:472] 2024-09-09 12:25:17,291 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-1197/config.json
[INFO|modeling_utils.py:2799] 2024-09-09 12:25:18,342 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-1197/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-09 12:25:18,343 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-1197/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 12:25:18,343 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-1197/special_tokens_map.json
[INFO|tokenization_utils_base.py:2684] 2024-09-09 12:25:23,192 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 12:25:23,192 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json
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[INFO|trainer.py:3819] 2024-09-09 12:26:42,162 >>
***** Running Evaluation *****
[INFO|trainer.py:3821] 2024-09-09 12:26:42,162 >> Num examples = 2519
[INFO|trainer.py:3824] 2024-09-09 12:26:42,162 >> Batch size = 8
{'eval_loss': 0.2653313875198364, 'eval_precision': 0.636231884057971, 'eval_recall': 0.7208538587848933, 'eval_f1': 0.6759045419553503, 'eval_accuracy': 0.9461804998556258, 'eval_runtime': 5.8972, 'eval_samples_per_second': 427.151, 'eval_steps_per_second': 53.415, 'epoch': 7.0}
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[A[INFO|trainer.py:3503] 2024-09-09 12:26:48,070 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-1368
[INFO|configuration_utils.py:472] 2024-09-09 12:26:48,072 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-1368/config.json
[INFO|modeling_utils.py:2799] 2024-09-09 12:26:49,101 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-1368/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-09 12:26:49,102 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-1368/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 12:26:49,103 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-1368/special_tokens_map.json
[INFO|tokenization_utils_base.py:2684] 2024-09-09 12:26:52,194 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 12:26:52,195 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json
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[INFO|trainer.py:3819] 2024-09-09 12:28:10,970 >>
***** Running Evaluation *****
[INFO|trainer.py:3821] 2024-09-09 12:28:10,970 >> Num examples = 2519
[INFO|trainer.py:3824] 2024-09-09 12:28:10,970 >> Batch size = 8
{'eval_loss': 0.28080618381500244, 'eval_precision': 0.6529382219989954, 'eval_recall': 0.7115489874110563, 'eval_f1': 0.680984808800419, 'eval_accuracy': 0.9473354935994097, 'eval_runtime': 5.9073, 'eval_samples_per_second': 426.421, 'eval_steps_per_second': 53.324, 'epoch': 8.0}
{'loss': 0.0082, 'grad_norm': 0.22467799484729767, 'learning_rate': 6.140350877192982e-06, 'epoch': 8.77}
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[A
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[A[INFO|trainer.py:3503] 2024-09-09 12:28:16,907 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-1539
[INFO|configuration_utils.py:472] 2024-09-09 12:28:16,908 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-1539/config.json
[INFO|modeling_utils.py:2799] 2024-09-09 12:28:17,937 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-1539/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-09 12:28:17,938 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-1539/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 12:28:17,939 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-1539/special_tokens_map.json
[INFO|tokenization_utils_base.py:2684] 2024-09-09 12:28:22,554 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 12:28:22,555 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json
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[INFO|configuration_utils.py:472] 2024-09-09 12:29:41,233 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-1710/config.json
[INFO|modeling_utils.py:2799] 2024-09-09 12:29:42,287 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-1710/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-09 12:29:42,288 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-1710/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 12:29:42,289 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-1710/special_tokens_map.json
[INFO|tokenization_utils_base.py:2684] 2024-09-09 12:29:47,798 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 12:29:47,799 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json
[INFO|trainer.py:811] 2024-09-09 12:29:47,847 >> The following columns in the evaluation set don't have a corresponding argument in `RobertaForTokenClassification.forward` and have been ignored: ner_tags, id, tokens. If ner_tags, id, tokens are not expected by `RobertaForTokenClassification.forward`, you can safely ignore this message.
[INFO|trainer.py:3819] 2024-09-09 12:29:47,850 >>
***** Running Evaluation *****
[INFO|trainer.py:3821] 2024-09-09 12:29:47,850 >> Num examples = 2519
[INFO|trainer.py:3824] 2024-09-09 12:29:47,850 >> Batch size = 8
{'eval_loss': 0.2917279005050659, 'eval_precision': 0.6458022851465475, 'eval_recall': 0.7115489874110563, 'eval_f1': 0.6770833333333333, 'eval_accuracy': 0.9466938304084186, 'eval_runtime': 5.9353, 'eval_samples_per_second': 424.408, 'eval_steps_per_second': 53.072, 'epoch': 9.0}
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[A
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[A[INFO|trainer.py:3503] 2024-09-09 12:29:53,786 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-1710
[INFO|configuration_utils.py:472] 2024-09-09 12:29:53,788 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-1710/config.json
[INFO|modeling_utils.py:2799] 2024-09-09 12:29:55,303 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-1710/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-09 12:29:55,306 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-1710/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 12:29:55,306 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-1710/special_tokens_map.json
[INFO|trainer.py:2394] 2024-09-09 12:29:57,247 >>
Training completed. Do not forget to share your model on huggingface.co/models =)
[INFO|trainer.py:2632] 2024-09-09 12:29:57,247 >> Loading best model from /content/dissertation/scripts/ner/output/checkpoint-1368 (score: 0.680984808800419).
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[INFO|trainer.py:4283] 2024-09-09 12:29:57,436 >> Waiting for the current checkpoint push to be finished, this might take a couple of minutes.
[INFO|trainer.py:3503] 2024-09-09 12:30:42,660 >> Saving model checkpoint to /content/dissertation/scripts/ner/output
[INFO|configuration_utils.py:472] 2024-09-09 12:30:42,661 >> Configuration saved in /content/dissertation/scripts/ner/output/config.json
[INFO|modeling_utils.py:2799] 2024-09-09 12:30:44,034 >> Model weights saved in /content/dissertation/scripts/ner/output/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-09 12:30:44,035 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 12:30:44,035 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json
[INFO|trainer.py:3503] 2024-09-09 12:30:44,082 >> Saving model checkpoint to /content/dissertation/scripts/ner/output
[INFO|configuration_utils.py:472] 2024-09-09 12:30:44,083 >> Configuration saved in /content/dissertation/scripts/ner/output/config.json
[INFO|modeling_utils.py:2799] 2024-09-09 12:30:47,113 >> Model weights saved in /content/dissertation/scripts/ner/output/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-09 12:30:47,114 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 12:30:47,114 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json
{'eval_loss': 0.2930145561695099, 'eval_precision': 0.6548403446528129, 'eval_recall': 0.7071702244116037, 'eval_f1': 0.6799999999999999, 'eval_accuracy': 0.9481215310083737, 'eval_runtime': 5.9346, 'eval_samples_per_second': 424.463, 'eval_steps_per_second': 53.079, 'epoch': 10.0}
{'train_runtime': 901.7971, 'train_samples_per_second': 121.269, 'train_steps_per_second': 1.896, 'train_loss': 0.047100042948248794, 'epoch': 10.0}
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***** train metrics *****
epoch = 10.0
total_flos = 4927248GF
train_loss = 0.0471
train_runtime = 0:15:01.79
train_samples = 10936
train_samples_per_second = 121.269
train_steps_per_second = 1.896
09/09/2024 12:30:53 - INFO - __main__ - *** Evaluate ***
[INFO|trainer.py:811] 2024-09-09 12:30:53,235 >> The following columns in the evaluation set don't have a corresponding argument in `RobertaForTokenClassification.forward` and have been ignored: ner_tags, id, tokens. If ner_tags, id, tokens are not expected by `RobertaForTokenClassification.forward`, you can safely ignore this message.
[INFO|trainer.py:3819] 2024-09-09 12:30:53,237 >>
***** Running Evaluation *****
[INFO|trainer.py:3821] 2024-09-09 12:30:53,238 >> Num examples = 2519
[INFO|trainer.py:3824] 2024-09-09 12:30:53,238 >> Batch size = 8
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***** eval metrics *****
epoch = 10.0
eval_accuracy = 0.9473
eval_f1 = 0.681
eval_loss = 0.2808
eval_precision = 0.6529
eval_recall = 0.7115
eval_runtime = 0:00:06.16
eval_samples = 2519
eval_samples_per_second = 408.28
eval_steps_per_second = 51.055
09/09/2024 12:30:59 - INFO - __main__ - *** Predict ***
[INFO|trainer.py:811] 2024-09-09 12:30:59,410 >> The following columns in the test set don't have a corresponding argument in `RobertaForTokenClassification.forward` and have been ignored: ner_tags, id, tokens. If ner_tags, id, tokens are not expected by `RobertaForTokenClassification.forward`, you can safely ignore this message.
[INFO|trainer.py:3819] 2024-09-09 12:30:59,412 >>
***** Running Prediction *****
[INFO|trainer.py:3821] 2024-09-09 12:30:59,412 >> Num examples = 4047
[INFO|trainer.py:3824] 2024-09-09 12:30:59,412 >> Batch size = 8
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[INFO|trainer.py:3503] 2024-09-09 12:31:09,424 >> Saving model checkpoint to /content/dissertation/scripts/ner/output
[INFO|configuration_utils.py:472] 2024-09-09 12:31:09,425 >> Configuration saved in /content/dissertation/scripts/ner/output/config.json
[INFO|modeling_utils.py:2799] 2024-09-09 12:31:10,791 >> Model weights saved in /content/dissertation/scripts/ner/output/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-09 12:31:10,792 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 12:31:10,793 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json
***** predict metrics *****
predict_accuracy = 0.9476
predict_f1 = 0.691
predict_loss = 0.3017
predict_precision = 0.6776
predict_recall = 0.7049
predict_runtime = 0:00:09.84
predict_samples_per_second = 410.896
predict_steps_per_second = 51.375
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