2024-09-09 19:52:37.575296: 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 19:52:37.593097: 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 19:52:37.614026: 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 19:52:37.620451: 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 19:52:37.635714: 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 19:52:38.893385: 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 19:52:41 - WARNING - __main__ -   Process rank: 0, device: cuda:0, n_gpu: 1distributed training: True, 16-bits training: False
09/09/2024 19:52:41 - 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 19:53:03,685 >> 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 19:53:03,694 >> 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-ENFERMEDAD",
    "2": "I-ENFERMEDAD"
  },
  "initializer_range": 0.02,
  "intermediate_size": 3072,
  "label2id": {
    "B-ENFERMEDAD": 1,
    "I-ENFERMEDAD": 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 19:53:04,218 >> 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 19:53:04,219 >> 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 19:53:07,137 >> 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 19:53:07,137 >> 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 19:53:07,137 >> loading file tokenizer.json from cache at None
[INFO|tokenization_utils_base.py:2269] 2024-09-09 19:53:07,138 >> loading file added_tokens.json from cache at None
[INFO|tokenization_utils_base.py:2269] 2024-09-09 19:53:07,138 >> 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 19:53:07,138 >> 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 19:53:07,138 >> 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 19:53:07,139 >> 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 19:53:07,228 >> 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 19:53:07,229 >> 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 19:53:09,768 >> 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 19:53:09,921 >> 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 19:53:09,921 >> 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)

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Downloading builder script: 6.33kB [00:00, 10.8MB/s]                   
[INFO|trainer.py:811] 2024-09-09 19:53:18,675 >> The following columns in the training set don't have a corresponding argument in `RobertaForTokenClassification.forward` and have been ignored: id, tokens, ner_tags. If id, tokens, ner_tags are not expected by `RobertaForTokenClassification.forward`,  you can safely ignore this message.
[INFO|trainer.py:2134] 2024-09-09 19:53:19,367 >> ***** Running training *****
[INFO|trainer.py:2135] 2024-09-09 19:53:19,367 >>   Num examples = 44,938
[INFO|trainer.py:2136] 2024-09-09 19:53:19,367 >>   Num Epochs = 10
[INFO|trainer.py:2137] 2024-09-09 19:53:19,367 >>   Instantaneous batch size per device = 32
[INFO|trainer.py:2140] 2024-09-09 19:53:19,367 >>   Total train batch size (w. parallel, distributed & accumulation) = 64
[INFO|trainer.py:2141] 2024-09-09 19:53:19,367 >>   Gradient Accumulation steps = 2
[INFO|trainer.py:2142] 2024-09-09 19:53:19,367 >>   Total optimization steps = 7,020
[INFO|trainer.py:2143] 2024-09-09 19:53:19,368 >>   Number of trainable parameters = 124,055,043

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 10%|█         | 702/7020 [02:51<22:52,  4.60it/s][INFO|trainer.py:811] 2024-09-09 19:56:10,636 >> The following columns in the evaluation set don't have a corresponding argument in `RobertaForTokenClassification.forward` and have been ignored: id, tokens, ner_tags. If id, tokens, ner_tags are not expected by `RobertaForTokenClassification.forward`,  you can safely ignore this message.
[INFO|trainer.py:3819] 2024-09-09 19:56:10,639 >> 
***** Running Evaluation *****
[INFO|trainer.py:3821] 2024-09-09 19:56:10,639 >>   Num examples = 6810
[INFO|trainer.py:3824] 2024-09-09 19:56:10,639 >>   Batch size = 8
{'loss': 0.1165, 'grad_norm': 0.739061713218689, 'learning_rate': 4.643874643874644e-05, 'epoch': 0.71}


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                                                 [INFO|trainer.py:3503] 2024-09-09 19:56:25,189 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-702
[INFO|configuration_utils.py:472] 2024-09-09 19:56:25,191 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-702/config.json
[INFO|modeling_utils.py:2799] 2024-09-09 19:56:26,216 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-702/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-09 19:56:26,217 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-702/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 19:56:26,217 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-702/special_tokens_map.json
[INFO|tokenization_utils_base.py:2684] 2024-09-09 19:56:31,997 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 19:56:31,997 >> 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 19:59:21,777 >> 
***** Running Evaluation *****
[INFO|trainer.py:3821] 2024-09-09 19:59:21,777 >>   Num examples = 6810
[INFO|trainer.py:3824] 2024-09-09 19:59:21,777 >>   Batch size = 8
{'eval_loss': 0.08085697889328003, 'eval_precision': 0.7454979388153613, 'eval_recall': 0.8039307440336921, 'eval_f1': 0.7736125182933693, 'eval_accuracy': 0.9735468988149999, 'eval_runtime': 14.5471, 'eval_samples_per_second': 468.136, 'eval_steps_per_second': 58.569, 'epoch': 1.0}
{'loss': 0.0461, 'grad_norm': 0.5600239038467407, 'learning_rate': 4.287749287749288e-05, 'epoch': 1.42}


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                                                 [INFO|trainer.py:3503] 2024-09-09 19:59:36,031 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-1405
[INFO|configuration_utils.py:472] 2024-09-09 19:59:36,032 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-1405/config.json
[INFO|modeling_utils.py:2799] 2024-09-09 19:59:37,034 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-1405/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-09 19:59:37,035 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-1405/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 19:59:37,035 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-1405/special_tokens_map.json
[INFO|tokenization_utils_base.py:2684] 2024-09-09 19:59:39,881 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 19:59:39,882 >> 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 20:02:30,245 >> 
***** Running Evaluation *****
[INFO|trainer.py:3821] 2024-09-09 20:02:30,245 >>   Num examples = 6810
[INFO|trainer.py:3824] 2024-09-09 20:02:30,245 >>   Batch size = 8
{'eval_loss': 0.09561743587255478, 'eval_precision': 0.7611479028697572, 'eval_recall': 0.8067384183434722, 'eval_f1': 0.7832803271240344, 'eval_accuracy': 0.9746797204333557, 'eval_runtime': 14.2524, 'eval_samples_per_second': 477.815, 'eval_steps_per_second': 59.779, 'epoch': 2.0}
{'loss': 0.0273, 'grad_norm': 0.4771190881729126, 'learning_rate': 3.931623931623932e-05, 'epoch': 2.14}
{'loss': 0.0165, 'grad_norm': 0.29212868213653564, 'learning_rate': 3.575498575498576e-05, 'epoch': 2.85}


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                                                 [INFO|trainer.py:3503] 2024-09-09 20:02:44,502 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-2107
[INFO|configuration_utils.py:472] 2024-09-09 20:02:44,504 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-2107/config.json
[INFO|modeling_utils.py:2799] 2024-09-09 20:02:45,536 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-2107/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-09 20:02:45,537 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-2107/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 20:02:45,537 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-2107/special_tokens_map.json
[INFO|tokenization_utils_base.py:2684] 2024-09-09 20:02:47,634 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 20:02:47,634 >> 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 20:05:37,044 >> 
***** Running Evaluation *****
[INFO|trainer.py:3821] 2024-09-09 20:05:37,044 >>   Num examples = 6810
[INFO|trainer.py:3824] 2024-09-09 20:05:37,044 >>   Batch size = 8
{'eval_loss': 0.10571284592151642, 'eval_precision': 0.7721003843545106, 'eval_recall': 0.7990173139915769, 'eval_f1': 0.7853282741175118, 'eval_accuracy': 0.974439424938553, 'eval_runtime': 14.2529, 'eval_samples_per_second': 477.797, 'eval_steps_per_second': 59.777, 'epoch': 3.0}
{'loss': 0.011, 'grad_norm': 0.08157803863286972, 'learning_rate': 3.2193732193732194e-05, 'epoch': 3.56}


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                                                 [INFO|trainer.py:3503] 2024-09-09 20:05:51,657 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-2810
[INFO|configuration_utils.py:472] 2024-09-09 20:05:51,658 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-2810/config.json
[INFO|modeling_utils.py:2799] 2024-09-09 20:05:52,654 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-2810/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-09 20:05:52,655 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-2810/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 20:05:52,655 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-2810/special_tokens_map.json
[INFO|tokenization_utils_base.py:2684] 2024-09-09 20:05:57,732 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 20:05:57,732 >> 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 20:08:47,445 >> 
***** Running Evaluation *****
[INFO|trainer.py:3821] 2024-09-09 20:08:47,445 >>   Num examples = 6810
[INFO|trainer.py:3824] 2024-09-09 20:08:47,445 >>   Batch size = 8
{'eval_loss': 0.12744221091270447, 'eval_precision': 0.775858250276855, 'eval_recall': 0.8196069255966308, 'eval_f1': 0.7971327796108773, 'eval_accuracy': 0.9750504620539086, 'eval_runtime': 14.6113, 'eval_samples_per_second': 466.079, 'eval_steps_per_second': 58.311, 'epoch': 4.0}
{'loss': 0.0079, 'grad_norm': 0.45329615473747253, 'learning_rate': 2.863247863247863e-05, 'epoch': 4.27}
{'loss': 0.006, 'grad_norm': 0.4696062207221985, 'learning_rate': 2.5071225071225073e-05, 'epoch': 4.98}


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                                                 [INFO|trainer.py:3503] 2024-09-09 20:09:01,801 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-3512
[INFO|configuration_utils.py:472] 2024-09-09 20:09:01,802 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-3512/config.json
[INFO|modeling_utils.py:2799] 2024-09-09 20:09:02,828 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-3512/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-09 20:09:02,829 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-3512/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 20:09:02,829 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-3512/special_tokens_map.json
[INFO|tokenization_utils_base.py:2684] 2024-09-09 20:09:12,195 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 20:09:12,195 >> 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 20:12:02,892 >> 
***** Running Evaluation *****
[INFO|trainer.py:3821] 2024-09-09 20:12:02,892 >>   Num examples = 6810
[INFO|trainer.py:3824] 2024-09-09 20:12:02,892 >>   Batch size = 8
{'eval_loss': 0.13583102822303772, 'eval_precision': 0.7904411764705882, 'eval_recall': 0.8048666354702855, 'eval_f1': 0.7975886853698122, 'eval_accuracy': 0.9744943496230793, 'eval_runtime': 14.3515, 'eval_samples_per_second': 474.516, 'eval_steps_per_second': 59.367, 'epoch': 5.0}
{'loss': 0.0045, 'grad_norm': 0.6243860721588135, 'learning_rate': 2.150997150997151e-05, 'epoch': 5.69}


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                                                 [INFO|trainer.py:3503] 2024-09-09 20:12:17,233 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-4215
[INFO|configuration_utils.py:472] 2024-09-09 20:12:17,234 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-4215/config.json
[INFO|modeling_utils.py:2799] 2024-09-09 20:12:18,228 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-4215/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-09 20:12:18,229 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-4215/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 20:12:18,229 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-4215/special_tokens_map.json
[INFO|tokenization_utils_base.py:2684] 2024-09-09 20:12:20,733 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 20:12:20,733 >> 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 20:15:10,174 >> 
***** Running Evaluation *****
[INFO|trainer.py:3821] 2024-09-09 20:15:10,174 >>   Num examples = 6810
[INFO|trainer.py:3824] 2024-09-09 20:15:10,174 >>   Batch size = 8
{'eval_loss': 0.14197705686092377, 'eval_precision': 0.7911451089476125, 'eval_recall': 0.7985493682732803, 'eval_f1': 0.7948299953423382, 'eval_accuracy': 0.9746179301632636, 'eval_runtime': 14.3392, 'eval_samples_per_second': 474.921, 'eval_steps_per_second': 59.417, 'epoch': 6.0}
{'loss': 0.0037, 'grad_norm': 0.2963380813598633, 'learning_rate': 1.794871794871795e-05, 'epoch': 6.41}


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                                                 [INFO|trainer.py:3503] 2024-09-09 20:15:24,350 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-4917
[INFO|configuration_utils.py:472] 2024-09-09 20:15:24,351 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-4917/config.json
[INFO|modeling_utils.py:2799] 2024-09-09 20:15:25,349 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-4917/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-09 20:15:25,350 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-4917/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 20:15:25,350 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-4917/special_tokens_map.json
[INFO|tokenization_utils_base.py:2684] 2024-09-09 20:15:27,457 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 20:15:27,458 >> 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 20:18:17,379 >> 
***** Running Evaluation *****
[INFO|trainer.py:3821] 2024-09-09 20:18:17,379 >>   Num examples = 6810
[INFO|trainer.py:3824] 2024-09-09 20:18:17,379 >>   Batch size = 8
{'eval_loss': 0.160146102309227, 'eval_precision': 0.7925359295317571, 'eval_recall': 0.7999532054281703, 'eval_f1': 0.796227293898463, 'eval_accuracy': 0.9748925535858953, 'eval_runtime': 14.175, 'eval_samples_per_second': 480.423, 'eval_steps_per_second': 60.106, 'epoch': 7.0}
{'loss': 0.0028, 'grad_norm': 0.4029097557067871, 'learning_rate': 1.4387464387464389e-05, 'epoch': 7.12}
{'loss': 0.0022, 'grad_norm': 0.04551521688699722, 'learning_rate': 1.0826210826210826e-05, 'epoch': 7.83}


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                                                 [INFO|trainer.py:3503] 2024-09-09 20:18:31,861 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-5620
[INFO|configuration_utils.py:472] 2024-09-09 20:18:31,862 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-5620/config.json
[INFO|modeling_utils.py:2799] 2024-09-09 20:18:32,887 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-5620/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-09 20:18:32,888 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-5620/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 20:18:32,888 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-5620/special_tokens_map.json
[INFO|tokenization_utils_base.py:2684] 2024-09-09 20:18:35,056 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 20:18:35,057 >> 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 20:21:23,981 >> 
***** Running Evaluation *****
[INFO|trainer.py:3821] 2024-09-09 20:21:23,982 >>   Num examples = 6810
[INFO|trainer.py:3824] 2024-09-09 20:21:23,982 >>   Batch size = 8
{'eval_loss': 0.1620824635028839, 'eval_precision': 0.7999537999538, 'eval_recall': 0.8102480112306972, 'eval_f1': 0.8050679995350458, 'eval_accuracy': 0.9758331388084089, 'eval_runtime': 14.4773, 'eval_samples_per_second': 470.391, 'eval_steps_per_second': 58.851, 'epoch': 8.0}
{'loss': 0.0016, 'grad_norm': 0.3445014953613281, 'learning_rate': 7.264957264957266e-06, 'epoch': 8.54}


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                                                 [INFO|trainer.py:3503] 2024-09-09 20:21:38,180 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-6322
[INFO|configuration_utils.py:472] 2024-09-09 20:21:38,181 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-6322/config.json
[INFO|modeling_utils.py:2799] 2024-09-09 20:21:39,183 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-6322/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-09 20:21:39,184 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-6322/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 20:21:39,184 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-6322/special_tokens_map.json
[INFO|tokenization_utils_base.py:2684] 2024-09-09 20:21:44,602 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 20:21:44,602 >> 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 20:24:33,137 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-7020/config.json
[INFO|modeling_utils.py:2799] 2024-09-09 20:24:34,167 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-7020/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-09 20:24:34,168 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-7020/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 20:24:34,168 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-7020/special_tokens_map.json
[INFO|tokenization_utils_base.py:2684] 2024-09-09 20:24:42,854 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 20:24:42,854 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json
[INFO|trainer.py:811] 2024-09-09 20:24:42,905 >> The following columns in the evaluation set don't have a corresponding argument in `RobertaForTokenClassification.forward` and have been ignored: id, tokens, ner_tags. If id, tokens, ner_tags are not expected by `RobertaForTokenClassification.forward`,  you can safely ignore this message.
[INFO|trainer.py:3819] 2024-09-09 20:24:42,908 >> 
***** Running Evaluation *****
[INFO|trainer.py:3821] 2024-09-09 20:24:42,908 >>   Num examples = 6810
[INFO|trainer.py:3824] 2024-09-09 20:24:42,908 >>   Batch size = 8
{'eval_loss': 0.16807720065116882, 'eval_precision': 0.7972318339100346, 'eval_recall': 0.8086102012166588, 'eval_f1': 0.8028807062376583, 'eval_accuracy': 0.9757713485383168, 'eval_runtime': 14.1973, 'eval_samples_per_second': 479.67, 'eval_steps_per_second': 60.012, 'epoch': 9.0}
{'loss': 0.0013, 'grad_norm': 0.12732785940170288, 'learning_rate': 3.7037037037037037e-06, 'epoch': 9.25}
{'loss': 0.0013, 'grad_norm': 0.05971187725663185, 'learning_rate': 1.4245014245014247e-07, 'epoch': 9.96}


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                                                 [INFO|trainer.py:3503] 2024-09-09 20:24:57,503 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-7020
[INFO|configuration_utils.py:472] 2024-09-09 20:24:57,505 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-7020/config.json
[INFO|modeling_utils.py:2799] 2024-09-09 20:24:58,994 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-7020/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-09 20:24:58,997 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-7020/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 20:24:58,997 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-7020/special_tokens_map.json
[INFO|trainer.py:2394] 2024-09-09 20:25:00,772 >> 

Training completed. Do not forget to share your model on huggingface.co/models =)


[INFO|trainer.py:2632] 2024-09-09 20:25:00,772 >> Loading best model from /content/dissertation/scripts/ner/output/checkpoint-7020 (score: 0.8053395240858967).

                                                   

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[INFO|trainer.py:4283] 2024-09-09 20:25:00,936 >> Waiting for the current checkpoint push to be finished, this might take a couple of minutes.
[INFO|trainer.py:3503] 2024-09-09 20:25:10,934 >> Saving model checkpoint to /content/dissertation/scripts/ner/output
[INFO|configuration_utils.py:472] 2024-09-09 20:25:10,935 >> Configuration saved in /content/dissertation/scripts/ner/output/config.json
[INFO|modeling_utils.py:2799] 2024-09-09 20:25:12,412 >> Model weights saved in /content/dissertation/scripts/ner/output/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-09 20:25:12,413 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 20:25:12,413 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json
[INFO|trainer.py:3503] 2024-09-09 20:25:12,462 >> Saving model checkpoint to /content/dissertation/scripts/ner/output
[INFO|configuration_utils.py:472] 2024-09-09 20:25:12,464 >> Configuration saved in /content/dissertation/scripts/ner/output/config.json
[INFO|modeling_utils.py:2799] 2024-09-09 20:25:13,924 >> Model weights saved in /content/dissertation/scripts/ner/output/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-09 20:25:13,925 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 20:25:13,925 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json
{'eval_loss': 0.17113561928272247, 'eval_precision': 0.7991246256622898, 'eval_recall': 0.8116518483855872, 'eval_f1': 0.8053395240858967, 'eval_accuracy': 0.9758743323218038, 'eval_runtime': 14.5918, 'eval_samples_per_second': 466.7, 'eval_steps_per_second': 58.389, 'epoch': 9.99}
{'train_runtime': 1901.5665, 'train_samples_per_second': 236.321, 'train_steps_per_second': 3.692, 'train_loss': 0.017706579814779112, 'epoch': 9.99}

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***** train metrics *****
  epoch                    =     9.9929
  total_flos               = 19840127GF
  train_loss               =     0.0177
  train_runtime            = 0:31:41.56
  train_samples            =      44938
  train_samples_per_second =    236.321
  train_steps_per_second   =      3.692
09/09/2024 20:25:19 - INFO - __main__ -   *** Evaluate ***
[INFO|trainer.py:811] 2024-09-09 20:25:19,522 >> The following columns in the evaluation set don't have a corresponding argument in `RobertaForTokenClassification.forward` and have been ignored: id, tokens, ner_tags. If id, tokens, ner_tags are not expected by `RobertaForTokenClassification.forward`,  you can safely ignore this message.
[INFO|trainer.py:3819] 2024-09-09 20:25:19,525 >> 
***** Running Evaluation *****
[INFO|trainer.py:3821] 2024-09-09 20:25:19,525 >>   Num examples = 6810
[INFO|trainer.py:3824] 2024-09-09 20:25:19,525 >>   Batch size = 8

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***** eval metrics *****
  epoch                   =     9.9929
  eval_accuracy           =     0.9759
  eval_f1                 =     0.8053
  eval_loss               =     0.1711
  eval_precision          =     0.7991
  eval_recall             =     0.8117
  eval_runtime            = 0:00:14.18
  eval_samples            =       6810
  eval_samples_per_second =     480.21
  eval_steps_per_second   =     60.079
09/09/2024 20:25:33 - INFO - __main__ -   *** Predict ***
[INFO|trainer.py:811] 2024-09-09 20:25:33,712 >> The following columns in the test set don't have a corresponding argument in `RobertaForTokenClassification.forward` and have been ignored: id, tokens, ner_tags. If id, tokens, ner_tags are not expected by `RobertaForTokenClassification.forward`,  you can safely ignore this message.
[INFO|trainer.py:3819] 2024-09-09 20:25:33,714 >> 
***** Running Prediction *****
[INFO|trainer.py:3821] 2024-09-09 20:25:33,714 >>   Num examples = 14614
[INFO|trainer.py:3824] 2024-09-09 20:25:33,714 >>   Batch size = 8

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[INFO|trainer.py:3503] 2024-09-09 20:26:03,483 >> Saving model checkpoint to /content/dissertation/scripts/ner/output
[INFO|configuration_utils.py:472] 2024-09-09 20:26:03,485 >> Configuration saved in /content/dissertation/scripts/ner/output/config.json
[INFO|modeling_utils.py:2799] 2024-09-09 20:26:04,957 >> Model weights saved in /content/dissertation/scripts/ner/output/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-09 20:26:04,958 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 20:26:04,958 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json
***** predict metrics *****
  predict_accuracy           =     0.9755
  predict_f1                 =     0.8018
  predict_loss               =     0.1611
  predict_precision          =     0.7862
  predict_recall             =     0.8181
  predict_runtime            = 0:00:29.14
  predict_samples_per_second =    501.411
  predict_steps_per_second   =     62.685

events.out.tfevents.1725913533.0ada7e7d1d89.3073.1:   0%|          | 0.00/560 [00:00<?, ?B/s]
events.out.tfevents.1725913533.0ada7e7d1d89.3073.1: 100%|██████████| 560/560 [00:00<00:00, 2.24kB/s]