HuggingFace Trainer logging train data
I'm following this tutorial to train some models:
https://huggingface.co/transformers/training.html
I'd like to track not only the evaluation loss and accuracy but also the train loss and accuracy, to monitor overfitting. While running the code in Jupyter, I do see all of htis:
Epoch   Training Loss   Validation Loss Accuracy    Glue
1   0.096500    0.928782    {'accuracy': 0.625} {'accuracy': 0.625, 'f1': 0.0}
2   0.096500    1.203832    {'accuracy': 0.625} {'accuracy': 0.625, 'f1': 0.0}
3   0.096500    1.643788    {'accuracy': 0.625} {'accuracy': 0.625, 'f1': 0.0}

but when I go into trainer.state.log_history, that stuff is not there. This really doesn't make sense to me.
for obj in trainer.state.log_history:
    print(obj)

{'loss': 0.0965, 'learning_rate': 4.5833333333333334e-05, 'epoch': 0.25, 'step': 1}
{'eval_loss': 0.9287818074226379, 'eval_accuracy': {'accuracy': 0.625}, 'eval_glue': {'accuracy': 0.625, 'f1': 0.0}, 'eval_runtime': 1.3266, 'eval_samples_per_second': 6.03, 'eval_steps_per_second': 0.754, 'epoch': 1.0, 'step': 4}
{'eval_loss': 1.2038320302963257, 'eval_accuracy': {'accuracy': 0.625}, 'eval_glue': {'accuracy': 0.625, 'f1': 0.0}, 'eval_runtime': 1.3187, 'eval_samples_per_second': 6.067, 'eval_steps_per_second': 0.758, 'epoch': 2.0, 'step': 8}
{'eval_loss': 1.6437877416610718, 'eval_accuracy': {'accuracy': 0.625}, 'eval_glue': {'accuracy': 0.625, 'f1': 0.0}, 'eval_runtime': 1.3931, 'eval_samples_per_second': 5.742, 'eval_steps_per_second': 0.718, 'epoch': 3.0, 'step': 12}
{'train_runtime': 20.9407, 'train_samples_per_second': 1.146, 'train_steps_per_second': 0.573, 'total_flos': 6314665328640.0, 'train_loss': 0.07855576276779175, 'epoch': 3.0, 'step': 12}

How do I get these back in an object, and not a printout?
Thanks
Edit: Reproducable code below:
import numpy as np
from datasets import load_metric, load_dataset
from transformers import TrainingArguments, AutoModelForSequenceClassification, Trainer, AutoTokenizer
from datasets import list_metrics

raw_datasets = load_dataset(&quot;imdb&quot;)

tokenizer = AutoTokenizer.from_pretrained(&quot;bert-base-cased&quot;)
def tokenize_function(examples):
    return tokenizer(examples[&quot;text&quot;], padding=&quot;max_length&quot;, truncation=True)

tokenized_datasets = raw_datasets.map(tokenize_function, batched=True)

small_train_dataset = tokenized_datasets[&quot;train&quot;].shuffle(seed=42).select(range(8))
small_eval_dataset = tokenized_datasets[&quot;test&quot;].shuffle(seed=42).select(range(8))
model = AutoModelForSequenceClassification.from_pretrained(&quot;bert-base-cased&quot;, num_labels=2)
    
training_args = TrainingArguments(&quot;IntroToBERT&quot;, evaluation_strategy=&quot;epoch&quot;)
training_args.logging_strategy = 'step'
training_args.logging_first_step = True
training_args.logging_steps = 1
training_args.num_train_epochs = 3
training_args.per_device_train_batch_size = 2
training_args.eval_steps = 1

metrics = {}
for metric in ['accuracy','glue']:
    metrics[metric] = load_metric(metric,'mrpc')


def compute_metrics(eval_pred):
    logits, labels = eval_pred
    predictions = np.argmax(logits, axis=-1)
    out = {}
    for metric in metrics.keys():
        out[metric] = metrics[metric].compute(predictions=predictions, references=labels)
    return out

trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=small_train_dataset,
    eval_dataset=small_eval_dataset,
    compute_metrics=compute_metrics,
)

trainer.train() 

# here the printout is as shown

for obj in trainer.state.log_history:
    print(obj)

# here the logging data is displayed


You can use the methods log_metrics to format your logs and save_metrics to save them. Here is the code:
# rest of the training args
# ...
training_args.logging_dir = 'logs' # or any dir you want to save logs

# training
train_result = trainer.train() 

# compute train results
metrics = train_result.metrics
max_train_samples = len(small_train_dataset)
metrics[&quot;train_samples&quot;] = min(max_train_samples, len(small_train_dataset))

# save train results
trainer.log_metrics(&quot;train&quot;, metrics)
trainer.save_metrics(&quot;train&quot;, metrics)

# compute evaluation results
metrics = trainer.evaluate()
max_val_samples = len(small_eval_dataset)
metrics[&quot;eval_samples&quot;] = min(max_val_samples, len(small_eval_dataset))

# save evaluation results
trainer.log_metrics(&quot;eval&quot;, metrics)
trainer.save_metrics(&quot;eval&quot;, metrics)

You can also save all logs at once by setting the split parameter in log_metrics and save_metrics to &quot;all&quot; i.e. trainer.save_metrics(&quot;all&quot;, metrics); but I prefer this way as you can customize the results based on your need.
Here is the complete source provided by transformers  from which you can read more.
