# Training: "'Trainer' object has no attribute 'epoch'"

**URL:** </static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Ft%2Ftraining-trainer-object-has-no-attribute-epoch%2F1878%3E%5C
**Category:** 🤗Transformers\
**Created:** [November 3, 2020, 6:14pm UTC](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Ft%2Ftraining-trainer-object-has-no-attribute-epoch%2F1878 "2020-11-03T18:14:42Z")\
**Posts on this page:** 1\
**Page:** 1

<div class="post-metadata">

**Author:** ![zanderbush](https://avatars.discourse-cdn.com/v4/letter/z/13edae/32.png) [@zanderbush](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Fu%2Fzanderbush)%5C
**Post date:** [November 3, 2020, 6:14pm UTC](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Ft%2Ftraining-trainer-object-has-no-attribute-epoch%2F1878%2F1 "2020-11-03T18:14:42Z")

</div>

def finetune():

progress = widgets.FloatProgress(value=0.1, min=0.0, max=1.0, bar\_style = ‘info’)

block\_size = tokenizer.max\_len

train\_dataset = TextDataset(tokenizer=tokenizer, file\_path=’/content/Armenian Para.txt’, block\_size=block\_size)

data\_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=False)

epochs = 2.5 # limit before overfitting

training\_args = TrainingArguments(

```
                output_dir= '/content/ParaCorpus',

                overwrite_output_dir=True,

                do_train=True,

                num_train_epochs=2.5,

                per_gpu_train_batch_size=1,

                prediction_loss_only=True,

                logging_steps=5,

                save_steps=0,

                seed=random.randint(0,2**32-1))

```

trainer = Trainer(

```
                model=model,

                tokenizer=tokenizer,

                args=training_args,

                data_collator=data_collator,

                train_dataset=train_dataset,)

```

progress.value = 0.4

p\_start, p\_end = 0.4, 1.

def progressify(f):

```
def inner(*args, **kwargs):

  if trainer.epoch is not None:

    progress.value = p_start + trainer.epoch / epochs * (p_end - p_start)

    return f(*args, **kwargs)

return inner

```

try:

```
trainer.training_step = progressify(trainer.training_step)

trainer.train()

```

except KeyboardInterrupt:

```
print('Model will be saved')

```

finally:

```
trainer.save_model('/content/ParaCorpus')

tokenizer.save_pretrained('/content/ParaCorpus')

```

def clean\_prediction(text):

token = ‘\<|endoftext|\>’

while len(token)\>1:

```
text = text.replace(token, '')

token = token[:-1]

```

text = text.strip()

if len(text) != 0:

```
if text[-1] == '"' and text.count('"') % 2: text = text[:-1]

return text.strip()

```

finetune()