from transformers import BertTokenizer, BertForSequenceClassification, TrainingArguments, Trainer, DataCollatorWithPadding
import datasets

model = BertForSequenceClassification.from_pretrained('VityaVitalich/bert-tiny-sst2')
tokenizer = BertTokenizer.from_pretrained('VityaVitalich/bert-tiny-sst2')

def create_data(tokenizer):
  train_set = datasets.load_dataset('sst2', split='train').remove_columns(['idx'])
  val_set = datasets.load_dataset('sst2', split='validation').remove_columns(['idx'])

  def tokenize_func(examples):
      return tokenizer(examples["sentence"], max_length=128, padding='max_length', truncation=True)

  encoded_dataset_train = train_set.map(tokenize_func, batched=True)
  encoded_dataset_test = val_set.map(tokenize_func, batched=True)
  data_collator = DataCollatorWithPadding(tokenizer)
  return encoded_dataset_train, encoded_dataset_test, data_collator

encoded_dataset_train, encoded_dataset_test, data_collator = create_data(tokenizer)

training_args = TrainingArguments(
    output_dir='./results',
    learning_rate=3e-5,
    per_device_train_batch_size=128,
    per_device_eval_batch_size=128,
    load_best_model_at_end=True,
    num_train_epochs=5,
    weight_decay=0.1,
    fp16=True,
    fp16_full_eval=True,
    evaluation_strategy="epoch",
    seed=42,
    save_strategy = "epoch",
    save_total_limit=5,
    logging_strategy="epoch",
    report_to="all",
)


trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=encoded_dataset_train,
    eval_dataset=encoded_dataset_test,
    data_collator=data_collator,
    compute_metrics=compute_metrics,
)

trainer.evaluate(encoded_dataset_test)

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 3e-05
train_batch_size: 128
eval_batch_size: 128
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 5

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.2313	1.0	527	0.4771	0.8280
0.2057	2.0	1054	0.4937	0.8257
0.1949	3.0	1581	0.5121	0.8177
0.1904	4.0	2108	0.5100	0.8200
0.1879	5.0	2635	0.5137	0.8211

Framework versions

Transformers 4.34.0.dev0
Pytorch 2.0.1+cu117
Datasets 2.14.5
Tokenizers 0.14.0