bert-tiny-sst2

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Bert Tiny for SST2

This model is a fine-tuned version of M-FAC/bert-tiny-finetuned-sst2 on the sst2 dataset. It achieves the following results on the evaluation set:

• Loss: 0.4771
• Accuracy: 0.8280

Usage Example

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

Framework versions

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