metadata

base_model: FacebookAI/roberta-base
library_name: peft
license: mit
metrics:
  - accuracy
tags:
  - generated_from_trainer
model-index:
  - name: spam_not_spam
    results: []

RoBERTa-PEFT-ForSequenceClassification

This model is a fine-tuned version of FacebookAI/roberta-base on spam_not_spam dataset. It achieves the following results on the evaluation set:
- Loss: 0.0414
- Accuracy: 0.9839

Model description

Performing Parameter-Efficient Fine-Tuning

We used low rank adaptation (LoRA) from PEFT library in HuggineFace.
Base-model is finetuned using LoRA config below:
peft_config = LoraConfig(task_type=TaskType.SEQ_CLS, inference_mode=False, r=8, lora_alpha=32, lora_dropout=0.1)

Training

Use script below for model fine-tuning:

def compute_metrics(eval_pred):
    predictions, labels = eval_pred
    predictions = np.argmax(predictions, axis=1)
    return {"accuracy": (predictions == labels).mean()}

trainer = Trainer(
    model=lora_model,
    args=TrainingArguments(
        output_dir="./data/spam_not_spam",
        # Set the learning rate
        learning_rate = 2e-5,
        # Set the per device train batch size and eval batch size
        per_device_train_batch_size=16,
        per_device_eval_batch_size=64,
        # Evaluate and save the model after each epoch
        evaluation_strategy = "epoch",
        save_strategy = "epoch",
        num_train_epochs=5,
        weight_decay=0.01,
        load_best_model_at_end=True,
    ),
    train_dataset= tokenized_dataset_train,
    eval_dataset= tokenized_dataset_test,
    tokenizer=tokenizer,
    data_collator=DataCollatorWithPadding(tokenizer=tokenizer),
    compute_metrics=compute_metrics,
)

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 2e-05
train_batch_size: 16
eval_batch_size: 64
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
No log	1.0	279	0.0414	0.9839
0.1982	2.0	558	0.0433	0.9865
0.1982	3.0	837	0.0465	0.9892
0.0415	4.0	1116	0.0426	0.9874
0.0415	5.0	1395	0.0415	0.9883

Framework versions

PEFT 0.12.0
Transformers 4.42.4
Pytorch 2.4.0+cu121
Datasets 2.15.0
Tokenizers 0.19.1