metadata

tags:
  - generated_from_trainer
datasets:
  - sms_spam
metrics:
  - accuracy
model-index:
  - name: MiniLMv2-L12-H384-distilled-finetuned-spam-detection
    results:
      - task:
          name: Text Classification
          type: text-classification
        dataset:
          name: sms_spam
          type: sms_spam
          args: plain_text
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.9928263988522238

MiniLMv2-L12-H384-distilled-finetuned-spam-detection

This model is a fine-tuned version of nreimers/MiniLMv2-L12-H384-distilled-from-RoBERTa-Large on the sms_spam dataset. It achieves the following results on the evaluation set:

Loss: 0.0938
Accuracy: 0.9928

Model description

More information needed

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: 0.0001
train_batch_size: 32
eval_batch_size: 32
seed: 33
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 6
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
1.4101	1.0	131	0.4930	0.9763
0.8003	2.0	262	0.3999	0.9799
0.377	3.0	393	0.3196	0.9828
0.302	4.0	524	0.3462	0.9828
0.1945	5.0	655	0.1094	0.9928
0.1393	6.0	786	0.0938	0.9928

Framework versions

Transformers 4.17.0
Pytorch 1.10.2+cu113
Datasets 1.18.4
Tokenizers 0.12.1