training_dir
This model is a fine-tuned version of albert-base-v2 on an Spam Data Collection dataset. It achieves the following results on the evaluation set:
- Loss: 0.0393
- Accuracy: 0.9946
- F1 Score: 0.9946
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
SMS 1:
Message: Hey, I'll be there in 10 minutes. See you soon!
Label: label_0 (ham)
SMS 2:
Message: Congratulations! You've won a $1000 gift card. Claim it now by clicking the link.
Label: label_1 (spam)
In this SMS classification example, the first message is labeled as "label_0" because it appears to be a legitimate text message (ham) with someone informing they will arrive shortly. The second message is labeled as "label_1" because it is clearly spam, offering a prize and urging the recipient to click a link, which is a common characteristic of spam messages. The classification model uses these labels to identify and filter out spammy SMS messages, ensuring that legitimate messages reach the user's inbox (ham).
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-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: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Score |
|---|---|---|---|---|---|
| No log | 1.0 | 244 | 0.1070 | 0.9785 | 0.9785 |
| No log | 2.0 | 488 | 0.0673 | 0.9880 | 0.9880 |
| 0.0885 | 3.0 | 732 | 0.0293 | 0.9946 | 0.9946 |
| 0.0885 | 4.0 | 976 | 0.0280 | 0.9964 | 0.9964 |
| 0.0306 | 5.0 | 1220 | 0.0355 | 0.9952 | 0.9952 |
| 0.0306 | 6.0 | 1464 | 0.0364 | 0.9952 | 0.9952 |
| 0.0087 | 7.0 | 1708 | 0.0448 | 0.9946 | 0.9946 |
| 0.0087 | 8.0 | 1952 | 0.0618 | 0.9922 | 0.9922 |
| 0.0047 | 9.0 | 2196 | 0.0420 | 0.9946 | 0.9946 |
| 0.0047 | 10.0 | 2440 | 0.0393 | 0.9946 | 0.9946 |
Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3