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: 0.978494623655914
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: 0.4473
- Accuracy: 0.9785
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: 10
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 4.1186 | 1.0 | 18 | 3.4012 | 0.8351 |
| 2.9893 | 2.0 | 36 | 2.9206 | 0.8351 |
| 2.6718 | 3.0 | 54 | 2.8932 | 0.8351 |
| 2.5495 | 4.0 | 72 | 2.8916 | 0.8351 |
| 1.7213 | 5.0 | 90 | 0.6804 | 0.9821 |
| 0.7464 | 6.0 | 108 | 0.6017 | 0.9713 |
| 1.2052 | 7.0 | 126 | 0.3425 | 0.9857 |
| 0.438 | 8.0 | 144 | 0.2136 | 0.9857 |
| 0.2282 | 9.0 | 162 | 0.4539 | 0.9785 |
| 0.438 | 10.0 | 180 | 0.4473 | 0.9785 |
Framework versions
- Transformers 4.17.0
- Pytorch 1.10.2+cu113
- Datasets 1.18.4
- Tokenizers 0.12.1