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---
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
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# MiniLMv2-L12-H384-distilled-finetuned-spam-detection
This model is a fine-tuned version of [nreimers/MiniLMv2-L12-H384-distilled-from-RoBERTa-Large](https://huggingface.co/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
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