---
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
---

<!-- 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: 0.4473
- 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