---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- sms_spam
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-spam
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: sms_spam
      type: sms_spam
      config: plain_text
      split: train
      args: plain_text
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9883408071748879
    - name: F1
      type: f1
      value: 0.9882535196626446
---

<!-- 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. -->

# distilbert-base-uncased-finetuned-spam

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the sms_spam dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0370
- Accuracy: 0.9883
- F1: 0.9883

## Model description

More information needed

### Label Key
- LABEL_1 = SPAM
- LABEL_0 = HAM

## 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: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.174         | 1.0   | 70   | 0.0444          | 0.9865   | 0.9866 |
| 0.0303        | 2.0   | 140  | 0.0370          | 0.9883   | 0.9883 |


### Framework versions

- Transformers 4.27.1
- Pytorch 2.0.0
- Datasets 2.10.1
- Tokenizers 0.13.2