transformers pytorch tensorboard bert text-classification generated_from_trainer dataset:sms_spam base_model:google-bert/bert-base-uncased base_model:finetune:google-bert/bert-base-uncased license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
---
base_model: bert-base-uncased
datasets:
- sms_spam
license: apache-2.0
metrics:
- accuracy
- precision
- recall
- f1
tags:
- generated_from_trainer
model-index:
- name: bert-base-uncased-finetuned-smsspam
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: sms_spam
      type: sms_spam
      config: plain_text
      split: train
      args: plain_text
    metrics:
    - type: accuracy
      value: 0.9904420549581839
      name: Accuracy
    - type: precision
      value: 0.9814814814814815
      name: Precision
    - type: recall
      value: 0.9464285714285714
      name: Recall
    - type: f1
      value: 0.9636363636363636
      name: F1
---

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# bert-base-uncased-finetuned-smsspam

This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the sms_spam dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0637
- Accuracy: 0.9904
- Precision: 0.9815
- Recall: 0.9464
- F1: 0.9636

## 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.0828        | 1.0   | 593  | 0.0538          | 0.9892   | 0.9725    | 0.9464 | 0.9593 |
| 0.0269        | 2.0   | 1186 | 0.1792          | 0.9677   | 0.8244    | 0.9643 | 0.8889 |
| 0.0229        | 3.0   | 1779 | 0.0623          | 0.9916   | 0.9817    | 0.9554 | 0.9683 |
| 0.0043        | 4.0   | 2372 | 0.0637          | 0.9904   | 0.9815    | 0.9464 | 0.9636 |


### Framework versions

- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
