---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: results
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: imdb
      type: imdb
      config: plain_text
      split: train
      args: plain_text
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9286666512489319
    - name: Precision
      type: precision
      value: 0.9286666512489319
    - name: Recall
      type: recall
      value: 0.9286666512489319
    - name: F1
      type: f1
      value: 0.9286666512489319
---

<!-- 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. -->

# results

This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2691
- Accuracy: 0.9287
- Precision: 0.9287
- Recall: 0.9287
- F1: 0.9287
- Auroc: 0.9772

## 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: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     | Auroc  |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:------:|
| 0.1764        | 0.46  | 500  | 0.2698          | 0.9044   | 0.9044    | 0.9044 | 0.9044 | 0.9738 |
| 0.3348        | 0.91  | 1000 | 0.2755          | 0.9117   | 0.9117    | 0.9117 | 0.9117 | 0.9686 |
| 0.1478        | 1.37  | 1500 | 0.3275          | 0.9109   | 0.9109    | 0.9109 | 0.9109 | 0.9771 |
| 0.2051        | 1.83  | 2000 | 0.2575          | 0.9309   | 0.9309    | 0.9309 | 0.9309 | 0.9793 |
| 0.1435        | 2.29  | 2500 | 0.3140          | 0.9245   | 0.9245    | 0.9245 | 0.9245 | 0.9783 |
| 0.1425        | 2.74  | 3000 | 0.2691          | 0.9287   | 0.9287    | 0.9287 | 0.9287 | 0.9772 |


### Framework versions

- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1