---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: results
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: imdb
      type: imdb
      config: plain_text
      split: test
      args: plain_text
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9133333333333333
    - name: F1
      type: f1
      value: 0.9161290322580645
    - name: Precision
      type: precision
      value: 0.8875
    - name: Recall
      type: recall
      value: 0.9466666666666667
---

<!-- 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: 0.2250
- Accuracy: 0.9133
- F1: 0.9161
- Precision: 0.8875
- Recall: 0.9467

## 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: 8
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.6922        | 0.98  | 46   | 0.6867          | 0.7433   | 0.6778 | 0.9101    | 0.54   |
| 0.2634        | 1.98  | 93   | 0.3428          | 0.8833   | 0.8736 | 0.9528    | 0.8067 |
| 0.1736        | 2.94  | 138  | 0.2250          | 0.9133   | 0.9161 | 0.8875    | 0.9467 |


### Framework versions

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