---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: roberta-hateful-meme
  results: []
---

<!-- 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. -->

# roberta-hateful-meme

This model is a fine-tuned version of [FacebookAI/roberta-base](https://huggingface.co/FacebookAI/roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4643
- Accuracy: 0.631
- Precision: 0.6057
- Recall: 0.6081
- F1: 0.6066

## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| No log        | 1.0   | 282  | 0.5978          | 0.696    | 0.6714    | 0.6698 | 0.6705 |
| 0.5742        | 2.0   | 564  | 0.6133          | 0.696    | 0.6691    | 0.6558 | 0.6599 |
| 0.5742        | 3.0   | 846  | 0.6326          | 0.694    | 0.6681    | 0.6385 | 0.6430 |
| 0.4682        | 4.0   | 1128 | 0.7503          | 0.662    | 0.6473    | 0.6564 | 0.6482 |
| 0.4682        | 5.0   | 1410 | 0.8875          | 0.687    | 0.6805    | 0.6004 | 0.5918 |
| 0.3907        | 6.0   | 1692 | 0.8072          | 0.651    | 0.6393    | 0.6489 | 0.6389 |
| 0.3907        | 7.0   | 1974 | 1.0383          | 0.639    | 0.6205    | 0.6266 | 0.6215 |
| 0.3445        | 8.0   | 2256 | 1.0678          | 0.643    | 0.6212    | 0.6257 | 0.6225 |
| 0.2996        | 9.0   | 2538 | 1.1221          | 0.649    | 0.6131    | 0.6025 | 0.6046 |
| 0.2996        | 10.0  | 2820 | 1.3531          | 0.646    | 0.6315    | 0.6397 | 0.6319 |
| 0.2749        | 11.0  | 3102 | 1.1258          | 0.655    | 0.6186    | 0.6031 | 0.6053 |
| 0.2749        | 12.0  | 3384 | 1.3594          | 0.641    | 0.6149    | 0.6166 | 0.6156 |
| 0.2635        | 13.0  | 3666 | 1.6264          | 0.637    | 0.5983    | 0.5884 | 0.5899 |
| 0.2635        | 14.0  | 3948 | 1.7278          | 0.636    | 0.6101    | 0.6120 | 0.6109 |
| 0.2382        | 15.0  | 4230 | 1.5670          | 0.635    | 0.6125    | 0.6165 | 0.6137 |
| 0.2387        | 16.0  | 4512 | 1.6334          | 0.63     | 0.6098    | 0.6149 | 0.6108 |
| 0.2387        | 17.0  | 4794 | 1.8990          | 0.632    | 0.5963    | 0.5908 | 0.5923 |
| 0.2214        | 18.0  | 5076 | 1.5244          | 0.644    | 0.6116    | 0.6073 | 0.6088 |
| 0.2214        | 19.0  | 5358 | 1.7948          | 0.639    | 0.6017    | 0.5928 | 0.5946 |
| 0.2138        | 20.0  | 5640 | 1.7595          | 0.638    | 0.6040    | 0.5991 | 0.6006 |
| 0.2138        | 21.0  | 5922 | 1.9552          | 0.633    | 0.6086    | 0.6114 | 0.6096 |
| 0.2038        | 22.0  | 6204 | 1.9481          | 0.636    | 0.6160    | 0.6214 | 0.6171 |
| 0.2038        | 23.0  | 6486 | 1.7951          | 0.648    | 0.6216    | 0.6227 | 0.6221 |
| 0.2003        | 24.0  | 6768 | 2.0499          | 0.636    | 0.6042    | 0.6016 | 0.6026 |
| 0.188         | 25.0  | 7050 | 2.3175          | 0.64     | 0.6147    | 0.6169 | 0.6156 |
| 0.188         | 26.0  | 7332 | 2.3284          | 0.634    | 0.6109    | 0.6145 | 0.6120 |
| 0.1838        | 27.0  | 7614 | 2.4239          | 0.634    | 0.6049    | 0.6046 | 0.6048 |
| 0.1838        | 28.0  | 7896 | 2.4116          | 0.639    | 0.6118    | 0.6127 | 0.6122 |
| 0.177         | 29.0  | 8178 | 2.4789          | 0.63     | 0.6052    | 0.6079 | 0.6062 |
| 0.177         | 30.0  | 8460 | 2.4643          | 0.631    | 0.6057    | 0.6081 | 0.6066 |


### Framework versions

- Transformers 4.30.2
- Pytorch 1.13.1+cu117
- Datasets 2.13.2
- Tokenizers 0.11.0