---
library_name: transformers
license: apache-2.0
base_model: distilbert/distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- hate_speech18
metrics:
- accuracy
- f1
- recall
- precision
model-index:
- name: distilbert-base-uncased-finetuned_on_hata_dateset
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: hate_speech18
      type: hate_speech18
      config: default
      split: train
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9178338001867413
    - name: F1
      type: f1
      value: 0.9154943774479662
    - name: Recall
      type: recall
      value: 0.9178338001867413
    - name: Precision
      type: precision
      value: 0.9137800286953446
---

<!-- 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_on_hata_dateset

This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on the hate_speech18 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0451
- Accuracy: 0.9178
- F1: 0.9155
- Recall: 0.9178
- Precision: 0.9138

## 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: 2e-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: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Recall | Precision |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|
| 0.3342        | 1.0   | 268  | 0.3774          | 0.8497   | 0.8702 | 0.8497 | 0.9131    |
| 0.2411        | 2.0   | 536  | 0.4330          | 0.9020   | 0.9097 | 0.9020 | 0.9237    |
| 0.1374        | 3.0   | 804  | 0.5690          | 0.8964   | 0.9050 | 0.8964 | 0.9206    |
| 0.0804        | 4.0   | 1072 | 1.0798          | 0.9188   | 0.9140 | 0.9188 | 0.9117    |
| 0.0428        | 5.0   | 1340 | 1.0451          | 0.9178   | 0.9155 | 0.9178 | 0.9138    |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1