---
library_name: transformers
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- jigsaw_toxicity_pred
metrics:
- f1
- accuracy
model-index:
- name: final_model_toxicity_classification
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: jigsaw_toxicity_pred
      type: jigsaw_toxicity_pred
      config: default
      split: test
      args: default
    metrics:
    - name: F1
      type: f1
      value: 0.6775510204081633
    - name: Accuracy
      type: accuracy
      value: 0.921
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# final_model_toxicity_classification

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the jigsaw_toxicity_pred dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2702
- F1: 0.6776
- Accuracy: 0.921

## 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: 4.910748967246961e-05
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 2
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:------:|:--------:|
| 0.0959        | 1.0   | 4987 | 0.1663          | 0.7248 | 0.94     |
| 0.0527        | 2.0   | 9974 | 0.2702          | 0.6776 | 0.921    |


### Framework versions

- Transformers 4.52.4
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1