---
library_name: transformers
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- hatexplain
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: distilbert-hatexplain
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: hatexplain
      type: hatexplain
      config: plain_text
      split: validation
      args: plain_text
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.6990644490644491
    - name: Precision
      type: precision
      value: 0.6974890380019948
    - name: Recall
      type: recall
      value: 0.6990644490644491
    - name: F1
      type: f1
      value: 0.6978790945993021
---

<!-- 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-hatexplain

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the hatexplain dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8165
- Accuracy: 0.6991
- Precision: 0.6975
- Recall: 0.6991
- F1: 0.6979

## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use 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: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.6131        | 1.0   | 1923 | 0.7399          | 0.6925   | 0.6877    | 0.6925 | 0.6847 |
| 0.7386        | 2.0   | 3846 | 0.7254          | 0.7040   | 0.7033    | 0.7040 | 0.7036 |
| 0.6471        | 3.0   | 5769 | 0.8259          | 0.7019   | 0.6995    | 0.7019 | 0.7005 |


### Framework versions

- Transformers 4.47.0
- Pytorch 2.5.1+cu118
- Datasets 3.1.0
- Tokenizers 0.21.0