---
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: finetuned-distilbert-hatexplainV2
  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: 0.6845114345114345
    - name: Precision
      type: precision
      value: 0.6875552661807551
    - name: Recall
      type: recall
      value: 0.6845114345114345
    - name: F1
      type: f1
      value: 0.6848681152421926
---

<!-- 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. -->

# finetuned-distilbert-hatexplainV2

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: 1.1306
- Accuracy: 0.6845
- Precision: 0.6876
- Recall: 0.6845
- F1: 0.6849

## 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: 16
- eval_batch_size: 16
- 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: 4

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.723         | 1.0   | 962  | 0.7320          | 0.6826   | 0.6766    | 0.6826 | 0.6703 |
| 0.6337        | 2.0   | 1924 | 0.7344          | 0.6857   | 0.6847    | 0.6857 | 0.6852 |
| 0.3821        | 3.0   | 2886 | 0.9051          | 0.6722   | 0.6885    | 0.6722 | 0.6759 |
| 0.1811        | 4.0   | 3848 | 1.1789          | 0.6743   | 0.6787    | 0.6743 | 0.6759 |


### Framework versions

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