metadata

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

finetuned-distilbert-hatexplainV2

This model is a fine-tuned version of 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