hatespeech_distilbert

This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 0.9977
• Accuracy: 0.7737
• Recall: 0.8118
• Precision: 0.7526
• F1: 0.7811

And the following results on the test set:

• Loss: 1.0640
• Accuracy: 0.7544
• Recall: 0.7930
• Precision: 0.7406
• F1: 0.7659

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: 8e-05
• train_batch_size: 32
• eval_batch_size: 32
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 128
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_ratio: 0.1
• num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Recall	Precision	F1
0.4863	0.9935	77	0.4678	0.7701	0.7421	0.7841	0.7625
0.3935	2.0	155	0.4595	0.7834	0.7340	0.8124	0.7712
0.2792	2.9935	232	0.5285	0.7850	0.7291	0.8188	0.7713
0.1408	4.0	310	0.7130	0.7785	0.7940	0.7684	0.7810
0.0945	4.9935	387	0.8230	0.7806	0.7551	0.7937	0.7739
0.0541	6.0	465	0.9977	0.7737	0.8118	0.7526	0.7811
0.0331	6.9935	542	1.1107	0.7753	0.7859	0.7678	0.7768
0.0151	8.0	620	1.1703	0.7789	0.7543	0.7915	0.7724
0.0106	8.9935	697	1.2741	0.7785	0.7616	0.7864	0.7738
0.0051	9.9355	770	1.2964	0.7753	0.7851	0.7683	0.7766

Framework versions

• Transformers 4.40.2
• Pytorch 2.2.1+cu121
• Datasets 2.19.1
• Tokenizers 0.19.1