c36b645c1020dd32cb60aba40a1a9fd9

This model is a fine-tuned version of google-bert/bert-base-uncased on the contemmcm/hate-speech-and-offensive-language dataset. It achieves the following results on the evaluation set:

• Loss: 0.4845
• Data Size: 1.0
• Epoch Runtime: 34.5907
• Accuracy: 0.9000
• F1 Macro: 0.7495

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: 8
• eval_batch_size: 8
• seed: 42
• distributed_type: multi-GPU
• num_devices: 4
• total_train_batch_size: 32
• total_eval_batch_size: 32
• optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: constant
• num_epochs: 50

Training results

Training Loss	Epoch	Step	Validation Loss	Data Size	Epoch Runtime	Accuracy	F1 Macro
No log	0	0	1.0234	0	2.9912	0.6575	0.2838
No log	1	619	0.6829	0.0078	3.5883	0.7672	0.2894
No log	2	1238	0.5880	0.0156	3.8899	0.8007	0.4195
0.0142	3	1857	0.3699	0.0312	4.1766	0.8699	0.5673
0.0142	4	2476	0.3050	0.0625	5.5494	0.8981	0.5962
0.2802	5	3095	0.3105	0.125	7.3106	0.8870	0.7294
0.0231	6	3714	0.2784	0.25	10.9548	0.9134	0.6543
0.2501	7	4333	0.2731	0.5	18.7168	0.8979	0.7641
0.2237	8.0	4952	0.2508	1.0	34.7097	0.9148	0.7205
0.1534	9.0	5571	0.2624	1.0	32.4199	0.9123	0.7741
0.1109	10.0	6190	0.3618	1.0	33.9418	0.9136	0.7558
0.0705	11.0	6809	0.4931	1.0	32.1609	0.9034	0.7593
0.0484	12.0	7428	0.4845	1.0	34.5907	0.9000	0.7495

Framework versions

• Transformers 4.57.0
• Pytorch 2.8.0+cu128
• Datasets 4.3.0
• Tokenizers 0.22.1