mix_ensemble_v1

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

• Loss: 0.4263
• Accuracy Offensive: 0.8097
• F1 Offensive: 0.7871
• Accuracy Targeted: 0.8788
• F1 Targeted: 0.8574
• Accuracy Stance: 0.8263
• F1 Stance: 0.7841

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: 1e-05
• train_batch_size: 8
• eval_batch_size: 8
• 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: 20

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy Offensive	F1 Offensive	Accuracy Targeted	F1 Targeted	Accuracy Stance	F1 Stance
0.7638	1.0	1324	0.7520	0.6545	0.5178	0.6545	0.5178	0.7009	0.5777
0.7218	2.0	2648	0.7018	0.6643	0.5449	0.6616	0.5360	0.7009	0.5777
0.6952	3.0	3972	0.6439	0.7035	0.6312	0.7311	0.6883	0.7009	0.5779
0.6489	4.0	5296	0.6076	0.7073	0.6355	0.7560	0.7174	0.7349	0.6592
0.6195	5.0	6620	0.5649	0.7557	0.7159	0.7919	0.7696	0.7685	0.7236
0.6161	6.0	7944	0.5540	0.7224	0.6572	0.7946	0.7637	0.7715	0.7168
0.5808	7.0	9268	0.5206	0.7602	0.7198	0.8248	0.8013	0.7847	0.7393
0.5684	8.0	10592	0.5034	0.7591	0.7176	0.8353	0.8124	0.7912	0.7477
0.5512	9.0	11916	0.4976	0.7519	0.7044	0.8346	0.8102	0.7987	0.7523
0.5487	10.0	13240	0.4687	0.7851	0.7549	0.8618	0.8407	0.8131	0.7719
0.5367	11.0	14564	0.4690	0.7704	0.7325	0.8512	0.8282	0.8063	0.7610
0.5446	12.0	15888	0.4558	0.7893	0.7588	0.8652	0.8433	0.8119	0.7689
0.5243	13.0	17212	0.4497	0.8032	0.7789	0.8705	0.8493	0.8138	0.7720
0.5283	14.0	18536	0.4415	0.7866	0.7561	0.8731	0.8511	0.8191	0.7758
0.5333	15.0	19860	0.4354	0.7934	0.7649	0.8776	0.8561	0.8285	0.7854
0.5228	16.0	21184	0.4395	0.7863	0.7552	0.8712	0.8488	0.8206	0.7760
0.5323	17.0	22508	0.4262	0.8199	0.8005	0.8829	0.8617	0.8278	0.7866
0.5186	18.0	23832	0.4304	0.8108	0.7889	0.8765	0.8551	0.8252	0.7832
0.5198	19.0	25156	0.4262	0.8104	0.7881	0.8833	0.8618	0.8319	0.7896
0.5076	20.0	26480	0.4255	0.8104	0.7885	0.8803	0.8588	0.8233	0.7823

Framework versions

• Transformers 4.50.2
• Pytorch 2.6.0+cu124
• Datasets 3.0.1
• Tokenizers 0.21.1