early_fusion_sentiment_True_hate_speech_False_extra_layer_False

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

• Loss: 0.5078
• Accuracy Offensive: 0.8187
• F1 Offensive: 0.8084
• Accuracy Targeted: 0.8187
• F1 Targeted: 0.7972
• Accuracy Stance: 0.7798
• F1 Stance: 0.7353

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: 1000

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy Offensive	F1 Offensive	Accuracy Targeted	F1 Targeted	Accuracy Stance	F1 Stance
0.7749	1.0	1324	0.7476	0.6545	0.5178	0.6545	0.5178	0.7009	0.5777
0.7179	2.0	2648	0.7035	0.6911	0.6029	0.6609	0.5326	0.7009	0.5777
0.6854	3.0	3972	0.6557	0.7232	0.6740	0.7251	0.6828	0.7028	0.5836
0.6539	4.0	5296	0.6435	0.7130	0.6442	0.7270	0.6749	0.7209	0.6329
0.637	5.0	6620	0.6033	0.7504	0.7128	0.7613	0.7312	0.7413	0.6786
0.6257	6.0	7944	0.6013	0.7436	0.6972	0.7621	0.7284	0.7440	0.6805
0.612	7.0	9268	0.5766	0.7825	0.7650	0.7851	0.7666	0.7489	0.7088
0.6053	8.0	10592	0.5653	0.7840	0.7610	0.7908	0.7662	0.7647	0.7166
0.5894	9.0	11916	0.5526	0.7723	0.7416	0.7987	0.7721	0.7632	0.7114
0.5787	10.0	13240	0.5415	0.8059	0.7932	0.8017	0.7807	0.7715	0.7266
0.58	11.0	14564	0.5382	0.7946	0.7746	0.8044	0.7836	0.7742	0.7290
0.5828	12.0	15888	0.5302	0.7953	0.7759	0.8100	0.7859	0.7696	0.7196
0.5656	13.0	17212	0.5292	0.8176	0.8086	0.8070	0.7894	0.7768	0.7380
0.5602	14.0	18536	0.5215	0.8078	0.7932	0.8100	0.7888	0.7753	0.7321
0.5623	15.0	19860	0.5133	0.8206	0.8112	0.8138	0.7940	0.7787	0.7370
0.5465	16.0	21184	0.5286	0.8044	0.7873	0.8093	0.7842	0.7772	0.7276
0.5624	17.0	22508	0.5027	0.8312	0.8222	0.8206	0.7992	0.7881	0.7426
0.5416	18.0	23832	0.5235	0.8063	0.7896	0.8066	0.7815	0.7783	0.7285
0.5442	19.0	25156	0.5141	0.8142	0.8001	0.8089	0.7841	0.7825	0.7306

Framework versions

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