learning_rate: 3e-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: 3

Training results

Training Loss	Epoch	Step	Validation Loss	Model Preparation Time	Accuracy	Auc Score	F1	Precision	Recall
0.6024	0.1054	100	0.6052	0.0024	0.6806	0.7925	0.6496	0.8453	0.5274
0.5439	0.2107	200	0.5120	0.0024	0.7514	0.8372	0.7941	0.7419	0.8542
0.515	0.3161	300	0.5180	0.0024	0.7538	0.8469	0.8035	0.7278	0.8969
0.5225	0.4215	400	0.5000	0.0024	0.7698	0.8393	0.7863	0.8210	0.7544
0.4935	0.5269	500	0.5008	0.0024	0.768	0.8457	0.7961	0.7855	0.8070
0.5196	0.6322	600	0.5069	0.0024	0.7674	0.8473	0.8023	0.767	0.8410
0.4918	0.7376	700	0.5011	0.0024	0.7655	0.8565	0.8109	0.7407	0.8958
0.5182	0.8430	800	0.4873	0.0024	0.7902	0.8616	0.8150	0.8067	0.8235
0.4749	0.9484	900	0.4606	0.0024	0.7815	0.8674	0.8109	0.7886	0.8344
0.4042	1.0537	1000	0.5453	0.0024	0.7852	0.8735	0.8211	0.7709	0.8783
0.3593	1.1591	1100	0.5650	0.0024	0.7791	0.8745	0.8193	0.7572	0.8925
0.3911	1.2645	1200	0.5108	0.0024	0.8025	0.8783	0.8264	0.8154	0.8377
0.3445	1.3699	1300	0.6231	0.0024	0.7902	0.8815	0.8265	0.7711	0.8904
0.4027	1.4752	1400	0.5336	0.0024	0.8062	0.8796	0.8239	0.8404	0.8081
0.3058	1.5806	1500	0.6094	0.0024	0.7957	0.8760	0.8232	0.8002	0.8476
0.3535	1.6860	1600	0.5834	0.0024	0.7951	0.8810	0.8254	0.7910	0.8629
0.3713	1.7914	1700	0.5286	0.0024	0.7969	0.8817	0.8278	0.7898	0.8695
0.359	1.8967	1800	0.5292	0.0024	0.8086	0.8819	0.8290	0.8313	0.8268
0.3762	2.0021	1900	0.5222	0.0024	0.8037	0.8814	0.8297	0.8085	0.8520
0.2101	2.1075	2000	0.6738	0.0024	0.8055	0.8793	0.8271	0.8253	0.8289
0.2307	2.2129	2100	0.7485	0.0024	0.8012	0.8845	0.8324	0.7901	0.8794
0.2403	2.3182	2200	0.7186	0.0024	0.8049	0.8818	0.8322	0.8045	0.8618
0.221	2.4236	2300	0.7233	0.0024	0.8074	0.8818	0.8334	0.8097	0.8586
0.2112	2.5290	2400	0.7259	0.0024	0.8123	0.8844	0.8345	0.8260	0.8432
0.2155	2.6344	2500	0.7302	0.0024	0.8117	0.8854	0.8342	0.8244	0.8443
0.1997	2.7397	2600	0.7658	0.0024	0.8074	0.8832	0.8289	0.8266	0.8311
0.2761	2.8451	2700	0.7838	0.0024	0.8037	0.8869	0.8334	0.7956	0.875
0.1878	2.9505	2800	0.7742	0.0024	0.8037	0.8861	0.8318	0.8010	0.8651

Framework versions

Transformers 4.53.0
Pytorch 2.7.1+cu126
Datasets 3.6.0
Tokenizers 0.21.2

franac1/hate_speech

hate_speech

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

franac1/
hate_speech