FP_Classifcation-V1

This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 0.2940
• Accuracy: 0.9026
• Precision Macro: 0.8742
• Recall Macro: 0.9098
• F1 Macro: 0.8895

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: 0.001
• train_batch_size: 4
• eval_batch_size: 16
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 16
• optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: cosine
• num_epochs: 50
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision Macro	Recall Macro	F1 Macro
1.0869	1.0	483	0.7657	0.6792	0.6837	0.7053	0.6710
0.9801	2.0	966	0.7458	0.7507	0.7370	0.7004	0.7055
0.9227	3.0	1449	0.6545	0.7143	0.7075	0.7831	0.6999
0.7036	4.0	1932	0.5989	0.7733	0.7476	0.8058	0.7593
0.6729	5.0	2415	0.5175	0.7830	0.7617	0.8409	0.7753
0.8200	6.0	2898	0.4929	0.7863	0.7541	0.8398	0.7735
0.7750	7.0	3381	0.4570	0.8154	0.7830	0.8476	0.8038
0.8015	8.0	3864	0.4494	0.8384	0.8090	0.8493	0.8243
0.6326	9.0	4347	0.4928	0.8263	0.8066	0.8151	0.8039
0.7675	10.0	4830	0.4397	0.8347	0.8021	0.8591	0.8175
0.6092	11.0	5313	0.4220	0.8554	0.8220	0.8603	0.8352
0.4704	12.0	5796	0.5017	0.8489	0.8190	0.8564	0.8321
0.6863	13.0	6279	0.4050	0.8533	0.8123	0.8687	0.8313
0.5217	14.0	6762	0.3839	0.8473	0.8123	0.8753	0.8296
0.4879	15.0	7245	0.4319	0.8687	0.8556	0.8531	0.8527
0.5894	16.0	7728	0.4071	0.8339	0.8098	0.8596	0.8245
0.4710	17.0	8211	0.3890	0.8622	0.8350	0.8626	0.8453
0.6325	18.0	8694	0.3555	0.8768	0.8425	0.8848	0.8589
0.6084	19.0	9177	0.3408	0.8865	0.8524	0.8901	0.8680
0.5211	20.0	9660	0.3399	0.8812	0.8484	0.8828	0.8627
0.3481	21.0	10143	0.3592	0.8905	0.8673	0.8818	0.8733
0.6450	22.0	10626	0.3644	0.8877	0.8632	0.8799	0.8706
0.4031	23.0	11109	0.3248	0.8962	0.8704	0.8944	0.8813
0.4193	24.0	11592	0.3284	0.8836	0.8587	0.8806	0.8677
0.4325	25.0	12075	0.3051	0.8881	0.8622	0.8958	0.8767
0.3674	26.0	12558	0.3227	0.8861	0.8626	0.8839	0.8713
0.3794	27.0	13041	0.3084	0.8982	0.8777	0.8920	0.8844
0.3150	28.0	13524	0.3058	0.8954	0.8680	0.8913	0.8777
0.4406	29.0	14007	0.2965	0.8877	0.8552	0.8943	0.8700
0.5185	30.0	14490	0.2986	0.9018	0.8768	0.8992	0.8868
0.5979	31.0	14973	0.3107	0.8986	0.8741	0.8921	0.8824
0.4205	32.0	15456	0.3153	0.9018	0.8760	0.9054	0.8882
0.3071	33.0	15939	0.2935	0.9010	0.8774	0.8998	0.8873
0.2884	34.0	16422	0.2940	0.9026	0.8742	0.9098	0.8895
0.3953	35.0	16905	0.3187	0.8970	0.8743	0.8911	0.8808
0.2838	36.0	17388	0.3100	0.9059	0.8814	0.8995	0.8896
0.3631	37.0	17871	0.3186	0.9063	0.8841	0.9014	0.8920
0.2681	38.0	18354	0.2929	0.9087	0.8841	0.9093	0.8951
0.2766	39.0	18837	0.2944	0.9022	0.8793	0.9006	0.8890

Framework versions

• Transformers 5.17.0
• Pytorch 2.11.0+cu128
• Datasets 5.0.1
• Tokenizers 0.23.1