Cytotoxicity-Nanoparticles_BioGPT_20250428-012904

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

• Loss: 194.2566
• R Squared: 0.808
• Matthews Correlation: 0.7421
• Accuracy: 0.8708

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: 0.0001
• train_batch_size: 30
• eval_batch_size: 30
• 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
• lr_scheduler_warmup_ratio: 0.1
• num_epochs: 50

Training results

Training Loss	Epoch	Step	Validation Loss	R Squared	Matthews Correlation	Accuracy
3954.792	1.0	54	1939.9783	-0.921	0.0	0.4888
1857.1345	2.0	108	1701.0602	-0.684	0.0	0.4888
1586.262	3.0	162	1444.4485	-0.43	0.0	0.4888
1303.3843	4.0	216	1183.9807	-0.172	0.0	0.4888
1069.758	5.0	270	936.9963	0.072	0.0	0.4888
852.7255	6.0	324	1015.5709	-0.005	0.0	0.4888
943.7232	7.0	378	853.4596	0.155	0.0	0.4888
768.215	8.0	432	673.1525	0.334	0.0	0.4888
657.1353	9.0	486	646.2881	0.36	0.0	0.4888
610.8252	10.0	540	609.7901	0.396	0.4332	0.7135
520.4049	11.0	594	574.8264	0.431	0.5034	0.7472
490.2998	12.0	648	557.5573	0.448	0.4859	0.7416
486.3869	13.0	702	450.8120	0.554	0.4859	0.7416
436.2832	14.0	756	480.4896	0.524	0.4978	0.7472
424.0476	15.0	810	439.2729	0.565	0.5830	0.7865
395.0732	16.0	864	504.2526	0.501	0.5329	0.7640
365.5696	17.0	918	418.4317	0.586	0.6062	0.7921
345.289	18.0	972	370.4825	0.633	0.6495	0.8202
305.6927	19.0	1026	380.0273	0.624	0.6516	0.8258
299.6194	20.0	1080	302.3536	0.701	0.6349	0.8146
269.0756	21.0	1134	366.7752	0.637	0.6874	0.8427
233.5834	22.0	1188	280.7651	0.722	0.6966	0.8483
220.198	23.0	1242	306.2237	0.697	0.6628	0.8315
214.9455	24.0	1296	330.7322	0.673	0.6856	0.8427
182.1093	25.0	1350	280.3594	0.722	0.6971	0.8483
199.767	26.0	1404	299.1470	0.704	0.6928	0.8427
185.5706	27.0	1458	255.0808	0.747	0.7191	0.8596
160.4716	28.0	1512	296.7401	0.706	0.7196	0.8596
153.9474	29.0	1566	241.4597	0.761	0.7648	0.8820
139.2026	30.0	1620	231.8831	0.77	0.7189	0.8596
137.9976	31.0	1674	258.4150	0.744	0.7253	0.8596
138.4847	32.0	1728	245.1484	0.757	0.7879	0.8933
122.4346	33.0	1782	238.7276	0.764	0.7421	0.8708
120.9553	34.0	1836	255.5294	0.747	0.7312	0.8652
104.6512	35.0	1890	221.7303	0.78	0.7421	0.8708
110.154	36.0	1944	253.2245	0.749	0.7548	0.8764
106.2475	37.0	1998	251.0776	0.751	0.7642	0.8820
98.1837	38.0	2052	235.0105	0.767	0.7654	0.8820
103.8237	39.0	2106	229.4165	0.773	0.7429	0.8708
83.0148	40.0	2160	204.9174	0.797	0.7667	0.8820
88.5942	41.0	2214	201.9303	0.8	0.7667	0.8820
81.0979	42.0	2268	203.3398	0.799	0.7429	0.8708
80.9213	43.0	2322	202.2505	0.8	0.7421	0.8708
74.3284	44.0	2376	198.2076	0.804	0.7773	0.8876
72.1458	45.0	2430	196.0490	0.806	0.7537	0.8764
70.7258	46.0	2484	192.0745	0.81	0.7421	0.8708
64.7731	47.0	2538	191.2959	0.811	0.7654	0.8820
69.1521	48.0	2592	196.5774	0.805	0.7421	0.8708
67.6264	49.0	2646	192.4344	0.809	0.7421	0.8708
65.597	50.0	2700	194.2566	0.808	0.7421	0.8708

Framework versions

• Transformers 4.51.3
• Pytorch 2.5.1+cu124
• Datasets 3.5.0
• Tokenizers 0.21.1