final_roberta_with_new_400k_plus_37k

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

• Loss: 0.2668
• Accuracy: 0.9031
• F1: 0.9027
• Precision: 0.9042
• Recall: 0.9031

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: 5e-05
• train_batch_size: 16
• eval_batch_size: 64
• seed: 42
• gradient_accumulation_steps: 2
• total_train_batch_size: 32
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 500
• num_epochs: 3
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Precision	Recall
0.3174	0.01	100	0.3254	0.8928	0.8919	0.8964	0.8928
0.3285	0.01	200	0.2578	0.8956	0.8951	0.8968	0.8956
0.247	0.02	300	0.3913	0.8621	0.8588	0.8783	0.8621
0.2853	0.03	400	0.3394	0.8737	0.8711	0.8871	0.8737
0.3031	0.04	500	0.3924	0.8537	0.8491	0.8770	0.8537
0.2747	0.04	600	0.2532	0.9079	0.9079	0.9080	0.9079
0.2797	0.05	700	0.3607	0.8637	0.8607	0.8781	0.8637
0.2211	0.06	800	0.2910	0.8880	0.8872	0.8909	0.8880
0.2769	0.06	900	0.2834	0.8824	0.8810	0.8884	0.8824
0.2412	0.07	1000	0.2394	0.9063	0.9061	0.9069	0.9063
0.3386	0.08	1100	0.2400	0.9016	0.9013	0.9020	0.9016
0.2743	0.09	1200	0.2421	0.9047	0.9048	0.9048	0.9047
0.2682	0.09	1300	0.2833	0.8768	0.8752	0.8839	0.8768
0.3219	0.1	1400	0.2383	0.9071	0.9070	0.9071	0.9071
0.2211	0.11	1500	0.2454	0.9047	0.9047	0.9047	0.9047
0.2606	0.11	1600	0.2083	0.9223	0.9221	0.9228	0.9223
0.1966	0.12	1700	0.2688	0.9004	0.9001	0.9007	0.9004
0.2205	0.13	1800	0.3076	0.8776	0.8752	0.8911	0.8776
0.2242	0.14	1900	0.2171	0.9151	0.9150	0.9153	0.9151
0.257	0.14	2000	0.2643	0.8912	0.8905	0.8932	0.8912
0.2238	0.15	2100	0.2165	0.9131	0.9128	0.9141	0.9131
0.2313	0.16	2200	0.2312	0.8996	0.8996	0.8996	0.8996
0.1856	0.16	2300	0.2269	0.9107	0.9108	0.9109	0.9107
0.2201	0.17	2400	0.2425	0.9059	0.9056	0.9065	0.9059
0.3332	0.18	2500	0.2254	0.9043	0.9044	0.9048	0.9043
0.1843	0.19	2600	0.2524	0.8980	0.8971	0.9020	0.8980
0.2728	0.19	2700	0.2348	0.8968	0.8957	0.9017	0.8968
0.2131	0.2	2800	0.2210	0.9135	0.9136	0.9138	0.9135
0.19	0.21	2900	0.2259	0.9123	0.9120	0.9130	0.9123
0.2099	0.21	3000	0.2814	0.9024	0.9016	0.9054	0.9024
0.2209	0.22	3100	0.2473	0.9051	0.9046	0.9070	0.9051
0.2366	0.23	3200	0.2561	0.8992	0.8983	0.9029	0.8992
0.3156	0.24	3300	0.2192	0.9095	0.9094	0.9095	0.9095
0.197	0.24	3400	0.2382	0.9063	0.9057	0.9093	0.9063
0.2371	0.25	3500	0.2243	0.9139	0.9141	0.9166	0.9139
0.2273	0.26	3600	0.2362	0.9135	0.9131	0.9153	0.9135
0.2504	0.26	3700	0.2671	0.8888	0.8873	0.8965	0.8888
0.1978	0.27	3800	0.2049	0.9171	0.9170	0.9172	0.9171
0.2189	0.28	3900	0.2268	0.9099	0.9099	0.9099	0.9099
0.2171	0.29	4000	0.2135	0.9163	0.9162	0.9164	0.9163
0.2325	0.29	4100	0.2624	0.8916	0.8905	0.8966	0.8916
0.1888	0.3	4200	0.2878	0.8924	0.8911	0.8987	0.8924
0.2345	0.31	4300	0.2444	0.8964	0.8953	0.9013	0.8964
0.1688	0.31	4400	0.2479	0.9083	0.9077	0.9109	0.9083
0.2083	0.32	4500	0.2200	0.9135	0.9131	0.9150	0.9135
0.2475	0.33	4600	0.2353	0.9035	0.9030	0.9052	0.9035
0.1928	0.34	4700	0.2987	0.8944	0.8933	0.8992	0.8944
0.2008	0.34	4800	0.2993	0.8760	0.8735	0.8897	0.8760
0.22	0.35	4900	0.2431	0.9035	0.9033	0.9039	0.9035
0.1844	0.36	5000	0.2590	0.9171	0.9171	0.9171	0.9171
0.2235	0.36	5100	0.2421	0.9047	0.9041	0.9072	0.9047
0.2222	0.37	5200	0.2958	0.8948	0.8941	0.8973	0.8948
0.2241	0.38	5300	0.2031	0.9211	0.9209	0.9216	0.9211
0.2307	0.39	5400	0.2277	0.9043	0.9036	0.9076	0.9043
0.1926	0.39	5500	0.2817	0.8900	0.8887	0.8959	0.8900
0.2119	0.4	5600	0.2151	0.9175	0.9174	0.9176	0.9175
0.1747	0.41	5700	0.2404	0.9123	0.9121	0.9126	0.9123
0.1809	0.41	5800	0.3013	0.8920	0.8908	0.8980	0.8920
0.1748	0.42	5900	0.3084	0.9063	0.9056	0.9097	0.9063
0.2101	0.43	6000	0.2129	0.9175	0.9173	0.9180	0.9175
0.202	0.44	6100	0.3794	0.8848	0.8834	0.8914	0.8848
0.1671	0.44	6200	0.2678	0.9043	0.9041	0.9046	0.9043
0.2808	0.45	6300	0.2613	0.9075	0.9070	0.9098	0.9075
0.2853	0.46	6400	0.2270	0.9087	0.9088	0.9088	0.9087
0.187	0.46	6500	0.2400	0.9111	0.9112	0.9115	0.9111
0.1382	0.47	6600	0.2454	0.9139	0.9136	0.9146	0.9139
0.2259	0.48	6700	0.3165	0.8904	0.8890	0.8976	0.8904
0.164	0.49	6800	0.3091	0.9031	0.9023	0.9074	0.9031
0.2557	0.49	6900	0.2708	0.9024	0.9015	0.9064	0.9024
0.1586	0.5	7000	0.2139	0.9247	0.9246	0.9247	0.9247
0.2391	0.51	7100	0.2087	0.9143	0.9141	0.9147	0.9143
0.1974	0.51	7200	0.2438	0.9171	0.9171	0.9171	0.9171
0.2507	0.52	7300	0.2323	0.9051	0.9044	0.9084	0.9051
0.226	0.53	7400	0.2465	0.9063	0.9056	0.9096	0.9063
0.1859	0.54	7500	0.2762	0.8960	0.8949	0.9012	0.8960
0.2208	0.54	7600	0.2705	0.8948	0.8937	0.9002	0.8948
0.2073	0.55	7700	0.2419	0.9008	0.9006	0.9007	0.9008
0.1557	0.56	7800	0.3004	0.8900	0.8899	0.8899	0.8900
0.1872	0.56	7900	0.2520	0.9059	0.9056	0.9066	0.9059
0.1749	0.57	8000	0.2757	0.9067	0.9067	0.9067	0.9067
0.2298	0.58	8100	0.2617	0.9075	0.9071	0.9092	0.9075
0.1781	0.59	8200	0.2380	0.9139	0.9137	0.9144	0.9139
0.1448	0.59	8300	0.2668	0.9031	0.9027	0.9042	0.9031

Framework versions

• Transformers 4.39.3
• Pytorch 2.1.2+cu121
• Datasets 2.16.1
• Tokenizers 0.15.1