Trail_run_final_roberta

ManojAlexender/Trail_run_final_roberta

Trail_run_final_roberta is a text classification model from ManojAlexender. Use it when you need a label for a piece of text. It is set up for transformers.

should probably proofread and complete it, then remove this comment. --

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At a glance

Task: Text Classification
Library: transformers
Model type: roberta
Access: Public
Created: May 3, 2024
Updated: May 3, 2024
SHA: 679dd574

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Base models

ManojAlexender/roberta-base_MLM

Model card

From the Hugging Face model README

Trail_run_final_roberta

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

Loss: 0.2322
Accuracy: 0.9163
F1: 0.9159
Precision: 0.9181
Recall: 0.9163

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
0.4057	0.01	100	0.3391	0.8808	0.8795	0.8865	0.8808
0.3447	0.01	200	0.3667	0.8470	0.8417	0.8728	0.8470
0.3202	0.02	300	0.3871	0.8689	0.8656	0.8869	0.8689
0.2354	0.03	400	0.4020	0.8597	0.8557	0.8805	0.8597
0.3268	0.04	500	0.3679	0.8318	0.8252	0.8610	0.8318
0.2771	0.04	600	0.2474	0.8924	0.8912	0.8982	0.8924
0.2288	0.05	700	0.2297	0.9103	0.9103	0.9103	0.9103
0.2307	0.06	800	0.2633	0.8944	0.8939	0.8957	0.8944
0.3375	0.06	900	0.2458	0.8988	0.8979	0.9024	0.8988
0.26	0.07	1000	0.2428	0.9071	0.9065	0.9099	0.9071
0.274	0.08	1100	0.2395	0.9035	0.9036	0.9036	0.9035
0.2513	0.09	1200	0.4167	0.8569	0.8532	0.8751	0.8569
0.2281	0.09	1300	0.3968	0.8633	0.8598	0.8815	0.8633
0.249	0.1	1400	0.2548	0.8804	0.8783	0.8920	0.8804
0.1986	0.11	1500	0.2590	0.9020	0.9020	0.9021	0.9020
0.26	0.11	1600	0.3084	0.8804	0.8784	0.8913	0.8804
0.2272	0.12	1700	0.2827	0.8884	0.8870	0.8956	0.8884
0.2312	0.13	1800	0.2373	0.9067	0.9068	0.9068	0.9067
0.2563	0.14	1900	0.2628	0.9008	0.9008	0.9011	0.9008
0.1876	0.14	2000	0.2744	0.8852	0.8840	0.8906	0.8852
0.284	0.15	2100	0.2751	0.8928	0.8914	0.9002	0.8928
0.203	0.16	2200	0.2406	0.9031	0.9034	0.9054	0.9031
0.2278	0.16	2300	0.2378	0.9115	0.9112	0.9123	0.9115
0.2204	0.17	2400	0.4288	0.8677	0.8646	0.8837	0.8677
0.2323	0.18	2500	0.2331	0.9115	0.9113	0.9118	0.9115
0.2508	0.19	2600	0.2932	0.8956	0.8955	0.8955	0.8956
0.2838	0.19	2700	0.2454	0.9035	0.9036	0.9037	0.9035
0.221	0.2	2800	0.3153	0.8800	0.8783	0.8881	0.8800
0.2167	0.21	2900	0.3200	0.8745	0.8724	0.8838	0.8745
0.2336	0.21	3000	0.2842	0.8880	0.8866	0.8947	0.8880
0.2653	0.22	3100	0.2353	0.9059	0.9059	0.9059	0.9059
0.2953	0.23	3200	0.2374	0.9051	0.9044	0.9087	0.9051
0.174	0.24	3300	0.2810	0.8964	0.8954	0.9006	0.8964
0.2184	0.24	3400	0.2127	0.9127	0.9125	0.9131	0.9127
0.2519	0.25	3500	0.2286	0.9083	0.9085	0.9126	0.9083
0.2326	0.26	3600	0.2904	0.8948	0.8944	0.8956	0.8948
0.1862	0.26	3700	0.2203	0.9259	0.9258	0.9259	0.9259
0.2098	0.27	3800	0.2350	0.9075	0.9074	0.9077	0.9075
0.2152	0.28	3900	0.2319	0.9063	0.9063	0.9063	0.9063
0.3154	0.29	4000	0.2184	0.9071	0.9070	0.9072	0.9071
0.1679	0.29	4100	0.4091	0.8764	0.8740	0.8892	0.8764
0.1535	0.3	4200	0.2574	0.9091	0.9090	0.9092	0.9091
0.1487	0.31	4300	0.2510	0.9063	0.9060	0.9072	0.9063
0.2337	0.31	4400	0.2163	0.9131	0.9128	0.9138	0.9131
0.3144	0.32	4500	0.2627	0.9051	0.9047	0.9062	0.9051
0.2487	0.33	4600	0.2557	0.8992	0.8985	0.9014	0.8992
0.2194	0.34	4700	0.2363	0.9159	0.9157	0.9163	0.9159
0.2602	0.34	4800	0.2374	0.9051	0.9053	0.9058	0.9051
0.2353	0.35	4900	0.2482	0.9059	0.9057	0.9062	0.9059
0.2107	0.36	5000	0.2903	0.9008	0.8998	0.9052	0.9008
0.2364	0.36	5100	0.2901	0.8760	0.8746	0.8815	0.8760
0.2009	0.37	5200	0.2491	0.9091	0.9086	0.9116	0.9091
0.2469	0.38	5300	0.3049	0.8992	0.8988	0.9000	0.8992
0.162	0.39	5400	0.2847	0.9059	0.9055	0.9071	0.9059
0.24	0.39	5500	0.2146	0.9135	0.9132	0.9143	0.9135
0.2667	0.4	5600	0.2379	0.9075	0.9072	0.9085	0.9075
0.2165	0.41	5700	0.2662	0.8844	0.8829	0.8915	0.8844
0.2007	0.41	5800	0.2539	0.9047	0.9039	0.9087	0.9047
0.221	0.42	5900	0.2272	0.9047	0.9046	0.9047	0.9047
0.2028	0.43	6000	0.3618	0.8669	0.8638	0.8826	0.8669
0.3003	0.44	6100	0.2454	0.9071	0.9071	0.9071	0.9071
0.2025	0.44	6200	0.2103	0.9175	0.9175	0.9175	0.9175
0.253	0.45	6300	0.2470	0.8992	0.8981	0.9044	0.8992
0.1955	0.46	6400	0.2887	0.9000	0.8992	0.9031	0.9000
0.1621	0.46	6500	0.2245	0.9151	0.9149	0.9155	0.9151
0.2532	0.47	6600	0.2493	0.8912	0.8907	0.8924	0.8912
0.1898	0.48	6700	0.2313	0.9083	0.9082	0.9083	0.9083
0.1858	0.49	6800	0.2514	0.9031	0.9026	0.9049	0.9031
0.1977	0.49	6900	0.2155	0.9167	0.9166	0.9167	0.9167
0.2247	0.5	7000	0.2280	0.9059	0.9056	0.9070	0.9059
0.1931	0.51	7100	0.2431	0.9047	0.9042	0.9066	0.9047
0.1746	0.51	7200	0.2400	0.9155	0.9152	0.9164	0.9155
0.2579	0.52	7300	0.2707	0.9107	0.9102	0.9125	0.9107
0.2139	0.53	7400	0.2625	0.8920	0.8910	0.8965	0.8920
0.2703	0.54	7500	0.2500	0.8980	0.8972	0.9013	0.8980
0.1412	0.54	7600	0.2210	0.9159	0.9158	0.9160	0.9159
0.2382	0.55	7700	0.2712	0.9028	0.9020	0.9064	0.9028
0.2498	0.56	7800	0.2200	0.9195	0.9193	0.9197	0.9195
0.2002	0.56	7900	0.3254	0.8832	0.8813	0.8935	0.8832
0.2359	0.57	8000	0.3023	0.8928	0.8918	0.8973	0.8928
0.2193	0.58	8100	0.2837	0.8892	0.8875	0.8988	0.8892
0.2436	0.59	8200	0.2221	0.9143	0.9142	0.9143	0.9143
0.1704	0.59	8300	0.2402	0.9123	0.9119	0.9136	0.9123
0.1979	0.6	8400	0.2722	0.8912	0.8896	0.9003	0.8912
0.2476	0.61	8500	0.2165	0.9211	0.9209	0.9216	0.9211
0.1996	0.61	8600	0.2374	0.9151	0.9148	0.9163	0.9151
0.2278	0.62	8700	0.2357	0.9079	0.9080	0.9083	0.9079
0.1625	0.63	8800	0.2205	0.9231	0.9228	0.9237	0.9231
0.2197	0.64	8900	0.3041	0.9020	0.9011	0.9063	0.9020
0.1868	0.64	9000	0.2280	0.9207	0.9205	0.9212	0.9207
0.2979	0.65	9100	0.2931	0.8948	0.8935	0.9011	0.8948
0.1973	0.66	9200	0.2322	0.9163	0.9159	0.9181	0.9163

Framework versions

Transformers 4.39.3
Pytorch 2.1.2+cu121
Datasets 2.16.1
Tokenizers 0.15.1

Task: Text Classification
Library: transformers
Type: roberta
Created: May 3, 2024
Updated: May 3, 2024