roberta-base-emotion-prediction-phr

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

• Loss: 0.3301
• Accuracy: 0.2814
• Micro Precision: 0.7422
• Micro Recall: 0.6510
• Micro F1: 0.6945
• Micro Roc Auc: 0.7940

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: 2e-05
• train_batch_size: 8
• eval_batch_size: 8
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Micro Precision	Micro Recall	Micro F1	Micro Roc Auc
0.4952	0.12	100	0.4515	0.1574	0.5861	0.3505	0.4386	0.6404
0.4152	0.23	200	0.3839	0.2041	0.7102	0.4593	0.5578	0.7033
0.3878	0.35	300	0.3625	0.2341	0.7384	0.5198	0.6101	0.7340
0.3764	0.47	400	0.3506	0.2412	0.7666	0.5092	0.6119	0.7328
0.372	0.58	500	0.3450	0.2375	0.7686	0.5251	0.6239	0.7403
0.3588	0.7	600	0.3464	0.2249	0.7804	0.4964	0.6068	0.7286
0.3383	0.82	700	0.3471	0.2470	0.7503	0.5578	0.6398	0.7528
0.3489	0.94	800	0.3284	0.2620	0.7702	0.5682	0.6539	0.7603
0.3287	1.05	900	0.3214	0.2820	0.7707	0.5936	0.6706	0.7720
0.3158	1.17	1000	0.3352	0.2657	0.7580	0.5814	0.6580	0.7646
0.3247	1.29	1100	0.3219	0.2811	0.7696	0.6031	0.6763	0.7762
0.3159	1.4	1200	0.3237	0.2688	0.7479	0.6138	0.6743	0.7778
0.3207	1.52	1300	0.3217	0.2461	0.7676	0.5767	0.6586	0.7638
0.3087	1.64	1400	0.3253	0.2424	0.7484	0.5883	0.6587	0.7663
0.3057	1.75	1500	0.3174	0.2728	0.7587	0.6116	0.6773	0.7785
0.3099	1.87	1600	0.3150	0.2774	0.7683	0.6001	0.6738	0.7746
0.3006	1.99	1700	0.3176	0.2633	0.7636	0.5881	0.6645	0.7685
0.285	2.11	1800	0.3177	0.2722	0.7363	0.6484	0.6896	0.7915
0.2886	2.22	1900	0.3156	0.2768	0.7734	0.5935	0.6716	0.7723
0.2785	2.34	2000	0.3101	0.2808	0.7692	0.6151	0.6836	0.7816
0.2801	2.46	2100	0.3121	0.2728	0.7739	0.5956	0.6732	0.7734
0.2876	2.57	2200	0.3166	0.2777	0.7577	0.6157	0.6794	0.7802
0.2769	2.69	2300	0.3143	0.2881	0.7691	0.6124	0.6819	0.7803
0.2755	2.81	2400	0.3133	0.2792	0.7577	0.6263	0.6857	0.7850
0.2815	2.92	2500	0.3197	0.2716	0.7406	0.6466	0.6904	0.7914
0.2671	3.04	2600	0.3133	0.2857	0.7549	0.6438	0.6949	0.7925
0.2431	3.16	2700	0.3225	0.2722	0.7515	0.6320	0.6866	0.7866
0.2512	3.27	2800	0.3221	0.2743	0.7616	0.6106	0.6778	0.7784
0.2574	3.39	2900	0.3191	0.2737	0.7561	0.6214	0.6822	0.7825
0.2527	3.51	3000	0.3207	0.2666	0.7443	0.6315	0.6833	0.7852
0.2615	3.63	3100	0.3170	0.2670	0.7443	0.6471	0.6923	0.7923
0.2583	3.74	3200	0.3122	0.2685	0.7729	0.6068	0.6799	0.7783
0.2543	3.86	3300	0.3175	0.2709	0.7492	0.6432	0.6921	0.7913
0.2546	3.98	3400	0.3164	0.2752	0.7661	0.6186	0.6845	0.7828
0.2274	4.09	3500	0.3172	0.2759	0.7437	0.6426	0.6895	0.7902
0.2328	4.21	3600	0.3214	0.2737	0.7548	0.6297	0.6866	0.7861
0.2354	4.33	3700	0.3192	0.2792	0.7546	0.6310	0.6872	0.7866
0.2238	4.44	3800	0.3199	0.2709	0.7453	0.6444	0.6912	0.7912
0.2376	4.56	3900	0.3176	0.2734	0.7599	0.6247	0.6857	0.7846
0.2344	4.68	4000	0.3189	0.2639	0.7437	0.6390	0.6874	0.7885
0.2222	4.8	4100	0.3222	0.2636	0.7436	0.6409	0.6884	0.7894
0.232	4.91	4200	0.3227	0.2725	0.7472	0.6426	0.6910	0.7907
0.2367	5.03	4300	0.3243	0.2670	0.7463	0.6339	0.6855	0.7866
0.2154	5.15	4400	0.3257	0.2593	0.7366	0.6513	0.6913	0.7929
0.2089	5.26	4500	0.3261	0.2700	0.7416	0.6453	0.6901	0.7910
0.2081	5.38	4600	0.3269	0.2731	0.7602	0.6133	0.6789	0.7794
0.2116	5.5	4700	0.3308	0.2593	0.7229	0.6687	0.6947	0.7983
0.2128	5.61	4800	0.3263	0.2660	0.7422	0.6432	0.6891	0.7902
0.2059	5.73	4900	0.3295	0.2728	0.7356	0.6550	0.6929	0.7944
0.2103	5.85	5000	0.3301	0.2814	0.7442	0.6510	0.6945	0.7940
0.2151	5.96	5100	0.3300	0.2541	0.7221	0.6598	0.6896	0.7942
0.1954	6.08	5200	0.3325	0.2765	0.7476	0.6381	0.6885	0.7887
0.2028	6.2	5300	0.3316	0.2559	0.7364	0.6400	0.6848	0.7878
0.1911	6.32	5400	0.3332	0.2553	0.7370	0.6386	0.6843	0.7873
0.2015	6.43	5500	0.3349	0.2645	0.7308	0.6538	0.6902	0.7931
0.1901	6.55	5600	0.3389	0.2587	0.7197	0.6682	0.6930	0.7975
0.197	6.67	5700	0.3349	0.2728	0.7400	0.6424	0.6878	0.7895
0.1907	6.78	5800	0.3354	0.2627	0.7454	0.6349	0.6857	0.7870
0.1853	6.9	5900	0.3420	0.2657	0.7356	0.6513	0.6909	0.7927
0.1841	7.02	6000	0.3399	0.2584	0.7308	0.6554	0.6910	0.7937
0.1739	7.13	6100	0.3409	0.2620	0.7364	0.6446	0.6874	0.7898
0.1768	7.25	6200	0.3417	0.2593	0.7314	0.6474	0.6868	0.7902
0.1762	7.37	6300	0.3384	0.2654	0.7398	0.6373	0.6847	0.7871
0.177	7.49	6400	0.3448	0.2541	0.7237	0.6547	0.6875	0.7922
0.1787	7.6	6500	0.3422	0.2513	0.7317	0.6425	0.6842	0.7881
0.1793	7.72	6600	0.3452	0.2611	0.7231	0.6582	0.6891	0.7936
0.1772	7.84	6700	0.3470	0.2587	0.7193	0.6618	0.6894	0.7946
0.1799	7.95	6800	0.3459	0.2547	0.7238	0.6494	0.6846	0.7898
0.1726	8.07	6900	0.3477	0.2507	0.7259	0.6419	0.6813	0.7869
0.1672	8.19	7000	0.3489	0.2492	0.7215	0.6499	0.6838	0.7897
0.1664	8.3	7100	0.3474	0.2498	0.7197	0.6491	0.6826	0.7890
0.1712	8.42	7200	0.3477	0.2516	0.7309	0.6404	0.6827	0.7870
0.166	8.54	7300	0.3487	0.2553	0.7209	0.6547	0.6862	0.7917
0.1706	8.65	7400	0.3487	0.2538	0.7239	0.6518	0.6860	0.7909
0.1674	8.77	7500	0.3506	0.2538	0.7216	0.6541	0.6862	0.7916
0.1655	8.89	7600	0.3476	0.2553	0.7283	0.6465	0.6849	0.7893
0.1609	9.01	7700	0.3498	0.2495	0.7273	0.6443	0.6833	0.7882
0.1647	9.12	7800	0.3507	0.2522	0.7255	0.6423	0.6814	0.7870
0.1531	9.24	7900	0.3503	0.2522	0.7292	0.6426	0.6832	0.7878
0.1577	9.36	8000	0.3524	0.2528	0.7212	0.6569	0.6875	0.7927
0.1592	9.47	8100	0.3517	0.2519	0.7186	0.6536	0.6845	0.7908
0.1615	9.59	8200	0.3514	0.2510	0.7183	0.6529	0.6841	0.7905
0.1529	9.71	8300	0.3515	0.2516	0.7221	0.6489	0.6835	0.7893
0.1607	9.82	8400	0.3520	0.2528	0.7212	0.6499	0.6837	0.7896
0.1506	9.94	8500	0.3524	0.2522	0.7220	0.6522	0.6853	0.7908

Framework versions

• Transformers 4.31.0
• Pytorch 2.1.0+cu121
• Datasets 2.14.5
• Tokenizers 0.13.3