roberta-base-emotion-prediction-phr

vibhorag101/roberta-base-emotion-prediction-phr

roberta-base-emotion-prediction-phr is a text classification model from vibhorag101. Use it when you need a label for a piece of text. It is set up for transformers. The card lists the license as mit.

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

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At a glance

Task: Text Classification
Library: transformers
License: mit
Model type: roberta
Access: Public
Created: Nov 26, 2023
Updated: Nov 27, 2023
SHA: f05d9dd6

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From the Hugging Face model README

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

Task: Text Classification
Library: transformers
Type: roberta
License: mit
Languages: en
Created: Nov 26, 2023
Updated: Nov 27, 2023