bertweet-finetuned_twitch-sentiment-analysis

This model is a fine-tuned version of finiteautomata/bertweet-base-sentiment-analysis on the None dataset. It achieves the following results on the evaluation set:

• Loss: 2.3828
• Accuracy: 0.6513
• F1: 0.6513

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: 16
• eval_batch_size: 16
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 100

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1
No log	1.0	79	0.9173	0.5424	0.5424
0.9476	2.0	158	0.9454	0.5701	0.5701
0.8032	3.0	237	0.8781	0.6107	0.6107
0.7289	4.0	316	0.9143	0.6218	0.6218
0.7289	5.0	395	0.8310	0.6513	0.6513
0.5873	6.0	474	0.9353	0.6624	0.6624
0.4568	7.0	553	0.9365	0.6734	0.6734
0.3544	8.0	632	1.0126	0.6494	0.6494
0.3161	9.0	711	1.0378	0.6494	0.6494
0.3161	10.0	790	1.2249	0.6568	0.6568
0.2757	11.0	869	1.1352	0.6808	0.6808
0.2619	12.0	948	1.2467	0.6697	0.6697
0.2292	13.0	1027	1.3262	0.6716	0.6716
0.2115	14.0	1106	1.3367	0.6697	0.6697
0.2115	15.0	1185	1.3757	0.6882	0.6882
0.1848	16.0	1264	1.3650	0.6697	0.6697
0.1916	17.0	1343	1.4940	0.6587	0.6587
0.1734	18.0	1422	1.5929	0.6808	0.6808
0.1715	19.0	1501	1.5662	0.6734	0.6734
0.1715	20.0	1580	1.6073	0.6845	0.6845
0.1711	21.0	1659	1.5038	0.6808	0.6808
0.1735	22.0	1738	1.8104	0.6587	0.6587
0.142	23.0	1817	1.4715	0.6900	0.6900
0.142	24.0	1896	1.7028	0.6863	0.6863
0.1504	25.0	1975	1.5413	0.6900	0.6900
0.1536	26.0	2054	1.7148	0.6624	0.6624
0.1405	27.0	2133	1.5510	0.6624	0.6624
0.1296	28.0	2212	1.6857	0.6863	0.6863
0.1296	29.0	2291	1.6228	0.6679	0.6679
0.1247	30.0	2370	1.7248	0.6716	0.6716
0.1181	31.0	2449	1.7833	0.6716	0.6716
0.1342	32.0	2528	1.9463	0.6661	0.6661
0.1412	33.0	2607	1.9416	0.6734	0.6734
0.1412	34.0	2686	1.7277	0.6679	0.6679
0.1114	35.0	2765	1.7833	0.6734	0.6734
0.1139	36.0	2844	1.8031	0.6753	0.6753
0.1143	37.0	2923	1.7150	0.6716	0.6716
0.1031	38.0	3002	1.9060	0.6827	0.6827
0.1031	39.0	3081	1.8854	0.6587	0.6587
0.1162	40.0	3160	1.8868	0.6753	0.6753
0.1115	41.0	3239	1.7967	0.6808	0.6808
0.1118	42.0	3318	1.9692	0.6661	0.6661
0.1118	43.0	3397	1.9876	0.6661	0.6661
0.1017	44.0	3476	1.9332	0.6642	0.6642
0.1172	45.0	3555	1.8807	0.6679	0.6679
0.1128	46.0	3634	1.9357	0.7011	0.7011
0.1196	47.0	3713	2.0208	0.6679	0.6679
0.1196	48.0	3792	1.9668	0.6679	0.6679
0.0955	49.0	3871	2.0051	0.6661	0.6661
0.0959	50.0	3950	1.9267	0.6661	0.6661
0.1144	51.0	4029	2.0940	0.6716	0.6716
0.107	52.0	4108	2.1097	0.6697	0.6697
0.107	53.0	4187	2.0383	0.6624	0.6624
0.1176	54.0	4266	1.9996	0.6587	0.6587
0.112	55.0	4345	2.0815	0.6716	0.6716
0.1033	56.0	4424	1.8365	0.6661	0.6661
0.116	57.0	4503	2.0785	0.6679	0.6679
0.116	58.0	4582	2.0580	0.6624	0.6624
0.1048	59.0	4661	2.0619	0.6863	0.6863
0.0907	60.0	4740	2.0260	0.6753	0.6753
0.1021	61.0	4819	2.0572	0.6753	0.6753
0.1021	62.0	4898	1.9949	0.6753	0.6753
0.0921	63.0	4977	2.0043	0.6808	0.6808
0.099	64.0	5056	2.1510	0.6697	0.6697
0.0792	65.0	5135	2.1658	0.6642	0.6642
0.1056	66.0	5214	2.0118	0.6734	0.6734
0.1056	67.0	5293	2.1683	0.6661	0.6661
0.0994	68.0	5372	2.1810	0.6734	0.6734
0.1054	69.0	5451	2.0225	0.6900	0.6900
0.0975	70.0	5530	2.1230	0.6679	0.6679
0.0885	71.0	5609	2.0770	0.6808	0.6808
0.0885	72.0	5688	2.0654	0.6771	0.6771
0.0939	73.0	5767	2.1239	0.6624	0.6624
0.1028	74.0	5846	2.1897	0.6771	0.6771
0.0851	75.0	5925	2.0848	0.6790	0.6790
0.0783	76.0	6004	2.1199	0.6734	0.6734
0.0783	77.0	6083	2.2011	0.6734	0.6734
0.0874	78.0	6162	2.1734	0.6679	0.6679
0.0878	79.0	6241	2.1986	0.6624	0.6624
0.0939	80.0	6320	2.2401	0.6642	0.6642
0.0939	81.0	6399	2.3477	0.6605	0.6605
0.0835	82.0	6478	2.3740	0.6605	0.6605
0.0887	83.0	6557	2.3200	0.6661	0.6661
0.0943	84.0	6636	2.3248	0.6642	0.6642
0.0875	85.0	6715	2.3079	0.6605	0.6605
0.0875	86.0	6794	2.3209	0.6568	0.6568
0.0822	87.0	6873	2.3303	0.6587	0.6587
0.0846	88.0	6952	2.3620	0.6531	0.6531
0.0909	89.0	7031	2.3498	0.6587	0.6587
0.0871	90.0	7110	2.3323	0.6513	0.6513
0.0871	91.0	7189	2.3494	0.6513	0.6513
0.0796	92.0	7268	2.3677	0.6513	0.6513
0.0797	93.0	7347	2.3887	0.6513	0.6513
0.0959	94.0	7426	2.3747	0.6513	0.6513
0.0861	95.0	7505	2.3896	0.6550	0.6550
0.0861	96.0	7584	2.3786	0.6531	0.6531
0.089	97.0	7663	2.3692	0.6531	0.6531
0.0764	98.0	7742	2.3789	0.6494	0.6494
0.0874	99.0	7821	2.3833	0.6513	0.6513
0.0852	100.0	7900	2.3828	0.6513	0.6513

Framework versions

• Transformers 4.35.2
• Pytorch 2.1.1+cu121
• Datasets 2.15.0
• Tokenizers 0.15.0