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Amala3/
bertweet-base_regression_7_seed7_EN

Name: bertweet-base_regression_7_seed7_EN
Availability: InStock

safetensors

roberta

generated_from_trainer

vinai/bertweet-base

finetune

mit

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bertweet-base_regression_7_seed7_EN

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

Loss: 1.0534
Mse: 5.3168
Rmse: 2.3058
Mae: 1.3446
R2: 0.2546
F1: 0.7813
Precision: 0.7833
Recall: 0.7850
Accuracy: 0.7850

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-06
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
lr_scheduler_warmup_steps: 200
num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Mse	Rmse	Mae	R2	F1	Precision	Recall	Accuracy
1.7289	0.4630	100	1.8447	9.6783	3.1110	2.2934	-0.3936	0.4570	0.3669	0.6057	0.6057
1.6915	0.9259	200	1.8018	8.9257	2.9876	2.2727	-0.2852	0.4570	0.3669	0.6057	0.6057
1.6504	1.3889	300	1.6921	7.9979	2.8280	2.1201	-0.1516	0.4570	0.3669	0.6057	0.6057
1.4742	1.8519	400	1.5208	6.7985	2.6074	1.8924	0.0211	0.4570	0.3669	0.6057	0.6057
1.2813	2.3148	500	1.4051	6.0745	2.4647	1.7582	0.1253	0.4570	0.3669	0.6057	0.6057
1.2569	2.7778	600	1.3427	5.7894	2.4061	1.6773	0.1664	0.4570	0.3669	0.6057	0.6057
1.1022	3.2407	700	1.2733	5.3487	2.3127	1.6098	0.2298	0.4570	0.3669	0.6057	0.6057
1.005	3.7037	800	1.2122	4.6814	2.1637	1.5743	0.3259	0.4570	0.3669	0.6057	0.6057
0.9814	4.1667	900	1.1237	4.6217	2.1498	1.4468	0.3345	0.7191	0.7827	0.7467	0.7467
0.8354	4.6296	1000	1.1104	4.8814	2.2094	1.4152	0.2971	0.7799	0.8035	0.7911	0.7911
0.8169	5.0926	1100	1.0778	4.5463	2.1322	1.4105	0.3454	0.7926	0.7955	0.7963	0.7963
0.7157	5.5556	1200	1.0451	4.6311	2.1520	1.3624	0.3332	0.7979	0.8010	0.8016	0.8016
0.6988	6.0185	1300	1.0387	4.5981	2.1443	1.3629	0.3379	0.7823	0.7843	0.7859	0.7859
0.6048	6.4815	1400	1.0342	4.8049	2.1920	1.3377	0.3081	0.7823	0.7843	0.7859	0.7859
0.5695	6.9444	1500	1.0254	4.9339	2.2212	1.3273	0.2896	0.7844	0.7875	0.7885	0.7885
0.5511	7.4074	1600	1.0084	5.0070	2.2376	1.3057	0.2790	0.7899	0.7992	0.7963	0.7963
0.5313	7.8704	1700	1.0131	4.9329	2.2210	1.3143	0.2897	0.7859	0.7866	0.7885	0.7885
0.4934	8.3333	1800	0.9928	4.8939	2.2122	1.2763	0.2953	0.7913	0.7970	0.7963	0.7963
0.4688	8.7963	1900	1.0044	4.9293	2.2202	1.2969	0.2902	0.7859	0.7866	0.7885	0.7885
0.4762	9.2593	2000	0.9942	4.8929	2.2120	1.2808	0.2955	0.7922	0.7959	0.7963	0.7963
0.457	9.7222	2100	1.0085	4.9806	2.2317	1.2983	0.2828	0.7884	0.7894	0.7911	0.7911

Framework versions

Transformers 4.40.2
Pytorch 2.1.2
Datasets 2.18.0
Tokenizers 0.19.1

Amala3/bertweet-base_regression_7_seed7_EN

bertweet-base_regression_7_seed7_EN

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Amala3/
bertweet-base_regression_7_seed7_EN