learning_rate: 2e-05
train_batch_size: 6
eval_batch_size: 6
seed: 42
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 5

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1 Score
0.6406	0.0960	50	0.5975	0.6615	0.6397
0.5559	0.1919	100	0.5361	0.7410	0.7358
0.5287	0.2879	150	0.5146	0.7718	0.7640
0.478	0.3839	200	0.4520	0.8128	0.8126
0.59	0.4798	250	0.3588	0.8590	0.8586
0.4079	0.5758	300	0.3321	0.8564	0.8563
0.3735	0.6718	350	0.3595	0.8795	0.8792
0.3082	0.7678	400	0.3796	0.8974	0.8974
0.3858	0.8637	450	0.3084	0.9128	0.9127
0.3758	0.9597	500	0.2758	0.9051	0.9051
0.3364	1.0557	550	0.2653	0.9077	0.9074
0.2208	1.1516	600	0.3032	0.9231	0.9230
0.2239	1.2476	650	0.3953	0.9051	0.9049
0.204	1.3436	700	0.3044	0.9333	0.9333
0.1212	1.4395	750	0.3261	0.9308	0.9307
0.1064	1.5355	800	0.3929	0.9256	0.9255
0.1557	1.6315	850	0.3203	0.9359	0.9358
0.1929	1.7274	900	0.3786	0.9231	0.9228
0.18	1.8234	950	0.3728	0.9205	0.9202
0.1931	1.9194	1000	0.2890	0.9359	0.9357
0.1239	2.0154	1050	0.3465	0.9205	0.9201
0.1063	2.1113	1100	0.2408	0.9385	0.9384
0.1151	2.2073	1150	0.2417	0.9538	0.9538
0.0666	2.3033	1200	0.3010	0.9359	0.9358
0.0811	2.3992	1250	0.2797	0.9462	0.9461
0.0688	2.4952	1300	0.3099	0.9436	0.9435
0.023	2.5912	1350	0.2864	0.9564	0.9563
0.0818	2.6871	1400	0.3087	0.9487	0.9487
0.1275	2.7831	1450	0.3015	0.9487	0.9487
0.0317	2.8791	1500	0.3420	0.9462	0.9461
0.1034	2.9750	1550	0.2924	0.9487	0.9487
0.0225	3.0710	1600	0.3018	0.9538	0.9537
0.0175	3.1670	1650	0.3316	0.9513	0.9512
0.0232	3.2630	1700	0.4221	0.9333	0.9330
0.042	3.3589	1750	0.3193	0.9513	0.9512
0.0171	3.4549	1800	0.3282	0.9513	0.9512
0.014	3.5509	1850	0.3753	0.9462	0.9460
0.0455	3.6468	1900	0.2947	0.9513	0.9512
0.0078	3.7428	1950	0.2745	0.9590	0.9589
0.0456	3.8388	2000	0.2527	0.9564	0.9564
0.0474	3.9347	2050	0.2896	0.9513	0.9512
0.0242	4.0307	2100	0.3211	0.9487	0.9487
0.0004	4.1267	2150	0.3032	0.9590	0.9589
0.0003	4.2226	2200	0.3176	0.9564	0.9564
0.0002	4.3186	2250	0.3216	0.9564	0.9564
0.0198	4.4146	2300	0.3205	0.9564	0.9564
0.0002	4.5106	2350	0.3267	0.9590	0.9589
0.0002	4.6065	2400	0.3274	0.9513	0.9512
0.0007	4.7025	2450	0.3268	0.9564	0.9564
0.0046	4.7985	2500	0.3245	0.9538	0.9538
0.0301	4.8944	2550	0.3238	0.9564	0.9564
0.0002	4.9904	2600	0.3240	0.9564	0.9564

Framework versions

Transformers 4.53.1
Pytorch 2.6.0+cu124
Datasets 2.14.4
Tokenizers 0.21.2

wildansofhal/IndoBERT-Sentiment-Analysis

IndoBERT-Sentiment-Analysis

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

wildansofhal/
IndoBERT-Sentiment-Analysis