DGurgurov/indonesian-wiki-lang-adapter

ms

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

• Loss: 1.4335
• Accuracy: 0.7183

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-05
• train_batch_size: 16
• eval_batch_size: 16
• seed: 42
• distributed_type: multi-GPU
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• training_steps: 50000

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
1.8607	0.05	500	1.6380	0.6876
1.7966	0.1	1000	1.6063	0.6916
1.7581	0.15	1500	1.5902	0.6949
1.7313	0.2	2000	1.5726	0.6975
1.7143	0.25	2500	1.5630	0.6992
1.7074	0.3	3000	1.5562	0.6997
1.699	0.34	3500	1.5440	0.7020
1.6797	0.39	4000	1.5376	0.7026
1.6813	0.44	4500	1.5355	0.7028
1.6641	0.49	5000	1.5276	0.7041
1.672	0.54	5500	1.5212	0.7048
1.6506	0.59	6000	1.5167	0.7058
1.6592	0.64	6500	1.5083	0.7069
1.6386	0.69	7000	1.5050	0.7071
1.6449	0.74	7500	1.5033	0.7078
1.6362	0.79	8000	1.5032	0.7073
1.6337	0.84	8500	1.4978	0.7082
1.622	0.89	9000	1.4971	0.7089
1.6279	0.94	9500	1.4919	0.7094
1.6199	0.98	10000	1.4900	0.7096
1.6218	1.03	10500	1.4914	0.7099
1.6144	1.08	11000	1.4814	0.7113
1.6017	1.13	11500	1.4789	0.7113
1.6092	1.18	12000	1.4755	0.7119
1.6083	1.23	12500	1.4766	0.7119
1.6081	1.28	13000	1.4791	0.7113
1.615	1.33	13500	1.4722	0.7127
1.605	1.38	14000	1.4725	0.7124
1.598	1.43	14500	1.4763	0.7119
1.6004	1.48	15000	1.4661	0.7132
1.6074	1.53	15500	1.4713	0.7123
1.603	1.58	16000	1.4658	0.7135
1.5928	1.62	16500	1.4646	0.7135
1.5942	1.67	17000	1.4676	0.7132
1.5914	1.72	17500	1.4604	0.7145
1.5931	1.77	18000	1.4580	0.7142
1.5808	1.82	18500	1.4606	0.7148
1.5911	1.87	19000	1.4592	0.7143
1.5842	1.92	19500	1.4622	0.7143
1.5838	1.97	20000	1.4584	0.7146
1.585	2.02	20500	1.4630	0.7139
1.5772	2.07	21000	1.4557	0.7146
1.589	2.12	21500	1.4556	0.7151
1.5653	2.17	22000	1.4533	0.7154
1.5753	2.22	22500	1.4574	0.7152
1.5707	2.26	23000	1.4531	0.7155
1.5744	2.31	23500	1.4534	0.7153
1.5741	2.36	24000	1.4531	0.7155
1.5696	2.41	24500	1.4480	0.7161
1.5844	2.46	25000	1.4526	0.7157
1.576	2.51	25500	1.4478	0.7160
1.5621	2.56	26000	1.4497	0.7158
1.5707	2.61	26500	1.4514	0.7159
1.5819	2.66	27000	1.4478	0.7164
1.5663	2.71	27500	1.4503	0.7159
1.5834	2.76	28000	1.4507	0.7160
1.5726	2.81	28500	1.4426	0.7165
1.5695	2.86	29000	1.4466	0.7166
1.5791	2.9	29500	1.4464	0.7167
1.5711	2.95	30000	1.4434	0.7170
1.5724	3.0	30500	1.4423	0.7169
1.5648	3.05	31000	1.4410	0.7173
1.5666	3.1	31500	1.4427	0.7168
1.567	3.15	32000	1.4424	0.7170
1.5569	3.2	32500	1.4441	0.7168
1.5717	3.25	33000	1.4385	0.7182
1.5585	3.3	33500	1.4416	0.7175
1.5572	3.35	34000	1.4415	0.7174
1.5585	3.4	34500	1.4368	0.7181
1.5705	3.45	35000	1.4400	0.7178
1.5712	3.5	35500	1.4420	0.7173
1.5651	3.55	36000	1.4355	0.7186
1.5595	3.59	36500	1.4363	0.7179
1.5613	3.64	37000	1.4385	0.7179
1.5594	3.69	37500	1.4422	0.7172
1.5574	3.74	38000	1.4370	0.7179
1.5557	3.79	38500	1.4410	0.7177
1.5649	3.84	39000	1.4349	0.7181
1.5635	3.89	39500	1.4406	0.7174
1.5569	3.94	40000	1.4362	0.7182
1.5661	3.99	40500	1.4369	0.7180
1.5612	4.04	41000	1.4380	0.7178
1.5632	4.09	41500	1.4374	0.7180
1.5617	4.14	42000	1.4374	0.7178
1.5452	4.19	42500	1.4341	0.7185
1.5644	4.23	43000	1.4358	0.7181
1.5448	4.28	43500	1.4392	0.7178
1.559	4.33	44000	1.4363	0.7180
1.5599	4.38	44500	1.4332	0.7185
1.5586	4.43	45000	1.4391	0.7179
1.5527	4.48	45500	1.4355	0.7183
1.5592	4.53	46000	1.4314	0.7187
1.55	4.58	46500	1.4352	0.7179
1.5592	4.63	47000	1.4367	0.7180
1.5586	4.68	47500	1.4346	0.7183
1.5547	4.73	48000	1.4321	0.7192
1.5572	4.78	48500	1.4397	0.7174
1.5537	4.83	49000	1.4334	0.7186
1.5546	4.87	49500	1.4347	0.7181
1.5518	4.92	50000	1.4365	0.7183

Framework versions

• Transformers 4.36.2
• Pytorch 2.0.0
• Datasets 2.15.0
• Tokenizers 0.15.0