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haurajahra/
QA_using_indoBERT_LORA_qv2

Name: QA_using_indoBERT_LORA_qv2
Availability: InStock

tensorboard

safetensors

generated_from_trainer

indolem/indobert-base-uncased

finetune

mit

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QA_using_indoBERT_LORA_qv2

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

Loss: 1.9435

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: 0.0001
train_batch_size: 4
eval_batch_size: 64
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 2

Training results

Training Loss	Epoch	Step	Validation Loss
5.7426	0.02	500	6.2378
5.1601	0.03	1000	4.0267
3.466	0.05	1500	3.0399
2.9304	0.06	2000	2.8011
2.7403	0.08	2500	2.7113
2.599	0.09	3000	2.6337
2.4993	0.11	3500	2.4798
2.4454	0.12	4000	2.4486
2.3938	0.14	4500	2.3848
2.3124	0.15	5000	2.3729
2.2595	0.17	5500	2.4021
2.241	0.18	6000	2.3487
2.3296	0.2	6500	2.2819
2.21	0.21	7000	2.2588
2.2386	0.23	7500	2.3498
2.164	0.25	8000	2.2315
2.2535	0.26	8500	2.2315
2.2621	0.28	9000	2.3788
2.364	0.29	9500	2.8077
2.2345	0.31	10000	2.2495
2.1571	0.32	10500	2.2306
2.0452	0.34	11000	2.2417
2.1279	0.35	11500	2.1814
2.1482	0.37	12000	2.1762
2.1064	0.38	12500	2.1931
1.9992	0.4	13000	2.1902
2.1265	0.41	13500	2.1558
2.0659	0.43	14000	2.2007
2.0314	0.44	14500	2.1326
2.0086	0.46	15000	2.1282
2.0168	0.48	15500	2.1372
2.024	0.49	16000	2.1111
2.0636	0.51	16500	2.0926
1.9673	0.52	17000	2.1200
2.0207	0.54	17500	2.1710
2.0857	0.55	18000	2.1886
2.1617	0.57	18500	2.1123
1.9912	0.58	19000	2.0999
2.1166	0.6	19500	2.0940
2.0312	0.61	20000	2.1436
2.1124	0.63	20500	2.1743
2.0399	0.64	21000	2.0801
1.9246	0.66	21500	2.0535
1.9792	0.67	22000	2.0926
1.9713	0.69	22500	2.0666
1.9285	0.71	23000	2.0699
1.9454	0.72	23500	2.0873
1.9255	0.74	24000	2.0515
1.9428	0.75	24500	2.0771
1.9093	0.77	25000	2.0538
1.933	0.78	25500	2.0308
1.8628	0.8	26000	2.0554
1.906	0.81	26500	2.0581
1.9255	0.83	27000	2.0167
1.8795	0.84	27500	2.0423
1.8987	0.86	28000	2.0300
1.8464	0.87	28500	2.0540
1.9619	0.89	29000	2.0068
1.9475	0.9	29500	2.0079
1.9399	0.92	30000	1.9889
1.8473	0.94	30500	2.0135
1.8775	0.95	31000	2.0096
1.8049	0.97	31500	2.0289
1.8029	0.98	32000	2.0561
1.9167	1.0	32500	2.0199
1.873	1.01	33000	2.0081
1.7915	1.03	33500	2.0418
1.8741	1.04	34000	2.0087
1.8528	1.06	34500	2.0023
1.8255	1.07	35000	2.0275
1.8667	1.09	35500	2.0227
1.7821	1.1	36000	1.9990
1.7809	1.12	36500	2.0067
1.8287	1.13	37000	1.9984
1.8026	1.15	37500	2.0272
1.8299	1.16	38000	2.0259
1.7972	1.18	38500	2.0382
1.8505	1.2	39000	1.9803
1.8319	1.21	39500	1.9699
1.8171	1.23	40000	1.9931
1.7986	1.24	40500	1.9933
1.8228	1.26	41000	1.9807
1.8793	1.27	41500	1.9999
1.7724	1.29	42000	1.9779
1.7328	1.3	42500	1.9725
1.8083	1.32	43000	1.9603
1.7829	1.33	43500	1.9790
1.7823	1.35	44000	1.9777
1.7715	1.36	44500	1.9831
1.8368	1.38	45000	1.9531
1.7688	1.39	45500	1.9666
1.7946	1.41	46000	1.9662
1.8104	1.43	46500	1.9799
1.758	1.44	47000	1.9697
1.802	1.46	47500	1.9617
1.7628	1.47	48000	1.9645
1.8014	1.49	48500	1.9642
1.8153	1.5	49000	1.9449
1.7997	1.52	49500	1.9682
1.8021	1.53	50000	1.9567
1.766	1.55	50500	1.9740
1.7886	1.56	51000	1.9513
1.7865	1.58	51500	1.9411
1.8403	1.59	52000	1.9396
1.7257	1.61	52500	1.9590
1.7743	1.62	53000	1.9408
1.7903	1.64	53500	1.9469
1.8302	1.66	54000	1.9370
1.7979	1.67	54500	1.9394
1.8109	1.69	55000	1.9440
1.7397	1.7	55500	1.9579
1.7374	1.72	56000	1.9501
1.7373	1.73	56500	1.9518
1.7273	1.75	57000	1.9474
1.8064	1.76	57500	1.9368
1.7913	1.78	58000	1.9426
1.8166	1.79	58500	1.9331
1.8238	1.81	59000	1.9341
1.8049	1.82	59500	1.9464
1.8735	1.84	60000	1.9397
1.8169	1.85	60500	1.9388
1.7689	1.87	61000	1.9393
1.7612	1.89	61500	1.9433
1.7768	1.9	62000	1.9402
1.6952	1.92	62500	1.9478
1.7951	1.93	63000	1.9395
1.764	1.95	63500	1.9381
1.7895	1.96	64000	1.9362
1.6671	1.98	64500	1.9428
1.7535	1.99	65000	1.9435

Framework versions

Transformers 4.35.2
Pytorch 2.1.0+cu118
Datasets 2.15.0
Tokenizers 0.15.0

haurajahra/QA_using_indoBERT_LORA_qv2

QA_using_indoBERT_LORA_qv2

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

haurajahra/
QA_using_indoBERT_LORA_qv2