fine-tuned-DatasetQAS-Squad-ID-with-indobert-base-uncased-with-ITTL-without-freeze-LR-1e-05 – AI Model by afaji | AlphaNeural AI

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fine-tuned-DatasetQAS-Squad-ID-with-indobert-base-uncased-with-ITTL-without-freeze-LR-1e-05

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fine-tuned-DatasetQAS-Squad-ID-with-indobert-base-uncased-with-ITTL-without-freeze-LR-1e-05

This model is a fine-tuned version of indolem/indobert-base-uncased on the None dataset. It achieves the following results on the evaluation set:

Loss: 1.6868
Exact Match: 45.1218
F1: 61.3575
Precision: 62.6921
Recall: 67.7425

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: 1e-05
train_batch_size: 16
eval_batch_size: 16
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 64
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.06
num_epochs: 16

Training results

Training Loss	Epoch	Step	Validation Loss	Exact Match	F1	Precision	Recall
2.4472	0.5	1023	2.2865	29.5933	42.4059	43.5205	52.5488
1.8722	1.0	2046	1.8562	39.5825	55.5286	56.6097	63.1751
1.6329	1.5	3069	1.7271	42.6516	59.0745	60.4591	65.8650
1.5391	2.0	4092	1.6703	43.7412	60.5632	62.1124	67.0088
1.4011	2.5	5115	1.6518	44.4565	61.1817	62.7045	66.6741
1.3955	3.0	6138	1.6557	45.0803	62.3404	63.2191	68.5525
1.2678	3.5	7161	1.6473	45.3131	61.8217	63.1073	67.9864
1.2641	4.0	8184	1.6277	45.5627	61.9713	63.2872	68.0141
1.1456	4.5	9207	1.6868	45.1218	61.3575	62.6921	67.7425

Framework versions

Transformers 4.26.1
Pytorch 1.13.1+cu117
Datasets 2.2.0
Tokenizers 0.13.2