fine-tuned-DatasetQAS-IDK-MRC-with-indobert-base-uncased-without-ITTL – AI Model by muhammadravi251001 | AlphaNeural AI

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fine-tuned-DatasetQAS-IDK-MRC-with-indobert-base-uncased-without-ITTL

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fine-tuned-DatasetQAS-IDK-MRC-with-indobert-base-uncased-without-ITTL

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: 1.3953
Exact Match: 57.0681
F1: 60.6263
Precision: 60.5659
Recall: 61.6128

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: 4

Training results

Training Loss	Epoch	Step	Validation Loss	Exact Match	F1	Precision	Recall
4.9796	0.49	72	2.7419	5.8901	10.4346	9.5399	14.3017
2.9811	0.98	144	1.9199	49.6073	52.8796	52.8599	52.9123
2.053	1.47	216	1.7355	49.2147	53.3398	53.1633	54.0246
1.9358	1.96	288	1.6560	49.6073	55.0960	54.6997	56.6932
1.6543	2.45	360	1.5563	53.5340	57.7517	57.4674	59.0722
1.6505	2.94	432	1.4367	56.6754	60.2111	60.1301	61.1952
1.4604	3.43	504	1.4737	54.3194	58.1394	58.0354	59.2380
1.4891	3.92	576	1.3953	57.0681	60.6263	60.5659	61.6128

Framework versions

Transformers 4.26.1
Pytorch 1.13.1+cu117
Datasets 2.2.0
Tokenizers 0.13.2