fine-tuned-DatasetQAS-IDK-MRC-with-indobert-base-uncased-with-ITTL-with-freeze – AI Model by muhammadravi251001 | AlphaNeural AI

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fine-tuned-DatasetQAS-IDK-MRC-with-indobert-base-uncased-with-ITTL-with-freeze

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fine-tuned-DatasetQAS-IDK-MRC-with-indobert-base-uncased-with-ITTL-with-freeze

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: 1.3111
Exact Match: 56.5445
F1: 62.0693
Precision: 62.6451
Recall: 66.3389

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
4.8072	0.49	73	2.1488	49.8691	49.8691	49.8691	49.8691
2.499	0.99	146	1.8266	49.4764	50.1167	50.0416	51.3416
1.9561	1.49	219	1.6905	48.8220	52.2799	52.0974	55.2549
1.8359	1.98	292	1.5588	51.3089	56.6619	56.4524	61.1890
1.5685	2.48	365	1.4738	51.7016	57.4150	57.7753	61.7455
1.5727	2.98	438	1.3563	56.9372	62.9376	63.1871	67.0306
1.3775	3.47	511	1.3414	55.6283	61.2198	61.8070	65.9293
1.3928	3.97	584	1.3111	56.5445	62.0693	62.6451	66.3389

Framework versions

Transformers 4.26.1
Pytorch 1.13.1+cu117
Datasets 2.2.0
Tokenizers 0.13.2