muhammadravi251001/fine-tuned-DatasetQAS-TYDI-QA-ID-with-indobert-base-uncased-with-ITTL-with-freeze

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fine-tuned-DatasetQAS-TYDI-QA-ID-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: 1.8879
• —Exact Match: 37.8709
• —F1: 49.7154
• —Precision: 49.7719
• —Recall: 58.0834

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
• —trainbatchsize: 16
• —evalbatchsize: 16
• —seed: 42
• —gradientaccumulationsteps: 4
• —totaltrainbatch_size: 64
• —optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• —lrschedulertype: linear
• —lrschedulerwarmup_ratio: 0.06
• —num_epochs: 4

Training results

Framework versions

• —Transformers 4.26.1
• —Pytorch 1.13.1+cu117
• —Datasets 2.2.0
• —Tokenizers 0.13.2