Jnjnpx/fine-tuned-bert-extractive-summarization

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fine-tuned-bert-extractive-summarization

This model is a fine-tuned version of Twitter/twhin-bert-base on the LaoNews dataset for Lao text extractive summarization. It achieves the following results on the evaluation set:

• —Loss: 0.5566
• —Accuracy: 0.6995
• —Precision: 0.6947
• —Recall: 0.6995
• —F1: 0.6961

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: 2e-05
• —trainbatchsize: 16
• —evalbatchsize: 16
• —seed: 42
• —gradientaccumulationsteps: 2
• —totaltrainbatch_size: 32
• —optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• —lrschedulertype: linear
• —lrschedulerwarmup_steps: 500
• —num_epochs: 3
• —mixedprecisiontraining: Native AMP

Training results

Framework versions

• —Transformers 4.39.3
• —Pytorch 2.3.0+cu121
• —Datasets 2.18.0
• —Tokenizers 0.15.2