📋 BUOD: bert2bert Transformer Model

This model is a fine-tuned version of patrickvonplaten/bert2bert-cnn_dailymail-fp16 on on KAMI-3000 for the task of Filipino Text Summarization. Bert2Bert is a EncoderDecoderModel, meaning that both the encoder and the decoder are bert-base-uncased BERT models.

It achieves the following results on the evaluation set:

• Loss: 2.3346
• Rouge1: 46.3609
• Rouge2: 18.8105
• Rougel: 30.215
• Rougelsum: 42.3642

🔧 Finetuning/ Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 5e-05
• train_batch_size: 4
• eval_batch_size: 4
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 2

Training results

Framework versions

• Transformers 4.26.1
• Pytorch 1.13.1
• Datasets 2.10.0
• Tokenizers 0.13.2