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--- |
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tags: |
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- generated_from_trainer |
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metrics: |
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- rouge |
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model-index: |
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- name: bert2bert-cnn_dailymail-fp16-finetuned-1.0.0 |
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results: [] |
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datasets: |
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- ateneoscsl/BUOD_articlescraper |
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- cnn_dailymail |
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language: |
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- tl |
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- en |
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--- |
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# 📋 BUOD: bert2bert Transformer Model |
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[](https://huggingface.co/0xhaz/bert2bert-cnn_dailymail-fp16-finetuned-1.0.0) |
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This model is a fine-tuned version of [patrickvonplaten/bert2bert-cnn_dailymail-fp16](https://huggingface.co/patrickvonplaten/bert2bert-cnn_dailymail-fp16) on on KAMI-3000 for the task of Filipino Text Summarization. |
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Bert2Bert is a EncoderDecoderModel, meaning that both the encoder and the decoder are bert-base-uncased BERT models. |
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It achieves the following results on the evaluation set: |
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- Loss: 2.3346 |
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- Rouge1: 46.3609 |
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- Rouge2: 18.8105 |
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- Rougel: 30.215 |
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- Rougelsum: 42.3642 |
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## 🔧 Finetuning/ Training procedure |
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#### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-05 |
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- train_batch_size: 4 |
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- eval_batch_size: 4 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 2 |
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#### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |
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|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:------:|:---------:| |
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| 2.8263 | 1.0 | 586 | 2.4478 | 45.3367 | 18.3604 | 29.713 | 41.2805 | |
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| 2.1264 | 2.0 | 1172 | 2.3346 | 46.3609 | 18.8105 | 30.215 | 42.3642 | |
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#### Framework versions |
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- Transformers 4.26.1 |
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- Pytorch 1.13.1 |
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- Datasets 2.10.0 |
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- Tokenizers 0.13.2 |
