File size: 1,690 Bytes
3d6ba30 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 |
---
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: bert2bert-cnn_dailymail-fp16-finetuned-1.0.0
results: []
datasets:
- ateneoscsl/BUOD_articlescraper
- cnn_dailymail
language:
- tl
- en
---
# 📋 BUOD: bert2bert Transformer Model
[](https://huggingface.co/0xhaz/bert2bert-cnn_dailymail-fp16-finetuned-1.0.0)
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.
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
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:------:|:---------:|
| 2.8263 | 1.0 | 586 | 2.4478 | 45.3367 | 18.3604 | 29.713 | 41.2805 |
| 2.1264 | 2.0 | 1172 | 2.3346 | 46.3609 | 18.8105 | 30.215 | 42.3642 |
#### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1
- Datasets 2.10.0
- Tokenizers 0.13.2 |