
# 📋  BUOD: bert2bert Transformer Model
[![Model:Bert2Bert](https://img.shields.io/badge/model-bert2bert-green)](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
{"language": ["tl", "en"], "tags": ["generated_from_trainer"], "datasets": ["ateneoscsl/BUOD_articlescraper", "cnn_dailymail"], "metrics": ["rouge"], "model-index": [{"name": "bert2bert-cnn_dailymail-fp16-finetuned-1.0.0", "results": []}]}
generated_from_trainer tl en dataset:ateneoscsl/BUOD_articlescraper dataset:cnn_dailymail region:us
TAGS
#generated_from_trainer #tl #en #dataset-ateneoscsl/BUOD_articlescraper #dataset-cnn_dailymail #region-us 

BUOD: bert2bert Transformer Model
=================================


![Model:Bert2Bert](URL
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



#### 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
TAGS
#generated_from_trainer #tl #en #dataset-ateneoscsl/BUOD_articlescraper #dataset-cnn_dailymail #region-us 
 #### 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