
# 📋 BUOD: distilBART Transformer Model
[![Model:distilBART](https://img.shields.io/badge/model-distilBART-green)](https://huggingface.co/jamesesguerra/distilbart-cnn-12-6-finetuned-1.3.1)
Authors: [James Esguerra](https://huggingface.co/jamesesguerra), [Julia Avila](), [Hazielle Bugayong](https://huggingface.co/0xhaz)

This model is a fine-tuned version of [sshleifer/distilbart-cnn-12-6](https://huggingface.co/sshleifer/distilbart-cnn-12-6) on the KAMI-3000 dataset, for the task of Filipino Text Summarization.

It achieves the following results on the evaluation set:
- Loss: 1.8049
- Rouge1: 50.5143
- Rouge2: 23.2481
- Rougel: 34.135
- Rougelsum: 46.4261

## 🔧 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 |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|
| 2.1377        | 1.0   | 586  | 1.8792          | 49.8737 | 22.7881 | 33.6698 | 45.8037   |
| 1.5731        | 2.0   | 1172 | 1.8049          | 50.5143 | 23.2481 | 34.135  | 46.4261   |


#### Framework versions
- Transformers 4.25.1
- Pytorch 1.12.1+cu113
- Datasets 2.7.1
- Tokenizers 0.13.2
{"language": ["tl", "en"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["ateneoscsl/BUOD_articlescraper", "cnn_dailymail", "xsum"], "metrics": ["rouge", "meteor"], "model-index": [{"name": "distilbart-cnn-12-6-finetuned-1.3.2", "results": []}]}
generated_from_trainer tl en dataset:ateneoscsl/BUOD_articlescraper dataset:cnn_dailymail dataset:xsum license:apache-2.0 region:us
TAGS
#generated_from_trainer #tl #en #dataset-ateneoscsl/BUOD_articlescraper #dataset-cnn_dailymail #dataset-xsum #license-apache-2.0 #region-us 

BUOD: distilBART Transformer Model
==================================


![Model:distilBART](URL
Authors: James Esguerra, Julia Avila, Hazielle Bugayong


This model is a fine-tuned version of sshleifer/distilbart-cnn-12-6 on the KAMI-3000 dataset, for the task of Filipino Text Summarization.


It achieves the following results on the evaluation set:


* Loss: 1.8049
* Rouge1: 50.5143
* Rouge2: 23.2481
* Rougel: 34.135
* Rougelsum: 46.4261


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 4.25.1
* Pytorch 1.12.1+cu113
* Datasets 2.7.1
* 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 4.25.1
* Pytorch 1.12.1+cu113
* Datasets 2.7.1
* Tokenizers 0.13.2
TAGS
#generated_from_trainer #tl #en #dataset-ateneoscsl/BUOD_articlescraper #dataset-cnn_dailymail #dataset-xsum #license-apache-2.0 #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 4.25.1
* Pytorch 1.12.1+cu113
* Datasets 2.7.1
* Tokenizers 0.13.2