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# t5-base-indonesian-summarization-cased-finetuned-indosum

This model is a fine-tuned version of [panggi/t5-base-indonesian-summarization-cased](https://huggingface.co/panggi/t5-base-indonesian-summarization-cased) on the indosum dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5884
- Rouge1: 0.3278
- Rouge2: 0.2868
- Rougel: 0.32
- Rougelsum: 0.3198
- Gen Len: 19.0

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5.6e-05
- train_batch_size: 3
- eval_batch_size: 3
- 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 | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| 0.3815        | 1.0   | 4754 | 0.5584          | 0.3281 | 0.2866 | 0.3202 | 0.32      | 19.0    |
| 0.3207        | 2.0   | 9508 | 0.5884          | 0.3278 | 0.2868 | 0.32   | 0.3198    | 19.0    |


### Framework versions

- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3

{"tags": ["generated_from_trainer"], "datasets": ["indosum"], "metrics": ["rouge"], "model-index": [{"name": "t5-base-indonesian-summarization-cased-finetuned-indosum", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "indosum", "type": "indosum", "config": "indosum_fold0_source", "split": "validation", "args": "indosum_fold0_source"}, "metrics": [{"type": "rouge", "value": 0.3278, "name": "Rouge1"}]}]}]}
transformers pytorch tensorboard t5 text2text-generation generated_from_trainer dataset:indosum model-index autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-indosum #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 

t5-base-indonesian-summarization-cased-finetuned-indosum
========================================================


This model is a fine-tuned version of panggi/t5-base-indonesian-summarization-cased on the indosum dataset.
It achieves the following results on the evaluation set:


* Loss: 0.5884
* Rouge1: 0.3278
* Rouge2: 0.2868
* Rougel: 0.32
* Rougelsum: 0.3198
* Gen Len: 19.0


Model description
-----------------


More information needed


Intended uses & limitations
---------------------------


More information needed


Training and evaluation data
----------------------------


More information needed


Training procedure
------------------


### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5.6e-05
* train\_batch\_size: 3
* eval\_batch\_size: 3
* 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 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5.6e-05
* train\_batch\_size: 3
* eval\_batch\_size: 3
* 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 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-indosum #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5.6e-05
* train\_batch\_size: 3
* eval\_batch\_size: 3
* 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 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3