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# indo-t5-base

This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on the lazarus-project/alkitab-sabda-mt dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5657
- Bleu: 18.7296
- Gen Len: 49.6657

## 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: 0.0008
- train_batch_size: 32
- eval_batch_size: 16
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 16
- total_train_batch_size: 4096
- total_eval_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 5000

### Training results

| Training Loss | Epoch | Step | Validation Loss | Bleu   | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|
| 1.5774        | 7.64  | 1000 | 1.5461          | 3.3312 | 18.8762 |
| 1.137         | 15.28 | 2000 | 1.4426          | 3.8148 | 18.8755 |
| 0.9109        | 22.92 | 3000 | 1.4754          | 3.9571 | 18.8752 |
| 0.7807        | 30.56 | 4000 | 1.5373          | 3.9767 | 18.8761 |
| 0.7288        | 38.2  | 5000 | 1.5657          | 3.9838 | 18.8778 |


### Framework versions

- Transformers 4.27.1
- Pytorch 2.0.0+cu117
- Datasets 2.9.0
- Tokenizers 0.13.2

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["bleu"], "model-index": [{"name": "indo-t5-base", "results": []}]}
transformers pytorch tensorboard mt5 text2text-generation generated_from_trainer license:apache-2.0 autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #tensorboard #mt5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 

indo-t5-base
============


This model is a fine-tuned version of google/mt5-base on the lazarus-project/alkitab-sabda-mt dataset.
It achieves the following results on the evaluation set:


* Loss: 1.5657
* Bleu: 18.7296
* Gen Len: 49.6657


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: 0.0008
* train\_batch\_size: 32
* eval\_batch\_size: 16
* seed: 42
* distributed\_type: multi-GPU
* num\_devices: 8
* gradient\_accumulation\_steps: 16
* total\_train\_batch\_size: 4096
* total\_eval\_batch\_size: 128
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* training\_steps: 5000


### Training results



### Framework versions


* Transformers 4.27.1
* Pytorch 2.0.0+cu117
* Datasets 2.9.0
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0008
* train\_batch\_size: 32
* eval\_batch\_size: 16
* seed: 42
* distributed\_type: multi-GPU
* num\_devices: 8
* gradient\_accumulation\_steps: 16
* total\_train\_batch\_size: 4096
* total\_eval\_batch\_size: 128
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* training\_steps: 5000 ### Training results ### Framework versions


* Transformers 4.27.1
* Pytorch 2.0.0+cu117
* Datasets 2.9.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #mt5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0008
* train\_batch\_size: 32
* eval\_batch\_size: 16
* seed: 42
* distributed\_type: multi-GPU
* num\_devices: 8
* gradient\_accumulation\_steps: 16
* total\_train\_batch\_size: 4096
* total\_eval\_batch\_size: 128
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* training\_steps: 5000 ### Training results ### Framework versions


* Transformers 4.27.1
* Pytorch 2.0.0+cu117
* Datasets 2.9.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #mt5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0008
* train\_batch\_size: 32
* eval\_batch\_size: 16
* seed: 42
* distributed\_type: multi-GPU
* num\_devices: 8
* gradient\_accumulation\_steps: 16
* total\_train\_batch\_size: 4096
* total\_eval\_batch\_size: 128
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* training\_steps: 5000### Training results### Framework versions


* Transformers 4.27.1
* Pytorch 2.0.0+cu117
* Datasets 2.9.0
* Tokenizers 0.13.2