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# sinMT5-tuned

This model is a fine-tuned version of [google/mT5](https://huggingface.co/csebuetnlp/mT5_multilingual_XLSum) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8573
- Rouge1: 20.2531
- Rouge2: 8.1307
- Rougel: 19.3917
- Rougelsum: 20.0592

## 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: 0.00015652249866150822
- 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: 7

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Rouge1  | Rouge2 | Rougel  | Rougelsum |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:------:|:-------:|:---------:|
| 1.8651        | 1.0   | 1500  | 1.8070          | 17.676  | 7.1418 | 16.8638 | 17.457    |
| 1.5527        | 2.0   | 3000  | 1.7804          | 21.1357 | 8.1386 | 20.122  | 20.8652   |
| 1.3755        | 3.0   | 4500  | 1.7769          | 21.4151 | 8.5692 | 20.3204 | 21.1152   |
| 1.2473        | 4.0   | 6000  | 1.7937          | 21.2434 | 8.2325 | 20.1332 | 21.0657   |
| 1.1548        | 5.0   | 7500  | 1.8035          | 20.4298 | 8.2314 | 19.5909 | 20.2116   |
| 1.0835        | 6.0   | 9000  | 1.8367          | 20.5427 | 8.2226 | 19.6134 | 20.2918   |
| 1.0387        | 7.0   | 10500 | 1.8573          | 20.2531 | 8.1307 | 19.3917 | 20.0592   |


### Framework versions

- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Tokenizers 0.13.3

{"tags": ["summarization", "generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "sinMT5-tuned", "results": []}]}
transformers pytorch tensorboard mt5 text2text-generation summarization generated_from_trainer autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #tensorboard #mt5 #text2text-generation #summarization #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 

sinMT5-tuned
============


This model is a fine-tuned version of google/mT5 on an unknown dataset.
It achieves the following results on the evaluation set:


* Loss: 1.8573
* Rouge1: 20.2531
* Rouge2: 8.1307
* Rougel: 19.3917
* Rougelsum: 20.0592


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: 0.00015652249866150822
* 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: 7


### Training results



### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.00015652249866150822
* 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: 7 ### Training results ### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #mt5 #text2text-generation #summarization #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.00015652249866150822
* 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: 7 ### Training results ### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Tokenizers 0.13.3