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# mt5-base-finetune-ar-xlsum

This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on the xlsum dataset.
It achieves the following results on the evaluation set:
- Loss: 3.2546
- Rouge-1: 22.2
- Rouge-2: 9.57
- Rouge-l: 20.26
- Gen Len: 19.0
- Bertscore: 71.43

## 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: 0.0005
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 250
- num_epochs: 10
- label_smoothing_factor: 0.1

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge-1 | Rouge-2 | Rouge-l | Gen Len | Bertscore |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:-------:|:---------:|
| 4.9261        | 1.0   | 585  | 3.6314          | 18.19   | 6.49    | 16.37   | 19.0    | 70.17     |
| 3.8429        | 2.0   | 1170 | 3.4253          | 19.45   | 7.58    | 17.73   | 19.0    | 70.35     |
| 3.6311        | 3.0   | 1755 | 3.3569          | 20.83   | 8.54    | 18.9    | 19.0    | 70.89     |
| 3.4917        | 4.0   | 2340 | 3.3101          | 20.77   | 8.53    | 18.89   | 19.0    | 70.98     |
| 3.3873        | 5.0   | 2925 | 3.2867          | 21.47   | 9.0     | 19.54   | 19.0    | 71.23     |
| 3.3037        | 6.0   | 3510 | 3.2693          | 21.41   | 9.0     | 19.5    | 19.0    | 71.21     |
| 3.2357        | 7.0   | 4095 | 3.2581          | 22.05   | 9.36    | 20.04   | 19.0    | 71.43     |
| 3.1798        | 8.0   | 4680 | 3.2522          | 22.21   | 9.56    | 20.23   | 19.0    | 71.41     |
| 3.1359        | 9.0   | 5265 | 3.2546          | 22.27   | 9.58    | 20.23   | 19.0    | 71.46     |
| 3.0997        | 10.0  | 5850 | 3.2546          | 22.2    | 9.57    | 20.26   | 19.0    | 71.43     |


### Framework versions

- Transformers 4.19.4
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1

{"license": "apache-2.0", "tags": ["summarization", "mT5_multilingual_XLSum", "mt5", "abstractive summarization", "ar", "xlsum", "generated_from_trainer"], "datasets": ["xlsum"], "model-index": [{"name": "mt5-base-finetune-ar-xlsum", "results": []}]}
transformers pytorch tensorboard mt5 text2text-generation summarization mT5_multilingual_XLSum abstractive summarization ar xlsum generated_from_trainer dataset:xlsum license:apache-2.0 autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #tensorboard #mt5 #text2text-generation #summarization #mT5_multilingual_XLSum #abstractive summarization #ar #xlsum #generated_from_trainer #dataset-xlsum #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 

mt5-base-finetune-ar-xlsum
==========================


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


* Loss: 3.2546
* Rouge-1: 22.2
* Rouge-2: 9.57
* Rouge-l: 20.26
* Gen Len: 19.0
* Bertscore: 71.43


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: 0.0005
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 42
* gradient\_accumulation\_steps: 16
* total\_train\_batch\_size: 64
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 250
* num\_epochs: 10
* label\_smoothing\_factor: 0.1


### Training results



### Framework versions


* Transformers 4.19.4
* Pytorch 1.11.0+cu113
* Datasets 2.2.2
* Tokenizers 0.12.1



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0005
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 42
* gradient\_accumulation\_steps: 16
* total\_train\_batch\_size: 64
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 250
* num\_epochs: 10
* label\_smoothing\_factor: 0.1 ### Training results ### Framework versions


* Transformers 4.19.4
* Pytorch 1.11.0+cu113
* Datasets 2.2.2
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #tensorboard #mt5 #text2text-generation #summarization #mT5_multilingual_XLSum #abstractive summarization #ar #xlsum #generated_from_trainer #dataset-xlsum #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0005
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 42
* gradient\_accumulation\_steps: 16
* total\_train\_batch\_size: 64
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 250
* num\_epochs: 10
* label\_smoothing\_factor: 0.1 ### Training results ### Framework versions


* Transformers 4.19.4
* Pytorch 1.11.0+cu113
* Datasets 2.2.2
* Tokenizers 0.12.1
passage: TAGS
#transformers #pytorch #tensorboard #mt5 #text2text-generation #summarization #mT5_multilingual_XLSum #abstractive summarization #ar #xlsum #generated_from_trainer #dataset-xlsum #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0005
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 42
* gradient\_accumulation\_steps: 16
* total\_train\_batch\_size: 64
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 250
* num\_epochs: 10
* label\_smoothing\_factor: 0.1### Training results### Framework versions


* Transformers 4.19.4
* Pytorch 1.11.0+cu113
* Datasets 2.2.2
* Tokenizers 0.12.1