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# AraBART-finetuned-ar

This model is a fine-tuned version of [moussaKam/AraBART](https://huggingface.co/moussaKam/AraBART) on the xlsum dataset.
It achieves the following results on the evaluation set:
- Loss: 3.7449
- Rouge-1: 31.08
- Rouge-2: 14.68
- Rouge-l: 27.36
- Gen Len: 19.64
- Bertscore: 73.86

## 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: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:-------:|:---------:|
| 4.4318        | 1.0   | 2345  | 3.7996          | 28.93   | 13.2    | 25.56   | 19.51   | 73.17     |
| 4.0338        | 2.0   | 4690  | 3.7483          | 30.29   | 14.24   | 26.73   | 19.5    | 73.59     |
| 3.8586        | 3.0   | 7035  | 3.7281          | 30.44   | 14.44   | 26.92   | 19.75   | 73.58     |
| 3.7289        | 4.0   | 9380  | 3.7204          | 30.55   | 14.49   | 26.88   | 19.66   | 73.73     |
| 3.6245        | 5.0   | 11725 | 3.7199          | 30.73   | 14.63   | 27.11   | 19.69   | 73.68     |
| 3.5392        | 6.0   | 14070 | 3.7221          | 30.85   | 14.65   | 27.21   | 19.7    | 73.77     |
| 3.4694        | 7.0   | 16415 | 3.7286          | 31.08   | 14.8    | 27.41   | 19.62   | 73.84     |
| 3.4126        | 8.0   | 18760 | 3.7384          | 31.06   | 14.77   | 27.41   | 19.64   | 73.82     |
| 3.3718        | 9.0   | 21105 | 3.7398          | 31.18   | 14.89   | 27.49   | 19.67   | 73.87     |
| 3.3428        | 10.0  | 23450 | 3.7449          | 31.19   | 14.88   | 27.44   | 19.68   | 73.87     |


### Framework versions

- Transformers 4.18.0
- Pytorch 1.10.0+cu111
- Datasets 2.1.0
- Tokenizers 0.12.1

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

AraBART-finetuned-ar
====================


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


* Loss: 3.7449
* Rouge-1: 31.08
* Rouge-2: 14.68
* Rouge-l: 27.36
* Gen Len: 19.64
* Bertscore: 73.86


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: 5e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* 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 4.18.0
* Pytorch 1.10.0+cu111
* Datasets 2.1.0
* Tokenizers 0.12.1



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* 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 4.18.0
* Pytorch 1.10.0+cu111
* Datasets 2.1.0
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #tensorboard #mbart #text2text-generation #summarization #generated_from_trainer #dataset-xlsum #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* 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 4.18.0
* Pytorch 1.10.0+cu111
* Datasets 2.1.0
* Tokenizers 0.12.1
passage: TAGS
#transformers #pytorch #tensorboard #mbart #text2text-generation #summarization #generated_from_trainer #dataset-xlsum #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* 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 4.18.0
* Pytorch 1.10.0+cu111
* Datasets 2.1.0
* Tokenizers 0.12.1