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# arabert2arabert-finetuned-ar-wikilingua

This model is a fine-tuned version of [](https://huggingface.co/) on the wiki_lingua dataset.
It achieves the following results on the evaluation set:
- Loss: 4.6877
- Rouge-1: 13.2
- Rouge-2: 3.43
- Rouge-l: 12.45
- Gen Len: 20.0
- Bertscore: 64.88

## 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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 250
- num_epochs: 8
- label_smoothing_factor: 0.1

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge-1 | Rouge-2 | Rouge-l | Gen Len | Bertscore |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:-------:|:---------:|
| 6.7667        | 1.0   | 156  | 5.3846          | 3.36    | 0.56    | 3.27    | 20.0    | 60.6      |
| 5.257         | 2.0   | 312  | 5.0424          | 5.44    | 0.88    | 5.35    | 20.0    | 60.56     |
| 4.743         | 3.0   | 468  | 4.8294          | 9.21    | 1.8     | 8.93    | 20.0    | 62.91     |
| 4.3832        | 4.0   | 624  | 4.7240          | 9.88    | 2.19    | 9.6     | 20.0    | 62.65     |
| 4.1166        | 5.0   | 780  | 4.6861          | 11.61   | 2.86    | 11.13   | 20.0    | 63.71     |
| 3.91          | 6.0   | 936  | 4.6692          | 12.27   | 3.11    | 11.76   | 20.0    | 64.07     |
| 3.7569        | 7.0   | 1092 | 4.6805          | 12.93   | 3.38    | 12.28   | 20.0    | 64.61     |
| 3.6454        | 8.0   | 1248 | 4.6877          | 13.2    | 3.43    | 12.45   | 20.0    | 64.88     |


### Framework versions

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

{"tags": ["summarization", "ar", "encoder-decoder", "arabert", "arabert2arabert", "Abstractive Summarization", "generated_from_trainer"], "datasets": ["wiki_lingua"], "model-index": [{"name": "arabert2arabert-finetuned-ar-wikilingua", "results": []}]}
transformers pytorch tensorboard encoder-decoder text2text-generation summarization ar arabert arabert2arabert Abstractive Summarization generated_from_trainer dataset:wiki_lingua autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #encoder-decoder #text2text-generation #summarization #ar #arabert #arabert2arabert #Abstractive Summarization #generated_from_trainer #dataset-wiki_lingua #autotrain_compatible #endpoints_compatible #region-us 

arabert2arabert-finetuned-ar-wikilingua
=======================================


This model is a fine-tuned version of [](URL on the wiki\_lingua dataset.
It achieves the following results on the evaluation set:


* Loss: 4.6877
* Rouge-1: 13.2
* Rouge-2: 3.43
* Rouge-l: 12.45
* Gen Len: 20.0
* Bertscore: 64.88


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: 8
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 16
* total\_train\_batch\_size: 128
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 250
* num\_epochs: 8
* 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: 5e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 16
* total\_train\_batch\_size: 128
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 250
* num\_epochs: 8
* 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 #encoder-decoder #text2text-generation #summarization #ar #arabert #arabert2arabert #Abstractive Summarization #generated_from_trainer #dataset-wiki_lingua #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 16
* total\_train\_batch\_size: 128
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 250
* num\_epochs: 8
* 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 #encoder-decoder #text2text-generation #summarization #ar #arabert #arabert2arabert #Abstractive Summarization #generated_from_trainer #dataset-wiki_lingua #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 16
* total\_train\_batch\_size: 128
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 250
* num\_epochs: 8
* 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