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# AraT5-base-finetune-ar-wikilingua

This model is a fine-tuned version of [UBC-NLP/AraT5-base](https://huggingface.co/UBC-NLP/AraT5-base) on the wiki_lingua dataset.
It achieves the following results on the evaluation set:
- Loss: 4.6110
- Rouge-1: 19.97
- Rouge-2: 6.9
- Rouge-l: 18.25
- Gen Len: 18.45
- Bertscore: 69.44

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:-------:|:---------:|
| 11.5412       | 1.0   | 312  | 6.8825          | 5.2     | 0.69    | 5.04    | 19.0    | 63.2      |
| 6.5212        | 2.0   | 624  | 5.8992          | 8.89    | 1.4     | 8.36    | 17.92   | 63.9      |
| 5.8302        | 3.0   | 936  | 5.3712          | 9.99    | 2.21    | 9.54    | 15.87   | 65.08     |
| 5.406         | 4.0   | 1248 | 5.0632          | 13.94   | 3.5     | 13.0    | 15.95   | 66.83     |
| 5.1109        | 5.0   | 1560 | 4.8718          | 15.28   | 4.34    | 14.27   | 18.26   | 66.83     |
| 4.9004        | 6.0   | 1872 | 4.7631          | 16.65   | 4.92    | 15.46   | 17.73   | 67.75     |
| 4.754         | 7.0   | 2184 | 4.6920          | 18.31   | 5.79    | 16.9    | 18.17   | 68.55     |
| 4.6369        | 8.0   | 2496 | 4.6459          | 18.6    | 6.12    | 17.16   | 18.16   | 68.66     |
| 4.5595        | 9.0   | 2808 | 4.6153          | 18.94   | 6.1     | 17.39   | 17.82   | 68.99     |
| 4.4967        | 10.0  | 3120 | 4.6110          | 19.15   | 6.25    | 17.55   | 17.91   | 69.09     |


### Framework versions

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

{"tags": ["summarization", "ar", "Abstractive Summarization", "generated_from_trainer"], "datasets": ["wiki_lingua"], "model-index": [{"name": "AraT5-base-finetune-ar-wikilingua", "results": []}]}
transformers pytorch tensorboard t5 text2text-generation summarization ar Abstractive Summarization generated_from_trainer dataset:wiki_lingua autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #summarization #ar #Abstractive Summarization #generated_from_trainer #dataset-wiki_lingua #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 

AraT5-base-finetune-ar-wikilingua
=================================


This model is a fine-tuned version of UBC-NLP/AraT5-base on the wiki\_lingua dataset.
It achieves the following results on the evaluation set:


* Loss: 4.6110
* Rouge-1: 19.97
* Rouge-2: 6.9
* Rouge-l: 18.25
* Gen Len: 18.45
* Bertscore: 69.44


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 #t5 #text2text-generation #summarization #ar #Abstractive Summarization #generated_from_trainer #dataset-wiki_lingua #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 #t5 #text2text-generation #summarization #ar #Abstractive Summarization #generated_from_trainer #dataset-wiki_lingua #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