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# AraT5-base-finetune-ar-xlsum

This model is a fine-tuned version of [UBC-NLP/AraT5-base](https://huggingface.co/UBC-NLP/AraT5-base) on the xlsum dataset.
It achieves the following results on the evaluation set:
- Loss: 4.4714
- Rouge-1: 29.55
- Rouge-2: 12.63
- Rouge-l: 25.8
- Gen Len: 18.76
- Bertscore: 73.3

## 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: 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: 10
- label_smoothing_factor: 0.1

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge-1 | Rouge-2 | Rouge-l | Gen Len | Bertscore |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:-------:|:---------:|
| 11.9753       | 1.0   | 293  | 7.0887          | 11.93   | 2.56    | 10.93   | 17.19   | 63.85     |
| 6.7818        | 2.0   | 586  | 5.7712          | 19.94   | 6.34    | 17.65   | 18.64   | 69.0      |
| 5.9434        | 3.0   | 879  | 5.1083          | 23.51   | 8.56    | 20.66   | 18.88   | 70.78     |
| 5.451         | 4.0   | 1172 | 4.8538          | 25.84   | 10.05   | 22.63   | 18.42   | 72.04     |
| 5.1643        | 5.0   | 1465 | 4.6910          | 27.23   | 11.13   | 23.83   | 18.78   | 72.45     |
| 4.9693        | 6.0   | 1758 | 4.5950          | 28.42   | 11.71   | 24.82   | 18.74   | 72.94     |
| 4.8308        | 7.0   | 2051 | 4.5323          | 28.95   | 12.19   | 25.3    | 18.74   | 73.13     |
| 4.7284        | 8.0   | 2344 | 4.4956          | 29.19   | 12.37   | 25.53   | 18.76   | 73.18     |
| 4.653         | 9.0   | 2637 | 4.4757          | 29.44   | 12.48   | 25.63   | 18.78   | 73.23     |
| 4.606         | 10.0  | 2930 | 4.4714          | 29.55   | 12.63   | 25.8    | 18.76   | 73.3      |


### Framework versions

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

{"tags": ["summarization", "Arat5-base", "abstractive summarization", "ar", "xlsum", "generated_from_trainer"], "datasets": ["xlsum"], "model-index": [{"name": "AraT5-base-finetune-ar-xlsum", "results": []}]}
transformers pytorch tensorboard t5 text2text-generation summarization Arat5-base abstractive summarization ar xlsum generated_from_trainer dataset:xlsum autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #summarization #Arat5-base #abstractive summarization #ar #xlsum #generated_from_trainer #dataset-xlsum #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 

AraT5-base-finetune-ar-xlsum
============================


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


* Loss: 4.4714
* Rouge-1: 29.55
* Rouge-2: 12.63
* Rouge-l: 25.8
* Gen Len: 18.76
* Bertscore: 73.3


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: 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: 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: 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: 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 #Arat5-base #abstractive summarization #ar #xlsum #generated_from_trainer #dataset-xlsum #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: 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: 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 #Arat5-base #abstractive summarization #ar #xlsum #generated_from_trainer #dataset-xlsum #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: 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: 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