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# AraT5-base-title-generation-finetune-ar-xlsum

This model is a fine-tuned version of [UBC-NLP/AraT5-base-title-generation](https://huggingface.co/UBC-NLP/AraT5-base-title-generation) on the xlsum dataset.
It achieves the following results on the evaluation set:
- Loss: 4.2837
- Rouge-1: 32.46
- Rouge-2: 15.15
- Rouge-l: 28.38
- Gen Len: 18.48
- Bertscore: 74.24

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:-------:|:---------:|
| 5.815         | 1.0   | 293  | 4.7437          | 27.05   | 10.49   | 23.56   | 18.03   | 72.56     |
| 5.0818        | 2.0   | 586  | 4.5004          | 28.92   | 11.97   | 25.09   | 18.61   | 73.08     |
| 4.7855        | 3.0   | 879  | 4.3910          | 29.66   | 12.57   | 25.79   | 18.58   | 73.3      |
| 4.588         | 4.0   | 1172 | 4.3469          | 30.22   | 13.05   | 26.36   | 18.59   | 73.61     |
| 4.4388        | 5.0   | 1465 | 4.3226          | 30.88   | 13.81   | 27.01   | 18.65   | 73.78     |
| 4.3162        | 6.0   | 1758 | 4.2990          | 30.9    | 13.6    | 26.92   | 18.68   | 73.78     |
| 4.2178        | 7.0   | 2051 | 4.2869          | 31.35   | 14.01   | 27.41   | 18.57   | 73.96     |
| 4.1387        | 8.0   | 2344 | 4.2794          | 31.28   | 13.98   | 27.34   | 18.6    | 73.87     |
| 4.0787        | 9.0   | 2637 | 4.2806          | 31.45   | 14.17   | 27.46   | 18.66   | 73.97     |
| 4.0371        | 10.0  | 2930 | 4.2837          | 31.55   | 14.19   | 27.52   | 18.65   | 74.0      |


### Framework versions

- Transformers 4.20.0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1

{"tags": ["summarization", "Arat5-base", "abstractive summarization", "ar", "xlsum", "generated_from_trainer"], "datasets": ["xlsum"], "model-index": [{"name": "AraT5-base-title-generation-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-title-generation-finetune-ar-xlsum
=============================================


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


* Loss: 4.2837
* Rouge-1: 32.46
* Rouge-2: 15.15
* Rouge-l: 28.38
* Gen Len: 18.48
* Bertscore: 74.24


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 4.20.0
* Pytorch 1.11.0+cu113
* Datasets 2.3.2
* 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 4.20.0
* Pytorch 1.11.0+cu113
* Datasets 2.3.2
* 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 4.20.0
* Pytorch 1.11.0+cu113
* Datasets 2.3.2
* 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 4.20.0
* Pytorch 1.11.0+cu113
* Datasets 2.3.2
* Tokenizers 0.12.1