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# mt5-base-finetuned-english-finetuned-english-arabic

This model is a fine-tuned version of [eslamxm/mt5-base-finetuned-english](https://huggingface.co/eslamxm/mt5-base-finetuned-english) on the xlsum dataset.
It achieves the following results on the evaluation set:
- Loss: 3.4788
- Rouge-1: 22.55
- Rouge-2: 9.84
- Rouge-l: 20.5
- Gen Len: 19.0
- Bertscore: 71.39

## 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: 8
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- label_smoothing_factor: 0.1

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge-1 | Rouge-2 | Rouge-l | Gen Len | Bertscore |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:-------:|:---------:|
| 4.999         | 1.0   | 1172 | 3.9343          | 17.67   | 5.93    | 15.86   | 19.0    | 69.69     |
| 4.008         | 2.0   | 2344 | 3.6655          | 19.48   | 7.67    | 17.67   | 19.0    | 70.49     |
| 3.7463        | 3.0   | 3516 | 3.5503          | 20.47   | 8.24    | 18.6    | 19.0    | 70.86     |
| 3.5924        | 4.0   | 4688 | 3.4942          | 20.95   | 8.45    | 19.05   | 19.0    | 71.0      |
| 3.4979        | 5.0   | 5860 | 3.4788          | 21.34   | 8.75    | 19.39   | 19.0    | 71.11     |


### Framework versions

- Transformers 4.19.0
- Pytorch 1.11.0+cu113
- Datasets 2.2.1
- Tokenizers 0.12.1

{"license": "apache-2.0", "tags": ["summarization", "arabic", "ar", "en", "mt5", "Abstractive Summarization", "generated_from_trainer"], "datasets": ["xlsum"], "model-index": [{"name": "mt5-base-finetuned-english-finetuned-english-arabic", "results": []}]}
transformers pytorch mt5 text2text-generation summarization arabic ar en Abstractive Summarization generated_from_trainer dataset:xlsum license:apache-2.0 autotrain_compatible endpoints_compatible has_space text-generation-inference region:us
TAGS
#transformers #pytorch #mt5 #text2text-generation #summarization #arabic #ar #en #Abstractive Summarization #generated_from_trainer #dataset-xlsum #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us 

mt5-base-finetuned-english-finetuned-english-arabic
===================================================


This model is a fine-tuned version of eslamxm/mt5-base-finetuned-english on the xlsum dataset.
It achieves the following results on the evaluation set:


* Loss: 3.4788
* Rouge-1: 22.55
* Rouge-2: 9.84
* Rouge-l: 20.5
* Gen Len: 19.0
* Bertscore: 71.39


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: 8
* total\_train\_batch\_size: 32
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 5
* label\_smoothing\_factor: 0.1


### Training results



### Framework versions


* Transformers 4.19.0
* Pytorch 1.11.0+cu113
* Datasets 2.2.1
* 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: 8
* total\_train\_batch\_size: 32
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 5
* label\_smoothing\_factor: 0.1 ### Training results ### Framework versions


* Transformers 4.19.0
* Pytorch 1.11.0+cu113
* Datasets 2.2.1
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #mt5 #text2text-generation #summarization #arabic #ar #en #Abstractive Summarization #generated_from_trainer #dataset-xlsum #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #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: 8
* total\_train\_batch\_size: 32
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 5
* label\_smoothing\_factor: 0.1 ### Training results ### Framework versions


* Transformers 4.19.0
* Pytorch 1.11.0+cu113
* Datasets 2.2.1
* Tokenizers 0.12.1
passage: TAGS
#transformers #pytorch #mt5 #text2text-generation #summarization #arabic #ar #en #Abstractive Summarization #generated_from_trainer #dataset-xlsum #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #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: 8
* total\_train\_batch\_size: 32
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 5
* label\_smoothing\_factor: 0.1### Training results### Framework versions


* Transformers 4.19.0
* Pytorch 1.11.0+cu113
* Datasets 2.2.1
* Tokenizers 0.12.1