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# mbert2mbert-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: 3.6753
- Rouge-1: 15.19
- Rouge-2: 5.45
- Rouge-l: 14.64
- Gen Len: 20.0
- Bertscore: 67.86

## 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

{"tags": ["summarization", "ar", "encoder-decoder", "mbert", "mbert2mbert", "Abstractive Summarization", "generated_from_trainer"], "datasets": ["wiki_lingua"], "model-index": [{"name": "mbert2mbert-finetuned-ar-wikilingua", "results": []}]}
transformers pytorch tensorboard encoder-decoder text2text-generation summarization ar mbert mbert2mbert Abstractive Summarization generated_from_trainer dataset:wiki_lingua autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #encoder-decoder #text2text-generation #summarization #ar #mbert #mbert2mbert #Abstractive Summarization #generated_from_trainer #dataset-wiki_lingua #autotrain_compatible #endpoints_compatible #region-us 




# mbert2mbert-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: 3.6753
- Rouge-1: 15.19
- Rouge-2: 5.45
- Rouge-l: 14.64
- Gen Len: 20.0
- Bertscore: 67.86

## 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

# mbert2mbert-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: 3.6753
- Rouge-1: 15.19
- Rouge-2: 5.45
- Rouge-l: 14.64
- Gen Len: 20.0
- Bertscore: 67.86 ## 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
TAGS
#transformers #pytorch #tensorboard #encoder-decoder #text2text-generation #summarization #ar #mbert #mbert2mbert #Abstractive Summarization #generated_from_trainer #dataset-wiki_lingua #autotrain_compatible #endpoints_compatible #region-us 
 # mbert2mbert-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: 3.6753
- Rouge-1: 15.19
- Rouge-2: 5.45
- Rouge-l: 14.64
- Gen Len: 20.0
- Bertscore: 67.86 ## 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
passage: TAGS
#transformers #pytorch #tensorboard #encoder-decoder #text2text-generation #summarization #ar #mbert #mbert2mbert #Abstractive Summarization #generated_from_trainer #dataset-wiki_lingua #autotrain_compatible #endpoints_compatible #region-us 
# mbert2mbert-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: 3.6753
- Rouge-1: 15.19
- Rouge-2: 5.45
- Rouge-l: 14.64
- Gen Len: 20.0
- Bertscore: 67.86## 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