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# mT5_multilingual_XLSum-finetuned-en-cnn

This model is a fine-tuned version of [csebuetnlp/mT5_multilingual_XLSum](https://huggingface.co/csebuetnlp/mT5_multilingual_XLSum) on the cnn_dailymail dataset.
It achieves the following results on the evaluation set:
- Loss: 3.0025
- Rouge-1: 36.87
- Rouge-2: 15.31
- Rouge-l: 33.74
- Gen Len: 77.93
- Bertscore: 88.28

## 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: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 250
- num_epochs: 4
- label_smoothing_factor: 0.1

### Training results



### Framework versions

- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1

{"tags": ["summarization", "en", "mt5", "Abstractive Summarization", "generated_from_trainer"], "datasets": ["cnn_dailymail"], "model-index": [{"name": "mT5_multilingual_XLSum-finetuned-en-cnn", "results": []}]}
transformers pytorch tensorboard mt5 text2text-generation summarization en Abstractive Summarization generated_from_trainer dataset:cnn_dailymail autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #tensorboard #mt5 #text2text-generation #summarization #en #Abstractive Summarization #generated_from_trainer #dataset-cnn_dailymail #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 




# mT5_multilingual_XLSum-finetuned-en-cnn

This model is a fine-tuned version of csebuetnlp/mT5_multilingual_XLSum on the cnn_dailymail dataset.
It achieves the following results on the evaluation set:
- Loss: 3.0025
- Rouge-1: 36.87
- Rouge-2: 15.31
- Rouge-l: 33.74
- Gen Len: 77.93
- Bertscore: 88.28

## 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: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 250
- num_epochs: 4
- label_smoothing_factor: 0.1

### Training results



### Framework versions

- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1

# mT5_multilingual_XLSum-finetuned-en-cnn

This model is a fine-tuned version of csebuetnlp/mT5_multilingual_XLSum on the cnn_dailymail dataset.
It achieves the following results on the evaluation set:
- Loss: 3.0025
- Rouge-1: 36.87
- Rouge-2: 15.31
- Rouge-l: 33.74
- Gen Len: 77.93
- Bertscore: 88.28 ## 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: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 250
- num_epochs: 4
- label_smoothing_factor: 0.1 ### Training results ### Framework versions

- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
TAGS
#transformers #pytorch #tensorboard #mt5 #text2text-generation #summarization #en #Abstractive Summarization #generated_from_trainer #dataset-cnn_dailymail #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
 # mT5_multilingual_XLSum-finetuned-en-cnn

This model is a fine-tuned version of csebuetnlp/mT5_multilingual_XLSum on the cnn_dailymail dataset.
It achieves the following results on the evaluation set:
- Loss: 3.0025
- Rouge-1: 36.87
- Rouge-2: 15.31
- Rouge-l: 33.74
- Gen Len: 77.93
- Bertscore: 88.28 ## 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: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 250
- num_epochs: 4
- label_smoothing_factor: 0.1 ### Training results ### Framework versions

- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
passage: TAGS
#transformers #pytorch #tensorboard #mt5 #text2text-generation #summarization #en #Abstractive Summarization #generated_from_trainer #dataset-cnn_dailymail #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
# mT5_multilingual_XLSum-finetuned-en-cnn

This model is a fine-tuned version of csebuetnlp/mT5_multilingual_XLSum on the cnn_dailymail dataset.
It achieves the following results on the evaluation set:
- Loss: 3.0025
- Rouge-1: 36.87
- Rouge-2: 15.31
- Rouge-l: 33.74
- Gen Len: 77.93
- Bertscore: 88.28## 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: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 250
- num_epochs: 4
- label_smoothing_factor: 0.1### Training results### Framework versions

- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1