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# mt5_large_riju_data

This model is a fine-tuned version of [MahdiSUST/bn_sum](https://huggingface.co/MahdiSUST/bn_sum) on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 1.3531
- eval_rouge1: {'precision': 41.8541697896367, 'recall': 41.3654041539666, 'fmeasure': 40.98106539607074}
- eval_rougeL: {'precision': 39.824364163293794, 'recall': 39.31906760605605, 'fmeasure': 38.97108720823315}
- eval_runtime: 229.14
- eval_samples_per_second: 4.159
- eval_steps_per_second: 2.082
- epoch: 9

## 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: 5.6e-05
- train_batch_size: 9
- eval_batch_size: 9
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10

### Framework versions

- Transformers 4.27.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2

{"license": "bigscience-openrail-m", "tags": ["generated_from_trainer"], "model-index": [{"name": "Bangla summarization using mt5-base", "results": []}]}
transformers pytorch tensorboard mt5 text2text-generation generated_from_trainer license:bigscience-openrail-m autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #tensorboard #mt5 #text2text-generation #generated_from_trainer #license-bigscience-openrail-m #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 




# mt5_large_riju_data

This model is a fine-tuned version of MahdiSUST/bn_sum on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 1.3531
- eval_rouge1: {'precision': 41.8541697896367, 'recall': 41.3654041539666, 'fmeasure': 40.98106539607074}
- eval_rougeL: {'precision': 39.824364163293794, 'recall': 39.31906760605605, 'fmeasure': 38.97108720823315}
- eval_runtime: 229.14
- eval_samples_per_second: 4.159
- eval_steps_per_second: 2.082
- epoch: 9

## 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: 5.6e-05
- train_batch_size: 9
- eval_batch_size: 9
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10

### Framework versions

- Transformers 4.27.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2

# mt5_large_riju_data

This model is a fine-tuned version of MahdiSUST/bn_sum on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 1.3531
- eval_rouge1: {'precision': 41.8541697896367, 'recall': 41.3654041539666, 'fmeasure': 40.98106539607074}
- eval_rougeL: {'precision': 39.824364163293794, 'recall': 39.31906760605605, 'fmeasure': 38.97108720823315}
- eval_runtime: 229.14
- eval_samples_per_second: 4.159
- eval_steps_per_second: 2.082
- epoch: 9 ## 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: 5.6e-05
- train_batch_size: 9
- eval_batch_size: 9
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10 ### Framework versions

- Transformers 4.27.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #mt5 #text2text-generation #generated_from_trainer #license-bigscience-openrail-m #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
 # mt5_large_riju_data

This model is a fine-tuned version of MahdiSUST/bn_sum on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 1.3531
- eval_rouge1: {'precision': 41.8541697896367, 'recall': 41.3654041539666, 'fmeasure': 40.98106539607074}
- eval_rougeL: {'precision': 39.824364163293794, 'recall': 39.31906760605605, 'fmeasure': 38.97108720823315}
- eval_runtime: 229.14
- eval_samples_per_second: 4.159
- eval_steps_per_second: 2.082
- epoch: 9 ## 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: 5.6e-05
- train_batch_size: 9
- eval_batch_size: 9
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10 ### Framework versions

- Transformers 4.27.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #mt5 #text2text-generation #generated_from_trainer #license-bigscience-openrail-m #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
# mt5_large_riju_data

This model is a fine-tuned version of MahdiSUST/bn_sum on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 1.3531
- eval_rouge1: {'precision': 41.8541697896367, 'recall': 41.3654041539666, 'fmeasure': 40.98106539607074}
- eval_rougeL: {'precision': 39.824364163293794, 'recall': 39.31906760605605, 'fmeasure': 38.97108720823315}
- eval_runtime: 229.14
- eval_samples_per_second: 4.159
- eval_steps_per_second: 2.082
- epoch: 9## 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: 5.6e-05
- train_batch_size: 9
- eval_batch_size: 9
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10### Framework versions

- Transformers 4.27.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2