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# t5-end2end-questions-generation

This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the squad_modified_for_t5_qg dataset.
It achieves the following results on the evaluation set:
- eval_loss: 1.6634
- eval_runtime: 95.9639
- eval_samples_per_second: 21.539
- eval_steps_per_second: 5.387
- epoch: 1.02
- step: 300

## 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: 0.0001
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 7

### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad_modified_for_t5_qg"], "model-index": [{"name": "t5-end2end-questions-generation", "results": []}]}
transformers pytorch t5 text2text-generation generated_from_trainer dataset:squad_modified_for_t5_qg license:apache-2.0 autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #t5 #text2text-generation #generated_from_trainer #dataset-squad_modified_for_t5_qg #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 




# t5-end2end-questions-generation

This model is a fine-tuned version of t5-base on the squad_modified_for_t5_qg dataset.
It achieves the following results on the evaluation set:
- eval_loss: 1.6634
- eval_runtime: 95.9639
- eval_samples_per_second: 21.539
- eval_steps_per_second: 5.387
- epoch: 1.02
- step: 300

## 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: 0.0001
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 7

### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2

# t5-end2end-questions-generation

This model is a fine-tuned version of t5-base on the squad_modified_for_t5_qg dataset.
It achieves the following results on the evaluation set:
- eval_loss: 1.6634
- eval_runtime: 95.9639
- eval_samples_per_second: 21.539
- eval_steps_per_second: 5.387
- epoch: 1.02
- step: 300 ## 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: 0.0001
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 7 ### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
TAGS
#transformers #pytorch #t5 #text2text-generation #generated_from_trainer #dataset-squad_modified_for_t5_qg #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
 # t5-end2end-questions-generation

This model is a fine-tuned version of t5-base on the squad_modified_for_t5_qg dataset.
It achieves the following results on the evaluation set:
- eval_loss: 1.6634
- eval_runtime: 95.9639
- eval_samples_per_second: 21.539
- eval_steps_per_second: 5.387
- epoch: 1.02
- step: 300 ## 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: 0.0001
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 7 ### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
TAGS
#transformers #pytorch #t5 #text2text-generation #generated_from_trainer #dataset-squad_modified_for_t5_qg #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
# t5-end2end-questions-generation

This model is a fine-tuned version of t5-base on the squad_modified_for_t5_qg dataset.
It achieves the following results on the evaluation set:
- eval_loss: 1.6634
- eval_runtime: 95.9639
- eval_samples_per_second: 21.539
- eval_steps_per_second: 5.387
- epoch: 1.02
- step: 300## 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: 0.0001
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 7### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2