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# Waynehills-NLP-doogie-AIHub-paper-summary

This model is a fine-tuned version of [mimi/Waynehills-NLP-doogie](https://huggingface.co/mimi/Waynehills-NLP-doogie) on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 2.6206
- eval_runtime: 309.223
- eval_samples_per_second: 38.167
- eval_steps_per_second: 4.773
- epoch: 3.75
- step: 60000

## 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: 2e-05
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4

### Framework versions

- Transformers 4.12.2
- Pytorch 1.10.0+cu111
- Datasets 1.5.0
- Tokenizers 0.10.3

{"tags": ["generated_from_trainer"], "model-index": [{"name": "Waynehills-NLP-doogie-AIHub-paper-summary", "results": []}]}
transformers pytorch t5 text2text-generation generated_from_trainer autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #t5 #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 




# Waynehills-NLP-doogie-AIHub-paper-summary

This model is a fine-tuned version of mimi/Waynehills-NLP-doogie on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 2.6206
- eval_runtime: 309.223
- eval_samples_per_second: 38.167
- eval_steps_per_second: 4.773
- epoch: 3.75
- step: 60000

## 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: 2e-05
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4

### Framework versions

- Transformers 4.12.2
- Pytorch 1.10.0+cu111
- Datasets 1.5.0
- Tokenizers 0.10.3

# Waynehills-NLP-doogie-AIHub-paper-summary

This model is a fine-tuned version of mimi/Waynehills-NLP-doogie on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 2.6206
- eval_runtime: 309.223
- eval_samples_per_second: 38.167
- eval_steps_per_second: 4.773
- epoch: 3.75
- step: 60000 ## 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: 2e-05
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4 ### Framework versions

- Transformers 4.12.2
- Pytorch 1.10.0+cu111
- Datasets 1.5.0
- Tokenizers 0.10.3
TAGS
#transformers #pytorch #t5 #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
 # Waynehills-NLP-doogie-AIHub-paper-summary

This model is a fine-tuned version of mimi/Waynehills-NLP-doogie on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 2.6206
- eval_runtime: 309.223
- eval_samples_per_second: 38.167
- eval_steps_per_second: 4.773
- epoch: 3.75
- step: 60000 ## 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: 2e-05
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4 ### Framework versions

- Transformers 4.12.2
- Pytorch 1.10.0+cu111
- Datasets 1.5.0
- Tokenizers 0.10.3
passage: TAGS
#transformers #pytorch #t5 #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
# Waynehills-NLP-doogie-AIHub-paper-summary

This model is a fine-tuned version of mimi/Waynehills-NLP-doogie on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 2.6206
- eval_runtime: 309.223
- eval_samples_per_second: 38.167
- eval_steps_per_second: 4.773
- epoch: 3.75
- step: 60000## 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: 2e-05
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4### Framework versions

- Transformers 4.12.2
- Pytorch 1.10.0+cu111
- Datasets 1.5.0
- Tokenizers 0.10.3