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# vit

This model is a fine-tuned version of [nlpconnect/vit-gpt2-image-captioning](https://huggingface.co/nlpconnect/vit-gpt2-image-captioning) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2973
- Rouge1: 67.9673
- Rouge2: 58.9518
- Rougel: 67.1789
- Rougelsum: 67.324
- Bleu: 54.4707
- Gen Len: 7.7647

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

### Training results



### Framework versions

- Transformers 4.28.0
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["rouge", "bleu"], "model-index": [{"name": "vit", "results": []}]}
transformers pytorch tensorboard vision-encoder-decoder generated_from_trainer license:apache-2.0 endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #vision-encoder-decoder #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us 




# vit

This model is a fine-tuned version of nlpconnect/vit-gpt2-image-captioning on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2973
- Rouge1: 67.9673
- Rouge2: 58.9518
- Rougel: 67.1789
- Rougelsum: 67.324
- Bleu: 54.4707
- Gen Len: 7.7647

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

### Training results



### Framework versions

- Transformers 4.28.0
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3

# vit

This model is a fine-tuned version of nlpconnect/vit-gpt2-image-captioning on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2973
- Rouge1: 67.9673
- Rouge2: 58.9518
- Rougel: 67.1789
- Rougelsum: 67.324
- Bleu: 54.4707
- Gen Len: 7.7647 ## 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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4 ### Training results ### Framework versions

- Transformers 4.28.0
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #vision-encoder-decoder #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us 
 # vit

This model is a fine-tuned version of nlpconnect/vit-gpt2-image-captioning on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2973
- Rouge1: 67.9673
- Rouge2: 58.9518
- Rougel: 67.1789
- Rougelsum: 67.324
- Bleu: 54.4707
- Gen Len: 7.7647 ## 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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4 ### Training results ### Framework versions

- Transformers 4.28.0
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #vision-encoder-decoder #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us 
# vit

This model is a fine-tuned version of nlpconnect/vit-gpt2-image-captioning on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2973
- Rouge1: 67.9673
- Rouge2: 58.9518
- Rougel: 67.1789
- Rougelsum: 67.324
- Bleu: 54.4707
- Gen Len: 7.7647## 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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4### Training results### Framework versions

- Transformers 4.28.0
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3