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# bridgetower-newyorker-a100-8x

> Disclaimer: Those are the results I obtained with an older version of software. It is possible to fit batches of 48 samples and to get better throughput as mentioned in this blog post: https://huggingface.co/blog/bridgetower

This model is a fine-tuned version of [BridgeTower/bridgetower-large-itm-mlm-itc](https://huggingface.co/BridgeTower/bridgetower-large-itm-mlm-itc) on the jmhessel/newyorker_caption_contest matching dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1148

## 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: 1e-05
- train_batch_size: 32
- eval_batch_size: 16
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- total_train_batch_size: 256
- total_eval_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5.0

### Training results



### Framework versions

- Transformers 4.29.2
- Pytorch 1.13.1+cu116
- Datasets 2.12.0
- Tokenizers 0.13.3

{"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["jmhessel/newyorker_caption_contest"], "base_model": "BridgeTower/bridgetower-large-itm-mlm-itc", "model-index": [{"name": "bridgetower-newyorker-a100-8x", "results": []}]}
transformers pytorch tensorboard bridgetower generated_from_trainer dataset:jmhessel/newyorker_caption_contest base_model:BridgeTower/bridgetower-large-itm-mlm-itc license:mit endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #bridgetower #generated_from_trainer #dataset-jmhessel/newyorker_caption_contest #base_model-BridgeTower/bridgetower-large-itm-mlm-itc #license-mit #endpoints_compatible #region-us 




# bridgetower-newyorker-a100-8x

> Disclaimer: Those are the results I obtained with an older version of software. It is possible to fit batches of 48 samples and to get better throughput as mentioned in this blog post: URL

This model is a fine-tuned version of BridgeTower/bridgetower-large-itm-mlm-itc on the jmhessel/newyorker_caption_contest matching dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1148

## 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: 1e-05
- train_batch_size: 32
- eval_batch_size: 16
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- total_train_batch_size: 256
- total_eval_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5.0

### Training results



### Framework versions

- Transformers 4.29.2
- Pytorch 1.13.1+cu116
- Datasets 2.12.0
- Tokenizers 0.13.3

# bridgetower-newyorker-a100-8x

> Disclaimer: Those are the results I obtained with an older version of software. It is possible to fit batches of 48 samples and to get better throughput as mentioned in this blog post: URL

This model is a fine-tuned version of BridgeTower/bridgetower-large-itm-mlm-itc on the jmhessel/newyorker_caption_contest matching dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1148 ## 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: 1e-05
- train_batch_size: 32
- eval_batch_size: 16
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- total_train_batch_size: 256
- total_eval_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5.0 ### Training results ### Framework versions

- Transformers 4.29.2
- Pytorch 1.13.1+cu116
- Datasets 2.12.0
- Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #bridgetower #generated_from_trainer #dataset-jmhessel/newyorker_caption_contest #base_model-BridgeTower/bridgetower-large-itm-mlm-itc #license-mit #endpoints_compatible #region-us 
 # bridgetower-newyorker-a100-8x

> Disclaimer: Those are the results I obtained with an older version of software. It is possible to fit batches of 48 samples and to get better throughput as mentioned in this blog post: URL

This model is a fine-tuned version of BridgeTower/bridgetower-large-itm-mlm-itc on the jmhessel/newyorker_caption_contest matching dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1148 ## 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: 1e-05
- train_batch_size: 32
- eval_batch_size: 16
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- total_train_batch_size: 256
- total_eval_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5.0 ### Training results ### Framework versions

- Transformers 4.29.2
- Pytorch 1.13.1+cu116
- Datasets 2.12.0
- Tokenizers 0.13.3