

# long-t5-tglobal-large-pubmed-3k-booksum-16384-WIP13 

> Evaluating some metric results before merging with the "main" wip version

This model is a fine-tuned version of [pszemraj/long-t5-tglobal-large-pubmed-3k-booksum-16384-WIP12](https://huggingface.co/pszemraj/long-t5-tglobal-large-pubmed-3k-booksum-16384-WIP12) on the `kmfoda/booksum`. 

The "base" checkpoint that I update when a training session is productive is [here](https://huggingface.co/pszemraj/long-t5-tglobal-large-pubmed-3k-booksum-16384-WIP)


## 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: 0.0006
- train_batch_size: 2
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 64
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.01
- num_epochs: 1.1

### Framework versions

- Transformers 4.21.2
- Pytorch 1.10.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1

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transformers pytorch longt5 text2text-generation summarization summary booksum long-document long-form dataset:kmfoda/booksum base_model:pszemraj/long-t5-tglobal-large-pubmed-3k-booksum-16384-WIP12 license:apache-2.0 license:bsd-3-clause model-index autotrain_compatible region:us
TAGS
#transformers #pytorch #longt5 #text2text-generation #summarization #summary #booksum #long-document #long-form #dataset-kmfoda/booksum #base_model-pszemraj/long-t5-tglobal-large-pubmed-3k-booksum-16384-WIP12 #license-apache-2.0 #license-bsd-3-clause #model-index #autotrain_compatible #region-us 



# long-t5-tglobal-large-pubmed-3k-booksum-16384-WIP13 

> Evaluating some metric results before merging with the "main" wip version

This model is a fine-tuned version of pszemraj/long-t5-tglobal-large-pubmed-3k-booksum-16384-WIP12 on the 'kmfoda/booksum'. 

The "base" checkpoint that I update when a training session is productive is here


## 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: 0.0006
- train_batch_size: 2
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 64
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.01
- num_epochs: 1.1

### Framework versions

- Transformers 4.21.2
- Pytorch 1.10.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1

# long-t5-tglobal-large-pubmed-3k-booksum-16384-WIP13 

> Evaluating some metric results before merging with the "main" wip version

This model is a fine-tuned version of pszemraj/long-t5-tglobal-large-pubmed-3k-booksum-16384-WIP12 on the 'kmfoda/booksum'. 

The "base" checkpoint that I update when a training session is productive is here ## 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: 0.0006
- train_batch_size: 2
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 64
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.01
- num_epochs: 1.1 ### Framework versions

- Transformers 4.21.2
- Pytorch 1.10.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
TAGS
#transformers #pytorch #longt5 #text2text-generation #summarization #summary #booksum #long-document #long-form #dataset-kmfoda/booksum #base_model-pszemraj/long-t5-tglobal-large-pubmed-3k-booksum-16384-WIP12 #license-apache-2.0 #license-bsd-3-clause #model-index #autotrain_compatible #region-us 
 # long-t5-tglobal-large-pubmed-3k-booksum-16384-WIP13 

> Evaluating some metric results before merging with the "main" wip version

This model is a fine-tuned version of pszemraj/long-t5-tglobal-large-pubmed-3k-booksum-16384-WIP12 on the 'kmfoda/booksum'. 

The "base" checkpoint that I update when a training session is productive is here ## 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: 0.0006
- train_batch_size: 2
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 64
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.01
- num_epochs: 1.1 ### Framework versions

- Transformers 4.21.2
- Pytorch 1.10.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
passage: TAGS
#transformers #pytorch #longt5 #text2text-generation #summarization #summary #booksum #long-document #long-form #dataset-kmfoda/booksum #base_model-pszemraj/long-t5-tglobal-large-pubmed-3k-booksum-16384-WIP12 #license-apache-2.0 #license-bsd-3-clause #model-index #autotrain_compatible #region-us 
# long-t5-tglobal-large-pubmed-3k-booksum-16384-WIP13 

> Evaluating some metric results before merging with the "main" wip version

This model is a fine-tuned version of pszemraj/long-t5-tglobal-large-pubmed-3k-booksum-16384-WIP12 on the 'kmfoda/booksum'. 

The "base" checkpoint that I update when a training session is productive is here## 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: 0.0006
- train_batch_size: 2
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 64
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.01
- num_epochs: 1.1### Framework versions

- Transformers 4.21.2
- Pytorch 1.10.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1