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# TextRank_Longformer

This model is a fine-tuned version of [hyesunyun/update-summarization-bart-large-longformer](https://huggingface.co/hyesunyun/update-summarization-bart-large-longformer) on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 2.7145
- eval_rouge1: 17.8877
- eval_rouge2: 4.4572
- eval_rougeL: 12.5184
- eval_rougeLsum: 13.8276
- eval_gen_len: 108.16
- eval_runtime: 224.8675
- eval_samples_per_second: 0.111
- eval_steps_per_second: 0.111
- epoch: 0.18
- step: 11

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

### Framework versions

- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "TextRank_Longformer", "results": []}]}
transformers pytorch tensorboard led text2text-generation generated_from_trainer license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #led #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 




# TextRank_Longformer

This model is a fine-tuned version of hyesunyun/update-summarization-bart-large-longformer on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 2.7145
- eval_rouge1: 17.8877
- eval_rouge2: 4.4572
- eval_rougeL: 12.5184
- eval_rougeLsum: 13.8276
- eval_gen_len: 108.16
- eval_runtime: 224.8675
- eval_samples_per_second: 0.111
- eval_steps_per_second: 0.111
- epoch: 0.18
- step: 11

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

### Framework versions

- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3

# TextRank_Longformer

This model is a fine-tuned version of hyesunyun/update-summarization-bart-large-longformer on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 2.7145
- eval_rouge1: 17.8877
- eval_rouge2: 4.4572
- eval_rougeL: 12.5184
- eval_rougeLsum: 13.8276
- eval_gen_len: 108.16
- eval_runtime: 224.8675
- eval_samples_per_second: 0.111
- eval_steps_per_second: 0.111
- epoch: 0.18
- step: 11 ## 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: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP ### Framework versions

- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #led #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
 # TextRank_Longformer

This model is a fine-tuned version of hyesunyun/update-summarization-bart-large-longformer on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 2.7145
- eval_rouge1: 17.8877
- eval_rouge2: 4.4572
- eval_rougeL: 12.5184
- eval_rougeLsum: 13.8276
- eval_gen_len: 108.16
- eval_runtime: 224.8675
- eval_samples_per_second: 0.111
- eval_steps_per_second: 0.111
- epoch: 0.18
- step: 11 ## 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: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP ### Framework versions

- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3