
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# bart-large-cnn-finetuned-weaksup-10000-pad-early

This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on an unknown dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.3541
- eval_rouge1: 27.8229
- eval_rouge2: 12.9484
- eval_rougeL: 21.4909
- eval_rougeLsum: 24.7737
- eval_gen_len: 67.365
- eval_runtime: 1162.9446
- eval_samples_per_second: 0.86
- eval_steps_per_second: 0.86
- epoch: 2.0
- step: 20000

## 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: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP

### Framework versions

- Transformers 4.16.2
- Pytorch 1.10.2
- Datasets 1.18.3
- Tokenizers 0.11.0

{"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "bart-large-cnn-finetuned-weaksup-10000-pad-early", "results": []}]}
transformers pytorch bart text2text-generation generated_from_trainer license:mit autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bart #text2text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us 




# bart-large-cnn-finetuned-weaksup-10000-pad-early

This model is a fine-tuned version of facebook/bart-large-cnn on an unknown dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.3541
- eval_rouge1: 27.8229
- eval_rouge2: 12.9484
- eval_rougeL: 21.4909
- eval_rougeLsum: 24.7737
- eval_gen_len: 67.365
- eval_runtime: 1162.9446
- eval_samples_per_second: 0.86
- eval_steps_per_second: 0.86
- epoch: 2.0
- step: 20000

## 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: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP

### Framework versions

- Transformers 4.16.2
- Pytorch 1.10.2
- Datasets 1.18.3
- Tokenizers 0.11.0

# bart-large-cnn-finetuned-weaksup-10000-pad-early

This model is a fine-tuned version of facebook/bart-large-cnn on an unknown dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.3541
- eval_rouge1: 27.8229
- eval_rouge2: 12.9484
- eval_rougeL: 21.4909
- eval_rougeLsum: 24.7737
- eval_gen_len: 67.365
- eval_runtime: 1162.9446
- eval_samples_per_second: 0.86
- eval_steps_per_second: 0.86
- epoch: 2.0
- step: 20000 ## 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: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP ### Framework versions

- Transformers 4.16.2
- Pytorch 1.10.2
- Datasets 1.18.3
- Tokenizers 0.11.0
TAGS
#transformers #pytorch #bart #text2text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us 
 # bart-large-cnn-finetuned-weaksup-10000-pad-early

This model is a fine-tuned version of facebook/bart-large-cnn on an unknown dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.3541
- eval_rouge1: 27.8229
- eval_rouge2: 12.9484
- eval_rougeL: 21.4909
- eval_rougeLsum: 24.7737
- eval_gen_len: 67.365
- eval_runtime: 1162.9446
- eval_samples_per_second: 0.86
- eval_steps_per_second: 0.86
- epoch: 2.0
- step: 20000 ## 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: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP ### Framework versions

- Transformers 4.16.2
- Pytorch 1.10.2
- Datasets 1.18.3
- Tokenizers 0.11.0
passage: TAGS
#transformers #pytorch #bart #text2text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us 
# bart-large-cnn-finetuned-weaksup-10000-pad-early

This model is a fine-tuned version of facebook/bart-large-cnn on an unknown dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.3541
- eval_rouge1: 27.8229
- eval_rouge2: 12.9484
- eval_rougeL: 21.4909
- eval_rougeLsum: 24.7737
- eval_gen_len: 67.365
- eval_runtime: 1162.9446
- eval_samples_per_second: 0.86
- eval_steps_per_second: 0.86
- epoch: 2.0
- step: 20000## 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: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP### Framework versions

- Transformers 4.16.2
- Pytorch 1.10.2
- Datasets 1.18.3
- Tokenizers 0.11.0