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# bart_summarization_pretrained

This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on the billsum dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7402
- Rouge1: 0.5264
- Rouge2: 0.2745
- Rougel: 0.3432
- Rougelsum: 0.4049
- Gen Len: 131.0645

## 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: 4

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len  |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:--------:|
| 1.7347        | 1.0   | 989  | 1.6263          | 0.5044 | 0.254  | 0.3219 | 0.3734    | 121.8306 |
| 1.2029        | 2.0   | 1978 | 1.6037          | 0.5278 | 0.2723 | 0.3351 | 0.3977    | 136.4718 |
| 0.8435        | 3.0   | 2967 | 1.6054          | 0.513  | 0.2661 | 0.3357 | 0.3957    | 129.1048 |
| 0.6326        | 4.0   | 3956 | 1.7402          | 0.5264 | 0.2745 | 0.3432 | 0.4049    | 131.0645 |


### Framework versions

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

{"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["billsum"], "metrics": ["rouge"], "model-index": [{"name": "bart_summarization_pretrained", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "billsum", "type": "billsum", "config": "default", "split": "ca_test", "args": "default"}, "metrics": [{"type": "rouge", "value": 0.5264, "name": "Rouge1"}]}]}]}
transformers pytorch tensorboard bart text2text-generation generated_from_trainer dataset:billsum license:mit model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #dataset-billsum #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us 

bart\_summarization\_pretrained
===============================


This model is a fine-tuned version of facebook/bart-large-cnn on the billsum dataset.
It achieves the following results on the evaluation set:


* Loss: 1.7402
* Rouge1: 0.5264
* Rouge2: 0.2745
* Rougel: 0.3432
* Rougelsum: 0.4049
* Gen Len: 131.0645


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: 4


### Training results



### Framework versions


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



### 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: 4 ### Training results ### Framework versions


* Transformers 4.29.2
* Pytorch 2.0.1+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #dataset-billsum #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us 
 ### 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: 4 ### Training results ### Framework versions


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