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# CS685-text-summarizer-2

This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the billsum dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7516
- Rouge1: 17.4066
- Rouge2: 14.022
- Rougel: 16.9378
- Rougelsum: 17.0519

## 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: 5.6e-05
- train_batch_size: 6
- eval_batch_size: 6
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1  | Rouge2  | Rougel  | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|
| 2.3529        | 1.0   | 1052 | 1.9277          | 17.1288 | 13.5932 | 16.6346 | 16.7728   |
| 1.9686        | 2.0   | 2104 | 1.8297          | 17.2756 | 13.7685 | 16.7924 | 16.9242   |
| 1.789         | 3.0   | 3156 | 1.7903          | 17.4219 | 14.0205 | 16.9082 | 17.0564   |
| 1.6619        | 4.0   | 4208 | 1.7632          | 17.5055 | 14.1186 | 16.996  | 17.1265   |
| 1.5819        | 5.0   | 5260 | 1.7516          | 17.4066 | 14.022  | 16.9378 | 17.0519   |


### Framework versions

- Transformers 4.28.0
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3

{"license": "apache-2.0", "tags": ["summarization", "generated_from_trainer"], "datasets": ["billsum"], "metrics": ["rouge"], "model-index": [{"name": "CS685-text-summarizer-2", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "billsum", "type": "billsum", "config": "default", "split": "train[:37%]", "args": "default"}, "metrics": [{"type": "rouge", "value": 17.4066, "name": "Rouge1"}]}]}]}
transformers pytorch tensorboard bart text2text-generation summarization generated_from_trainer dataset:billsum license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #bart #text2text-generation #summarization #generated_from_trainer #dataset-billsum #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 

CS685-text-summarizer-2
=======================


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


* Loss: 1.7516
* Rouge1: 17.4066
* Rouge2: 14.022
* Rougel: 16.9378
* Rougelsum: 17.0519


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: 5.6e-05
* train\_batch\_size: 6
* eval\_batch\_size: 6
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 5


### Training results



### Framework versions


* Transformers 4.28.0
* Pytorch 2.0.0+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5.6e-05
* train\_batch\_size: 6
* eval\_batch\_size: 6
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 5 ### Training results ### Framework versions


* Transformers 4.28.0
* Pytorch 2.0.0+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #bart #text2text-generation #summarization #generated_from_trainer #dataset-billsum #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5.6e-05
* train\_batch\_size: 6
* eval\_batch\_size: 6
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 5 ### Training results ### Framework versions


* Transformers 4.28.0
* Pytorch 2.0.0+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3