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# CS685-text-summarizer

This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the multi_news dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4960
- Rouge1: 9.8777
- Rouge2: 3.6509
- Rougel: 7.597
- Rougelsum: 9.0495

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|
| 2.8402        | 1.0   | 1349 | 2.5256          | 9.7548 | 3.5429 | 7.5078 | 8.9174    |
| 2.5581        | 2.0   | 2698 | 2.5011          | 9.7256 | 3.6262 | 7.5644 | 8.8904    |
| 2.3908        | 3.0   | 4047 | 2.4948          | 9.7569 | 3.5976 | 7.5209 | 8.8919    |
| 2.2744        | 4.0   | 5396 | 2.4875          | 9.9116 | 3.7758 | 7.6823 | 9.0881    |
| 2.1908        | 5.0   | 6745 | 2.4960          | 9.8777 | 3.6509 | 7.597  | 9.0495    |


### Framework versions

- Transformers 4.29.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3

{"license": "apache-2.0", "tags": ["summarization", "generated_from_trainer"], "datasets": ["multi_news"], "metrics": ["rouge"], "model-index": [{"name": "CS685-text-summarizer", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "multi_news", "type": "multi_news", "config": "default", "split": "train[:20%]", "args": "default"}, "metrics": [{"type": "rouge", "value": 9.8777, "name": "Rouge1"}]}]}]}
transformers pytorch tensorboard bart text2text-generation summarization generated_from_trainer dataset:multi_news license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #bart #text2text-generation #summarization #generated_from_trainer #dataset-multi_news #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 

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


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


* Loss: 2.4960
* Rouge1: 9.8777
* Rouge2: 3.6509
* Rougel: 7.597
* Rougelsum: 9.0495


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 4.29.1
* 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 4.29.1
* 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-multi_news #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 4.29.1
* Pytorch 2.0.0+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3