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# bart-paraphrase-finetuned-xsum-v2

This model is a fine-tuned version of [eugenesiow/bart-paraphrase](https://huggingface.co/eugenesiow/bart-paraphrase) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2329
- Rouge1: 100.0
- Rouge2: 100.0
- Rougel: 100.0
- Rougelsum: 100.0
- Gen Len: 9.2619

## 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: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1  | Rouge2  | Rougel  | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| No log        | 1.0   | 21   | 1.2954          | 66.7012 | 60.8612 | 66.5163 | 66.4352   | 13.2857 |
| No log        | 2.0   | 42   | 0.6866          | 86.8284 | 82.7835 | 86.7208 | 86.784    | 9.5238  |
| No log        | 3.0   | 63   | 0.4652          | 95.1892 | 93.5619 | 95.2567 | 95.1657   | 10.3095 |
| No log        | 4.0   | 84   | 0.4280          | 97.7463 | 97.1782 | 97.8708 | 97.718    | 9.5     |
| No log        | 5.0   | 105  | 0.3712          | 99.6435 | 99.5767 | 99.6435 | 99.6435   | 9.3571  |
| No log        | 6.0   | 126  | 0.4451          | 99.2695 | 98.9418 | 99.1883 | 99.3506   | 9.3095  |
| No log        | 7.0   | 147  | 0.3169          | 99.246  | 99.0232 | 99.246  | 99.4048   | 9.619   |
| No log        | 8.0   | 168  | 0.2942          | 100.0   | 100.0   | 100.0   | 100.0     | 9.4048  |
| No log        | 9.0   | 189  | 0.3105          | 100.0   | 100.0   | 100.0   | 100.0     | 9.1667  |
| No log        | 10.0  | 210  | 0.3035          | 100.0   | 100.0   | 100.0   | 100.0     | 9.2619  |
| No log        | 11.0  | 231  | 0.2983          | 100.0   | 100.0   | 100.0   | 100.0     | 10.5714 |
| No log        | 12.0  | 252  | 0.2497          | 100.0   | 100.0   | 100.0   | 100.0     | 9.4286  |
| No log        | 13.0  | 273  | 0.2911          | 100.0   | 100.0   | 100.0   | 100.0     | 9.1667  |
| No log        | 14.0  | 294  | 0.2619          | 100.0   | 100.0   | 100.0   | 100.0     | 9.2143  |
| No log        | 15.0  | 315  | 0.2510          | 100.0   | 100.0   | 100.0   | 100.0     | 9.2381  |
| No log        | 16.0  | 336  | 0.2647          | 100.0   | 100.0   | 100.0   | 100.0     | 9.9048  |
| No log        | 17.0  | 357  | 0.2438          | 100.0   | 100.0   | 100.0   | 100.0     | 9.2143  |
| No log        | 18.0  | 378  | 0.2324          | 100.0   | 100.0   | 100.0   | 100.0     | 9.3095  |
| No log        | 19.0  | 399  | 0.2296          | 100.0   | 100.0   | 100.0   | 100.0     | 9.3095  |
| No log        | 20.0  | 420  | 0.2329          | 100.0   | 100.0   | 100.0   | 100.0     | 9.2619  |


### Framework versions

- Transformers 4.19.1
- Pytorch 1.11.0+cu113
- Datasets 2.2.1
- Tokenizers 0.12.1

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "bart-paraphrase-finetuned-xsum-v2", "results": []}]}
transformers pytorch tensorboard bart text2text-generation generated_from_trainer license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

bart-paraphrase-finetuned-xsum-v2
=================================


This model is a fine-tuned version of eugenesiow/bart-paraphrase on an unknown dataset.
It achieves the following results on the evaluation set:


* Loss: 0.2329
* Rouge1: 100.0
* Rouge2: 100.0
* Rougel: 100.0
* Rougelsum: 100.0
* Gen Len: 9.2619


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: 2
* eval\_batch\_size: 2
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 20
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.19.1
* Pytorch 1.11.0+cu113
* Datasets 2.2.1
* Tokenizers 0.12.1



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 2
* eval\_batch\_size: 2
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 20
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.19.1
* Pytorch 1.11.0+cu113
* Datasets 2.2.1
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 2
* eval\_batch\_size: 2
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 20
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.19.1
* Pytorch 1.11.0+cu113
* Datasets 2.2.1
* Tokenizers 0.12.1
passage: TAGS
#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 2
* eval\_batch\_size: 2
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 20
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.19.1
* Pytorch 1.11.0+cu113
* Datasets 2.2.1
* Tokenizers 0.12.1