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# bart-paraphrase-finetuned-xsum-v3

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: 0.3377
- Rouge1: 99.9461
- Rouge2: 72.6619
- Rougel: 99.9461
- Rougelsum: 99.9461
- Gen Len: 9.0396

## 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   | 139  | 0.3653          | 96.4972 | 70.8271 | 96.5252 | 96.5085   | 9.7158  |
| No log        | 2.0   | 278  | 0.6624          | 98.3228 | 72.2829 | 98.2598 | 98.2519   | 9.0612  |
| No log        | 3.0   | 417  | 0.2880          | 98.2415 | 72.36   | 98.249  | 98.2271   | 9.4496  |
| 0.5019        | 4.0   | 556  | 0.4188          | 98.1123 | 70.8536 | 98.0746 | 98.0465   | 9.4065  |
| 0.5019        | 5.0   | 695  | 0.3718          | 98.8882 | 72.6619 | 98.8997 | 98.8882   | 10.7842 |
| 0.5019        | 6.0   | 834  | 0.4442          | 99.6076 | 72.6619 | 99.6076 | 99.598    | 9.0647  |
| 0.5019        | 7.0   | 973  | 0.2681          | 99.6076 | 72.6619 | 99.598  | 99.598    | 9.1403  |
| 0.2751        | 8.0   | 1112 | 0.3577          | 99.2479 | 72.6619 | 99.2536 | 99.2383   | 9.0612  |
| 0.2751        | 9.0   | 1251 | 0.2481          | 98.8785 | 72.6394 | 98.8882 | 98.8882   | 9.7914  |
| 0.2751        | 10.0  | 1390 | 0.2339          | 99.6076 | 72.6619 | 99.6076 | 99.6076   | 9.1942  |
| 0.2051        | 11.0  | 1529 | 0.2472          | 99.9461 | 72.6619 | 99.9461 | 99.9461   | 9.2338  |
| 0.2051        | 12.0  | 1668 | 0.3948          | 99.6076 | 72.6619 | 99.598  | 99.598    | 9.0468  |
| 0.2051        | 13.0  | 1807 | 0.4756          | 99.6076 | 72.6619 | 99.6076 | 99.6076   | 9.0576  |
| 0.2051        | 14.0  | 1946 | 0.3543          | 99.9461 | 72.6619 | 99.9461 | 99.9461   | 9.0396  |
| 0.1544        | 15.0  | 2085 | 0.2828          | 99.9461 | 72.6619 | 99.9461 | 99.9461   | 9.0576  |
| 0.1544        | 16.0  | 2224 | 0.2456          | 99.9461 | 72.6619 | 99.9461 | 99.9461   | 9.1079  |
| 0.1544        | 17.0  | 2363 | 0.2227          | 99.9461 | 72.6394 | 99.9461 | 99.9461   | 9.5072  |
| 0.1285        | 18.0  | 2502 | 0.3490          | 99.9461 | 72.6619 | 99.9461 | 99.9461   | 9.0396  |
| 0.1285        | 19.0  | 2641 | 0.3736          | 99.9461 | 72.6619 | 99.9461 | 99.9461   | 9.0396  |
| 0.1285        | 20.0  | 2780 | 0.3377          | 99.9461 | 72.6619 | 99.9461 | 99.9461   | 9.0396  |


### Framework versions

- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "bart-paraphrase-finetuned-xsum-v3", "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-v3
=================================


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: 0.3377
* Rouge1: 99.9461
* Rouge2: 72.6619
* Rougel: 99.9461
* Rougelsum: 99.9461
* Gen Len: 9.0396


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 4.19.2
* Pytorch 1.11.0+cu113
* Datasets 2.2.2
* 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 4.19.2
* Pytorch 1.11.0+cu113
* Datasets 2.2.2
* 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 4.19.2
* Pytorch 1.11.0+cu113
* Datasets 2.2.2
* 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 4.19.2
* Pytorch 1.11.0+cu113
* Datasets 2.2.2
* Tokenizers 0.12.1