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# bart-paraphrase-pubmed

This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6340
- Rouge2 Precision: 0.83
- Rouge2 Recall: 0.6526
- Rouge2 Fmeasure: 0.7144

## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 40
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Rouge2 Precision | Rouge2 Recall | Rouge2 Fmeasure |
|:-------------:|:-----:|:-----:|:---------------:|:----------------:|:-------------:|:---------------:|
| 0.6613        | 1.0   | 663   | 0.4750          | 0.8321           | 0.6552        | 0.7167          |
| 0.4993        | 2.0   | 1326  | 0.4404          | 0.8366           | 0.6583        | 0.7203          |
| 0.443         | 3.0   | 1989  | 0.4261          | 0.8319           | 0.6562        | 0.7176          |
| 0.3482        | 4.0   | 2652  | 0.4198          | 0.8348           | 0.6571        | 0.7191          |
| 0.3206        | 5.0   | 3315  | 0.4233          | 0.8344           | 0.656         | 0.7183          |
| 0.294         | 6.0   | 3978  | 0.4334          | 0.835            | 0.657         | 0.719           |
| 0.2404        | 7.0   | 4641  | 0.4437          | 0.8334           | 0.6559        | 0.7178          |
| 0.2228        | 8.0   | 5304  | 0.4438          | 0.8348           | 0.6565        | 0.7187          |
| 0.211         | 9.0   | 5967  | 0.4516          | 0.8329           | 0.6549        | 0.717           |
| 0.1713        | 10.0  | 6630  | 0.4535          | 0.8332           | 0.6547        | 0.7169          |
| 0.1591        | 11.0  | 7293  | 0.4763          | 0.8349           | 0.6561        | 0.7184          |
| 0.1555        | 12.0  | 7956  | 0.4824          | 0.8311           | 0.6534        | 0.7153          |
| 0.1262        | 13.0  | 8619  | 0.4883          | 0.8322           | 0.655         | 0.7167          |
| 0.1164        | 14.0  | 9282  | 0.5025          | 0.8312           | 0.6539        | 0.7158          |
| 0.1108        | 15.0  | 9945  | 0.5149          | 0.8321           | 0.6535        | 0.7157          |
| 0.0926        | 16.0  | 10608 | 0.5340          | 0.8315           | 0.6544        | 0.7159          |
| 0.0856        | 17.0  | 11271 | 0.5322          | 0.8306           | 0.6518        | 0.7142          |
| 0.0785        | 18.0  | 11934 | 0.5346          | 0.8324           | 0.6549        | 0.7167          |
| 0.071         | 19.0  | 12597 | 0.5488          | 0.8311           | 0.652         | 0.714           |
| 0.0635        | 20.0  | 13260 | 0.5624          | 0.8287           | 0.6517        | 0.7132          |
| 0.0608        | 21.0  | 13923 | 0.5612          | 0.8299           | 0.6527        | 0.7141          |
| 0.0531        | 22.0  | 14586 | 0.5764          | 0.8283           | 0.6498        | 0.7119          |
| 0.0486        | 23.0  | 15249 | 0.5832          | 0.8298           | 0.6532        | 0.7148          |
| 0.0465        | 24.0  | 15912 | 0.5866          | 0.83             | 0.6522        | 0.7142          |
| 0.0418        | 25.0  | 16575 | 0.5825          | 0.83             | 0.6523        | 0.7141          |
| 0.0391        | 26.0  | 17238 | 0.5997          | 0.8306           | 0.6545        | 0.716           |
| 0.0376        | 27.0  | 17901 | 0.5894          | 0.8315           | 0.6546        | 0.7164          |
| 0.035         | 28.0  | 18564 | 0.6045          | 0.8306           | 0.6529        | 0.7149          |
| 0.0316        | 29.0  | 19227 | 0.6168          | 0.8311           | 0.6546        | 0.7162          |
| 0.0314        | 30.0  | 19890 | 0.6203          | 0.8311           | 0.6552        | 0.7164          |
| 0.0292        | 31.0  | 20553 | 0.6173          | 0.8315           | 0.6548        | 0.7163          |
| 0.0265        | 32.0  | 21216 | 0.6226          | 0.832            | 0.6548        | 0.7166          |
| 0.0274        | 33.0  | 21879 | 0.6264          | 0.8314           | 0.6538        | 0.7155          |
| 0.0247        | 34.0  | 22542 | 0.6254          | 0.8289           | 0.6515        | 0.7132          |
| 0.0238        | 35.0  | 23205 | 0.6254          | 0.8307           | 0.6519        | 0.7142          |
| 0.0232        | 36.0  | 23868 | 0.6295          | 0.8287           | 0.6515        | 0.7133          |
| 0.0215        | 37.0  | 24531 | 0.6326          | 0.8293           | 0.6523        | 0.7138          |
| 0.0212        | 38.0  | 25194 | 0.6332          | 0.8295           | 0.6522        | 0.714           |
| 0.0221        | 39.0  | 25857 | 0.6335          | 0.8305           | 0.6528        | 0.7147          |
| 0.0202        | 40.0  | 26520 | 0.6340          | 0.83             | 0.6526        | 0.7144          |


### Framework versions

- Transformers 4.12.3
- Pytorch 1.9.0+cu111
- Datasets 1.15.1
- Tokenizers 0.10.3

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "bart-paraphrase-pubmed", "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-pubmed
======================


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


* Loss: 0.6340
* Rouge2 Precision: 0.83
* Rouge2 Recall: 0.6526
* Rouge2 Fmeasure: 0.7144


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: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 40
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.12.3
* Pytorch 1.9.0+cu111
* Datasets 1.15.1
* Tokenizers 0.10.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 40
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.12.3
* Pytorch 1.9.0+cu111
* Datasets 1.15.1
* Tokenizers 0.10.3
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: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 40
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.12.3
* Pytorch 1.9.0+cu111
* Datasets 1.15.1
* Tokenizers 0.10.3
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: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 40
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.12.3
* Pytorch 1.9.0+cu111
* Datasets 1.15.1
* Tokenizers 0.10.3