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# t5-small-paraphrase-pubmed

This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4032
- Rouge2 Precision: 0.8281
- Rouge2 Recall: 0.6346
- Rouge2 Fmeasure: 0.6996

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:----------------:|:-------------:|:---------------:|
| 0.5253        | 1.0   | 663   | 0.4895          | 0.8217           | 0.6309        | 0.695           |
| 0.5385        | 2.0   | 1326  | 0.4719          | 0.822            | 0.6307        | 0.6953          |
| 0.5255        | 3.0   | 1989  | 0.4579          | 0.8225           | 0.631         | 0.6954          |
| 0.4927        | 4.0   | 2652  | 0.4510          | 0.824            | 0.6315        | 0.6965          |
| 0.484         | 5.0   | 3315  | 0.4426          | 0.8254           | 0.6323        | 0.6974          |
| 0.4691        | 6.0   | 3978  | 0.4383          | 0.8241           | 0.6311        | 0.6962          |
| 0.4546        | 7.0   | 4641  | 0.4319          | 0.8248           | 0.6322        | 0.6969          |
| 0.4431        | 8.0   | 5304  | 0.4270          | 0.8254           | 0.633         | 0.6977          |
| 0.4548        | 9.0   | 5967  | 0.4257          | 0.8257           | 0.6322        | 0.6976          |
| 0.4335        | 10.0  | 6630  | 0.4241          | 0.8271           | 0.6333        | 0.6986          |
| 0.4234        | 11.0  | 7293  | 0.4203          | 0.827            | 0.6341        | 0.6992          |
| 0.433         | 12.0  | 7956  | 0.4185          | 0.8279           | 0.6347        | 0.6998          |
| 0.4108        | 13.0  | 8619  | 0.4161          | 0.8285           | 0.6352        | 0.7004          |
| 0.4101        | 14.0  | 9282  | 0.4133          | 0.8289           | 0.6356        | 0.7008          |
| 0.4155        | 15.0  | 9945  | 0.4149          | 0.8279           | 0.635         | 0.6998          |
| 0.3991        | 16.0  | 10608 | 0.4124          | 0.8289           | 0.6353        | 0.7005          |
| 0.3962        | 17.0  | 11271 | 0.4113          | 0.829            | 0.6353        | 0.7006          |
| 0.3968        | 18.0  | 11934 | 0.4114          | 0.8285           | 0.6352        | 0.7002          |
| 0.3962        | 19.0  | 12597 | 0.4100          | 0.8282           | 0.6346        | 0.6998          |
| 0.3771        | 20.0  | 13260 | 0.4078          | 0.829            | 0.6352        | 0.7005          |
| 0.3902        | 21.0  | 13923 | 0.4083          | 0.8295           | 0.6351        | 0.7006          |
| 0.3811        | 22.0  | 14586 | 0.4077          | 0.8276           | 0.6346        | 0.6995          |
| 0.38          | 23.0  | 15249 | 0.4076          | 0.8281           | 0.6346        | 0.6997          |
| 0.3695        | 24.0  | 15912 | 0.4059          | 0.8277           | 0.6344        | 0.6993          |
| 0.3665        | 25.0  | 16575 | 0.4043          | 0.8278           | 0.6343        | 0.6992          |
| 0.3728        | 26.0  | 17238 | 0.4059          | 0.8279           | 0.6346        | 0.6994          |
| 0.3669        | 27.0  | 17901 | 0.4048          | 0.8271           | 0.6342        | 0.6991          |
| 0.3702        | 28.0  | 18564 | 0.4058          | 0.8265           | 0.6338        | 0.6985          |
| 0.3674        | 29.0  | 19227 | 0.4049          | 0.8277           | 0.6345        | 0.6993          |
| 0.364         | 30.0  | 19890 | 0.4048          | 0.8273           | 0.6341        | 0.699           |
| 0.3618        | 31.0  | 20553 | 0.4041          | 0.828            | 0.6349        | 0.6997          |
| 0.3609        | 32.0  | 21216 | 0.4040          | 0.8275           | 0.6346        | 0.6994          |
| 0.357         | 33.0  | 21879 | 0.4037          | 0.8278           | 0.6348        | 0.6996          |
| 0.3638        | 34.0  | 22542 | 0.4038          | 0.8275           | 0.634         | 0.6989          |
| 0.3551        | 35.0  | 23205 | 0.4035          | 0.8275           | 0.6344        | 0.6992          |
| 0.358         | 36.0  | 23868 | 0.4035          | 0.8279           | 0.6347        | 0.6995          |
| 0.3519        | 37.0  | 24531 | 0.4034          | 0.8277           | 0.6343        | 0.6992          |
| 0.359         | 38.0  | 25194 | 0.4035          | 0.8281           | 0.6346        | 0.6996          |
| 0.3542        | 39.0  | 25857 | 0.4033          | 0.8281           | 0.6346        | 0.6996          |
| 0.3592        | 40.0  | 26520 | 0.4032          | 0.8281           | 0.6346        | 0.6996          |


### 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": "t5-small-paraphrase-pubmed", "results": []}]}
transformers pytorch tensorboard t5 text2text-generation generated_from_trainer license:apache-2.0 autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 

t5-small-paraphrase-pubmed
==========================


This model is a fine-tuned version of t5-small on an unknown dataset.
It achieves the following results on the evaluation set:


* Loss: 0.4032
* Rouge2 Precision: 0.8281
* Rouge2 Recall: 0.6346
* Rouge2 Fmeasure: 0.6996


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 #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #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 #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #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