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# t5-small-mlm-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: 0.8008
- Rouge2 Precision: 0.6071
- Rouge2 Recall: 0.4566
- Rouge2 Fmeasure: 0.5079

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:----------------:|:-------------:|:---------------:|
| 0.914         | 0.75  | 500   | 0.8691          | 0.5901           | 0.4357        | 0.4879          |
| 0.9093        | 1.51  | 1000  | 0.8646          | 0.5867           | 0.4372        | 0.488           |
| 0.895         | 2.26  | 1500  | 0.8618          | 0.5891           | 0.4387        | 0.49            |
| 0.8842        | 3.02  | 2000  | 0.8571          | 0.5899           | 0.4374        | 0.4891          |
| 0.8796        | 3.77  | 2500  | 0.8544          | 0.5903           | 0.4406        | 0.4916          |
| 0.8759        | 4.52  | 3000  | 0.8513          | 0.5921           | 0.4395        | 0.4912          |
| 0.8621        | 5.28  | 3500  | 0.8485          | 0.5934           | 0.4413        | 0.493           |
| 0.8613        | 6.03  | 4000  | 0.8442          | 0.5944           | 0.4428        | 0.4944          |
| 0.8537        | 6.79  | 4500  | 0.8406          | 0.594            | 0.4414        | 0.4932          |
| 0.8518        | 7.54  | 5000  | 0.8399          | 0.5956           | 0.4424        | 0.4945          |
| 0.8438        | 8.3   | 5500  | 0.8365          | 0.5953           | 0.4452        | 0.4964          |
| 0.8339        | 9.05  | 6000  | 0.8353          | 0.5983           | 0.4468        | 0.4983          |
| 0.8307        | 9.8   | 6500  | 0.8331          | 0.5979           | 0.4461        | 0.4976          |
| 0.8328        | 10.56 | 7000  | 0.8304          | 0.5975           | 0.4465        | 0.4979          |
| 0.8263        | 11.31 | 7500  | 0.8283          | 0.5977           | 0.4467        | 0.4981          |
| 0.8168        | 12.07 | 8000  | 0.8267          | 0.5971           | 0.4463        | 0.4976          |
| 0.8165        | 12.82 | 8500  | 0.8248          | 0.5969           | 0.4462        | 0.4976          |
| 0.8084        | 13.57 | 9000  | 0.8245          | 0.6018           | 0.4527        | 0.5035          |
| 0.8136        | 14.33 | 9500  | 0.8219          | 0.6023           | 0.4509        | 0.5023          |
| 0.8073        | 15.08 | 10000 | 0.8206          | 0.6002           | 0.4486        | 0.5001          |
| 0.808         | 15.84 | 10500 | 0.8185          | 0.6009           | 0.4506        | 0.5019          |
| 0.8027        | 16.59 | 11000 | 0.8173          | 0.5978           | 0.4478        | 0.4989          |
| 0.8061        | 17.35 | 11500 | 0.8169          | 0.6022           | 0.4513        | 0.5026          |
| 0.7922        | 18.1  | 12000 | 0.8152          | 0.6016           | 0.4501        | 0.5016          |
| 0.7928        | 18.85 | 12500 | 0.8141          | 0.6009           | 0.45          | 0.5012          |
| 0.7909        | 19.61 | 13000 | 0.8143          | 0.6019           | 0.4521        | 0.5028          |
| 0.7909        | 20.36 | 13500 | 0.8115          | 0.5997           | 0.4505        | 0.5011          |
| 0.7949        | 21.12 | 14000 | 0.8115          | 0.6043           | 0.4536        | 0.5048          |
| 0.7853        | 21.87 | 14500 | 0.8095          | 0.6033           | 0.4527        | 0.5038          |
| 0.7819        | 22.62 | 15000 | 0.8095          | 0.6054           | 0.4541        | 0.5056          |
| 0.7828        | 23.38 | 15500 | 0.8075          | 0.6036           | 0.453         | 0.5042          |
| 0.787         | 24.13 | 16000 | 0.8068          | 0.6031           | 0.4528        | 0.504           |
| 0.7739        | 24.89 | 16500 | 0.8072          | 0.6043           | 0.4529        | 0.5045          |
| 0.7782        | 25.64 | 17000 | 0.8073          | 0.606            | 0.4551        | 0.5063          |
| 0.7772        | 26.4  | 17500 | 0.8063          | 0.6055           | 0.4549        | 0.5062          |
| 0.7718        | 27.15 | 18000 | 0.8057          | 0.606            | 0.4546        | 0.5059          |
| 0.7747        | 27.9  | 18500 | 0.8045          | 0.6046           | 0.4543        | 0.5054          |
| 0.7738        | 28.66 | 19000 | 0.8035          | 0.6059           | 0.4549        | 0.506           |
| 0.7642        | 29.41 | 19500 | 0.8041          | 0.6053           | 0.4545        | 0.5058          |
| 0.7666        | 30.17 | 20000 | 0.8039          | 0.6066           | 0.457         | 0.508           |
| 0.7686        | 30.92 | 20500 | 0.8027          | 0.6075           | 0.4571        | 0.5081          |
| 0.7664        | 31.67 | 21000 | 0.8026          | 0.6062           | 0.4566        | 0.5076          |
| 0.77          | 32.43 | 21500 | 0.8022          | 0.6068           | 0.4571        | 0.5081          |
| 0.7618        | 33.18 | 22000 | 0.8015          | 0.6065           | 0.4563        | 0.5072          |
| 0.7615        | 33.94 | 22500 | 0.8013          | 0.6064           | 0.4565        | 0.5074          |
| 0.7611        | 34.69 | 23000 | 0.8017          | 0.607            | 0.4567        | 0.5078          |
| 0.7611        | 35.44 | 23500 | 0.8013          | 0.608            | 0.4565        | 0.5082          |
| 0.7604        | 36.2  | 24000 | 0.8012          | 0.6069           | 0.4561        | 0.5072          |
| 0.7599        | 36.95 | 24500 | 0.8013          | 0.6078           | 0.4571        | 0.5085          |
| 0.7542        | 37.71 | 25000 | 0.8016          | 0.6083           | 0.4579        | 0.5091          |
| 0.7637        | 38.46 | 25500 | 0.8009          | 0.6072           | 0.4569        | 0.5081          |
| 0.7596        | 39.22 | 26000 | 0.8008          | 0.6069           | 0.4566        | 0.5078          |
| 0.7604        | 39.97 | 26500 | 0.8008          | 0.6071           | 0.4566        | 0.5079          |


### 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-mlm-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-mlm-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: 0.8008
* Rouge2 Precision: 0.6071
* Rouge2 Recall: 0.4566
* Rouge2 Fmeasure: 0.5079


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