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# marimari-r2r-mlsum-clara-med

This model is a fine-tuned version of [IIC/marimari-r2r-mlsum](https://huggingface.co/IIC/marimari-r2r-mlsum) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.9618
- Rouge1: 42.6764
- Rouge2: 24.4569
- Rougel: 37.0033
- Rougelsum: 37.1595

## 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: 5.6e-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: 30

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1  | Rouge2  | Rougel  | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|
| No log        | 1.0   | 190  | 2.3970          | 40.7426 | 23.212  | 35.7093 | 35.8437   |
| No log        | 2.0   | 380  | 2.3165          | 42.5676 | 24.6494 | 37.1225 | 37.2619   |
| 1.9699        | 3.0   | 570  | 2.4711          | 42.0346 | 23.7633 | 36.3472 | 36.4433   |
| 1.9699        | 4.0   | 760  | 2.7339          | 41.1717 | 22.8419 | 35.3263 | 35.4823   |
| 0.6485        | 5.0   | 950  | 2.9593          | 40.714  | 22.6931 | 34.8859 | 35.0647   |
| 0.6485        | 6.0   | 1140 | 3.1316          | 41.3218 | 23.2054 | 35.3103 | 35.5063   |
| 0.6485        | 7.0   | 1330 | 3.2542          | 41.2786 | 23.4853 | 35.8236 | 35.972    |
| 0.1529        | 8.0   | 1520 | 3.3470          | 41.2991 | 22.8385 | 35.0524 | 35.2153   |
| 0.1529        | 9.0   | 1710 | 3.4324          | 41.3838 | 23.1045 | 35.3472 | 35.5779   |
| 0.0719        | 10.0  | 1900 | 3.5187          | 42.0833 | 23.8538 | 36.3282 | 36.5294   |
| 0.0719        | 11.0  | 2090 | 3.5527          | 41.2993 | 23.0323 | 35.3116 | 35.4687   |
| 0.0719        | 12.0  | 2280 | 3.6624          | 41.6524 | 23.8925 | 35.9281 | 36.1012   |
| 0.0393        | 13.0  | 2470 | 3.6536          | 41.188  | 23.2066 | 35.371  | 35.5616   |
| 0.0393        | 14.0  | 2660 | 3.6656          | 40.8222 | 22.5651 | 35.0515 | 35.1399   |
| 0.0266        | 15.0  | 2850 | 3.7349          | 41.844  | 23.7839 | 36.102  | 36.3169   |
| 0.0266        | 16.0  | 3040 | 3.7254          | 41.5535 | 23.3996 | 35.9619 | 36.0981   |
| 0.0266        | 17.0  | 3230 | 3.7919          | 41.5683 | 23.2824 | 36.0855 | 36.2475   |
| 0.0151        | 18.0  | 3420 | 3.8152          | 42.1272 | 24.0548 | 36.5784 | 36.785    |
| 0.0151        | 19.0  | 3610 | 3.8213          | 41.9185 | 23.5975 | 36.1182 | 36.3194   |
| 0.0087        | 20.0  | 3800 | 3.8501          | 41.3409 | 23.0081 | 35.7662 | 35.9451   |
| 0.0087        | 21.0  | 3990 | 3.8690          | 41.9496 | 23.7032 | 36.0116 | 36.1843   |
| 0.0087        | 22.0  | 4180 | 3.8809          | 42.5366 | 24.6413 | 37.2644 | 37.459    |
| 0.0044        | 23.0  | 4370 | 3.8865          | 42.4346 | 24.2278 | 36.7284 | 36.8846   |
| 0.0044        | 24.0  | 4560 | 3.9044          | 42.9781 | 24.8423 | 37.3582 | 37.4807   |
| 0.0024        | 25.0  | 4750 | 3.9138          | 42.6738 | 24.4737 | 36.8959 | 37.0031   |
| 0.0024        | 26.0  | 4940 | 3.9361          | 42.5267 | 24.4155 | 36.8414 | 36.9915   |
| 0.0024        | 27.0  | 5130 | 3.9477          | 42.4844 | 24.5483 | 36.8857 | 37.0219   |
| 0.0013        | 28.0  | 5320 | 3.9561          | 42.7199 | 24.5977 | 37.1206 | 37.2374   |
| 0.0013        | 29.0  | 5510 | 3.9599          | 42.7088 | 24.4474 | 37.0513 | 37.1971   |
| 0.001         | 30.0  | 5700 | 3.9618          | 42.6764 | 24.4569 | 37.0033 | 37.1595   |


### Framework versions

- Transformers 4.25.1
- Pytorch 1.13.0
- Datasets 2.8.0
- Tokenizers 0.12.1

{"tags": ["simplification", "generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "marimari-r2r-mlsum-clara-med", "results": []}]}
transformers pytorch tensorboard encoder-decoder text2text-generation simplification generated_from_trainer autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #encoder-decoder #text2text-generation #simplification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us 

marimari-r2r-mlsum-clara-med
============================


This model is a fine-tuned version of IIC/marimari-r2r-mlsum on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 3.9618
* Rouge1: 42.6764
* Rouge2: 24.4569
* Rougel: 37.0033
* Rougelsum: 37.1595


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: 5.6e-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: 30


### Training results



### Framework versions


* Transformers 4.25.1
* Pytorch 1.13.0
* Datasets 2.8.0
* Tokenizers 0.12.1



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5.6e-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: 30 ### Training results ### Framework versions


* Transformers 4.25.1
* Pytorch 1.13.0
* Datasets 2.8.0
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #tensorboard #encoder-decoder #text2text-generation #simplification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5.6e-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: 30 ### Training results ### Framework versions


* Transformers 4.25.1
* Pytorch 1.13.0
* Datasets 2.8.0
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #tensorboard #encoder-decoder #text2text-generation #simplification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5.6e-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: 30### Training results### Framework versions


* Transformers 4.25.1
* Pytorch 1.13.0
* Datasets 2.8.0
* Tokenizers 0.12.1