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# flan-t5-large-extraction-cnndm_4000-all-ep20

This model is a fine-tuned version of [google/flan-t5-large](https://huggingface.co/google/flan-t5-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7262
- Rouge1: 34.7376
- Rouge2: 15.0654
- Rougel: 29.8906
- Rougelsum: 29.9217
- Gen Len: 19.0

## 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: 8
- eval_batch_size: 24
- seed: 1799
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Rouge1  | Rouge2  | Rougel  | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 2.1456        | 0.4   | 200   | 1.8334          | 34.4069 | 14.6208 | 29.4432 | 29.4556   | 18.986  |
| 1.9791        | 0.8   | 400   | 1.7728          | 34.2402 | 14.6376 | 29.5349 | 29.5363   | 18.986  |
| 1.8807        | 1.2   | 600   | 1.7601          | 34.5439 | 15.1529 | 29.8476 | 29.803    | 18.986  |
| 1.8082        | 1.6   | 800   | 1.7609          | 34.6764 | 15.2571 | 30.0381 | 29.9641   | 18.99   |
| 1.8193        | 2.0   | 1000  | 1.7262          | 34.7376 | 15.0654 | 29.8906 | 29.9217   | 19.0    |
| 1.7098        | 2.4   | 1200  | 1.7295          | 35.1726 | 15.5835 | 30.3994 | 30.4092   | 18.998  |
| 1.702         | 2.8   | 1400  | 1.7391          | 34.3418 | 15.311  | 29.6477 | 29.6509   | 19.0    |
| 1.6659        | 3.2   | 1600  | 1.7512          | 34.5394 | 15.5073 | 30.1215 | 30.1061   | 18.996  |
| 1.6216        | 3.6   | 1800  | 1.7485          | 34.9325 | 15.5266 | 30.0803 | 30.0892   | 19.0    |
| 1.6217        | 4.0   | 2000  | 1.7286          | 34.8255 | 15.328  | 29.7924 | 29.8133   | 19.0    |
| 1.5645        | 4.4   | 2200  | 1.7570          | 35.0673 | 15.3704 | 29.9302 | 29.9333   | 19.0    |
| 1.5454        | 4.8   | 2400  | 1.7453          | 34.7481 | 15.1067 | 29.9309 | 29.873    | 19.0    |
| 1.5012        | 5.2   | 2600  | 1.7661          | 35.0534 | 15.4418 | 30.0802 | 30.0423   | 19.0    |
| 1.4867        | 5.6   | 2800  | 1.7647          | 35.5583 | 15.5157 | 30.4281 | 30.4351   | 19.0    |
| 1.4914        | 6.0   | 3000  | 1.7587          | 35.0749 | 15.5551 | 29.9916 | 30.0154   | 19.0    |
| 1.4307        | 6.4   | 3200  | 1.7922          | 34.8942 | 15.248  | 29.6589 | 29.6454   | 19.0    |
| 1.432         | 6.8   | 3400  | 1.7761          | 34.5489 | 15.0722 | 29.585  | 29.5869   | 19.0    |
| 1.4142        | 7.2   | 3600  | 1.7966          | 34.4441 | 15.3042 | 29.8935 | 29.8639   | 19.0    |
| 1.3605        | 7.6   | 3800  | 1.8043          | 34.511  | 15.4382 | 29.5617 | 29.564    | 19.0    |
| 1.3846        | 8.0   | 4000  | 1.7951          | 35.1945 | 15.5203 | 29.8749 | 29.879    | 19.0    |
| 1.3311        | 8.4   | 4200  | 1.8232          | 35.4176 | 15.8548 | 30.4327 | 30.3956   | 19.0    |
| 1.3329        | 8.8   | 4400  | 1.8223          | 35.4969 | 15.6317 | 30.2432 | 30.222    | 19.0    |
| 1.3214        | 9.2   | 4600  | 1.8441          | 35.4614 | 15.8153 | 29.9465 | 29.9406   | 19.0    |
| 1.2979        | 9.6   | 4800  | 1.8392          | 34.9863 | 15.5971 | 30.003  | 30.0149   | 19.0    |
| 1.2782        | 10.0  | 5000  | 1.8395          | 35.1628 | 15.8039 | 30.1611 | 30.1363   | 19.0    |
| 1.2708        | 10.4  | 5200  | 1.8701          | 34.9822 | 15.5759 | 29.7916 | 29.7935   | 19.0    |
| 1.2659        | 10.8  | 5400  | 1.8575          | 35.3481 | 15.5923 | 29.9914 | 29.9391   | 19.0    |
| 1.2302        | 11.2  | 5600  | 1.8695          | 35.4173 | 15.8071 | 30.3649 | 30.3185   | 19.0    |
| 1.229         | 11.6  | 5800  | 1.8685          | 35.2428 | 15.795  | 29.9917 | 29.9481   | 18.996  |
| 1.2185        | 12.0  | 6000  | 1.8837          | 34.8893 | 15.6796 | 29.9493 | 29.9607   | 19.0    |
| 1.1934        | 12.4  | 6200  | 1.8790          | 34.8951 | 15.4768 | 29.9075 | 29.9236   | 19.0    |
| 1.1922        | 12.8  | 6400  | 1.9001          | 35.1576 | 15.7902 | 29.9736 | 29.9386   | 19.0    |
| 1.2022        | 13.2  | 6600  | 1.8984          | 34.7087 | 15.3546 | 29.5658 | 29.5201   | 18.998  |
| 1.173         | 13.6  | 6800  | 1.9125          | 34.9104 | 15.3827 | 29.7326 | 29.7116   | 19.0    |
| 1.1873        | 14.0  | 7000  | 1.8984          | 35.3422 | 15.639  | 30.1525 | 30.1138   | 19.0    |
| 1.1561        | 14.4  | 7200  | 1.9096          | 35.2554 | 15.747  | 30.0491 | 29.9829   | 19.0    |
| 1.154         | 14.8  | 7400  | 1.9043          | 35.0523 | 15.5168 | 29.974  | 29.9442   | 18.992  |
| 1.1351        | 15.2  | 7600  | 1.9202          | 35.2258 | 15.8262 | 30.4862 | 30.4702   | 18.996  |
| 1.1528        | 15.6  | 7800  | 1.9253          | 35.2228 | 15.7183 | 30.1423 | 30.1282   | 18.996  |
| 1.1413        | 16.0  | 8000  | 1.9251          | 35.2414 | 15.5881 | 30.0468 | 30.0212   | 18.998  |
| 1.1375        | 16.4  | 8200  | 1.9256          | 34.8926 | 15.451  | 29.8252 | 29.8018   | 18.992  |
| 1.1025        | 16.8  | 8400  | 1.9359          | 35.3022 | 15.7612 | 30.0581 | 30.0105   | 19.0    |
| 1.1045        | 17.2  | 8600  | 1.9452          | 35.2059 | 15.7584 | 30.0534 | 30.0004   | 19.0    |
| 1.1052        | 17.6  | 8800  | 1.9353          | 35.2928 | 15.7749 | 30.1623 | 30.1519   | 19.0    |
| 1.1261        | 18.0  | 9000  | 1.9412          | 35.5988 | 16.0219 | 30.4918 | 30.4493   | 19.0    |
| 1.1044        | 18.4  | 9200  | 1.9454          | 35.4386 | 15.7284 | 30.2409 | 30.194    | 19.0    |
| 1.0999        | 18.8  | 9400  | 1.9449          | 35.2472 | 15.7148 | 30.1136 | 30.119    | 18.992  |
| 1.1044        | 19.2  | 9600  | 1.9466          | 35.2895 | 15.7895 | 30.1843 | 30.1468   | 19.0    |
| 1.1021        | 19.6  | 9800  | 1.9474          | 35.2796 | 15.7082 | 30.0929 | 30.0662   | 19.0    |
| 1.0954        | 20.0  | 10000 | 1.9488          | 35.319  | 15.7079 | 30.1428 | 30.1129   | 19.0    |


### Framework versions

- Transformers 4.18.0
- Pytorch 1.10.0+cu111
- Datasets 2.5.1
- Tokenizers 0.12.1

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "flan-t5-large-extraction-cnndm_4000-all-ep20", "results": []}]}
transformers pytorch t5 text2text-generation generated_from_trainer license:apache-2.0 autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 

flan-t5-large-extraction-cnndm\_4000-all-ep20
=============================================


This model is a fine-tuned version of google/flan-t5-large on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 1.7262
* Rouge1: 34.7376
* Rouge2: 15.0654
* Rougel: 29.8906
* Rougelsum: 29.9217
* Gen Len: 19.0


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: 8
* eval\_batch\_size: 24
* seed: 1799
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 20


### Training results



### Framework versions


* Transformers 4.18.0
* Pytorch 1.10.0+cu111
* Datasets 2.5.1
* Tokenizers 0.12.1



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 8
* eval\_batch\_size: 24
* seed: 1799
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 20 ### Training results ### Framework versions


* Transformers 4.18.0
* Pytorch 1.10.0+cu111
* Datasets 2.5.1
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #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: 8
* eval\_batch\_size: 24
* seed: 1799
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 20 ### Training results ### Framework versions


* Transformers 4.18.0
* Pytorch 1.10.0+cu111
* Datasets 2.5.1
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #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: 8
* eval\_batch\_size: 24
* seed: 1799
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 20### Training results### Framework versions


* Transformers 4.18.0
* Pytorch 1.10.0+cu111
* Datasets 2.5.1
* Tokenizers 0.12.1