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# t5-small_6_3-en-hi_en_LinCE

This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.2034
- Bleu: 7.8135
- Gen Len: 39.5564

## 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: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Bleu   | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|
| No log        | 0.99  | 94   | 3.5424          | 0.9187 | 16.7437 |
| No log        | 1.99  | 188  | 3.1434          | 1.2886 | 16.8158 |
| No log        | 2.99  | 282  | 2.9494          | 1.4577 | 16.7824 |
| No log        | 3.99  | 376  | 2.8233          | 1.4745 | 16.8879 |
| No log        | 4.99  | 470  | 2.7300          | 1.7116 | 16.6636 |
| 3.6303        | 5.99  | 564  | 2.6589          | 1.7857 | 16.6302 |
| 3.6303        | 6.99  | 658  | 2.6005          | 1.8572 | 16.4553 |
| 3.6303        | 7.99  | 752  | 2.5456          | 2.139  | 16.3925 |
| 3.6303        | 8.99  | 846  | 2.5023          | 2.3835 | 16.2911 |
| 3.6303        | 9.99  | 940  | 2.4725          | 2.5607 | 16.3271 |
| 2.9087        | 10.99 | 1034 | 2.4272          | 2.6614 | 16.3138 |
| 2.9087        | 11.99 | 1128 | 2.3977          | 2.9623 | 16.3338 |
| 2.9087        | 12.99 | 1222 | 2.3686          | 3.1248 | 16.2443 |
| 2.9087        | 13.99 | 1316 | 2.3438          | 3.3294 | 16.3458 |
| 2.9087        | 14.99 | 1410 | 2.3253          | 3.3885 | 16.3591 |
| 2.6588        | 15.99 | 1504 | 2.3028          | 3.3985 | 16.3124 |
| 2.6588        | 16.99 | 1598 | 2.2839          | 3.3772 | 16.3858 |
| 2.6588        | 17.99 | 1692 | 2.2704          | 3.5804 | 16.3872 |
| 2.6588        | 18.99 | 1786 | 2.2533          | 3.8751 | 16.2697 |
| 2.6588        | 19.99 | 1880 | 2.2378          | 4.0003 | 16.3271 |
| 2.6588        | 20.99 | 1974 | 2.2233          | 4.0271 | 16.3031 |
| 2.5079        | 21.99 | 2068 | 2.2160          | 4.1898 | 16.3057 |
| 2.5079        | 22.99 | 2162 | 2.2010          | 4.1216 | 16.3031 |
| 2.5079        | 23.99 | 2256 | 2.1935          | 4.1311 | 16.2644 |
| 2.5079        | 24.99 | 2350 | 2.1833          | 4.1373 | 16.3138 |
| 2.5079        | 25.99 | 2444 | 2.1725          | 4.3471 | 16.3057 |
| 2.4027        | 26.99 | 2538 | 2.1657          | 4.183  | 16.3298 |
| 2.4027        | 27.99 | 2632 | 2.1611          | 4.2867 | 16.3351 |
| 2.4027        | 28.99 | 2726 | 2.1531          | 4.2689 | 16.2737 |
| 2.4027        | 29.99 | 2820 | 2.1482          | 4.4802 | 16.2644 |
| 2.4027        | 30.99 | 2914 | 2.1443          | 4.469  | 16.231  |
| 2.3251        | 31.99 | 3008 | 2.1375          | 4.5295 | 16.227  |
| 2.3251        | 32.99 | 3102 | 2.1330          | 4.4799 | 16.2243 |
| 2.3251        | 33.99 | 3196 | 2.1307          | 4.7124 | 16.2417 |
| 2.3251        | 34.99 | 3290 | 2.1248          | 4.5954 | 16.3004 |
| 2.3251        | 35.99 | 3384 | 2.1215          | 4.7455 | 16.215  |
| 2.3251        | 36.99 | 3478 | 2.1166          | 4.6233 | 16.2016 |
| 2.2818        | 37.99 | 3572 | 2.1147          | 4.6843 | 16.219  |
| 2.2818        | 38.99 | 3666 | 2.1112          | 4.7068 | 16.2163 |
| 2.2818        | 39.99 | 3760 | 2.1071          | 4.684  | 16.223  |
| 2.2818        | 40.99 | 3854 | 2.1034          | 4.7323 | 16.2523 |
| 2.2818        | 41.99 | 3948 | 2.0998          | 4.6406 | 16.2016 |
| 2.2392        | 42.99 | 4042 | 2.1017          | 4.7609 | 16.1976 |
| 2.2392        | 43.99 | 4136 | 2.1021          | 4.7634 | 16.2069 |
| 2.2392        | 44.99 | 4230 | 2.0994          | 4.7854 | 16.1976 |
| 2.2392        | 45.99 | 4324 | 2.0980          | 4.7562 | 16.2243 |
| 2.2392        | 46.99 | 4418 | 2.0964          | 4.7921 | 16.219  |
| 2.2192        | 47.99 | 4512 | 2.0970          | 4.8029 | 16.2377 |
| 2.2192        | 48.99 | 4606 | 2.0967          | 4.7953 | 16.2176 |
| 2.2192        | 49.99 | 4700 | 2.0968          | 4.819  | 16.2457 |


### Framework versions

- Transformers 4.20.0.dev0
- Pytorch 1.8.0
- Datasets 2.1.0
- Tokenizers 0.12.1

{"tags": ["translation", "generated_from_trainer"], "metrics": ["bleu"], "model-index": [{"name": "t5-small_6_3-en-hi_en_LinCE", "results": []}]}
transformers pytorch tensorboard t5 text2text-generation translation generated_from_trainer autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #translation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 

t5-small\_6\_3-en-hi\_en\_LinCE
===============================


This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 2.2034
* Bleu: 7.8135
* Gen Len: 39.5564


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: 0.0001
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 8
* total\_train\_batch\_size: 64
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 50
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.20.0.dev0
* Pytorch 1.8.0
* Datasets 2.1.0
* Tokenizers 0.12.1



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0001
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 8
* total\_train\_batch\_size: 64
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 50
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.20.0.dev0
* Pytorch 1.8.0
* Datasets 2.1.0
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #translation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0001
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 8
* total\_train\_batch\_size: 64
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 50
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.20.0.dev0
* Pytorch 1.8.0
* Datasets 2.1.0
* Tokenizers 0.12.1
passage: TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #translation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0001
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 8
* total\_train\_batch\_size: 64
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 50
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.20.0.dev0
* Pytorch 1.8.0
* Datasets 2.1.0
* Tokenizers 0.12.1