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# English2AkuapemTwi

This model is a fine-tuned version of [allenai/unifiedqa-t5-small](https://huggingface.co/allenai/unifiedqa-t5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3098
- Bleu: 8.8875
- Gen Len: 17.5114

## 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: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Bleu   | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:-------:|
| 2.8304        | 1.0   | 2542  | 2.5363          | 0.2404 | 18.9072 |
| 2.5079        | 2.0   | 5084  | 2.2533          | 0.5763 | 18.6814 |
| 2.3362        | 3.0   | 7626  | 2.0838          | 0.8836 | 18.4972 |
| 2.2155        | 4.0   | 10168 | 1.9649          | 1.3748 | 18.2368 |
| 2.1066        | 5.0   | 12710 | 1.8767          | 2.0954 | 18.0588 |
| 2.0368        | 6.0   | 15252 | 1.8057          | 2.6294 | 17.9451 |
| 1.9641        | 7.0   | 17794 | 1.7436          | 3.0267 | 17.8342 |
| 1.9194        | 8.0   | 20336 | 1.6907          | 3.4947 | 18.0199 |
| 1.8474        | 9.0   | 22878 | 1.6438          | 4.0431 | 17.7758 |
| 1.8261        | 10.0  | 25420 | 1.6008          | 4.4608 | 17.7919 |
| 1.7833        | 11.0  | 27962 | 1.5642          | 4.9271 | 17.7868 |
| 1.7255        | 12.0  | 30504 | 1.5322          | 5.3659 | 17.7962 |
| 1.6913        | 13.0  | 33046 | 1.5020          | 5.9051 | 17.7301 |
| 1.6616        | 14.0  | 35588 | 1.4780          | 6.0824 | 17.7217 |
| 1.6522        | 15.0  | 38130 | 1.4550          | 6.6326 | 17.6227 |
| 1.6039        | 16.0  | 40672 | 1.4327          | 6.7955 | 17.6564 |
| 1.5926        | 17.0  | 43214 | 1.4119          | 7.0477 | 17.6876 |
| 1.5802        | 18.0  | 45756 | 1.3960          | 7.4328 | 17.6233 |
| 1.5438        | 19.0  | 48298 | 1.3816          | 7.4937 | 17.6359 |
| 1.5452        | 20.0  | 50840 | 1.3695          | 7.7696 | 17.5515 |
| 1.5423        | 21.0  | 53382 | 1.3569          | 7.9677 | 17.5596 |
| 1.5076        | 22.0  | 55924 | 1.3465          | 8.2829 | 17.5193 |
| 1.5183        | 23.0  | 58466 | 1.3386          | 8.4304 | 17.5486 |
| 1.4919        | 24.0  | 61008 | 1.3296          | 8.536  | 17.5714 |
| 1.4923        | 25.0  | 63550 | 1.3242          | 8.6446 | 17.4343 |
| 1.488         | 26.0  | 66092 | 1.3177          | 8.7099 | 17.5126 |
| 1.477         | 27.0  | 68634 | 1.3144          | 8.8431 | 17.5035 |
| 1.4724        | 28.0  | 71176 | 1.3116          | 8.8588 | 17.5277 |
| 1.4488        | 29.0  | 73718 | 1.3105          | 8.8989 | 17.5024 |
| 1.4837        | 30.0  | 76260 | 1.3098          | 8.8875 | 17.5114 |


### Framework versions

- Transformers 4.27.4
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3

{"tags": ["generated_from_trainer"], "metrics": ["bleu"], "model-index": [{"name": "English2AkuapemTwi", "results": []}]}
transformers pytorch tensorboard t5 text2text-generation generated_from_trainer autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 

English2AkuapemTwi
==================


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


* Loss: 1.3098
* Bleu: 8.8875
* Gen Len: 17.5114


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: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 30
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.27.4
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 30
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.27.4
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #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: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 30
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.27.4
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #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: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 30
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.27.4
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3