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# uitviquad_noseg_bart

This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7253

## 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: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 20.7767       | 0.45  | 100  | 10.0605         |
| 13.9887       | 0.9   | 200  | 7.6241          |
| 9.9698        | 1.35  | 300  | 5.6805          |
| 7.0255        | 1.8   | 400  | 3.2683          |
| 5.424         | 2.25  | 500  | 2.6617          |
| 4.731         | 2.7   | 600  | 1.9936          |
| 4.0452        | 3.15  | 700  | 1.6257          |
| 3.4385        | 3.6   | 800  | 1.4585          |
| 2.9751        | 4.05  | 900  | 1.3627          |
| 2.6369        | 4.5   | 1000 | 1.2824          |
| 2.3538        | 4.95  | 1100 | 1.2082          |
| 2.1737        | 5.4   | 1200 | 1.1418          |
| 2.0271        | 5.85  | 1300 | 1.0817          |
| 1.9121        | 6.3   | 1400 | 1.0290          |
| 1.8308        | 6.75  | 1500 | 0.9858          |
| 1.7694        | 7.2   | 1600 | 0.9456          |
| 1.7025        | 7.65  | 1700 | 0.9107          |
| 1.6458        | 8.1   | 1800 | 0.8782          |
| 1.6022        | 8.55  | 1900 | 0.8516          |
| 1.5802        | 9.0   | 2000 | 0.8288          |
| 1.5482        | 9.45  | 2100 | 0.8119          |
| 1.4982        | 9.9   | 2200 | 0.7938          |
| 1.4836        | 10.35 | 2300 | 0.7802          |
| 1.4647        | 10.8  | 2400 | 0.7680          |
| 1.4437        | 11.25 | 2500 | 0.7571          |
| 1.4165        | 11.7  | 2600 | 0.7498          |
| 1.4275        | 12.15 | 2700 | 0.7422          |
| 1.4045        | 12.59 | 2800 | 0.7375          |
| 1.4104        | 13.04 | 2900 | 0.7324          |
| 1.366         | 13.49 | 3000 | 0.7296          |
| 1.3912        | 13.94 | 3100 | 0.7276          |
| 1.3615        | 14.39 | 3200 | 0.7260          |
| 1.3801        | 14.84 | 3300 | 0.7253          |


### Framework versions

- Transformers 4.28.0
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "uitviquad_noseg_bart", "results": []}]}
transformers pytorch tensorboard mt5 text2text-generation generated_from_trainer license:apache-2.0 autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #tensorboard #mt5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 

uitviquad\_noseg\_bart
======================


This model is a fine-tuned version of google/mt5-small on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 0.7253


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: 1e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 16
* total\_train\_batch\_size: 128
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 15


### Training results



### Framework versions


* Transformers 4.28.0
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 16
* total\_train\_batch\_size: 128
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 15 ### Training results ### Framework versions


* Transformers 4.28.0
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #mt5 #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: 1e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 16
* total\_train\_batch\_size: 128
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 15 ### Training results ### Framework versions


* Transformers 4.28.0
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #mt5 #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: 1e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 16
* total\_train\_batch\_size: 128
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 15### Training results### Framework versions


* Transformers 4.28.0
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3