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# timesheet_estimator

This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5383
- Mse: 0.5383
- Rmse: 0.7337
- Mae: 0.5091
- R2: 0.4827
- Smape: 89.7730

## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Mae    | Mse    | R2     | Rmse   | Smape    |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:------:|:--------:|
| No log        | 0.46  | 300  | 0.7945          | 0.6404 | 0.7945 | 0.2018 | 0.8914 | 120.6558 |
| No log        | 0.91  | 600  | 0.6674          | 0.5948 | 0.6674 | 0.3295 | 0.8169 | 109.9213 |
| No log        | 1.37  | 900  | 0.6795          | 0.5998 | 0.6795 | 0.3174 | 0.8243 | 108.4883 |
| 0.7094        | 1.82  | 1200 | 0.6327          | 0.5916 | 0.6327 | 0.3643 | 0.7954 | 111.5772 |
| 0.7094        | 2.28  | 1500 | 0.6216          | 0.5712 | 0.6216 | 0.3755 | 0.7884 | 99.9257  |
| 0.7094        | 2.73  | 1800 | 0.5770          | 0.5397 | 0.5770 | 0.4203 | 0.7596 | 100.1235 |
| 0.5129        | 3.19  | 2100 | 0.5791          | 0.5391 | 0.5791 | 0.4182 | 0.7610 | 99.9525  |
| 0.5129        | 3.64  | 2400 | 0.5796          | 0.5421 | 0.5796 | 0.4177 | 0.7613 | 99.3905  |
| 0.5129        | 4.1   | 2700 | 0.5720          | 0.5354 | 0.5720 | 0.4254 | 0.7563 | 98.9299  |
| 0.4448        | 4.55  | 3000 | 0.5801          | 0.5381 | 0.5801 | 0.4173 | 0.7616 | 96.1430  |
| 0.4448        | 5.01  | 3300 | 0.5437          | 0.5185 | 0.5437 | 0.4775 | 0.7373 | 94.1203  |
| 0.4448        | 5.46  | 3600 | 0.5111          | 0.4949 | 0.5111 | 0.5088 | 0.7149 | 92.1147  |
| 0.4448        | 5.92  | 3900 | 0.5234          | 0.5106 | 0.5234 | 0.4970 | 0.7235 | 95.4636  |
| 0.4877        | 6.37  | 4200 | 0.5478          | 0.5249 | 0.5478 | 0.4735 | 0.7402 | 94.5022  |
| 0.4877        | 6.83  | 4500 | 0.5172          | 0.5172 | 0.7192 | 0.4998 | 0.5029 | 93.0563  |
| 0.4877        | 7.28  | 4800 | 0.5318          | 0.5318 | 0.7293 | 0.5083 | 0.4889 | 90.5273  |
| 0.3889        | 7.74  | 5100 | 0.5845          | 0.5845 | 0.7645 | 0.5377 | 0.4383 | 93.8608  |
| 0.3889        | 8.19  | 5400 | 0.5315          | 0.5315 | 0.7291 | 0.5014 | 0.4892 | 90.2302  |
| 0.3889        | 8.65  | 5700 | 0.5356          | 0.5356 | 0.7319 | 0.5010 | 0.4852 | 88.9946  |
| 0.324         | 9.1   | 6000 | 0.5345          | 0.5345 | 0.7311 | 0.5028 | 0.4864 | 89.7148  |
| 0.324         | 9.56  | 6300 | 0.5383          | 0.5383 | 0.7337 | 0.5091 | 0.4827 | 89.7730  |


### Framework versions

- Transformers 4.27.0.dev0
- Pytorch 1.13.1
- Datasets 2.9.0
- Tokenizers 0.13.2

{"tags": ["generated_from_trainer"], "model-index": [{"name": "timesheet_estimator", "results": []}]}
transformers pytorch feature-extraction generated_from_trainer custom_code region:us
TAGS
#transformers #pytorch #feature-extraction #generated_from_trainer #custom_code #region-us 

timesheet\_estimator
====================


This model is a fine-tuned version of [](URL on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 0.5383
* Mse: 0.5383
* Rmse: 0.7337
* Mae: 0.5091
* R2: 0.4827
* Smape: 89.7730


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: 32
* eval\_batch\_size: 32
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10


### Training results



### Framework versions


* Transformers 4.27.0.dev0
* Pytorch 1.13.1
* Datasets 2.9.0
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10 ### Training results ### Framework versions


* Transformers 4.27.0.dev0
* Pytorch 1.13.1
* Datasets 2.9.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #feature-extraction #generated_from_trainer #custom_code #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10 ### Training results ### Framework versions


* Transformers 4.27.0.dev0
* Pytorch 1.13.1
* Datasets 2.9.0
* Tokenizers 0.13.2