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# digikala_products_parsbert_model

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: 2.7245

## 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: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 100

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log        | 1.0   | 25   | 7.8477          |
| No log        | 2.0   | 50   | 7.0014          |
| No log        | 3.0   | 75   | 6.3235          |
| No log        | 4.0   | 100  | 5.6651          |
| No log        | 5.0   | 125  | 4.9101          |
| No log        | 6.0   | 150  | 4.2448          |
| No log        | 7.0   | 175  | 3.8656          |
| No log        | 8.0   | 200  | 3.4329          |
| No log        | 9.0   | 225  | 3.3204          |
| No log        | 10.0  | 250  | 3.0740          |
| No log        | 11.0  | 275  | 2.9556          |
| No log        | 12.0  | 300  | 2.9938          |
| No log        | 13.0  | 325  | 2.8620          |
| No log        | 14.0  | 350  | 2.7879          |
| No log        | 15.0  | 375  | 2.8619          |
| No log        | 16.0  | 400  | 2.8521          |
| No log        | 17.0  | 425  | 2.7920          |
| No log        | 18.0  | 450  | 2.8494          |
| No log        | 19.0  | 475  | 2.8209          |
| 4.1477        | 20.0  | 500  | 2.8471          |
| 4.1477        | 21.0  | 525  | 2.8478          |
| 4.1477        | 22.0  | 550  | 2.7904          |
| 4.1477        | 23.0  | 575  | 2.7961          |
| 4.1477        | 24.0  | 600  | 2.7494          |
| 4.1477        | 25.0  | 625  | 2.8250          |
| 4.1477        | 26.0  | 650  | 2.7439          |
| 4.1477        | 27.0  | 675  | 2.7539          |
| 4.1477        | 28.0  | 700  | 2.7635          |
| 4.1477        | 29.0  | 725  | 2.7742          |
| 4.1477        | 30.0  | 750  | 2.7711          |
| 4.1477        | 31.0  | 775  | 2.8243          |
| 4.1477        | 32.0  | 800  | 2.7547          |
| 4.1477        | 33.0  | 825  | 2.7690          |
| 4.1477        | 34.0  | 850  | 2.7178          |
| 4.1477        | 35.0  | 875  | 2.7554          |
| 4.1477        | 36.0  | 900  | 2.7701          |
| 4.1477        | 37.0  | 925  | 2.7953          |
| 4.1477        | 38.0  | 950  | 2.8062          |
| 4.1477        | 39.0  | 975  | 2.7637          |
| 2.772         | 40.0  | 1000 | 2.7675          |
| 2.772         | 41.0  | 1025 | 2.7953          |
| 2.772         | 42.0  | 1050 | 2.8003          |
| 2.772         | 43.0  | 1075 | 2.7484          |
| 2.772         | 44.0  | 1100 | 2.7292          |
| 2.772         | 45.0  | 1125 | 2.7287          |
| 2.772         | 46.0  | 1150 | 2.6998          |
| 2.772         | 47.0  | 1175 | 2.7381          |
| 2.772         | 48.0  | 1200 | 2.7196          |
| 2.772         | 49.0  | 1225 | 2.7450          |
| 2.772         | 50.0  | 1250 | 2.7293          |
| 2.772         | 51.0  | 1275 | 2.7216          |
| 2.772         | 52.0  | 1300 | 2.7981          |
| 2.772         | 53.0  | 1325 | 2.7405          |
| 2.772         | 54.0  | 1350 | 2.7895          |
| 2.772         | 55.0  | 1375 | 2.7092          |
| 2.772         | 56.0  | 1400 | 2.7977          |
| 2.772         | 57.0  | 1425 | 2.7012          |
| 2.772         | 58.0  | 1450 | 2.7752          |
| 2.772         | 59.0  | 1475 | 2.7469          |
| 2.742         | 60.0  | 1500 | 2.7205          |
| 2.742         | 61.0  | 1525 | 2.7752          |
| 2.742         | 62.0  | 1550 | 2.6942          |
| 2.742         | 63.0  | 1575 | 2.6916          |
| 2.742         | 64.0  | 1600 | 2.8169          |
| 2.742         | 65.0  | 1625 | 2.7256          |
| 2.742         | 66.0  | 1650 | 2.6844          |
| 2.742         | 67.0  | 1675 | 2.7544          |
| 2.742         | 68.0  | 1700 | 2.7083          |
| 2.742         | 69.0  | 1725 | 2.7286          |
| 2.742         | 70.0  | 1750 | 2.7492          |
| 2.742         | 71.0  | 1775 | 2.6946          |
| 2.742         | 72.0  | 1800 | 2.7395          |
| 2.742         | 73.0  | 1825 | 2.7597          |
| 2.742         | 74.0  | 1850 | 2.7953          |
| 2.742         | 75.0  | 1875 | 2.7468          |
| 2.742         | 76.0  | 1900 | 2.7274          |
| 2.742         | 77.0  | 1925 | 2.7507          |
| 2.742         | 78.0  | 1950 | 2.7174          |
| 2.742         | 79.0  | 1975 | 2.7233          |
| 2.7185        | 80.0  | 2000 | 2.7405          |
| 2.7185        | 81.0  | 2025 | 2.7781          |
| 2.7185        | 82.0  | 2050 | 2.7534          |
| 2.7185        | 83.0  | 2075 | 2.7588          |
| 2.7185        | 84.0  | 2100 | 2.7469          |
| 2.7185        | 85.0  | 2125 | 2.6929          |
| 2.7185        | 86.0  | 2150 | 2.6785          |
| 2.7185        | 87.0  | 2175 | 2.7098          |
| 2.7185        | 88.0  | 2200 | 2.7622          |
| 2.7185        | 89.0  | 2225 | 2.7726          |
| 2.7185        | 90.0  | 2250 | 2.7144          |
| 2.7185        | 91.0  | 2275 | 2.7877          |
| 2.7185        | 92.0  | 2300 | 2.7665          |
| 2.7185        | 93.0  | 2325 | 2.7794          |
| 2.7185        | 94.0  | 2350 | 2.6788          |
| 2.7185        | 95.0  | 2375 | 2.7398          |
| 2.7185        | 96.0  | 2400 | 2.7277          |
| 2.7185        | 97.0  | 2425 | 2.8053          |
| 2.7185        | 98.0  | 2450 | 2.7537          |
| 2.7185        | 99.0  | 2475 | 2.7467          |
| 2.7057        | 100.0 | 2500 | 2.7191          |


### Framework versions

- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2

{"tags": ["generated_from_trainer"], "model-index": [{"name": "digikala_products_parsbert_model", "results": []}]}
transformers pytorch tensorboard bert fill-mask generated_from_trainer autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us 

digikala\_products\_parsbert\_model
===================================


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


* Loss: 2.7245


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: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 100


### Training results



### Framework versions


* Transformers 4.26.0
* Pytorch 1.13.1+cu116
* 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: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 100 ### Training results ### Framework versions


* Transformers 4.26.0
* Pytorch 1.13.1+cu116
* Datasets 2.9.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 32
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 100 ### Training results ### Framework versions


* Transformers 4.26.0
* Pytorch 1.13.1+cu116
* Datasets 2.9.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 32
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 100### Training results### Framework versions


* Transformers 4.26.0
* Pytorch 1.13.1+cu116
* Datasets 2.9.0
* Tokenizers 0.13.2