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# Regression_albert_9_with_translation

This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3629
- Mse: 0.3629
- Mae: 0.4551
- R2: 0.1650
- Accuracy: 0.6333

## 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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20

### Training results

| Training Loss | Epoch | Step | Validation Loss | Mse    | Mae    | R2     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:--------:|
| No log        | 1.0   | 53   | 0.3421          | 0.3421 | 0.4573 | 0.2292 | 0.6167   |
| No log        | 2.0   | 106  | 0.2617          | 0.2617 | 0.3888 | 0.4104 | 0.6667   |
| No log        | 3.0   | 159  | 0.2117          | 0.2117 | 0.3422 | 0.5230 | 0.7667   |
| No log        | 4.0   | 212  | 0.3250          | 0.3250 | 0.4990 | 0.2677 | 0.55     |
| No log        | 5.0   | 265  | 0.2494          | 0.2494 | 0.3321 | 0.4380 | 0.7167   |
| No log        | 6.0   | 318  | 0.2477          | 0.2477 | 0.3488 | 0.4419 | 0.75     |
| No log        | 7.0   | 371  | 0.3209          | 0.3209 | 0.3599 | 0.2770 | 0.7833   |
| No log        | 8.0   | 424  | 0.2704          | 0.2704 | 0.3715 | 0.3909 | 0.7      |
| No log        | 9.0   | 477  | 0.2886          | 0.2886 | 0.3185 | 0.3498 | 0.7833   |
| 0.1507        | 10.0  | 530  | 0.2477          | 0.2477 | 0.3071 | 0.4418 | 0.7667   |
| 0.1507        | 11.0  | 583  | 0.2670          | 0.2670 | 0.3232 | 0.3984 | 0.7833   |
| 0.1507        | 12.0  | 636  | 0.2285          | 0.2285 | 0.2926 | 0.4851 | 0.75     |
| 0.1507        | 13.0  | 689  | 0.2378          | 0.2378 | 0.2980 | 0.4643 | 0.7833   |
| 0.1507        | 14.0  | 742  | 0.2544          | 0.2544 | 0.3194 | 0.4269 | 0.7667   |
| 0.1507        | 15.0  | 795  | 0.2571          | 0.2571 | 0.2904 | 0.4208 | 0.8      |
| 0.1507        | 16.0  | 848  | 0.2505          | 0.2505 | 0.2884 | 0.4357 | 0.8      |
| 0.1507        | 17.0  | 901  | 0.2654          | 0.2654 | 0.2846 | 0.4022 | 0.8      |
| 0.1507        | 18.0  | 954  | 0.2606          | 0.2606 | 0.2785 | 0.4128 | 0.8      |
| 0.0203        | 19.0  | 1007 | 0.2519          | 0.2519 | 0.2816 | 0.4324 | 0.8      |
| 0.0203        | 20.0  | 1060 | 0.2634          | 0.2634 | 0.2826 | 0.4065 | 0.8      |


### Framework versions

- Transformers 4.27.4
- Pytorch 1.13.1+cu116
- Datasets 2.11.0
- Tokenizers 0.13.2

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "Regression_albert_9_with_translation", "results": []}]}
transformers pytorch tensorboard albert text-classification generated_from_trainer license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #albert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

Regression\_albert\_9\_with\_translation
========================================


This model is a fine-tuned version of albert-base-v2 on an unknown dataset.
It achieves the following results on the evaluation set:


* Loss: 0.3629
* Mse: 0.3629
* Mae: 0.4551
* R2: 0.1650
* Accuracy: 0.6333


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: 4
* eval\_batch\_size: 4
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 20


### Training results



### Framework versions


* Transformers 4.27.4
* Pytorch 1.13.1+cu116
* Datasets 2.11.0
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 20 ### Training results ### Framework versions


* Transformers 4.27.4
* Pytorch 1.13.1+cu116
* Datasets 2.11.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #albert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 20 ### Training results ### Framework versions


* Transformers 4.27.4
* Pytorch 1.13.1+cu116
* Datasets 2.11.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #albert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 20### Training results### Framework versions


* Transformers 4.27.4
* Pytorch 1.13.1+cu116
* Datasets 2.11.0
* Tokenizers 0.13.2