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# albert_model

This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6560
- Accuracy: 0.9070
- F1: 0.8852
- Recall: 0.9122

## 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: 16
- eval_batch_size: 16
- seed: 42
- 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 | Accuracy | F1     | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|
| No log        | 1.0   | 167  | 0.3571          | 0.8351   | 0.8142 | 0.9198 |
| No log        | 2.0   | 334  | 0.2670          | 0.8891   | 0.8683 | 0.9313 |
| 0.3358        | 3.0   | 501  | 0.2643          | 0.9115   | 0.8885 | 0.8969 |
| 0.3358        | 4.0   | 668  | 0.3804          | 0.9130   | 0.8910 | 0.9046 |
| 0.3358        | 5.0   | 835  | 0.4376          | 0.9070   | 0.8848 | 0.9084 |
| 0.1007        | 6.0   | 1002 | 0.4957          | 0.9100   | 0.8859 | 0.8893 |
| 0.1007        | 7.0   | 1169 | 0.6375          | 0.8801   | 0.8601 | 0.9389 |
| 0.1007        | 8.0   | 1336 | 0.5978          | 0.8996   | 0.8780 | 0.9198 |
| 0.012         | 9.0   | 1503 | 0.6101          | 0.9025   | 0.8816 | 0.9237 |
| 0.012         | 10.0  | 1670 | 0.6209          | 0.9085   | 0.8847 | 0.8931 |
| 0.012         | 11.0  | 1837 | 0.6485          | 0.9010   | 0.8787 | 0.9122 |
| 0.0007        | 12.0  | 2004 | 0.6480          | 0.9070   | 0.8852 | 0.9122 |
| 0.0007        | 13.0  | 2171 | 0.6527          | 0.9055   | 0.8835 | 0.9122 |
| 0.0007        | 14.0  | 2338 | 0.6557          | 0.9055   | 0.8835 | 0.9122 |
| 0.0002        | 15.0  | 2505 | 0.6560          | 0.9070   | 0.8852 | 0.9122 |


### Framework versions

- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1", "recall"], "model-index": [{"name": "albert_model", "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 

albert\_model
=============


This model is a fine-tuned version of albert-base-v2 on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 0.6560
* Accuracy: 0.9070
* F1: 0.8852
* Recall: 0.9122


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: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 15


### Training results



### Framework versions


* Transformers 4.29.2
* Pytorch 2.0.1+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


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


* Transformers 4.29.2
* Pytorch 2.0.1+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3
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: 1e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 15 ### Training results ### Framework versions


* Transformers 4.29.2
* Pytorch 2.0.1+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3