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# user_class_L

This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5451
- Accuracy: 0.9237

## 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: 3.8e-05
- train_batch_size: 30
- eval_batch_size: 4
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1572        | 1.0   | 24   | 0.2433          | 0.9025   |
| 0.1649        | 2.0   | 48   | 0.2262          | 0.9237   |
| 0.2498        | 3.0   | 72   | 0.2584          | 0.9237   |
| 0.006         | 4.0   | 96   | 0.3393          | 0.9153   |
| 0.0035        | 5.0   | 120  | 0.3967          | 0.9153   |
| 0.0017        | 6.0   | 144  | 0.4777          | 0.9153   |
| 0.0006        | 7.0   | 168  | 0.6257          | 0.8898   |
| 0.0005        | 8.0   | 192  | 0.5752          | 0.9153   |
| 0.0002        | 9.0   | 216  | 0.5182          | 0.9237   |
| 0.0003        | 10.0  | 240  | 0.5041          | 0.9195   |
| 0.0002        | 11.0  | 264  | 0.5051          | 0.9195   |
| 0.0001        | 12.0  | 288  | 0.5292          | 0.9195   |
| 0.0002        | 13.0  | 312  | 0.5391          | 0.9237   |
| 0.0002        | 14.0  | 336  | 0.5437          | 0.9237   |
| 0.0002        | 15.0  | 360  | 0.5451          | 0.9237   |


### Framework versions

- Transformers 4.24.0
- Pytorch 1.13.0+cu117
- Datasets 2.12.0
- Tokenizers 0.13.2

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

user\_class\_L
==============


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


* Loss: 0.5451
* Accuracy: 0.9237


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: 3.8e-05
* train\_batch\_size: 30
* eval\_batch\_size: 4
* 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 4.24.0
* Pytorch 1.13.0+cu117
* Datasets 2.12.0
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 3.8e-05
* train\_batch\_size: 30
* eval\_batch\_size: 4
* 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 4.24.0
* Pytorch 1.13.0+cu117
* Datasets 2.12.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #bert #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: 3.8e-05
* train\_batch\_size: 30
* eval\_batch\_size: 4
* 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 4.24.0
* Pytorch 1.13.0+cu117
* Datasets 2.12.0
* Tokenizers 0.13.2