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# uroptest2

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: 0.1950
- Precision: 0.4434
- Recall: 0.4290
- F1: 0.4361
- Accuracy: 0.9699

## 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: 10
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0425        | 1.0   | 690  | 0.1567          | 0.3439    | 0.4198 | 0.3780 | 0.9637   |
| 0.0367        | 2.0   | 1380 | 0.1876          | 0.4529    | 0.3565 | 0.3990 | 0.9694   |
| 0.0251        | 3.0   | 2070 | 0.1603          | 0.3693    | 0.4599 | 0.4096 | 0.9662   |
| 0.0213        | 4.0   | 2760 | 0.1659          | 0.3842    | 0.4120 | 0.3976 | 0.9675   |
| 0.0166        | 5.0   | 3450 | 0.1732          | 0.3975    | 0.4429 | 0.4190 | 0.9677   |
| 0.0104        | 6.0   | 4140 | 0.1686          | 0.3871    | 0.4182 | 0.4021 | 0.9683   |
| 0.0105        | 7.0   | 4830 | 0.1809          | 0.4205    | 0.3920 | 0.4058 | 0.9688   |
| 0.0064        | 8.0   | 5520 | 0.1914          | 0.4452    | 0.4074 | 0.4255 | 0.9702   |
| 0.0047        | 9.0   | 6210 | 0.1908          | 0.4310    | 0.4244 | 0.4277 | 0.9696   |
| 0.004         | 10.0  | 6900 | 0.1950          | 0.4434    | 0.4290 | 0.4361 | 0.9699   |


### 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": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "uroptest2", "results": []}]}
transformers pytorch tensorboard bert token-classification generated_from_trainer license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

uroptest2
=========


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: 0.1950
* Precision: 0.4434
* Recall: 0.4290
* F1: 0.4361
* Accuracy: 0.9699


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: 10
* mixed\_precision\_training: Native AMP


### 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: 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: 10
* mixed\_precision\_training: Native AMP ### 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 #bert #token-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: 10
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


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