
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# distilbert-base-cased-finetuned-cv2

This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1933
- Precision: 0.5105
- Recall: 0.5893
- F1: 0.5471
- Accuracy: 0.9497

## 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: 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: 30

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 17   | 0.5040          | 0.0       | 0.0    | 0.0    | 0.9043   |
| No log        | 2.0   | 34   | 0.4049          | 0.0       | 0.0    | 0.0    | 0.9043   |
| No log        | 3.0   | 51   | 0.3508          | 0.2712    | 0.1744 | 0.2123 | 0.9125   |
| No log        | 4.0   | 68   | 0.3097          | 0.3073    | 0.2437 | 0.2718 | 0.9209   |
| No log        | 5.0   | 85   | 0.2815          | 0.3323    | 0.3246 | 0.3284 | 0.9256   |
| No log        | 6.0   | 102  | 0.2569          | 0.3522    | 0.3866 | 0.3686 | 0.9295   |
| No log        | 7.0   | 119  | 0.2479          | 0.3545    | 0.4391 | 0.3923 | 0.9318   |
| No log        | 8.0   | 136  | 0.2281          | 0.4159    | 0.4569 | 0.4354 | 0.9359   |
| No log        | 9.0   | 153  | 0.2310          | 0.5044    | 0.4202 | 0.4585 | 0.9401   |
| No log        | 10.0  | 170  | 0.2258          | 0.4144    | 0.5063 | 0.4558 | 0.9337   |
| No log        | 11.0  | 187  | 0.2127          | 0.4413    | 0.4979 | 0.4679 | 0.9383   |
| No log        | 12.0  | 204  | 0.2172          | 0.4056    | 0.5210 | 0.4561 | 0.9364   |
| No log        | 13.0  | 221  | 0.2093          | 0.4313    | 0.5441 | 0.4812 | 0.9404   |
| No log        | 14.0  | 238  | 0.2062          | 0.4263    | 0.5378 | 0.4756 | 0.9389   |
| No log        | 15.0  | 255  | 0.2010          | 0.4554    | 0.5578 | 0.5014 | 0.9429   |
| No log        | 16.0  | 272  | 0.1959          | 0.4683    | 0.5578 | 0.5091 | 0.9455   |
| No log        | 17.0  | 289  | 0.1955          | 0.4668    | 0.5620 | 0.5100 | 0.9449   |
| No log        | 18.0  | 306  | 0.1950          | 0.4768    | 0.5714 | 0.5198 | 0.9466   |
| No log        | 19.0  | 323  | 0.1942          | 0.4892    | 0.5735 | 0.5280 | 0.9474   |
| No log        | 20.0  | 340  | 0.1937          | 0.5408    | 0.5641 | 0.5522 | 0.9501   |
| No log        | 21.0  | 357  | 0.1993          | 0.4665    | 0.5924 | 0.5220 | 0.9457   |
| No log        | 22.0  | 374  | 0.1925          | 0.5086    | 0.5882 | 0.5455 | 0.9489   |
| No log        | 23.0  | 391  | 0.2035          | 0.4604    | 0.6050 | 0.5229 | 0.9444   |
| No log        | 24.0  | 408  | 0.1902          | 0.5190    | 0.5725 | 0.5445 | 0.9497   |
| No log        | 25.0  | 425  | 0.1937          | 0.4970    | 0.5998 | 0.5436 | 0.9481   |
| No log        | 26.0  | 442  | 0.1942          | 0.4969    | 0.5914 | 0.5400 | 0.9488   |
| No log        | 27.0  | 459  | 0.1937          | 0.5072    | 0.5924 | 0.5465 | 0.9489   |
| No log        | 28.0  | 476  | 0.1927          | 0.5146    | 0.5914 | 0.5503 | 0.9500   |
| No log        | 29.0  | 493  | 0.1937          | 0.5108    | 0.5956 | 0.5500 | 0.9495   |
| 0.1908        | 30.0  | 510  | 0.1933          | 0.5105    | 0.5893 | 0.5471 | 0.9497   |


### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "distilbert-base-cased-finetuned-cv2", "results": []}]}
transformers pytorch tensorboard distilbert token-classification generated_from_trainer license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

distilbert-base-cased-finetuned-cv2
===================================


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


* Loss: 0.1933
* Precision: 0.5105
* Recall: 0.5893
* F1: 0.5471
* Accuracy: 0.9497


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: 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: 30


### Training results



### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-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: 30 ### Training results ### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #distilbert #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: 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: 30 ### Training results ### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #distilbert #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: 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: 30### Training results### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2