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# distilbert-base-cased-finetuned-paper2

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: 0.1849
- Precision: 0.6124
- Recall: 0.6695
- F1: 0.6397
- Accuracy: 0.9714

## 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   | 68   | 0.1941          | 0.2474    | 0.2034 | 0.2233 | 0.9438   |
| No log        | 2.0   | 136  | 0.1462          | 0.3680    | 0.3602 | 0.3640 | 0.9593   |
| No log        | 3.0   | 204  | 0.1154          | 0.4921    | 0.5254 | 0.5082 | 0.9667   |
| No log        | 4.0   | 272  | 0.1121          | 0.5420    | 0.6017 | 0.5703 | 0.9675   |
| No log        | 5.0   | 340  | 0.1291          | 0.5714    | 0.6102 | 0.5902 | 0.9676   |
| No log        | 6.0   | 408  | 0.1355          | 0.5152    | 0.6483 | 0.5741 | 0.9649   |
| No log        | 7.0   | 476  | 0.1396          | 0.5694    | 0.6949 | 0.6260 | 0.9671   |
| 0.1425        | 8.0   | 544  | 0.1459          | 0.5484    | 0.6483 | 0.5942 | 0.9675   |
| 0.1425        | 9.0   | 612  | 0.1623          | 0.5290    | 0.6186 | 0.5703 | 0.9661   |
| 0.1425        | 10.0  | 680  | 0.1545          | 0.5938    | 0.6441 | 0.6179 | 0.9705   |
| 0.1425        | 11.0  | 748  | 0.1540          | 0.6171    | 0.7034 | 0.6574 | 0.9709   |
| 0.1425        | 12.0  | 816  | 0.1621          | 0.5985    | 0.6822 | 0.6376 | 0.9696   |
| 0.1425        | 13.0  | 884  | 0.1672          | 0.5664    | 0.6864 | 0.6207 | 0.9699   |
| 0.1425        | 14.0  | 952  | 0.1828          | 0.5563    | 0.7119 | 0.6245 | 0.9675   |
| 0.0068        | 15.0  | 1020 | 0.1751          | 0.6216    | 0.6822 | 0.6505 | 0.9714   |
| 0.0068        | 16.0  | 1088 | 0.1738          | 0.5933    | 0.6737 | 0.6310 | 0.9703   |
| 0.0068        | 17.0  | 1156 | 0.1740          | 0.6345    | 0.6695 | 0.6515 | 0.9717   |
| 0.0068        | 18.0  | 1224 | 0.1850          | 0.6062    | 0.6653 | 0.6343 | 0.9707   |
| 0.0068        | 19.0  | 1292 | 0.1856          | 0.6216    | 0.6822 | 0.6505 | 0.9714   |
| 0.0068        | 20.0  | 1360 | 0.1936          | 0.6015    | 0.6653 | 0.6318 | 0.9718   |
| 0.0068        | 21.0  | 1428 | 0.1844          | 0.6367    | 0.6907 | 0.6626 | 0.9724   |
| 0.0068        | 22.0  | 1496 | 0.1882          | 0.6092    | 0.6737 | 0.6398 | 0.9709   |
| 0.0021        | 23.0  | 1564 | 0.1887          | 0.6423    | 0.6695 | 0.6556 | 0.9721   |
| 0.0021        | 24.0  | 1632 | 0.1874          | 0.5985    | 0.6568 | 0.6263 | 0.9710   |
| 0.0021        | 25.0  | 1700 | 0.1861          | 0.5874    | 0.6695 | 0.6257 | 0.9707   |
| 0.0021        | 26.0  | 1768 | 0.1868          | 0.5918    | 0.6695 | 0.6282 | 0.9709   |
| 0.0021        | 27.0  | 1836 | 0.1858          | 0.5970    | 0.6780 | 0.6349 | 0.9714   |
| 0.0021        | 28.0  | 1904 | 0.1842          | 0.6       | 0.6737 | 0.6347 | 0.9713   |
| 0.0021        | 29.0  | 1972 | 0.1847          | 0.6124    | 0.6695 | 0.6397 | 0.9714   |
| 0.0012        | 30.0  | 2040 | 0.1849          | 0.6124    | 0.6695 | 0.6397 | 0.9714   |


### 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-paper2", "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-paper2
======================================


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: 0.1849
* Precision: 0.6124
* Recall: 0.6695
* F1: 0.6397
* Accuracy: 0.9714


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