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# distilbert-base-cased-finetuned-paper

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: 0.1417
- Precision: 0.5690
- Recall: 0.6226
- F1: 0.5946
- Accuracy: 0.9790

## 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   | 62   | 0.1183          | 0.3810    | 0.3019 | 0.3368 | 0.9702   |
| No log        | 2.0   | 124  | 0.0923          | 0.3986    | 0.5189 | 0.4508 | 0.9730   |
| No log        | 3.0   | 186  | 0.0808          | 0.5       | 0.5943 | 0.5431 | 0.9765   |
| No log        | 4.0   | 248  | 0.0951          | 0.6042    | 0.5472 | 0.5743 | 0.9790   |
| No log        | 5.0   | 310  | 0.0939          | 0.5794    | 0.5849 | 0.5822 | 0.9795   |
| No log        | 6.0   | 372  | 0.0949          | 0.5508    | 0.6132 | 0.5804 | 0.9792   |
| No log        | 7.0   | 434  | 0.1004          | 0.5075    | 0.6415 | 0.5667 | 0.9782   |
| No log        | 8.0   | 496  | 0.1110          | 0.5403    | 0.6321 | 0.5826 | 0.9784   |
| 0.0979        | 9.0   | 558  | 0.1139          | 0.5517    | 0.6038 | 0.5766 | 0.9789   |
| 0.0979        | 10.0  | 620  | 0.1153          | 0.5727    | 0.5943 | 0.5833 | 0.9795   |
| 0.0979        | 11.0  | 682  | 0.1238          | 0.5238    | 0.6226 | 0.5690 | 0.9776   |
| 0.0979        | 12.0  | 744  | 0.1249          | 0.5478    | 0.5943 | 0.5701 | 0.9786   |
| 0.0979        | 13.0  | 806  | 0.1263          | 0.5323    | 0.6226 | 0.5739 | 0.9786   |
| 0.0979        | 14.0  | 868  | 0.1303          | 0.5810    | 0.5755 | 0.5782 | 0.9792   |
| 0.0979        | 15.0  | 930  | 0.1358          | 0.4929    | 0.6509 | 0.5610 | 0.9773   |
| 0.0979        | 16.0  | 992  | 0.1305          | 0.5766    | 0.6038 | 0.5899 | 0.9793   |
| 0.0033        | 17.0  | 1054 | 0.1321          | 0.5323    | 0.6226 | 0.5739 | 0.9779   |
| 0.0033        | 18.0  | 1116 | 0.1353          | 0.5726    | 0.6321 | 0.6009 | 0.9789   |
| 0.0033        | 19.0  | 1178 | 0.1355          | 0.5462    | 0.6132 | 0.5778 | 0.9793   |
| 0.0033        | 20.0  | 1240 | 0.1346          | 0.5556    | 0.6132 | 0.5830 | 0.9796   |
| 0.0033        | 21.0  | 1302 | 0.1386          | 0.5403    | 0.6321 | 0.5826 | 0.9784   |
| 0.0033        | 22.0  | 1364 | 0.1389          | 0.5508    | 0.6132 | 0.5804 | 0.9790   |
| 0.0033        | 23.0  | 1426 | 0.1376          | 0.55      | 0.6226 | 0.5841 | 0.9790   |
| 0.0033        | 24.0  | 1488 | 0.1394          | 0.5641    | 0.6226 | 0.5919 | 0.9796   |
| 0.0012        | 25.0  | 1550 | 0.1408          | 0.55      | 0.6226 | 0.5841 | 0.9789   |
| 0.0012        | 26.0  | 1612 | 0.1413          | 0.5739    | 0.6226 | 0.5973 | 0.9792   |
| 0.0012        | 27.0  | 1674 | 0.1417          | 0.5455    | 0.6226 | 0.5815 | 0.9787   |
| 0.0012        | 28.0  | 1736 | 0.1424          | 0.5455    | 0.6226 | 0.5815 | 0.9787   |
| 0.0012        | 29.0  | 1798 | 0.1419          | 0.5546    | 0.6226 | 0.5867 | 0.9790   |
| 0.0012        | 30.0  | 1860 | 0.1417          | 0.5690    | 0.6226 | 0.5946 | 0.9790   |


### 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-paper", "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-paper
=====================================


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: 0.1417
* Precision: 0.5690
* Recall: 0.6226
* F1: 0.5946
* Accuracy: 0.9790


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