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# distilbert-base-cased-finetuned-paper3

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: 0.1966
- Precision: 0.6773
- Recall: 0.7350
- F1: 0.7050
- Accuracy: 0.9687

## 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: 5e-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: 20

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 73   | 0.1851          | 0.4383    | 0.4479 | 0.4431 | 0.9434   |
| No log        | 2.0   | 146  | 0.1473          | 0.5455    | 0.5678 | 0.5564 | 0.9593   |
| No log        | 3.0   | 219  | 0.1391          | 0.6509    | 0.6530 | 0.6520 | 0.9646   |
| No log        | 4.0   | 292  | 0.1236          | 0.6552    | 0.7192 | 0.6857 | 0.9702   |
| No log        | 5.0   | 365  | 0.1352          | 0.6724    | 0.7382 | 0.7038 | 0.9693   |
| No log        | 6.0   | 438  | 0.1594          | 0.6746    | 0.7129 | 0.6933 | 0.9673   |
| 0.0969        | 7.0   | 511  | 0.1693          | 0.6705    | 0.7382 | 0.7027 | 0.9683   |
| 0.0969        | 8.0   | 584  | 0.1806          | 0.6923    | 0.7382 | 0.7145 | 0.9692   |
| 0.0969        | 9.0   | 657  | 0.1594          | 0.6359    | 0.7603 | 0.6925 | 0.9687   |
| 0.0969        | 10.0  | 730  | 0.1740          | 0.6946    | 0.7319 | 0.7127 | 0.9683   |
| 0.0969        | 11.0  | 803  | 0.1881          | 0.6735    | 0.7287 | 0.7    | 0.9677   |
| 0.0969        | 12.0  | 876  | 0.1932          | 0.7064    | 0.7287 | 0.7174 | 0.9692   |
| 0.0969        | 13.0  | 949  | 0.1890          | 0.6907    | 0.7256 | 0.7077 | 0.9689   |
| 0.0025        | 14.0  | 1022 | 0.1860          | 0.6705    | 0.7445 | 0.7055 | 0.9696   |
| 0.0025        | 15.0  | 1095 | 0.1951          | 0.6706    | 0.7256 | 0.6970 | 0.9688   |
| 0.0025        | 16.0  | 1168 | 0.1936          | 0.6648    | 0.7319 | 0.6967 | 0.9681   |
| 0.0025        | 17.0  | 1241 | 0.1969          | 0.6725    | 0.7319 | 0.7009 | 0.9686   |
| 0.0025        | 18.0  | 1314 | 0.1953          | 0.6792    | 0.7413 | 0.7089 | 0.9692   |
| 0.0025        | 19.0  | 1387 | 0.1960          | 0.6754    | 0.7350 | 0.7039 | 0.9687   |
| 0.0025        | 20.0  | 1460 | 0.1966          | 0.6773    | 0.7350 | 0.7050 | 0.9687   |


### 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-paper3", "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-paper3
======================================


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: 0.1966
* Precision: 0.6773
* Recall: 0.7350
* F1: 0.7050
* Accuracy: 0.9687


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: 5e-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: 20


### 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: 5e-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: 20 ### 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: 5e-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: 20 ### 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: 5e-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: 20### Training results### Framework versions


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