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# bert-base-uncased-finetuned-paper

This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2326
- Precision: 0.7612
- Recall: 0.7456
- F1: 0.7533
- Accuracy: 0.9684

## 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   | 0.1917          | 0.6756    | 0.5175 | 0.5861 | 0.9484   |
| No log        | 2.0   | 146  | 0.1402          | 0.7516    | 0.6988 | 0.7242 | 0.9678   |
| No log        | 3.0   | 219  | 0.1747          | 0.7397    | 0.6813 | 0.7093 | 0.9659   |
| No log        | 4.0   | 292  | 0.1627          | 0.6797    | 0.7632 | 0.7190 | 0.9633   |
| No log        | 5.0   | 365  | 0.1720          | 0.7005    | 0.7456 | 0.7224 | 0.9661   |
| No log        | 6.0   | 438  | 0.2029          | 0.7515    | 0.7339 | 0.7426 | 0.9688   |
| 0.0876        | 7.0   | 511  | 0.1928          | 0.7415    | 0.7632 | 0.7522 | 0.9700   |
| 0.0876        | 8.0   | 584  | 0.2016          | 0.7579    | 0.7690 | 0.7634 | 0.9708   |
| 0.0876        | 9.0   | 657  | 0.2051          | 0.7371    | 0.7544 | 0.7457 | 0.9684   |
| 0.0876        | 10.0  | 730  | 0.2153          | 0.7477    | 0.7281 | 0.7378 | 0.9693   |
| 0.0876        | 11.0  | 803  | 0.2284          | 0.7626    | 0.7515 | 0.7570 | 0.9693   |
| 0.0876        | 12.0  | 876  | 0.2223          | 0.7139    | 0.7515 | 0.7322 | 0.9682   |
| 0.0876        | 13.0  | 949  | 0.2274          | 0.7471    | 0.7515 | 0.7493 | 0.9690   |
| 0.0022        | 14.0  | 1022 | 0.2321          | 0.7695    | 0.7515 | 0.7604 | 0.9695   |
| 0.0022        | 15.0  | 1095 | 0.2367          | 0.7590    | 0.7368 | 0.7478 | 0.9690   |
| 0.0022        | 16.0  | 1168 | 0.2327          | 0.7612    | 0.7456 | 0.7533 | 0.9695   |
| 0.0022        | 17.0  | 1241 | 0.2367          | 0.7704    | 0.7456 | 0.7578 | 0.9690   |
| 0.0022        | 18.0  | 1314 | 0.2309          | 0.7529    | 0.7485 | 0.7507 | 0.9691   |
| 0.0022        | 19.0  | 1387 | 0.2358          | 0.7711    | 0.7485 | 0.7596 | 0.9686   |
| 0.0022        | 20.0  | 1460 | 0.2326          | 0.7612    | 0.7456 | 0.7533 | 0.9684   |


### Framework versions

- Transformers 4.27.1
- 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": "bert-base-uncased-finetuned-paper", "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 

bert-base-uncased-finetuned-paper
=================================


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


* Loss: 0.2326
* Precision: 0.7612
* Recall: 0.7456
* F1: 0.7533
* Accuracy: 0.9684


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 4.27.1
* 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 4.27.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2
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: 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 4.27.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2
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: 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 4.27.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2