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# bert-base-uncased-finetuned-filtered-0609

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: 0.1749
- Accuracy: 0.9789
- Precision: 0.9790
- Recall: 0.9789
- F1: 0.9789

## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.1671        | 1.0   | 3180  | 0.1735          | 0.9632   | 0.9648    | 0.9632 | 0.9635 |
| 0.1384        | 2.0   | 6360  | 0.1120          | 0.9736   | 0.9738    | 0.9736 | 0.9736 |
| 0.1064        | 3.0   | 9540  | 0.1880          | 0.9635   | 0.9647    | 0.9635 | 0.9635 |
| 0.0823        | 4.0   | 12720 | 0.1495          | 0.9758   | 0.9759    | 0.9758 | 0.9757 |
| 0.0426        | 5.0   | 15900 | 0.1766          | 0.9742   | 0.9746    | 0.9742 | 0.9743 |
| 0.0254        | 6.0   | 19080 | 0.1724          | 0.9777   | 0.9778    | 0.9777 | 0.9777 |
| 0.0257        | 7.0   | 22260 | 0.1760          | 0.9764   | 0.9767    | 0.9764 | 0.9764 |
| 0.0017        | 8.0   | 25440 | 0.1672          | 0.9786   | 0.9787    | 0.9786 | 0.9786 |
| 0.0077        | 9.0   | 28620 | 0.1894          | 0.9789   | 0.9791    | 0.9789 | 0.9789 |
| 0.0014        | 10.0  | 31800 | 0.1749          | 0.9789   | 0.9790    | 0.9789 | 0.9789 |


### Framework versions

- Transformers 4.19.2
- Pytorch 1.9.1+cu111
- Datasets 1.16.1
- Tokenizers 0.12.1

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "precision", "recall", "f1"], "model-index": [{"name": "bert-base-uncased-finetuned-filtered-0609", "results": []}]}
transformers pytorch bert text-classification generated_from_trainer license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

bert-base-uncased-finetuned-filtered-0609
=========================================


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: 0.1749
* Accuracy: 0.9789
* Precision: 0.9790
* Recall: 0.9789
* F1: 0.9789


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: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 1000
* num\_epochs: 10


### Training results



### Framework versions


* Transformers 4.19.2
* Pytorch 1.9.1+cu111
* Datasets 1.16.1
* Tokenizers 0.12.1



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 1000
* num\_epochs: 10 ### Training results ### Framework versions


* Transformers 4.19.2
* Pytorch 1.9.1+cu111
* Datasets 1.16.1
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #bert #text-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: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 1000
* num\_epochs: 10 ### Training results ### Framework versions


* Transformers 4.19.2
* Pytorch 1.9.1+cu111
* Datasets 1.16.1
* Tokenizers 0.12.1
passage: TAGS
#transformers #pytorch #bert #text-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: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 1000
* num\_epochs: 10### Training results### Framework versions


* Transformers 4.19.2
* Pytorch 1.9.1+cu111
* Datasets 1.16.1
* Tokenizers 0.12.1