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# bert-base-uncased-ag-news-finetuned-dwnews-categories

This model is a fine-tuned version of [nateraw/bert-base-uncased-ag-news](https://huggingface.co/nateraw/bert-base-uncased-ag-news) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8659
- Accuracy: 0.74
- Precision Weighted: 0.7435
- Precision Macro: 0.7557
- Recall Weighted: 0.74
- Recall Macro: 0.7304
- F1 Weighted: 0.7294
- F1 Macro: 0.7250

## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision Weighted | Precision Macro | Recall Weighted | Recall Macro | F1 Weighted | F1 Macro |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------------------:|:---------------:|:---------------:|:------------:|:-----------:|:--------:|
| 1.8428        | 0.25  | 50   | 1.5099          | 0.53     | 0.5008             | 0.4655          | 0.53            | 0.3544       | 0.4363      | 0.3246   |
| 1.3134        | 0.5   | 100  | 1.2200          | 0.605    | 0.4765             | 0.4247          | 0.605           | 0.4681       | 0.5207      | 0.4261   |
| 1.1562        | 0.75  | 150  | 1.0473          | 0.68     | 0.7007             | 0.6603          | 0.68            | 0.5534       | 0.6483      | 0.5507   |
| 1.0008        | 1.0   | 200  | 0.9491          | 0.67     | 0.6281             | 0.5935          | 0.67            | 0.5881       | 0.6390      | 0.5778   |
| 0.8173        | 1.25  | 250  | 0.9218          | 0.7      | 0.7028             | 0.6599          | 0.7             | 0.6694       | 0.6874      | 0.6417   |
| 0.8385        | 1.5   | 300  | 0.8900          | 0.715    | 0.7250             | 0.7131          | 0.715           | 0.6600       | 0.7059      | 0.6637   |
| 0.6988        | 1.75  | 350  | 0.9198          | 0.7      | 0.6941             | 0.6875          | 0.7             | 0.6825       | 0.6866      | 0.6704   |
| 0.6851        | 2.0   | 400  | 0.8607          | 0.72     | 0.7248             | 0.7310          | 0.72            | 0.6978       | 0.7067      | 0.6965   |
| 0.548         | 2.25  | 450  | 0.8659          | 0.74     | 0.7435             | 0.7557          | 0.74            | 0.7304       | 0.7294      | 0.7250   |
| 0.4898        | 2.5   | 500  | 0.9184          | 0.73     | 0.7379             | 0.7079          | 0.73            | 0.7599       | 0.7229      | 0.7203   |
| 0.5683        | 2.75  | 550  | 0.9207          | 0.72     | 0.7188             | 0.7089          | 0.72            | 0.7429       | 0.7150      | 0.7195   |
| 0.4971        | 3.0   | 600  | 0.9256          | 0.72     | 0.7257             | 0.7104          | 0.72            | 0.7384       | 0.7141      | 0.7126   |


### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.0
- Tokenizers 0.13.2

{"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "bert-base-uncased-ag-news-finetuned-dwnews-categories", "results": []}]}
transformers pytorch tensorboard bert text-classification generated_from_trainer license:mit autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us 

bert-base-uncased-ag-news-finetuned-dwnews-categories
=====================================================


This model is a fine-tuned version of nateraw/bert-base-uncased-ag-news on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 0.8659
* Accuracy: 0.74
* Precision Weighted: 0.7435
* Precision Macro: 0.7557
* Recall Weighted: 0.74
* Recall Macro: 0.7304
* F1 Weighted: 0.7294
* F1 Macro: 0.7250


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: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 5


### Training results



### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.0
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-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
* num\_epochs: 5 ### Training results ### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-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
* num\_epochs: 5 ### Training results ### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-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
* num\_epochs: 5### Training results### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.0
* Tokenizers 0.13.2