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# bert_small

This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4537
- Accuracy: 0.88
- Precision: 0.625
- Recall: 0.3571
- F1: 0.4545
- D-index: 1.6429

## 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: 4
- 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: 1600
- num_epochs: 20
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     | D-index |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:-------:|
| No log        | 1.0   | 200  | 0.3773          | 0.86     | 0.0       | 0.0    | 0.0    | 1.4803  |
| No log        | 2.0   | 400  | 0.4271          | 0.86     | 0.0       | 0.0    | 0.0    | 1.4803  |
| 0.5126        | 3.0   | 600  | 0.4598          | 0.87     | 0.55      | 0.3929 | 0.4583 | 1.6431  |
| 0.5126        | 4.0   | 800  | 0.6620          | 0.865    | 0.52      | 0.4643 | 0.4906 | 1.6624  |
| 0.2953        | 5.0   | 1000 | 0.8149          | 0.855    | 0.4615    | 0.2143 | 0.2927 | 1.5575  |
| 0.2953        | 6.0   | 1200 | 0.7819          | 0.875    | 0.5714    | 0.4286 | 0.4898 | 1.6623  |
| 0.2953        | 7.0   | 1400 | 1.0426          | 0.86     | 0.5       | 0.3571 | 0.4167 | 1.6173  |
| 0.1565        | 8.0   | 1600 | 1.0078          | 0.885    | 0.7273    | 0.2857 | 0.4103 | 1.6231  |
| 0.1565        | 9.0   | 1800 | 1.2939          | 0.865    | 0.6       | 0.1071 | 0.1818 | 1.5294  |
| 0.0643        | 10.0  | 2000 | 1.2661          | 0.88     | 0.6429    | 0.3214 | 0.4286 | 1.6299  |
| 0.0643        | 11.0  | 2200 | 1.3556          | 0.87     | 0.5833    | 0.25   | 0.3500 | 1.5905  |
| 0.0643        | 12.0  | 2400 | 1.2393          | 0.87     | 0.625     | 0.1786 | 0.2778 | 1.5635  |
| 0.0306        | 13.0  | 2600 | 1.3059          | 0.88     | 0.625     | 0.3571 | 0.4545 | 1.6429  |
| 0.0306        | 14.0  | 2800 | 1.3446          | 0.88     | 0.625     | 0.3571 | 0.4545 | 1.6429  |
| 0.0019        | 15.0  | 3000 | 1.3618          | 0.885    | 0.6471    | 0.3929 | 0.4889 | 1.6622  |
| 0.0019        | 16.0  | 3200 | 1.3785          | 0.885    | 0.6471    | 0.3929 | 0.4889 | 1.6622  |
| 0.0019        | 17.0  | 3400 | 1.4361          | 0.88     | 0.625     | 0.3571 | 0.4545 | 1.6429  |
| 0.0098        | 18.0  | 3600 | 1.4466          | 0.88     | 0.625     | 0.3571 | 0.4545 | 1.6429  |
| 0.0098        | 19.0  | 3800 | 1.4518          | 0.88     | 0.625     | 0.3571 | 0.4545 | 1.6429  |
| 0.0           | 20.0  | 4000 | 1.4537          | 0.88     | 0.625     | 0.3571 | 0.4545 | 1.6429  |


### Framework versions

- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3

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

bert\_small
===========


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


* Loss: 1.4537
* Accuracy: 0.88
* Precision: 0.625
* Recall: 0.3571
* F1: 0.4545
* D-index: 1.6429


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: 4
* 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: 1600
* num\_epochs: 20
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.28.0
* Pytorch 2.0.1+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 4
* 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: 1600
* num\_epochs: 20
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.28.0
* Pytorch 2.0.1+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #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: 4
* 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: 1600
* num\_epochs: 20
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.28.0
* Pytorch 2.0.1+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3