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# bert_sm_gen1_cv_4

This model is a fine-tuned version of [wiorz/bert_sm_gen1](https://huggingface.co/wiorz/bert_sm_gen1) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4140
- Accuracy: 0.82
- Precision: 0.5758
- Recall: 0.2923
- F1: 0.3878
- D-index: 1.5600

## 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: 8000
- num_epochs: 20
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy | Precision | Recall | F1     | D-index |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:---------:|:------:|:------:|:-------:|
| 0.962         | 1.0   | 1000  | 0.6663          | 0.821    | 0.5930    | 0.2615 | 0.3630 | 1.5509  |
| 0.5779        | 2.0   | 2000  | 0.6352          | 0.818    | 0.5481    | 0.3795 | 0.4485 | 1.5864  |
| 0.4922        | 3.0   | 3000  | 0.9985          | 0.819    | 0.6667    | 0.1436 | 0.2363 | 1.5076  |
| 0.2595        | 4.0   | 4000  | 1.3708          | 0.806    | 0.5062    | 0.2103 | 0.2971 | 1.5130  |
| 0.1417        | 5.0   | 5000  | 1.5550          | 0.811    | 0.5326    | 0.2513 | 0.3415 | 1.5339  |
| 0.1007        | 6.0   | 6000  | 1.8121          | 0.808    | 0.5185    | 0.2154 | 0.3043 | 1.5175  |
| 0.1046        | 7.0   | 7000  | 1.9016          | 0.818    | 0.5657    | 0.2872 | 0.3810 | 1.5556  |
| 0.1286        | 8.0   | 8000  | 1.8942          | 0.815    | 0.5714    | 0.2051 | 0.3019 | 1.5235  |
| 0.108         | 9.0   | 9000  | 1.9444          | 0.802    | 0.4895    | 0.3590 | 0.4142 | 1.5581  |
| 0.0547        | 10.0  | 10000 | 1.8634          | 0.802    | 0.4887    | 0.3333 | 0.3963 | 1.5495  |
| 0.0288        | 11.0  | 11000 | 2.0029          | 0.83     | 0.6761    | 0.2462 | 0.3609 | 1.5578  |
| 0.0185        | 12.0  | 12000 | 2.2107          | 0.803    | 0.4926    | 0.3436 | 0.4048 | 1.5543  |
| 0.0088        | 13.0  | 13000 | 2.1847          | 0.817    | 0.5517    | 0.3282 | 0.4116 | 1.5680  |
| 0.0018        | 14.0  | 14000 | 2.3947          | 0.808    | 0.5118    | 0.3333 | 0.4037 | 1.5576  |
| 0.0152        | 15.0  | 15000 | 2.3443          | 0.823    | 0.5957    | 0.2872 | 0.3875 | 1.5623  |
| 0.016         | 16.0  | 16000 | 2.3187          | 0.815    | 0.5385    | 0.3590 | 0.4308 | 1.5756  |
| 0.0           | 17.0  | 17000 | 2.3557          | 0.817    | 0.5536    | 0.3179 | 0.4039 | 1.5646  |
| 0.0001        | 18.0  | 18000 | 2.4107          | 0.816    | 0.5433    | 0.3538 | 0.4286 | 1.5752  |
| 0.0           | 19.0  | 19000 | 2.4105          | 0.82     | 0.5758    | 0.2923 | 0.3878 | 1.5600  |
| 0.0           | 20.0  | 20000 | 2.4140          | 0.82     | 0.5758    | 0.2923 | 0.3878 | 1.5600  |


### 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_sm_gen1_cv_4", "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\_sm\_gen1\_cv\_4
=====================


This model is a fine-tuned version of wiorz/bert\_sm\_gen1 on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 2.4140
* Accuracy: 0.82
* Precision: 0.5758
* Recall: 0.2923
* F1: 0.3878
* D-index: 1.5600


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: 8000
* 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: 8000
* 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 #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: 8000
* 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