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# bert_sm_cv_summarized_4

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: 1.9996
- Accuracy: 0.802
- Precision: 0.48
- Recall: 0.1846
- F1: 0.2667
- D-index: 1.4986

## 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
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:-------:|
| No log        | 1.0   | 250  | 0.4713          | 0.812    | 0.5814    | 0.1282 | 0.2101 | 1.4926  |
| 0.5708        | 2.0   | 500  | 0.4584          | 0.811    | 0.5625    | 0.1385 | 0.2222 | 1.4948  |
| 0.5708        | 3.0   | 750  | 0.4557          | 0.813    | 0.5769    | 0.1538 | 0.2429 | 1.5029  |
| 0.4231        | 4.0   | 1000 | 0.4700          | 0.81     | 0.5316    | 0.2154 | 0.3066 | 1.5202  |
| 0.4231        | 5.0   | 1250 | 0.4979          | 0.812    | 0.5385    | 0.2513 | 0.3427 | 1.5353  |
| 0.3292        | 6.0   | 1500 | 0.5337          | 0.816    | 0.5647    | 0.2462 | 0.3429 | 1.5389  |
| 0.3292        | 7.0   | 1750 | 0.6282          | 0.797    | 0.4615    | 0.2462 | 0.3211 | 1.5131  |
| 0.2218        | 8.0   | 2000 | 0.7182          | 0.805    | 0.5       | 0.2513 | 0.3345 | 1.5257  |
| 0.2218        | 9.0   | 2250 | 0.8488          | 0.809    | 0.5208    | 0.2564 | 0.3436 | 1.5329  |
| 0.1478        | 10.0  | 2500 | 0.9830          | 0.809    | 0.5294    | 0.1846 | 0.2738 | 1.5082  |
| 0.1478        | 11.0  | 2750 | 1.0302          | 0.79     | 0.4419    | 0.2923 | 0.3519 | 1.5193  |
| 0.077         | 12.0  | 3000 | 1.0467          | 0.795    | 0.4658    | 0.3487 | 0.3988 | 1.5452  |
| 0.077         | 13.0  | 3250 | 1.2609          | 0.803    | 0.4931    | 0.3641 | 0.4189 | 1.5612  |
| 0.0328        | 14.0  | 3500 | 1.4127          | 0.806    | 0.5044    | 0.2923 | 0.3701 | 1.5411  |
| 0.0328        | 15.0  | 3750 | 1.6626          | 0.802    | 0.4835    | 0.2256 | 0.3077 | 1.5128  |
| 0.0189        | 16.0  | 4000 | 1.7062          | 0.81     | 0.5362    | 0.1897 | 0.2803 | 1.5113  |
| 0.0189        | 17.0  | 4250 | 1.9225          | 0.809    | 0.54      | 0.1385 | 0.2204 | 1.4921  |
| 0.0214        | 18.0  | 4500 | 1.8228          | 0.81     | 0.5269    | 0.2513 | 0.3403 | 1.5325  |
| 0.0214        | 19.0  | 4750 | 1.9544          | 0.789    | 0.4355    | 0.2769 | 0.3386 | 1.5127  |
| 0.0184        | 20.0  | 5000 | 1.9996          | 0.802    | 0.48      | 0.1846 | 0.2667 | 1.4986  |


### 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_cv_summarized_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\_cv\_summarized\_4
===========================


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: 1.9996
* Accuracy: 0.802
* Precision: 0.48
* Recall: 0.1846
* F1: 0.2667
* D-index: 1.4986


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
* 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: 16
* eval\_batch\_size: 16
* 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: 16
* eval\_batch\_size: 16
* 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