
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# bert_sm_gen1_summarized_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: 2.1011
- Accuracy: 0.801
- Precision: 0.4706
- Recall: 0.1641
- F1: 0.2433
- D-index: 1.4901

## 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  | 2.4563          | 0.79     | 0.44      | 0.2821 | 0.3438 | 1.5158  |
| 1.9188        | 2.0   | 500  | 0.7587          | 0.78     | 0.4126    | 0.3026 | 0.3491 | 1.5091  |
| 1.9188        | 3.0   | 750  | 0.4834          | 0.809    | 0.5345    | 0.1590 | 0.2451 | 1.4992  |
| 0.4332        | 4.0   | 1000 | 0.5400          | 0.811    | 0.5366    | 0.2256 | 0.3177 | 1.5251  |
| 0.4332        | 5.0   | 1250 | 0.6813          | 0.787    | 0.4286    | 0.2769 | 0.3364 | 1.5100  |
| 0.2633        | 6.0   | 1500 | 0.9358          | 0.794    | 0.4286    | 0.1692 | 0.2426 | 1.4822  |
| 0.2633        | 7.0   | 1750 | 1.5052          | 0.786    | 0.4159    | 0.2410 | 0.3052 | 1.4962  |
| 0.1124        | 8.0   | 2000 | 1.7146          | 0.791    | 0.4239    | 0.2    | 0.2718 | 1.4888  |
| 0.1124        | 9.0   | 2250 | 1.8601          | 0.794    | 0.4382    | 0.2    | 0.2746 | 1.4930  |
| 0.0465        | 10.0  | 2500 | 1.9701          | 0.774    | 0.3869    | 0.2718 | 0.3193 | 1.4903  |
| 0.0465        | 11.0  | 2750 | 2.0934          | 0.78     | 0.4101    | 0.2923 | 0.3413 | 1.5056  |
| 0.0297        | 12.0  | 3000 | 2.0712          | 0.79     | 0.4336    | 0.2513 | 0.3182 | 1.5053  |
| 0.0297        | 13.0  | 3250 | 2.1711          | 0.79     | 0.4299    | 0.2359 | 0.3046 | 1.4999  |
| 0.0328        | 14.0  | 3500 | 2.1590          | 0.795    | 0.45      | 0.2308 | 0.3051 | 1.5050  |
| 0.0328        | 15.0  | 3750 | 2.1184          | 0.803    | 0.4861    | 0.1795 | 0.2622 | 1.4982  |
| 0.0311        | 16.0  | 4000 | 2.1504          | 0.789    | 0.4355    | 0.2769 | 0.3386 | 1.5127  |
| 0.0311        | 17.0  | 4250 | 2.2112          | 0.773    | 0.3947    | 0.3077 | 0.3458 | 1.5013  |
| 0.0264        | 18.0  | 4500 | 2.2326          | 0.795    | 0.4519    | 0.2410 | 0.3144 | 1.5086  |
| 0.0264        | 19.0  | 4750 | 2.3306          | 0.774    | 0.3885    | 0.2769 | 0.3234 | 1.4921  |
| 0.0391        | 20.0  | 5000 | 2.1011          | 0.801    | 0.4706    | 0.1641 | 0.2433 | 1.4901  |


### 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_summarized_cv_4", "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\_sm\_gen1\_summarized\_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: 2.1011
* Accuracy: 0.801
* Precision: 0.4706
* Recall: 0.1641
* F1: 0.2433
* D-index: 1.4901


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 #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: 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