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# bert_sm_cv_defined_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: 1.8001
- Accuracy: 0.801
- Precision: 0.4677
- Recall: 0.1487
- F1: 0.2257
- D-index: 1.4847

## 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  | 0.4931          | 0.805    | 0.5       | 0.0308 | 0.0580 | 1.4481  |
| 0.5724        | 2.0   | 500  | 0.4850          | 0.806    | 0.5263    | 0.0513 | 0.0935 | 1.4569  |
| 0.5724        | 3.0   | 750  | 0.4842          | 0.811    | 0.6       | 0.0923 | 0.16   | 1.4785  |
| 0.4468        | 4.0   | 1000 | 0.4954          | 0.81     | 0.5806    | 0.0923 | 0.1593 | 1.4771  |
| 0.4468        | 5.0   | 1250 | 0.5307          | 0.81     | 0.5862    | 0.0872 | 0.1518 | 1.4753  |
| 0.381         | 6.0   | 1500 | 0.5312          | 0.809    | 0.5455    | 0.1231 | 0.2008 | 1.4866  |
| 0.381         | 7.0   | 1750 | 0.5354          | 0.807    | 0.5161    | 0.1641 | 0.2490 | 1.4983  |
| 0.283         | 8.0   | 2000 | 0.7003          | 0.811    | 0.6364    | 0.0718 | 0.1290 | 1.4712  |
| 0.283         | 9.0   | 2250 | 0.7079          | 0.798    | 0.4568    | 0.1897 | 0.2681 | 1.4949  |
| 0.1621        | 10.0  | 2500 | 0.9032          | 0.8      | 0.4603    | 0.1487 | 0.2248 | 1.4833  |
| 0.1621        | 11.0  | 2750 | 1.0875          | 0.797    | 0.4474    | 0.1744 | 0.2509 | 1.4881  |
| 0.0678        | 12.0  | 3000 | 1.2256          | 0.769    | 0.3861    | 0.3128 | 0.3456 | 1.4975  |
| 0.0678        | 13.0  | 3250 | 1.6378          | 0.793    | 0.4       | 0.1231 | 0.1882 | 1.4645  |
| 0.039         | 14.0  | 3500 | 1.7475          | 0.767    | 0.2841    | 0.1282 | 0.1767 | 1.4301  |
| 0.039         | 15.0  | 3750 | 1.8575          | 0.804    | 0.4848    | 0.0821 | 0.1404 | 1.4652  |
| 0.0295        | 16.0  | 4000 | 1.8151          | 0.775    | 0.3370    | 0.1590 | 0.2160 | 1.4522  |
| 0.0295        | 17.0  | 4250 | 1.8788          | 0.795    | 0.4219    | 0.1385 | 0.2085 | 1.4728  |
| 0.0416        | 18.0  | 4500 | 1.8193          | 0.765    | 0.3462    | 0.2308 | 0.2769 | 1.4636  |
| 0.0416        | 19.0  | 4750 | 1.6942          | 0.788    | 0.3896    | 0.1538 | 0.2206 | 1.4685  |
| 0.0322        | 20.0  | 5000 | 1.8001          | 0.801    | 0.4677    | 0.1487 | 0.2257 | 1.4847  |


### 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_defined_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\_defined\_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: 1.8001
* Accuracy: 0.801
* Precision: 0.4677
* Recall: 0.1487
* F1: 0.2257
* D-index: 1.4847


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