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# bert_sm_gen1

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: 2.1391
- Accuracy: 0.829
- Precision: 0.5241
- Recall: 0.4270
- F1: 0.4706
- D-index: 1.6122

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:---------:|:------:|:------:|:-------:|
| 0.4764        | 1.0   | 1000  | 0.4123          | 0.828    | 0.5455    | 0.2022 | 0.2951 | 1.5328  |
| 0.5783        | 2.0   | 2000  | 0.6138          | 0.83     | 0.5690    | 0.1854 | 0.2797 | 1.5295  |
| 0.5735        | 3.0   | 3000  | 0.7900          | 0.801    | 0.4525    | 0.5618 | 0.5013 | 1.6205  |
| 0.4248        | 4.0   | 4000  | 0.9244          | 0.84     | 0.6875    | 0.1854 | 0.2920 | 1.5429  |
| 0.2873        | 5.0   | 5000  | 1.0765          | 0.815    | 0.4774    | 0.4157 | 0.4444 | 1.5899  |
| 0.2717        | 6.0   | 6000  | 1.1807          | 0.814    | 0.4661    | 0.3090 | 0.3716 | 1.5518  |
| 0.2166        | 7.0   | 7000  | 1.2673          | 0.821    | 0.4970    | 0.4607 | 0.4781 | 1.6131  |
| 0.1294        | 8.0   | 8000  | 1.5151          | 0.808    | 0.4628    | 0.4888 | 0.4754 | 1.6054  |
| 0.0485        | 9.0   | 9000  | 1.6610          | 0.823    | 0.504     | 0.3539 | 0.4158 | 1.5794  |
| 0.0522        | 10.0  | 10000 | 1.8193          | 0.802    | 0.4519    | 0.5281 | 0.4870 | 1.6106  |
| 0.0307        | 11.0  | 11000 | 1.7044          | 0.828    | 0.5211    | 0.4157 | 0.4625 | 1.6071  |
| 0.0196        | 12.0  | 12000 | 1.8297          | 0.818    | 0.4873    | 0.4326 | 0.4583 | 1.5996  |
| 0.0048        | 13.0  | 13000 | 1.9419          | 0.827    | 0.5188    | 0.3876 | 0.4437 | 1.5962  |
| 0.0098        | 14.0  | 14000 | 2.0127          | 0.828    | 0.5211    | 0.4157 | 0.4625 | 1.6071  |
| 0.0082        | 15.0  | 15000 | 2.0195          | 0.833    | 0.5420    | 0.3989 | 0.4595 | 1.6079  |
| 0.0           | 16.0  | 16000 | 2.0748          | 0.827    | 0.5161    | 0.4494 | 0.4805 | 1.6172  |
| 0.0           | 17.0  | 17000 | 2.0948          | 0.831    | 0.5319    | 0.4213 | 0.4702 | 1.6129  |
| 0.0           | 18.0  | 18000 | 2.1141          | 0.831    | 0.5338    | 0.3989 | 0.4566 | 1.6053  |
| 0.0           | 19.0  | 19000 | 2.1411          | 0.828    | 0.5205    | 0.4270 | 0.4691 | 1.6109  |
| 0.0           | 20.0  | 20000 | 2.1391          | 0.829    | 0.5241    | 0.4270 | 0.4706 | 1.6122  |


### 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", "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
==============


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: 2.1391
* Accuracy: 0.829
* Precision: 0.5241
* Recall: 0.4270
* F1: 0.4706
* D-index: 1.6122


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 #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: 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