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# bert_sm_gen1_large

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: 1.1722
- Accuracy: 0.8151
- Precision: 0.5531
- Recall: 0.4006
- F1: 0.4647
- D-index: 1.5910

## 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: 32
- 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: 96000
- num_epochs: 20
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy | Precision | Recall | F1     | D-index |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:---------:|:------:|:------:|:-------:|
| 0.4737        | 1.0   | 1500  | 0.4508          | 0.7995   | 0.0       | 0.0    | 0.0    | 1.4325  |
| 0.4244        | 2.0   | 3000  | 0.4167          | 0.8041   | 0.6514    | 0.0474 | 0.0884 | 1.4558  |
| 0.3988        | 3.0   | 4500  | 0.4196          | 0.823    | 0.6737    | 0.2259 | 0.3383 | 1.5437  |
| 0.3789        | 4.0   | 6000  | 0.4097          | 0.8283   | 0.6564    | 0.3003 | 0.4121 | 1.5758  |
| 0.3565        | 5.0   | 7500  | 0.4138          | 0.8302   | 0.6614    | 0.3120 | 0.4240 | 1.5821  |
| 0.3248        | 6.0   | 9000  | 0.4094          | 0.826    | 0.5914    | 0.4251 | 0.4947 | 1.6135  |
| 0.2971        | 7.0   | 10500 | 0.4413          | 0.8291   | 0.6156    | 0.3910 | 0.4782 | 1.6066  |
| 0.2596        | 8.0   | 12000 | 0.5242          | 0.828    | 0.6206    | 0.3640 | 0.4588 | 1.5963  |
| 0.2266        | 9.0   | 13500 | 0.5538          | 0.8266   | 0.6133    | 0.3636 | 0.4565 | 1.5943  |
| 0.185         | 10.0  | 15000 | 0.7189          | 0.8206   | 0.6390    | 0.2400 | 0.3490 | 1.5452  |
| 0.1577        | 11.0  | 16500 | 0.6479          | 0.8176   | 0.5522    | 0.4734 | 0.5097 | 1.6178  |
| 0.126         | 12.0  | 18000 | 0.7646          | 0.8204   | 0.5915    | 0.3349 | 0.4276 | 1.5766  |
| 0.1149        | 13.0  | 19500 | 0.8697          | 0.8183   | 0.5874    | 0.3132 | 0.4086 | 1.5666  |
| 0.1041        | 14.0  | 21000 | 0.8395          | 0.8134   | 0.5485    | 0.3881 | 0.4546 | 1.5847  |
| 0.0879        | 15.0  | 22500 | 1.0069          | 0.81     | 0.5376    | 0.3686 | 0.4373 | 1.5737  |
| 0.0895        | 16.0  | 24000 | 1.0749          | 0.8122   | 0.5379    | 0.4426 | 0.4856 | 1.6007  |
| 0.0784        | 17.0  | 25500 | 1.1892          | 0.8177   | 0.6040    | 0.2621 | 0.3655 | 1.5488  |
| 0.0763        | 18.0  | 27000 | 1.2229          | 0.8169   | 0.6095    | 0.2396 | 0.3440 | 1.5401  |
| 0.0675        | 19.0  | 28500 | 1.2429          | 0.8196   | 0.6038    | 0.2891 | 0.3910 | 1.5603  |
| 0.0645        | 20.0  | 30000 | 1.1722          | 0.8151   | 0.5531    | 0.4006 | 0.4647 | 1.5910  |


### 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_large", "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\_large
=====================


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: 1.1722
* Accuracy: 0.8151
* Precision: 0.5531
* Recall: 0.4006
* F1: 0.4647
* D-index: 1.5910


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: 32
* 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: 96000
* 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: 32
* 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: 96000
* 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: 32
* 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: 96000
* 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