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# bert_sm_cv_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: 2.3264
- Accuracy: 0.822
- Precision: 0.5714
- Recall: 0.3487
- F1: 0.4331
- D-index: 1.5816

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:---------:|:------:|:------:|:-------:|
| 0.5342        | 1.0   | 1000  | 0.4182          | 0.828    | 0.6117    | 0.3231 | 0.4228 | 1.5811  |
| 0.5562        | 2.0   | 2000  | 0.5091          | 0.825    | 0.5943    | 0.3231 | 0.4186 | 1.5770  |
| 0.5531        | 3.0   | 3000  | 0.6970          | 0.821    | 0.5678    | 0.3436 | 0.4281 | 1.5785  |
| 0.4464        | 4.0   | 4000  | 0.9186          | 0.816    | 0.5495    | 0.3128 | 0.3987 | 1.5615  |
| 0.3459        | 5.0   | 5000  | 1.0847          | 0.815    | 0.5510    | 0.2769 | 0.3686 | 1.5480  |
| 0.2035        | 6.0   | 6000  | 1.2288          | 0.818    | 0.5504    | 0.3641 | 0.4383 | 1.5813  |
| 0.2029        | 7.0   | 7000  | 1.3880          | 0.811    | 0.5395    | 0.2103 | 0.3026 | 1.5198  |
| 0.0907        | 8.0   | 8000  | 1.6336          | 0.824    | 0.6       | 0.2923 | 0.3931 | 1.5654  |
| 0.1161        | 9.0   | 9000  | 1.6379          | 0.799    | 0.4821    | 0.4154 | 0.4463 | 1.5729  |
| 0.0516        | 10.0  | 10000 | 1.6650          | 0.812    | 0.5304    | 0.3128 | 0.3935 | 1.5561  |
| 0.0249        | 11.0  | 11000 | 1.8710          | 0.815    | 0.5410    | 0.3385 | 0.4164 | 1.5688  |
| 0.0097        | 12.0  | 12000 | 1.9980          | 0.821    | 0.5741    | 0.3179 | 0.4092 | 1.5700  |
| 0.0047        | 13.0  | 13000 | 2.1137          | 0.821    | 0.5930    | 0.2615 | 0.3630 | 1.5509  |
| 0.0001        | 14.0  | 14000 | 2.1541          | 0.825    | 0.5893    | 0.3385 | 0.4300 | 1.5822  |
| 0.0038        | 15.0  | 15000 | 2.2491          | 0.814    | 0.5338    | 0.3641 | 0.4329 | 1.5760  |
| 0.0063        | 16.0  | 16000 | 2.2822          | 0.818    | 0.5546    | 0.3385 | 0.4204 | 1.5728  |
| 0.0           | 17.0  | 17000 | 2.3280          | 0.815    | 0.5373    | 0.3692 | 0.4377 | 1.5790  |
| 0.011         | 18.0  | 18000 | 2.3034          | 0.822    | 0.5714    | 0.3487 | 0.4331 | 1.5816  |
| 0.0           | 19.0  | 19000 | 2.3205          | 0.822    | 0.5714    | 0.3487 | 0.4331 | 1.5816  |
| 0.0054        | 20.0  | 20000 | 2.3264          | 0.822    | 0.5714    | 0.3487 | 0.4331 | 1.5816  |


### 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_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\_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: 2.3264
* Accuracy: 0.822
* Precision: 0.5714
* Recall: 0.3487
* F1: 0.4331
* D-index: 1.5816


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 #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