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# bert_sm_cv_defined_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: 1.8439
- Accuracy: 0.802
- Precision: 0.4824
- Recall: 0.2103
- F1: 0.2929
- D-index: 1.5075

## 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  | 0.4642          | 0.813    | 0.5833    | 0.1436 | 0.2305 | 1.4993  |
| 0.5606        | 2.0   | 500  | 0.4524          | 0.816    | 0.6       | 0.1692 | 0.264  | 1.5124  |
| 0.5606        | 3.0   | 750  | 0.4448          | 0.82     | 0.6154    | 0.2051 | 0.3077 | 1.5303  |
| 0.4415        | 4.0   | 1000 | 0.4583          | 0.819    | 0.6591    | 0.1487 | 0.2427 | 1.5093  |
| 0.4415        | 5.0   | 1250 | 0.4659          | 0.817    | 0.5652    | 0.2667 | 0.3624 | 1.5473  |
| 0.3644        | 6.0   | 1500 | 0.4927          | 0.805    | 0.5       | 0.3077 | 0.3810 | 1.5449  |
| 0.3644        | 7.0   | 1750 | 0.5493          | 0.82     | 0.6230    | 0.1949 | 0.2969 | 1.5267  |
| 0.2821        | 8.0   | 2000 | 0.6262          | 0.815    | 0.5893    | 0.1692 | 0.2629 | 1.5110  |
| 0.2821        | 9.0   | 2250 | 0.6864          | 0.798    | 0.4685    | 0.2667 | 0.3399 | 1.5215  |
| 0.2084        | 10.0  | 2500 | 0.8556          | 0.816    | 0.5696    | 0.2308 | 0.3285 | 1.5337  |
| 0.2084        | 11.0  | 2750 | 0.9422          | 0.814    | 0.5542    | 0.2359 | 0.3309 | 1.5327  |
| 0.114         | 12.0  | 3000 | 1.0528          | 0.807    | 0.5109    | 0.2410 | 0.3275 | 1.5249  |
| 0.114         | 13.0  | 3250 | 1.2325          | 0.802    | 0.4889    | 0.3385 | 0.4000 | 1.5513  |
| 0.0612        | 14.0  | 3500 | 1.3032          | 0.782    | 0.4148    | 0.2872 | 0.3394 | 1.5066  |
| 0.0612        | 15.0  | 3750 | 1.7304          | 0.806    | 0.5102    | 0.1282 | 0.2049 | 1.4843  |
| 0.0454        | 16.0  | 4000 | 1.6902          | 0.797    | 0.4643    | 0.2667 | 0.3388 | 1.5201  |
| 0.0454        | 17.0  | 4250 | 1.6552          | 0.78     | 0.4172    | 0.3231 | 0.3642 | 1.5161  |
| 0.0379        | 18.0  | 4500 | 1.7798          | 0.806    | 0.5054    | 0.2410 | 0.3264 | 1.5236  |
| 0.0379        | 19.0  | 4750 | 1.8226          | 0.816    | 0.5753    | 0.2154 | 0.3134 | 1.5284  |
| 0.0487        | 20.0  | 5000 | 1.8439          | 0.802    | 0.4824    | 0.2103 | 0.2929 | 1.5075  |


### 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_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\_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: 1.8439
* Accuracy: 0.802
* Precision: 0.4824
* Recall: 0.2103
* F1: 0.2929
* D-index: 1.5075


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