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# legal_bert_sm_gen1_cv_4

This model is a fine-tuned version of [wiorz/legal_bert_sm_gen1](https://huggingface.co/wiorz/legal_bert_sm_gen1) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9479
- Accuracy: 0.832
- Precision: 0.6709
- Recall: 0.2718
- F1: 0.3869
- D-index: 1.5692

## 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  | 2.0287          | 0.827    | 0.5965    | 0.3487 | 0.4401 | 1.5883  |
| 1.656         | 2.0   | 500  | 0.7873          | 0.822    | 0.5556    | 0.4359 | 0.4885 | 1.6103  |
| 1.656         | 3.0   | 750  | 0.4216          | 0.834    | 0.6706    | 0.2923 | 0.4071 | 1.5788  |
| 0.3927        | 4.0   | 1000 | 0.4208          | 0.83     | 0.6033    | 0.3744 | 0.4620 | 1.6007  |
| 0.3927        | 5.0   | 1250 | 0.5522          | 0.832    | 0.5985    | 0.4205 | 0.4940 | 1.6185  |
| 0.2162        | 6.0   | 1500 | 0.6989          | 0.83     | 0.6016    | 0.3795 | 0.4654 | 1.6024  |
| 0.2162        | 7.0   | 1750 | 1.1381          | 0.826    | 0.5778    | 0.4    | 0.4727 | 1.6039  |
| 0.0856        | 8.0   | 2000 | 1.5135          | 0.831    | 0.6477    | 0.2923 | 0.4028 | 1.5748  |
| 0.0856        | 9.0   | 2250 | 1.6362          | 0.837    | 0.6667    | 0.3282 | 0.4399 | 1.5948  |
| 0.0378        | 10.0  | 2500 | 1.6518          | 0.839    | 0.6545    | 0.3692 | 0.4721 | 1.6110  |
| 0.0378        | 11.0  | 2750 | 1.7085          | 0.833    | 0.6186    | 0.3744 | 0.4665 | 1.6047  |
| 0.0226        | 12.0  | 3000 | 1.7801          | 0.832    | 0.6063    | 0.3949 | 0.4783 | 1.6101  |
| 0.0226        | 13.0  | 3250 | 1.7308          | 0.83     | 0.5887    | 0.4256 | 0.4940 | 1.6176  |
| 0.0218        | 14.0  | 3500 | 1.9745          | 0.829    | 0.6176    | 0.3231 | 0.4242 | 1.5824  |
| 0.0218        | 15.0  | 3750 | 1.8087          | 0.832    | 0.608     | 0.3897 | 0.475  | 1.6085  |
| 0.0316        | 16.0  | 4000 | 1.7999          | 0.826    | 0.5814    | 0.3846 | 0.4630 | 1.5988  |
| 0.0316        | 17.0  | 4250 | 1.9195          | 0.828    | 0.6095    | 0.3282 | 0.4267 | 1.5828  |
| 0.0266        | 18.0  | 4500 | 1.9005          | 0.825    | 0.5926    | 0.3282 | 0.4224 | 1.5788  |
| 0.0266        | 19.0  | 4750 | 1.8568          | 0.826    | 0.5814    | 0.3846 | 0.4630 | 1.5988  |
| 0.0243        | 20.0  | 5000 | 1.9479          | 0.832    | 0.6709    | 0.2718 | 0.3869 | 1.5692  |


### Framework versions

- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3

{"tags": ["generated_from_trainer"], "metrics": ["accuracy", "precision", "recall", "f1"], "model-index": [{"name": "legal_bert_sm_gen1_cv_4", "results": []}]}
transformers pytorch bert text-classification generated_from_trainer autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us 

legal\_bert\_sm\_gen1\_cv\_4
============================


This model is a fine-tuned version of wiorz/legal\_bert\_sm\_gen1 on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 1.9479
* Accuracy: 0.832
* Precision: 0.6709
* Recall: 0.2718
* F1: 0.3869
* D-index: 1.5692


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 #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