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# legal_bert_sm_cv_defined_summarized_4

This model is a fine-tuned version of [nlpaueb/legal-bert-base-uncased](https://huggingface.co/nlpaueb/legal-bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7595
- Accuracy: 0.811
- Precision: 0.5385
- Recall: 0.2154
- F1: 0.3077
- D-index: 1.5216

## 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  | 0.4882          | 0.805    | 0.0       | 0.0    | 0.0    | 1.4370  |
| 0.5662        | 2.0   | 500  | 0.4681          | 0.805    | 0.0       | 0.0    | 0.0    | 1.4370  |
| 0.5662        | 3.0   | 750  | 0.4649          | 0.807    | 0.625     | 0.0256 | 0.0493 | 1.4491  |
| 0.4397        | 4.0   | 1000 | 0.4675          | 0.819    | 0.7692    | 0.1026 | 0.1810 | 1.4931  |
| 0.4397        | 5.0   | 1250 | 0.5234          | 0.816    | 0.7391    | 0.0872 | 0.1560 | 1.4836  |
| 0.3492        | 6.0   | 1500 | 0.5137          | 0.825    | 0.6562    | 0.2154 | 0.3243 | 1.5406  |
| 0.3492        | 7.0   | 1750 | 0.5490          | 0.81     | 0.5490    | 0.1436 | 0.2276 | 1.4952  |
| 0.2409        | 8.0   | 2000 | 0.6896          | 0.82     | 0.5882    | 0.2564 | 0.3571 | 1.5478  |
| 0.2409        | 9.0   | 2250 | 0.7600          | 0.808    | 0.5155    | 0.2564 | 0.3425 | 1.5316  |
| 0.1506        | 10.0  | 2500 | 1.0232          | 0.813    | 0.5714    | 0.1641 | 0.2550 | 1.5065  |
| 0.1506        | 11.0  | 2750 | 1.0855          | 0.823    | 0.6731    | 0.1795 | 0.2834 | 1.5255  |
| 0.0851        | 12.0  | 3000 | 1.1956          | 0.797    | 0.4655    | 0.2769 | 0.3473 | 1.5236  |
| 0.0851        | 13.0  | 3250 | 1.2379          | 0.808    | 0.5190    | 0.2103 | 0.2993 | 1.5157  |
| 0.0538        | 14.0  | 3500 | 1.4613          | 0.807    | 0.5143    | 0.1846 | 0.2717 | 1.5055  |
| 0.0538        | 15.0  | 3750 | 1.4960          | 0.815    | 0.5658    | 0.2205 | 0.3173 | 1.5288  |
| 0.0334        | 16.0  | 4000 | 1.6423          | 0.806    | 0.5067    | 0.1949 | 0.2815 | 1.5076  |
| 0.0334        | 17.0  | 4250 | 1.6386          | 0.804    | 0.4958    | 0.3026 | 0.3758 | 1.5419  |
| 0.0364        | 18.0  | 4500 | 1.6520          | 0.797    | 0.45      | 0.1846 | 0.2618 | 1.4917  |
| 0.0364        | 19.0  | 4750 | 1.6842          | 0.804    | 0.4953    | 0.2718 | 0.3510 | 1.5314  |
| 0.0167        | 20.0  | 5000 | 1.7595          | 0.811    | 0.5385    | 0.2154 | 0.3077 | 1.5216  |


### Framework versions

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

{"license": "cc-by-sa-4.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "precision", "recall", "f1"], "model-index": [{"name": "legal_bert_sm_cv_defined_summarized_4", "results": []}]}
transformers pytorch bert text-classification generated_from_trainer license:cc-by-sa-4.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us 

legal\_bert\_sm\_cv\_defined\_summarized\_4
===========================================


This model is a fine-tuned version of nlpaueb/legal-bert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 1.7595
* Accuracy: 0.811
* Precision: 0.5385
* Recall: 0.2154
* F1: 0.3077
* D-index: 1.5216


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-cc-by-sa-4.0 #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