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# legal_bert_sm_cv_defined_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: 1.5637
- Accuracy: 0.817
- Precision: 0.5545
- Recall: 0.3128
- F1: 0.4
- D-index: 1.5629

## 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  | 0.4802          | 0.805    | 0.0       | 0.0    | 0.0    | 1.4370  |
| 0.5519        | 2.0   | 500  | 0.4506          | 0.809    | 0.8333    | 0.0256 | 0.0498 | 1.4518  |
| 0.5519        | 3.0   | 750  | 0.4347          | 0.828    | 0.7018    | 0.2051 | 0.3175 | 1.5411  |
| 0.4107        | 4.0   | 1000 | 0.4288          | 0.838    | 0.7037    | 0.2923 | 0.4130 | 1.5841  |
| 0.4107        | 5.0   | 1250 | 0.4980          | 0.834    | 0.7736    | 0.2103 | 0.3306 | 1.5510  |
| 0.3108        | 6.0   | 1500 | 0.4671          | 0.837    | 0.7353    | 0.2564 | 0.3802 | 1.5707  |
| 0.3108        | 7.0   | 1750 | 0.4817          | 0.835    | 0.6829    | 0.2872 | 0.4043 | 1.5784  |
| 0.2129        | 8.0   | 2000 | 0.6713          | 0.826    | 0.6981    | 0.1897 | 0.2984 | 1.5331  |
| 0.2129        | 9.0   | 2250 | 0.7290          | 0.825    | 0.6190    | 0.2667 | 0.3728 | 1.5581  |
| 0.1289        | 10.0  | 2500 | 0.8754          | 0.812    | 0.5402    | 0.2410 | 0.3333 | 1.5317  |
| 0.1289        | 11.0  | 2750 | 1.1026          | 0.822    | 0.6232    | 0.2205 | 0.3258 | 1.5383  |
| 0.0804        | 12.0  | 3000 | 1.2274          | 0.807    | 0.5109    | 0.2410 | 0.3275 | 1.5249  |
| 0.0804        | 13.0  | 3250 | 1.2411          | 0.824    | 0.5772    | 0.3641 | 0.4465 | 1.5894  |
| 0.0529        | 14.0  | 3500 | 1.2761          | 0.812    | 0.5263    | 0.3590 | 0.4268 | 1.5716  |
| 0.0529        | 15.0  | 3750 | 1.3524          | 0.823    | 0.5714    | 0.3692 | 0.4486 | 1.5897  |
| 0.0299        | 16.0  | 4000 | 1.6109          | 0.829    | 0.7308    | 0.1949 | 0.3077 | 1.5389  |
| 0.0299        | 17.0  | 4250 | 1.6461          | 0.824    | 0.6863    | 0.1795 | 0.2846 | 1.5269  |
| 0.0284        | 18.0  | 4500 | 1.7304          | 0.824    | 0.7879    | 0.1333 | 0.2281 | 1.5108  |
| 0.0284        | 19.0  | 4750 | 1.5481          | 0.809    | 0.5159    | 0.3333 | 0.4050 | 1.5590  |
| 0.0396        | 20.0  | 5000 | 1.5637          | 0.817    | 0.5545    | 0.3128 | 0.4    | 1.5629  |


### 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_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\_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: 1.5637
* Accuracy: 0.817
* Precision: 0.5545
* Recall: 0.3128
* F1: 0.4
* D-index: 1.5629


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