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# legal_bert_small_defined_summarized

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: 1.4178
- Accuracy: 0.87
- Precision: 0.6
- Recall: 0.2143
- F1: 0.3158
- D-index: 1.5771

## 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: 1600
- 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   | 200  | 0.4008          | 0.86     | 0.0       | 0.0    | 0.0    | 1.4803  |
| No log        | 2.0   | 400  | 0.3871          | 0.86     | 0.0       | 0.0    | 0.0    | 1.4803  |
| 0.5179        | 3.0   | 600  | 0.4293          | 0.87     | 0.625     | 0.1786 | 0.2778 | 1.5635  |
| 0.5179        | 4.0   | 800  | 0.6702          | 0.87     | 0.625     | 0.1786 | 0.2778 | 1.5635  |
| 0.3816        | 5.0   | 1000 | 0.7388          | 0.865    | 0.5455    | 0.2143 | 0.3077 | 1.5706  |
| 0.3816        | 6.0   | 1200 | 1.0422          | 0.86     | 0.5       | 0.1786 | 0.2632 | 1.5504  |
| 0.3816        | 7.0   | 1400 | 1.0804          | 0.875    | 0.7143    | 0.1786 | 0.2857 | 1.5700  |
| 0.0567        | 8.0   | 1600 | 1.1490          | 0.875    | 0.6364    | 0.25   | 0.3590 | 1.5970  |
| 0.0567        | 9.0   | 1800 | 1.3190          | 0.865    | 0.5556    | 0.1786 | 0.2703 | 1.5570  |
| 0.0125        | 10.0  | 2000 | 1.4220          | 0.835    | 0.3913    | 0.3214 | 0.3529 | 1.5718  |
| 0.0125        | 11.0  | 2200 | 1.3567          | 0.855    | 0.4706    | 0.2857 | 0.3556 | 1.5845  |
| 0.0125        | 12.0  | 2400 | 1.3349          | 0.875    | 0.7143    | 0.1786 | 0.2857 | 1.5700  |
| 0.0021        | 13.0  | 2600 | 1.3494          | 0.87     | 0.5714    | 0.2857 | 0.3810 | 1.6038  |
| 0.0021        | 14.0  | 2800 | 1.3747          | 0.87     | 0.6       | 0.2143 | 0.3158 | 1.5771  |
| 0.0           | 15.0  | 3000 | 1.3890          | 0.87     | 0.6       | 0.2143 | 0.3158 | 1.5771  |
| 0.0           | 16.0  | 3200 | 1.4069          | 0.875    | 0.6667    | 0.2143 | 0.3243 | 1.5835  |
| 0.0           | 17.0  | 3400 | 1.4185          | 0.875    | 0.6667    | 0.2143 | 0.3243 | 1.5835  |
| 0.0           | 18.0  | 3600 | 1.3945          | 0.865    | 0.5385    | 0.25   | 0.3415 | 1.5840  |
| 0.0           | 19.0  | 3800 | 1.3921          | 0.87     | 0.6       | 0.2143 | 0.3158 | 1.5771  |
| 0.0037        | 20.0  | 4000 | 1.4178          | 0.87     | 0.6       | 0.2143 | 0.3158 | 1.5771  |


### 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_small_defined_summarized", "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\_small\_defined\_summarized
=======================================


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: 1.4178
* Accuracy: 0.87
* Precision: 0.6
* Recall: 0.2143
* F1: 0.3158
* D-index: 1.5771


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: 1600
* 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: 1600
* 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: 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: 1600
* 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