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# legal_bert_small_summarized_defined

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: 1.8897
- Accuracy: 0.835
- Precision: 0.5
- Recall: 0.1515
- F1: 0.2326
- D-index: 1.5181

## 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  | 0.4467          | 0.835    | 0.0       | 0.0    | 0.0    | 1.4607  |
| No log        | 2.0   | 400  | 0.4909          | 0.835    | 0.0       | 0.0    | 0.0    | 1.4607  |
| 0.5409        | 3.0   | 600  | 0.4941          | 0.83     | 0.4545    | 0.1515 | 0.2273 | 1.5113  |
| 0.5409        | 4.0   | 800  | 0.5612          | 0.84     | 0.6       | 0.0909 | 0.1579 | 1.5021  |
| 0.4849        | 5.0   | 1000 | 0.6301          | 0.84     | 0.5714    | 0.1212 | 0.2    | 1.5135  |
| 0.4849        | 6.0   | 1200 | 0.8969          | 0.84     | 0.6       | 0.0909 | 0.1579 | 1.5021  |
| 0.4849        | 7.0   | 1400 | 1.3171          | 0.82     | 0.3636    | 0.1212 | 0.1818 | 1.4865  |
| 0.2104        | 8.0   | 1600 | 1.6653          | 0.775    | 0.2692    | 0.2121 | 0.2373 | 1.4593  |
| 0.2104        | 9.0   | 1800 | 1.7041          | 0.795    | 0.3182    | 0.2121 | 0.2545 | 1.4866  |
| 0.0314        | 10.0  | 2000 | 1.7495          | 0.815    | 0.3571    | 0.1515 | 0.2128 | 1.4911  |
| 0.0314        | 11.0  | 2200 | 1.7627          | 0.815    | 0.3571    | 0.1515 | 0.2128 | 1.4911  |
| 0.0314        | 12.0  | 2400 | 1.7892          | 0.825    | 0.375     | 0.0909 | 0.1463 | 1.4819  |
| 0.0067        | 13.0  | 2600 | 1.8211          | 0.83     | 0.4444    | 0.1212 | 0.1905 | 1.5000  |
| 0.0067        | 14.0  | 2800 | 1.8567          | 0.83     | 0.4444    | 0.1212 | 0.1905 | 1.5000  |
| 0.0           | 15.0  | 3000 | 1.8817          | 0.83     | 0.4444    | 0.1212 | 0.1905 | 1.5000  |
| 0.0           | 16.0  | 3200 | 1.8590          | 0.825    | 0.4167    | 0.1515 | 0.2222 | 1.5046  |
| 0.0           | 17.0  | 3400 | 1.8619          | 0.835    | 0.5       | 0.1515 | 0.2326 | 1.5181  |
| 0.0014        | 18.0  | 3600 | 1.8744          | 0.835    | 0.5       | 0.1515 | 0.2326 | 1.5181  |
| 0.0014        | 19.0  | 3800 | 1.8849          | 0.835    | 0.5       | 0.1515 | 0.2326 | 1.5181  |
| 0.0           | 20.0  | 4000 | 1.8897          | 0.835    | 0.5       | 0.1515 | 0.2326 | 1.5181  |


### 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_summarized_defined", "results": []}]}
transformers pytorch tensorboard bert text-classification generated_from_trainer license:cc-by-sa-4.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us 

legal\_bert\_small\_summarized\_defined
=======================================


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: 1.8897
* Accuracy: 0.835
* Precision: 0.5
* Recall: 0.1515
* F1: 0.2326
* D-index: 1.5181


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 #tensorboard #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