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# legal_bert_legal_test_sm

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: 0.5535
- Accuracy: 0.75
- Precision: 0.7892
- Recall: 0.6854
- F1: 0.7337
- D-index: 1.6150

## 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: 8
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 200
- num_epochs: 20

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     | D-index |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:-------:|
| No log        | 0.98  | 26   | 0.6956          | 0.5259   | 0.5180    | 0.8122 | 0.6325 | 1.2181  |
| No log        | 2.0   | 53   | 0.6949          | 0.5      | 0.5015    | 0.7934 | 0.6145 | 1.1686  |
| No log        | 2.98  | 79   | 0.6933          | 0.5259   | 0.575     | 0.2160 | 0.3140 | 1.2208  |
| No log        | 4.0   | 106  | 0.6035          | 0.6981   | 0.7607    | 0.5822 | 0.6596 | 1.5283  |
| No log        | 4.98  | 132  | 0.5535          | 0.75     | 0.7892    | 0.6854 | 0.7337 | 1.6150  |
| No log        | 6.0   | 159  | 0.6376          | 0.7146   | 0.7614    | 0.6291 | 0.6889 | 1.5561  |
| No log        | 6.98  | 185  | 0.7555          | 0.7358   | 0.7205    | 0.7746 | 0.7466 | 1.5911  |
| No log        | 8.0   | 212  | 0.9223          | 0.7217   | 0.7149    | 0.7418 | 0.7281 | 1.5676  |
| No log        | 8.98  | 238  | 1.0061          | 0.7311   | 0.8       | 0.6197 | 0.6984 | 1.5839  |
| No log        | 10.0  | 265  | 1.1761          | 0.7217   | 0.7844    | 0.6150 | 0.6895 | 1.5681  |
| No log        | 10.98 | 291  | 1.2807          | 0.7264   | 0.8345    | 0.5681 | 0.6760 | 1.5762  |
| No log        | 12.0  | 318  | 1.3035          | 0.7311   | 0.7735    | 0.6573 | 0.7107 | 1.5837  |
| No log        | 12.98 | 344  | 1.4680          | 0.7406   | 0.8503    | 0.5869 | 0.6944 | 1.5997  |
| No log        | 14.0  | 371  | 1.3238          | 0.7358   | 0.7327    | 0.7465 | 0.7395 | 1.5912  |
| No log        | 14.98 | 397  | 1.3373          | 0.7547   | 0.8303    | 0.6432 | 0.7249 | 1.6229  |
| No log        | 16.0  | 424  | 1.3234          | 0.7736   | 0.8162    | 0.7089 | 0.7588 | 1.6536  |
| No log        | 16.98 | 450  | 1.3853          | 0.7736   | 0.8162    | 0.7089 | 0.7588 | 1.6536  |
| No log        | 18.0  | 477  | 1.4619          | 0.7594   | 0.8323    | 0.6526 | 0.7316 | 1.6306  |
| 0.2167        | 18.98 | 503  | 1.4222          | 0.7571   | 0.8161    | 0.6667 | 0.7339 | 1.6267  |
| 0.2167        | 19.62 | 520  | 1.4074          | 0.7689   | 0.8212    | 0.6901 | 0.75   | 1.6460  |


### Framework versions

- Transformers 4.27.4
- Pytorch 1.13.1+cu116
- Datasets 2.11.0
- Tokenizers 0.13.2

{"license": "cc-by-sa-4.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "precision", "recall", "f1"], "model-index": [{"name": "legal_bert_legal_test_sm", "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\_legal\_test\_sm
============================


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: 0.5535
* Accuracy: 0.75
* Precision: 0.7892
* Recall: 0.6854
* F1: 0.7337
* D-index: 1.6150


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: 8
* eval\_batch\_size: 16
* seed: 42
* gradient\_accumulation\_steps: 8
* total\_train\_batch\_size: 64
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 200
* num\_epochs: 20


### Training results



### Framework versions


* Transformers 4.27.4
* Pytorch 1.13.1+cu116
* Datasets 2.11.0
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 8
* eval\_batch\_size: 16
* seed: 42
* gradient\_accumulation\_steps: 8
* total\_train\_batch\_size: 64
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 200
* num\_epochs: 20 ### Training results ### Framework versions


* Transformers 4.27.4
* Pytorch 1.13.1+cu116
* Datasets 2.11.0
* Tokenizers 0.13.2
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: 8
* eval\_batch\_size: 16
* seed: 42
* gradient\_accumulation\_steps: 8
* total\_train\_batch\_size: 64
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 200
* num\_epochs: 20 ### Training results ### Framework versions


* Transformers 4.27.4
* Pytorch 1.13.1+cu116
* Datasets 2.11.0
* Tokenizers 0.13.2
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: 8
* eval\_batch\_size: 16
* seed: 42
* gradient\_accumulation\_steps: 8
* total\_train\_batch\_size: 64
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 200
* num\_epochs: 20### Training results### Framework versions


* Transformers 4.27.4
* Pytorch 1.13.1+cu116
* Datasets 2.11.0
* Tokenizers 0.13.2