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# legal_bert_test2

This model is a fine-tuned version of [nlpaueb/legal-bert-small-uncased](https://huggingface.co/nlpaueb/legal-bert-small-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7119
- Accuracy: 0.4875
- Precision: 0.48
- Recall: 0.3
- F1: 0.3692
- D-index: 1.1458

## 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
- gradient_accumulation_steps: 16
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 50

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     | D-index |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:-------:|
| No log        | 0.8   | 1    | 0.7095          | 0.5      | 0.0       | 0.0    | 0.0    | 1.1699  |
| No log        | 1.8   | 2    | 0.7094          | 0.5      | 0.0       | 0.0    | 0.0    | 1.1699  |
| No log        | 2.8   | 3    | 0.7092          | 0.5      | 0.0       | 0.0    | 0.0    | 1.1699  |
| No log        | 3.8   | 4    | 0.7089          | 0.5      | 0.0       | 0.0    | 0.0    | 1.1699  |
| No log        | 4.8   | 5    | 0.7085          | 0.5      | 0.0       | 0.0    | 0.0    | 1.1699  |
| No log        | 5.8   | 6    | 0.7080          | 0.5      | 0.0       | 0.0    | 0.0    | 1.1699  |
| No log        | 6.8   | 7    | 0.7074          | 0.5      | 0.0       | 0.0    | 0.0    | 1.1699  |
| No log        | 7.8   | 8    | 0.7067          | 0.5      | 0.0       | 0.0    | 0.0    | 1.1699  |
| No log        | 8.8   | 9    | 0.7060          | 0.5      | 0.0       | 0.0    | 0.0    | 1.1699  |
| No log        | 9.8   | 10   | 0.7053          | 0.5125   | 1.0       | 0.025  | 0.0488 | 1.1939  |
| No log        | 10.8  | 11   | 0.7045          | 0.5125   | 1.0       | 0.025  | 0.0488 | 1.1939  |
| No log        | 11.8  | 12   | 0.7036          | 0.4875   | 0.3333    | 0.025  | 0.0465 | 1.1458  |
| No log        | 12.8  | 13   | 0.7028          | 0.5      | 0.5       | 0.05   | 0.0909 | 1.1699  |
| No log        | 13.8  | 14   | 0.7019          | 0.5      | 0.5       | 0.05   | 0.0909 | 1.1699  |
| No log        | 14.8  | 15   | 0.7011          | 0.5      | 0.5       | 0.05   | 0.0909 | 1.1699  |
| No log        | 15.8  | 16   | 0.7004          | 0.5      | 0.5       | 0.05   | 0.0909 | 1.1699  |
| No log        | 16.8  | 17   | 0.6997          | 0.4875   | 0.4       | 0.05   | 0.0889 | 1.1458  |
| No log        | 17.8  | 18   | 0.6991          | 0.5      | 0.5       | 0.1    | 0.1667 | 1.1699  |
| No log        | 18.8  | 19   | 0.6985          | 0.4875   | 0.4444    | 0.1    | 0.1633 | 1.1458  |
| No log        | 19.8  | 20   | 0.6981          | 0.5125   | 0.5333    | 0.2    | 0.2909 | 1.1939  |
| No log        | 20.8  | 21   | 0.6978          | 0.475    | 0.4444    | 0.2    | 0.2759 | 1.1214  |
| No log        | 21.8  | 22   | 0.6976          | 0.5125   | 0.5185    | 0.35   | 0.4179 | 1.1939  |
| No log        | 22.8  | 23   | 0.6975          | 0.525    | 0.5263    | 0.5    | 0.5128 | 1.2176  |
| No log        | 23.8  | 24   | 0.6977          | 0.5375   | 0.5366    | 0.55   | 0.5432 | 1.2412  |
| No log        | 24.8  | 25   | 0.6980          | 0.525    | 0.5227    | 0.575  | 0.5476 | 1.2176  |
| No log        | 25.8  | 26   | 0.6984          | 0.5      | 0.5       | 0.625  | 0.5556 | 1.1699  |
| No log        | 26.8  | 27   | 0.6989          | 0.4625   | 0.4727    | 0.65   | 0.5474 | 1.0969  |
| No log        | 27.8  | 28   | 0.6994          | 0.45     | 0.4667    | 0.7    | 0.56   | 1.0721  |
| No log        | 28.8  | 29   | 0.7001          | 0.45     | 0.4688    | 0.75   | 0.5769 | 1.0721  |
| No log        | 29.8  | 30   | 0.7006          | 0.4375   | 0.4615    | 0.75   | 0.5714 | 1.0471  |
| No log        | 30.8  | 31   | 0.7011          | 0.45     | 0.4697    | 0.775  | 0.5849 | 1.0721  |
| No log        | 31.8  | 32   | 0.7016          | 0.45     | 0.4697    | 0.775  | 0.5849 | 1.0721  |
| No log        | 32.8  | 33   | 0.7019          | 0.4375   | 0.4615    | 0.75   | 0.5714 | 1.0471  |
| No log        | 33.8  | 34   | 0.7022          | 0.4375   | 0.4615    | 0.75   | 0.5714 | 1.0471  |
| No log        | 34.8  | 35   | 0.7024          | 0.4625   | 0.4762    | 0.75   | 0.5825 | 1.0969  |
| No log        | 35.8  | 36   | 0.7028          | 0.4625   | 0.4762    | 0.75   | 0.5825 | 1.0969  |
| No log        | 36.8  | 37   | 0.7031          | 0.4625   | 0.4754    | 0.725  | 0.5743 | 1.0969  |
| No log        | 37.8  | 38   | 0.7036          | 0.45     | 0.4667    | 0.7    | 0.56   | 1.0721  |
| No log        | 38.8  | 39   | 0.7041          | 0.45     | 0.4630    | 0.625  | 0.5319 | 1.0721  |
| No log        | 39.8  | 40   | 0.7046          | 0.4375   | 0.4490    | 0.55   | 0.4944 | 1.0471  |
| No log        | 40.8  | 41   | 0.7051          | 0.4375   | 0.4390    | 0.45   | 0.4444 | 1.0471  |
| No log        | 41.8  | 42   | 0.7058          | 0.4625   | 0.4595    | 0.425  | 0.4416 | 1.0969  |
| No log        | 42.8  | 43   | 0.7064          | 0.475    | 0.4706    | 0.4    | 0.4324 | 1.1214  |
| No log        | 43.8  | 44   | 0.7072          | 0.4875   | 0.4839    | 0.375  | 0.4225 | 1.1458  |
| No log        | 44.8  | 45   | 0.7080          | 0.475    | 0.4643    | 0.325  | 0.3824 | 1.1214  |
| No log        | 45.8  | 46   | 0.7088          | 0.475    | 0.4615    | 0.3    | 0.3636 | 1.1214  |
| No log        | 46.8  | 47   | 0.7097          | 0.475    | 0.4615    | 0.3    | 0.3636 | 1.1214  |
| No log        | 47.8  | 48   | 0.7105          | 0.475    | 0.4615    | 0.3    | 0.3636 | 1.1214  |
| No log        | 48.8  | 49   | 0.7112          | 0.475    | 0.4615    | 0.3    | 0.3636 | 1.1214  |
| No log        | 49.8  | 50   | 0.7119          | 0.4875   | 0.48      | 0.3    | 0.3692 | 1.1458  |


### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2

{"license": "cc-by-sa-4.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "precision", "recall", "f1"], "model-index": [{"name": "legal_bert_test2", "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\_test2
==================


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


* Loss: 0.7119
* Accuracy: 0.4875
* Precision: 0.48
* Recall: 0.3
* F1: 0.3692
* D-index: 1.1458


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
* gradient\_accumulation\_steps: 16
* total\_train\_batch\_size: 256
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 50


### Training results



### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* gradient\_accumulation\_steps: 16
* total\_train\_batch\_size: 256
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 50 ### Training results ### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* 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: 16
* eval\_batch\_size: 16
* seed: 42
* gradient\_accumulation\_steps: 16
* total\_train\_batch\_size: 256
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 50 ### Training results ### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* 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: 16
* eval\_batch\_size: 16
* seed: 42
* gradient\_accumulation\_steps: 16
* total\_train\_batch\_size: 256
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 50### Training results### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2