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# bert_legal_test_sm_gen_1

This model is a fine-tuned version of [wiorz/bert_legal_test_sm](https://huggingface.co/wiorz/bert_legal_test_sm) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4297
- Accuracy: 0.7992
- Precision: 0.4576
- Recall: 0.2687
- F1: 0.3386
- D-index: 1.5225

## 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        | 0.99  | 65   | 0.4457          | 0.8002   | 0.4554    | 0.2289 | 0.3046 | 1.5100  |
| No log        | 1.99  | 131  | 0.4289          | 0.7973   | 0.4444    | 0.2388 | 0.3107 | 1.5095  |
| No log        | 3.0   | 197  | 0.5157          | 0.7555   | 0.4034    | 0.5821 | 0.4766 | 1.5683  |
| No log        | 4.0   | 263  | 0.6436          | 0.7983   | 0.4433    | 0.2139 | 0.2886 | 1.5022  |
| No log        | 4.99  | 328  | 0.6772          | 0.8021   | 0.4598    | 0.1990 | 0.2778 | 1.5021  |
| No log        | 5.99  | 394  | 0.7292          | 0.8078   | 0.4964    | 0.3383 | 0.4024 | 1.5578  |
| No log        | 7.0   | 460  | 0.9566          | 0.8021   | 0.4755    | 0.3383 | 0.3953 | 1.5501  |
| 0.2346        | 8.0   | 526  | 1.0280          | 0.8002   | 0.4651    | 0.2985 | 0.3636 | 1.5340  |
| 0.2346        | 8.99  | 591  | 1.0350          | 0.7840   | 0.4330    | 0.4179 | 0.4253 | 1.5526  |
| 0.2346        | 9.99  | 657  | 1.2664          | 0.8002   | 0.4444    | 0.1791 | 0.2553 | 1.4925  |
| 0.2346        | 11.0  | 723  | 1.2846          | 0.7812   | 0.4040    | 0.3035 | 0.3466 | 1.5098  |
| 0.2346        | 12.0  | 789  | 1.2157          | 0.7897   | 0.4351    | 0.3333 | 0.3775 | 1.5317  |
| 0.2346        | 12.99 | 854  | 1.3208          | 0.8030   | 0.4688    | 0.2239 | 0.3030 | 1.5121  |
| 0.2346        | 13.99 | 920  | 1.3100          | 0.7783   | 0.4101    | 0.3632 | 0.3852 | 1.5263  |
| 0.2346        | 15.0  | 986  | 1.2587          | 0.8154   | 0.5347    | 0.2687 | 0.3576 | 1.5444  |
| 0.0277        | 16.0  | 1052 | 1.3552          | 0.7878   | 0.4304    | 0.3383 | 0.3788 | 1.5308  |
| 0.0277        | 16.99 | 1117 | 1.3783          | 0.8059   | 0.4872    | 0.2836 | 0.3585 | 1.5366  |
| 0.0277        | 17.99 | 1183 | 1.4071          | 0.7907   | 0.4336    | 0.3085 | 0.3605 | 1.5245  |
| 0.0277        | 19.0  | 1249 | 1.4283          | 0.8011   | 0.4655    | 0.2687 | 0.3407 | 1.5251  |
| 0.0277        | 19.77 | 1300 | 1.4297          | 0.7992   | 0.4576    | 0.2687 | 0.3386 | 1.5225  |


### Framework versions

- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "precision", "recall", "f1"], "model-index": [{"name": "bert_legal_test_sm_gen_1", "results": []}]}
transformers pytorch tensorboard bert text-classification generated_from_trainer license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

bert\_legal\_test\_sm\_gen\_1
=============================


This model is a fine-tuned version of wiorz/bert\_legal\_test\_sm on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 1.4297
* Accuracy: 0.7992
* Precision: 0.4576
* Recall: 0.2687
* F1: 0.3386
* D-index: 1.5225


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 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3



### 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 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-apache-2.0 #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 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-apache-2.0 #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 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3