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# legal_bert_sm_cv_summarized_defined_4

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: 1.6665
- Accuracy: 0.811
- Precision: 0.5357
- Recall: 0.2308
- F1: 0.3226
- D-index: 1.5269

## 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 8000
- 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   | 250  | 0.4854          | 0.804    | 0.0       | 0.0    | 0.0    | 1.4356  |
| 0.5596        | 2.0   | 500  | 0.4545          | 0.805    | 0.0       | 0.0    | 0.0    | 1.4370  |
| 0.5596        | 3.0   | 750  | 0.4570          | 0.811    | 0.6667    | 0.0615 | 0.1127 | 1.4675  |
| 0.4293        | 4.0   | 1000 | 0.4673          | 0.815    | 0.6316    | 0.1231 | 0.2060 | 1.4949  |
| 0.4293        | 5.0   | 1250 | 0.4893          | 0.828    | 0.6949    | 0.2103 | 0.3228 | 1.5429  |
| 0.3311        | 6.0   | 1500 | 0.5062          | 0.828    | 0.6533    | 0.2513 | 0.3630 | 1.5569  |
| 0.3311        | 7.0   | 1750 | 0.5584          | 0.826    | 0.7059    | 0.1846 | 0.2927 | 1.5313  |
| 0.2126        | 8.0   | 2000 | 0.7423          | 0.821    | 0.6333    | 0.1949 | 0.2980 | 1.5281  |
| 0.2126        | 9.0   | 2250 | 0.8720          | 0.804    | 0.4933    | 0.1897 | 0.2741 | 1.5031  |
| 0.1327        | 10.0  | 2500 | 0.9116          | 0.811    | 0.5268    | 0.3026 | 0.3844 | 1.5513  |
| 0.1327        | 11.0  | 2750 | 0.9677          | 0.8      | 0.4809    | 0.3231 | 0.3865 | 1.5434  |
| 0.0803        | 12.0  | 3000 | 1.1951          | 0.795    | 0.4627    | 0.3179 | 0.3769 | 1.5349  |
| 0.0803        | 13.0  | 3250 | 1.3724          | 0.819    | 0.5946    | 0.2256 | 0.3271 | 1.5360  |
| 0.0584        | 14.0  | 3500 | 1.4260          | 0.806    | 0.5056    | 0.2308 | 0.3169 | 1.5201  |
| 0.0584        | 15.0  | 3750 | 1.4684          | 0.812    | 0.5327    | 0.2923 | 0.3775 | 1.5492  |
| 0.0437        | 16.0  | 4000 | 1.5562          | 0.815    | 0.5658    | 0.2205 | 0.3173 | 1.5288  |
| 0.0437        | 17.0  | 4250 | 1.5812          | 0.814    | 0.5763    | 0.1744 | 0.2677 | 1.5115  |
| 0.0357        | 18.0  | 4500 | 1.6058          | 0.805    | 0.5       | 0.2308 | 0.3158 | 1.5187  |
| 0.0357        | 19.0  | 4750 | 1.6784          | 0.813    | 0.5465    | 0.2410 | 0.3345 | 1.5331  |
| 0.0334        | 20.0  | 5000 | 1.6665          | 0.811    | 0.5357    | 0.2308 | 0.3226 | 1.5269  |


### 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_sm_cv_summarized_defined_4", "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\_sm\_cv\_summarized\_defined\_4
===========================================


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: 1.6665
* Accuracy: 0.811
* Precision: 0.5357
* Recall: 0.2308
* F1: 0.3226
* D-index: 1.5269


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
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 8000
* 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: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 8000
* 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: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 8000
* 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