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# bert_legal_test_sm

This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6233
- Accuracy: 0.6580
- Precision: 0.6683
- Recall: 0.6274
- F1: 0.6472
- D-index: 1.4589

## 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   | 0.6905          | 0.5542   | 0.5418    | 0.7028 | 0.6119 | 1.2724  |
| No log        | 2.0   | 53   | 0.6937          | 0.5024   | 0.5012    | 0.9623 | 0.6591 | 1.1745  |
| No log        | 2.98  | 79   | 0.6505          | 0.6439   | 0.6894    | 0.5236 | 0.5952 | 1.4342  |
| No log        | 4.0   | 106  | 0.6233          | 0.6580   | 0.6683    | 0.6274 | 0.6472 | 1.4589  |
| No log        | 4.98  | 132  | 0.6369          | 0.6840   | 0.7053    | 0.6321 | 0.6667 | 1.5037  |
| No log        | 6.0   | 159  | 0.9851          | 0.6085   | 0.7614    | 0.3160 | 0.4467 | 1.3714  |
| No log        | 6.98  | 185  | 0.8765          | 0.6604   | 0.7537    | 0.4764 | 0.5838 | 1.4630  |
| No log        | 8.0   | 212  | 0.9170          | 0.6745   | 0.7102    | 0.5896 | 0.6443 | 1.4875  |
| No log        | 8.98  | 238  | 1.1931          | 0.6557   | 0.7324    | 0.4906 | 0.5876 | 1.4548  |
| No log        | 10.0  | 265  | 1.0355          | 0.6840   | 0.7216    | 0.5991 | 0.6546 | 1.5037  |
| No log        | 10.98 | 291  | 1.1690          | 0.6675   | 0.6878    | 0.6132 | 0.6484 | 1.4753  |
| No log        | 12.0  | 318  | 1.1527          | 0.6651   | 0.64      | 0.7547 | 0.6926 | 1.4712  |
| No log        | 12.98 | 344  | 1.2299          | 0.6675   | 0.6940    | 0.5991 | 0.6430 | 1.4753  |
| No log        | 14.0  | 371  | 1.4807          | 0.6557   | 0.72      | 0.5094 | 0.5967 | 1.4548  |
| No log        | 14.98 | 397  | 1.4303          | 0.6887   | 0.7083    | 0.6415 | 0.6733 | 1.5118  |
| No log        | 16.0  | 424  | 1.5717          | 0.6792   | 0.6863    | 0.6604 | 0.6731 | 1.4956  |
| No log        | 16.98 | 450  | 1.7842          | 0.6509   | 0.6975    | 0.5330 | 0.6043 | 1.4466  |
| No log        | 18.0  | 477  | 1.6653          | 0.6698   | 0.6895    | 0.6179 | 0.6517 | 1.4794  |
| 0.2514        | 18.98 | 503  | 1.8285          | 0.6557   | 0.7143    | 0.5189 | 0.6011 | 1.4548  |
| 0.2514        | 19.62 | 520  | 1.8220          | 0.6486   | 0.7006    | 0.5189 | 0.5962 | 1.4425  |


### Framework versions

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

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "precision", "recall", "f1"], "model-index": [{"name": "bert_legal_test_sm", "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
=====================


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


* Loss: 0.6233
* Accuracy: 0.6580
* Precision: 0.6683
* Recall: 0.6274
* F1: 0.6472
* D-index: 1.4589


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-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 4.27.4
* Pytorch 1.13.1+cu116
* Datasets 2.11.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-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 4.27.4
* Pytorch 1.13.1+cu116
* Datasets 2.11.0
* Tokenizers 0.13.2