---
license: mit
tags:
- generated_from_trainer
base_model: law-ai/InLegalBERT
metrics:
- accuracy
- precision
- recall
model-index:
- name: InLegalBERT
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# InLegalBERT

This model is a fine-tuned version of [law-ai/InLegalBERT](https://huggingface.co/law-ai/InLegalBERT) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5914
- Accuracy: 0.8296
- Precision: 0.8293
- Recall: 0.8296
- Precision Macro: 0.7959
- Recall Macro: 0.8029
- Macro Fpr: 0.0150
- Weighted Fpr: 0.0145
- Weighted Specificity: 0.9774
- Macro Specificity: 0.9871
- Weighted Sensitivity: 0.8296
- Macro Sensitivity: 0.8029
- F1 Micro: 0.8296
- F1 Macro: 0.7954
- F1 Weighted: 0.8283

## 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: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | Precision Macro | Recall Macro | Macro Fpr | Weighted Fpr | Weighted Specificity | Macro Specificity | Weighted Sensitivity | Macro Sensitivity | F1 Micro | F1 Macro | F1 Weighted |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:---------------:|:------------:|:---------:|:------------:|:--------------------:|:-----------------:|:--------------------:|:-----------------:|:--------:|:--------:|:-----------:|
| 1.065         | 1.0   | 643  | 0.6395          | 0.7994   | 0.7818    | 0.7994 | 0.6194          | 0.6308       | 0.0185    | 0.0176       | 0.9714               | 0.9847            | 0.7994               | 0.6308            | 0.7994   | 0.6029   | 0.7804      |
| 0.5866        | 2.0   | 1286 | 0.6907          | 0.8187   | 0.8199    | 0.8187 | 0.7285          | 0.7366       | 0.0161    | 0.0156       | 0.9765               | 0.9864            | 0.8187               | 0.7366            | 0.8187   | 0.7276   | 0.8152      |
| 0.4622        | 3.0   | 1929 | 0.8056          | 0.8180   | 0.8137    | 0.8180 | 0.7227          | 0.7376       | 0.0162    | 0.0156       | 0.9764               | 0.9863            | 0.8180               | 0.7376            | 0.8180   | 0.7283   | 0.8150      |
| 0.2398        | 4.0   | 2572 | 0.9310          | 0.8172   | 0.8235    | 0.8172 | 0.7661          | 0.7425       | 0.0161    | 0.0157       | 0.9762               | 0.9862            | 0.8172               | 0.7425            | 0.8172   | 0.7407   | 0.8161      |
| 0.1611        | 5.0   | 3215 | 1.0763          | 0.8304   | 0.8363    | 0.8304 | 0.8174          | 0.7918       | 0.0148    | 0.0144       | 0.9784               | 0.9873            | 0.8304               | 0.7918            | 0.8304   | 0.7986   | 0.8304      |
| 0.1055        | 6.0   | 3858 | 1.1377          | 0.8257   | 0.8275    | 0.8257 | 0.8039          | 0.7810       | 0.0154    | 0.0149       | 0.9775               | 0.9869            | 0.8257               | 0.7810            | 0.8257   | 0.7863   | 0.8246      |
| 0.0463        | 7.0   | 4501 | 1.3215          | 0.8071   | 0.8111    | 0.8071 | 0.7692          | 0.7689       | 0.0172    | 0.0168       | 0.9761               | 0.9856            | 0.8071               | 0.7689            | 0.8071   | 0.7661   | 0.8078      |
| 0.031         | 8.0   | 5144 | 1.3483          | 0.8203   | 0.8170    | 0.8203 | 0.7773          | 0.7727       | 0.0161    | 0.0154       | 0.9751               | 0.9864            | 0.8203               | 0.7727            | 0.8203   | 0.7690   | 0.8175      |
| 0.0202        | 9.0   | 5787 | 1.3730          | 0.8280   | 0.8263    | 0.8280 | 0.7818          | 0.7803       | 0.0152    | 0.0146       | 0.9779               | 0.9871            | 0.8280               | 0.7803            | 0.8280   | 0.7753   | 0.8256      |
| 0.0133        | 10.0  | 6430 | 1.5407          | 0.8164   | 0.8163    | 0.8164 | 0.7688          | 0.7779       | 0.0165    | 0.0158       | 0.9751               | 0.9861            | 0.8164               | 0.7779            | 0.8164   | 0.7655   | 0.8135      |
| 0.0051        | 11.0  | 7073 | 1.5235          | 0.8226   | 0.8265    | 0.8226 | 0.7900          | 0.7680       | 0.0156    | 0.0152       | 0.9769               | 0.9866            | 0.8226               | 0.7680            | 0.8226   | 0.7744   | 0.8234      |
| 0.0027        | 12.0  | 7716 | 1.5643          | 0.8265   | 0.8259    | 0.8265 | 0.7805          | 0.7841       | 0.0154    | 0.0148       | 0.9772               | 0.9869            | 0.8265               | 0.7841            | 0.8265   | 0.7775   | 0.8245      |
| 0.002         | 13.0  | 8359 | 1.5516          | 0.8280   | 0.8273    | 0.8280 | 0.7882          | 0.7902       | 0.0152    | 0.0146       | 0.9779               | 0.9871            | 0.8280               | 0.7902            | 0.8280   | 0.7860   | 0.8262      |
| 0.0015        | 14.0  | 9002 | 1.5835          | 0.8273   | 0.8268    | 0.8273 | 0.7943          | 0.8022       | 0.0153    | 0.0147       | 0.9773               | 0.9870            | 0.8273               | 0.8022            | 0.8273   | 0.7943   | 0.8259      |
| 0.0007        | 15.0  | 9645 | 1.5914          | 0.8296   | 0.8293    | 0.8296 | 0.7959          | 0.8029       | 0.0150    | 0.0145       | 0.9774               | 0.9871            | 0.8296               | 0.8029            | 0.8296   | 0.7954   | 0.8283      |


### Framework versions

- Transformers 4.38.2
- Pytorch 2.1.2
- Datasets 2.1.0
- Tokenizers 0.15.2