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

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: 0.6344
- Accuracy: 0.8203
- Precision: 0.8092
- Recall: 0.8203
- Precision Macro: 0.6487
- Recall Macro: 0.6625
- Macro Fpr: 0.0160
- Weighted Fpr: 0.0154
- Weighted Specificity: 0.9771
- Macro Specificity: 0.9865
- Weighted Sensitivity: 0.8203
- Macro Sensitivity: 0.6625
- F1 Micro: 0.8203
- F1 Macro: 0.6461
- F1 Weighted: 0.8125

## 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
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15

### 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:---------------:|:------------:|:---------:|:------------:|:--------------------:|:-----------------:|:--------------------:|:-----------------:|:--------:|:--------:|:-----------:|
| No log        | 1.0   | 160  | 1.2013          | 0.6553   | 0.6007    | 0.6553 | 0.3279          | 0.3903       | 0.0365    | 0.0362       | 0.9556               | 0.9741            | 0.6553               | 0.3903            | 0.6553   | 0.3420   | 0.6147      |
| No log        | 2.0   | 321  | 0.8279          | 0.7382   | 0.7211    | 0.7382 | 0.4092          | 0.4658       | 0.0248    | 0.0247       | 0.9713               | 0.9806            | 0.7382               | 0.4658            | 0.7382   | 0.4250   | 0.7237      |
| No log        | 3.0   | 482  | 0.7130          | 0.7545   | 0.7255    | 0.7545 | 0.4800          | 0.4770       | 0.0233    | 0.0227       | 0.9701               | 0.9816            | 0.7545               | 0.4770            | 0.7545   | 0.4431   | 0.7305      |
| 1.1985        | 4.0   | 643  | 0.6922          | 0.7823   | 0.7594    | 0.7823 | 0.5188          | 0.5283       | 0.0200    | 0.0195       | 0.9740               | 0.9838            | 0.7823               | 0.5283            | 0.7823   | 0.5048   | 0.7660      |
| 1.1985        | 5.0   | 803  | 0.6710          | 0.7940   | 0.7734    | 0.7940 | 0.5450          | 0.5571       | 0.0190    | 0.0182       | 0.9739               | 0.9845            | 0.7940               | 0.5571            | 0.7940   | 0.5257   | 0.7718      |
| 1.1985        | 6.0   | 964  | 0.6455          | 0.7971   | 0.7757    | 0.7971 | 0.5353          | 0.5622       | 0.0184    | 0.0179       | 0.9754               | 0.9848            | 0.7971               | 0.5622            | 0.7971   | 0.5316   | 0.7790      |
| 0.5721        | 7.0   | 1125 | 0.6395          | 0.8002   | 0.7801    | 0.8002 | 0.5443          | 0.5784       | 0.0181    | 0.0175       | 0.9762               | 0.9851            | 0.8002               | 0.5784            | 0.8002   | 0.5486   | 0.7845      |
| 0.5721        | 8.0   | 1286 | 0.6317          | 0.8025   | 0.7833    | 0.8025 | 0.5439          | 0.5773       | 0.0178    | 0.0173       | 0.9765               | 0.9853            | 0.8025               | 0.5773            | 0.8025   | 0.5475   | 0.7874      |
| 0.5721        | 9.0   | 1446 | 0.6137          | 0.8009   | 0.7828    | 0.8009 | 0.5593          | 0.5842       | 0.0179    | 0.0174       | 0.9765               | 0.9852            | 0.8009               | 0.5842            | 0.8009   | 0.5609   | 0.7875      |
| 0.4166        | 10.0  | 1607 | 0.6249          | 0.8156   | 0.8055    | 0.8156 | 0.6398          | 0.6430       | 0.0165    | 0.0159       | 0.9772               | 0.9862            | 0.8156               | 0.6430            | 0.8156   | 0.6305   | 0.8067      |
| 0.4166        | 11.0  | 1768 | 0.6426          | 0.8125   | 0.8014    | 0.8125 | 0.6397          | 0.6520       | 0.0169    | 0.0162       | 0.9762               | 0.9859            | 0.8125               | 0.6520            | 0.8125   | 0.6372   | 0.8042      |
| 0.4166        | 12.0  | 1929 | 0.6305          | 0.8164   | 0.8050    | 0.8164 | 0.6358          | 0.6526       | 0.0164    | 0.0158       | 0.9770               | 0.9862            | 0.8164               | 0.6526            | 0.8164   | 0.6372   | 0.8083      |
| 0.3406        | 13.0  | 2089 | 0.6276          | 0.8203   | 0.8102    | 0.8203 | 0.6418          | 0.6467       | 0.0160    | 0.0154       | 0.9774               | 0.9865            | 0.8203               | 0.6467            | 0.8203   | 0.6353   | 0.8129      |
| 0.3406        | 14.0  | 2250 | 0.6428          | 0.8187   | 0.8079    | 0.8187 | 0.6467          | 0.6618       | 0.0162    | 0.0156       | 0.9771               | 0.9864            | 0.8187               | 0.6618            | 0.8187   | 0.6446   | 0.8107      |
| 0.3406        | 14.93 | 2400 | 0.6344          | 0.8203   | 0.8092    | 0.8203 | 0.6487          | 0.6625       | 0.0160    | 0.0154       | 0.9771               | 0.9865            | 0.8203               | 0.6625            | 0.8203   | 0.6461   | 0.8125      |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.1