---
library_name: transformers
license: cc-by-sa-4.0
base_model: nlpaueb/legal-bert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: legalbertcase_outcome_model
  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. -->

# legalbertcase_outcome_model

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: 1.5650
- Accuracy: 0.4393

## 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: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.6614        | 1.0   | 224  | 1.6261          | 0.4373   |
| 1.5774        | 2.0   | 448  | 1.5875          | 0.4449   |
| 1.5879        | 3.0   | 672  | 1.5650          | 0.4393   |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.4.0
- Datasets 3.0.0
- Tokenizers 0.19.1