---
library_name: transformers
license: cc-by-sa-4.0
base_model: nlpaueb/legal-bert-base-uncased
tags:
- generated_from_trainer
datasets:
- alayaran/bodo-monolingual-dataset
metrics:
- accuracy
model-index:
- name: legal-bert
  results:
  - task:
      name: Masked Language Modeling
      type: fill-mask
    dataset:
      name: alayaran/bodo-monolingual-dataset
      type: alayaran/bodo-monolingual-dataset
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.15121982702981798
---

<!-- 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. -->

# legal-bert

This model is a fine-tuned version of [nlpaueb/legal-bert-base-uncased](https://huggingface.co/nlpaueb/legal-bert-base-uncased) on the alayaran/bodo-monolingual-dataset dataset.
It achieves the following results on the evaluation set:
- Loss: 4.3914
- Accuracy: 0.1512

## 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: 0.0003
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.98) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 4.0

### Training results



### Framework versions

- Transformers 4.48.3
- Pytorch 2.0.1+cu117
- Datasets 3.2.0
- Tokenizers 0.21.0