---
library_name: transformers
license: mit
base_model: nsi319/legal-led-base-16384
tags:
- generated_from_trainer
datasets:
- Mwnthai/bodo-legal-summary-data
metrics:
- accuracy
model-index:
- name: legal-led
  results:
  - task:
      name: Masked Language Modeling
      type: fill-mask
    dataset:
      name: Mwnthai/bodo-legal-summary-data
      type: Mwnthai/bodo-legal-summary-data
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.1611527023992727
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# legal-led

This model is a fine-tuned version of [nsi319/legal-led-base-16384](https://huggingface.co/nsi319/legal-led-base-16384) on the Mwnthai/bodo-legal-summary-data dataset.
It achieves the following results on the evaluation set:
- Loss: 4.1035
- Accuracy: 0.1612

## 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