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---
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
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