metadata
license: cc-by-sa-4.0
base_model: nlpaueb/legal-bert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: legal-bert-base-uncased-regnlp-obligation-classifier
results: []
legal-bert-base-uncased-regnlp-obligation-classifier
This model is a fine-tuned version of nlpaueb/legal-bert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0205
- Accuracy: 0.9957
- F1: 0.9964
- Precision: 1.0
- Recall: 0.9928
This repo is created for easy sharing model weights among our team which is participating in the RegNLP challenge. Please don't request access for it
You can find the finetuning scripts at this Github Repo.
All credits for the work and data belong to the RegNLP Paper authors.
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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|---|---|---|---|---|---|---|---|
| 0.4237 | 1.0 | 115 | 0.1827 | 0.9783 | 0.9819 | 0.9855 | 0.9784 |
| 0.0429 | 2.0 | 230 | 0.0438 | 0.9870 | 0.9892 | 0.9928 | 0.9856 |
| 0.0781 | 3.0 | 345 | 0.0579 | 0.9891 | 0.9910 | 0.9928 | 0.9892 |
| 0.009 | 4.0 | 460 | 0.0753 | 0.9891 | 0.9909 | 1.0 | 0.9820 |
| 0.0004 | 5.0 | 575 | 0.0205 | 0.9957 | 0.9964 | 1.0 | 0.9928 |
| 0.0001 | 6.0 | 690 | 0.0640 | 0.9913 | 0.9928 | 0.9964 | 0.9892 |
| 0.0001 | 7.0 | 805 | 0.0671 | 0.9913 | 0.9928 | 0.9964 | 0.9892 |
| 0.0001 | 8.0 | 920 | 0.0696 | 0.9913 | 0.9928 | 0.9964 | 0.9892 |
Framework versions
- Transformers 4.43.1
- Pytorch 2.3.0+cu121
- Datasets 2.14.7
- Tokenizers 0.19.1