Instructions to use Harsh-2706/legal-ai-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Harsh-2706/legal-ai-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Harsh-2706/legal-ai-model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Harsh-2706/legal-ai-model") model = AutoModelForSequenceClassification.from_pretrained("Harsh-2706/legal-ai-model") - Notebooks
- Google Colab
- Kaggle
legal-ai-model
This model is a fine-tuned version of law-ai/InLegalBERT on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.5619
- Accuracy: 0.734
- F1: 0.5167
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: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|---|---|---|---|---|---|
| 3.9548 | 1.0 | 563 | 3.0482 | 0.5 | 0.2283 |
| 2.7400 | 2.0 | 1126 | 2.1695 | 0.662 | 0.4170 |
| 2.1137 | 3.0 | 1689 | 1.7869 | 0.7 | 0.4606 |
| 1.7650 | 4.0 | 2252 | 1.6146 | 0.726 | 0.5003 |
| 1.5814 | 5.0 | 2815 | 1.5619 | 0.734 | 0.5167 |
Framework versions
- Transformers 5.5.0
- Pytorch 2.11.0+cu130
- Datasets 4.0.0
- Tokenizers 0.22.2
- Downloads last month
- 6
Model tree for Harsh-2706/legal-ai-model
Base model
law-ai/InLegalBERT