Legal Ner Model

--- library_name: transformers license: mit base_model: roberta-base tags: - generated_from_trainer metrics: - accuracy - precision - recall - f1 model-index: - name: legal_ner_model results: [] --- # legal_ner_model This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1039 - Model Preparation Time: 0.0049 - Accuracy: 0.9740 - Precision: 0.9520 - Recall: 0.9430 - F1: 0.9475 ## 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: 2e-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: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Model Preparation Time | Accuracy | Precision | Recall | F1 | |:-------------:|:-----:|:----:|:---------------:|:----------------------:|:--------:|:---------:|:------:|:------:| | 0.1464 | 1.0 | 1375 | 0.1224 | 0.0049 | 0.9647 | 0.9364 | 0.9204 | 0.9284 | | 0.0969 | 2.0 | 2750 | 0.1027 | 0.0049 | 0.9723 | 0.9523 | 0.9355 | 0.9438 | | 0.0814 | 3.0 | 4125 | 0.1039 | 0.0049 | 0.9740 | 0.9520 | 0.9430 | 0.9475 | ### Framework versions - Transformers 4.55.4 - Pytorch 2.11.0+cu128 - Datasets 4.0.0 - Tokenizers 0.21.4