--- license: mit base_model: microsoft/deberta-v3-base tags: - generated_from_trainer model-index: - name: legal_deberta results: [] --- # legal_deberta This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2674 - Law Precision: 0.6932 - Law Recall: 0.8133 - Law F1: 0.7485 - Law Number: 75 - Violated by Precision: 0.8684 - Violated by Recall: 0.88 - Violated by F1: 0.8742 - Violated by Number: 75 - Violated on Precision: 0.5882 - Violated on Recall: 0.6667 - Violated on F1: 0.625 - Violated on Number: 75 - Violation Precision: 0.5287 - Violation Recall: 0.6429 - Violation F1: 0.5802 - Violation Number: 616 - Overall Precision: 0.5741 - Overall Recall: 0.6813 - Overall F1: 0.6232 - Overall Accuracy: 0.9461 ## 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 | Law Precision | Law Recall | Law F1 | Law Number | Violated by Precision | Violated by Recall | Violated by F1 | Violated by Number | Violated on Precision | Violated on Recall | Violated on F1 | Violated on Number | Violation Precision | Violation Recall | Violation F1 | Violation Number | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | |:-------------:|:-----:|:----:|:---------------:|:-------------:|:----------:|:------:|:----------:|:---------------------:|:------------------:|:--------------:|:------------------:|:---------------------:|:------------------:|:--------------:|:------------------:|:-------------------:|:----------------:|:------------:|:----------------:|:-----------------:|:--------------:|:----------:|:----------------:| | 1.9748 | 1.0 | 45 | 1.1555 | 0.0 | 0.0 | 0.0 | 75 | 0.0 | 0.0 | 0.0 | 75 | 0.0 | 0.0 | 0.0 | 75 | 0.0 | 0.0 | 0.0 | 616 | 0.0 | 0.0 | 0.0 | 0.7437 | | 0.4536 | 2.0 | 90 | 0.3670 | 0.0 | 0.0 | 0.0 | 75 | 0.0 | 0.0 | 0.0 | 75 | 0.0 | 0.0 | 0.0 | 75 | 0.1704 | 0.2955 | 0.2162 | 616 | 0.1704 | 0.2164 | 0.1907 | 0.8901 | | 0.2704 | 3.0 | 135 | 0.2199 | 0.7059 | 0.64 | 0.6713 | 75 | 0.3095 | 0.1733 | 0.2222 | 75 | 0.0909 | 0.0133 | 0.0233 | 75 | 0.3291 | 0.5097 | 0.4000 | 616 | 0.3498 | 0.4471 | 0.3925 | 0.9277 | | 0.1475 | 4.0 | 180 | 0.1959 | 0.6263 | 0.8267 | 0.7126 | 75 | 0.9153 | 0.72 | 0.8060 | 75 | 0.3182 | 0.3733 | 0.3436 | 75 | 0.4641 | 0.5974 | 0.5224 | 616 | 0.4928 | 0.6088 | 0.5447 | 0.9407 | | 0.0879 | 5.0 | 225 | 0.2038 | 0.5909 | 0.8667 | 0.7027 | 75 | 0.7590 | 0.84 | 0.7975 | 75 | 0.3982 | 0.6 | 0.4787 | 75 | 0.4692 | 0.6055 | 0.5287 | 616 | 0.4959 | 0.6492 | 0.5623 | 0.9434 | | 0.0499 | 6.0 | 270 | 0.2466 | 0.5913 | 0.9067 | 0.7158 | 75 | 0.7674 | 0.88 | 0.8199 | 75 | 0.4412 | 0.6 | 0.5085 | 75 | 0.4832 | 0.6071 | 0.5381 | 616 | 0.5135 | 0.6576 | 0.5766 | 0.9425 | | 0.0291 | 7.0 | 315 | 0.2980 | 0.5755 | 0.8133 | 0.6740 | 75 | 0.7976 | 0.8933 | 0.8428 | 75 | 0.3802 | 0.6133 | 0.4694 | 75 | 0.4929 | 0.5617 | 0.5250 | 616 | 0.5133 | 0.6183 | 0.5609 | 0.9389 | | 0.0341 | 8.0 | 360 | 0.2660 | 0.5739 | 0.88 | 0.6947 | 75 | 0.8193 | 0.9067 | 0.8608 | 75 | 0.48 | 0.64 | 0.5486 | 75 | 0.4800 | 0.6445 | 0.5502 | 616 | 0.5147 | 0.6885 | 0.5890 | 0.9366 | | 0.0228 | 9.0 | 405 | 0.3186 | 0.3505 | 0.9067 | 0.5056 | 75 | 0.6126 | 0.9067 | 0.7312 | 75 | 0.3216 | 0.7333 | 0.4472 | 75 | 0.4365 | 0.5519 | 0.4875 | 616 | 0.4231 | 0.6314 | 0.5067 | 0.9301 | | 0.0173 | 10.0 | 450 | 0.2674 | 0.6932 | 0.8133 | 0.7485 | 75 | 0.8684 | 0.88 | 0.8742 | 75 | 0.5882 | 0.6667 | 0.625 | 75 | 0.5287 | 0.6429 | 0.5802 | 616 | 0.5741 | 0.6813 | 0.6232 | 0.9461 | ### Framework versions - Transformers 4.31.0 - Pytorch 2.4.0+cu121 - Datasets 2.15.0 - Tokenizers 0.13.3