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legal-bert-lora

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+ ---
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+ license: cc-by-sa-4.0
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+ base_model: nlpaueb/legal-bert-base-uncased
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+ tags:
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+ - generated_from_trainer
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+ metrics:
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+ - accuracy
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+ - precision
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+ - recall
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+ model-index:
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+ - name: legal-bert-lora
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+ results: []
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+ ---
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+
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+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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+ should probably proofread and complete it, then remove this comment. -->
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+
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+ # legal-bert-lora
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+
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+ This model is a fine-tuned version of [nlpaueb/legal-bert-base-uncased](https://huggingface.co/nlpaueb/legal-bert-base-uncased) on an unknown dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.7058
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+ - Accuracy: 0.7847
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+ - Precision: 0.7702
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+ - Recall: 0.7847
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+ - Precision Macro: 0.5452
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+ - Recall Macro: 0.5400
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+ - Macro Fpr: 0.0199
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+ - Weighted Fpr: 0.0192
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+ - Weighted Specificity: 0.9737
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+ - Macro Specificity: 0.9839
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+ - Weighted Sensitivity: 0.7847
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+ - Macro Sensitivity: 0.5400
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+ - F1 Micro: 0.7847
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+ - F1 Macro: 0.5165
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+ - F1 Weighted: 0.7676
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 5e-05
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+ - train_batch_size: 8
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+ - eval_batch_size: 8
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+ - seed: 42
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+ - gradient_accumulation_steps: 4
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+ - total_train_batch_size: 32
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+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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+ - lr_scheduler_type: linear
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+ - num_epochs: 10
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | Precision Macro | Recall Macro | Macro Fpr | Weighted Fpr | Weighted Specificity | Macro Specificity | Weighted Sensitivity | Macro Sensitivity | F1 Micro | F1 Macro | F1 Weighted |
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+ |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:---------------:|:------------:|:---------:|:------------:|:--------------------:|:-----------------:|:--------------------:|:-----------------:|:--------:|:--------:|:-----------:|
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+ | No log | 1.0 | 160 | 1.3252 | 0.6297 | 0.5643 | 0.6297 | 0.2865 | 0.3110 | 0.0417 | 0.0403 | 0.9455 | 0.9717 | 0.6297 | 0.3110 | 0.6297 | 0.2742 | 0.5694 |
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+ | No log | 2.0 | 321 | 0.8870 | 0.7312 | 0.6873 | 0.7312 | 0.3742 | 0.4525 | 0.0257 | 0.0256 | 0.9690 | 0.9800 | 0.7312 | 0.4525 | 0.7312 | 0.3967 | 0.6996 |
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+ | No log | 3.0 | 482 | 0.7794 | 0.7483 | 0.7169 | 0.7483 | 0.4059 | 0.4680 | 0.0239 | 0.0235 | 0.9711 | 0.9813 | 0.7483 | 0.4680 | 0.7483 | 0.4262 | 0.7282 |
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+ | 1.2835 | 4.0 | 643 | 0.7481 | 0.7444 | 0.7085 | 0.7444 | 0.3997 | 0.4588 | 0.0243 | 0.0239 | 0.9700 | 0.9810 | 0.7444 | 0.4588 | 0.7444 | 0.4100 | 0.7146 |
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+ | 1.2835 | 5.0 | 803 | 0.7360 | 0.7630 | 0.7245 | 0.7630 | 0.4176 | 0.4763 | 0.0226 | 0.0217 | 0.9702 | 0.9822 | 0.7630 | 0.4763 | 0.7630 | 0.4350 | 0.7372 |
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+ | 1.2835 | 6.0 | 964 | 0.7146 | 0.7738 | 0.7790 | 0.7738 | 0.5020 | 0.4907 | 0.0209 | 0.0205 | 0.9730 | 0.9831 | 0.7738 | 0.4907 | 0.7738 | 0.4514 | 0.7549 |
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+ | 0.6494 | 7.0 | 1125 | 0.7362 | 0.7607 | 0.7519 | 0.7607 | 0.5232 | 0.4890 | 0.0225 | 0.0220 | 0.9724 | 0.9822 | 0.7607 | 0.4890 | 0.7607 | 0.4556 | 0.7390 |
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+ | 0.6494 | 8.0 | 1286 | 0.7271 | 0.7800 | 0.7639 | 0.7800 | 0.5348 | 0.5171 | 0.0205 | 0.0197 | 0.9731 | 0.9835 | 0.7800 | 0.5171 | 0.7800 | 0.4923 | 0.7617 |
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+ | 0.6494 | 9.0 | 1446 | 0.7068 | 0.7847 | 0.7739 | 0.7847 | 0.5495 | 0.5205 | 0.0199 | 0.0192 | 0.9744 | 0.9839 | 0.7847 | 0.5205 | 0.7847 | 0.4943 | 0.7665 |
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+ | 0.5284 | 9.95 | 1600 | 0.7058 | 0.7847 | 0.7702 | 0.7847 | 0.5452 | 0.5400 | 0.0199 | 0.0192 | 0.9737 | 0.9839 | 0.7847 | 0.5400 | 0.7847 | 0.5165 | 0.7676 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.35.2
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+ - Pytorch 2.1.0+cu121
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+ - Datasets 2.18.0
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+ - Tokenizers 0.15.1