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legal-small-NER-epoch

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+ ---
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+ license: cc-by-sa-4.0
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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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+ - f1
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+ model-index:
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+ - name: legal-small-NER-epoch
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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-small-NER-epoch
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+
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+ This model is a fine-tuned version of [nlpaueb/legal-bert-small-uncased](https://huggingface.co/nlpaueb/legal-bert-small-uncased) on the None dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.2430
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+ - Accuracy: 0.9557
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+ - Precision: 0.7506
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+ - Recall: 0.7934
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+ - F1: 0.7714
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+ - Classification Report: precision recall f1-score support
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+
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+ LOC 0.84 0.87 0.85 1668
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+ MISC 0.55 0.64 0.59 702
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+ ORG 0.66 0.69 0.68 1661
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+ PER 0.84 0.89 0.87 1617
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+
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+ micro avg 0.75 0.79 0.77 5648
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+ macro avg 0.72 0.77 0.75 5648
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+ weighted avg 0.75 0.79 0.77 5648
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+
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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: 2e-05
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+ - train_batch_size: 16
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+ - eval_batch_size: 32
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+ - seed: 42
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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: 3
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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 | F1 | Classification Report |
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+ |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|
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+ | 0.023 | 1.0 | 217 | 0.2438 | 0.9515 | 0.7291 | 0.7852 | 0.7561 | precision recall f1-score support
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+
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+ LOC 0.84 0.87 0.85 1668
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+ MISC 0.49 0.65 0.56 702
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+ ORG 0.66 0.64 0.65 1661
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+ PER 0.81 0.91 0.86 1617
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+
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+ micro avg 0.73 0.79 0.76 5648
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+ macro avg 0.70 0.77 0.73 5648
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+ weighted avg 0.73 0.79 0.76 5648
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+ |
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+ | 0.0365 | 2.0 | 434 | 0.2452 | 0.9547 | 0.7532 | 0.7893 | 0.7708 | precision recall f1-score support
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+
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+ LOC 0.86 0.85 0.85 1668
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+ MISC 0.58 0.63 0.61 702
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+ ORG 0.65 0.69 0.67 1661
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+ PER 0.83 0.90 0.87 1617
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+
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+ micro avg 0.75 0.79 0.77 5648
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+ macro avg 0.73 0.77 0.75 5648
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+ weighted avg 0.76 0.79 0.77 5648
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+ |
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+ | 0.0305 | 3.0 | 651 | 0.2430 | 0.9557 | 0.7506 | 0.7934 | 0.7714 | precision recall f1-score support
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+
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+ LOC 0.84 0.87 0.85 1668
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+ MISC 0.55 0.64 0.59 702
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+ ORG 0.66 0.69 0.68 1661
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+ PER 0.84 0.89 0.87 1617
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+
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+ micro avg 0.75 0.79 0.77 5648
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+ macro avg 0.72 0.77 0.75 5648
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+ weighted avg 0.75 0.79 0.77 5648
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+ |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.30.2
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+ - Pytorch 2.0.0
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+ - Datasets 2.1.0
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+ - Tokenizers 0.13.3