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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-bert-small-NER |
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results: [] |
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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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# legal-bert-small-NER |
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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: 0.2334 |
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- Accuracy: 0.9558 |
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- Precision: 0.7587 |
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- Recall: 0.7950 |
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- F1: 0.7764 |
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- Classification Report: precision recall f1-score support |
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LOC 0.85 0.86 0.86 1668 |
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MISC 0.56 0.67 0.61 702 |
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ORG 0.68 0.67 0.68 1661 |
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PER 0.83 0.91 0.87 1617 |
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micro avg 0.76 0.79 0.78 5648 |
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macro avg 0.73 0.78 0.75 5648 |
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weighted avg 0.76 0.79 0.78 5648 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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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: 2 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Classification Report | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:| |
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| 0.0289 | 1.0 | 434 | 0.2151 | 0.9555 | 0.7592 | 0.7890 | 0.7738 | precision recall f1-score support |
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LOC 0.86 0.85 0.86 1668 |
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MISC 0.57 0.67 0.62 702 |
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ORG 0.69 0.64 0.67 1661 |
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PER 0.81 0.92 0.86 1617 |
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micro avg 0.76 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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| 0.0193 | 2.0 | 868 | 0.2334 | 0.9558 | 0.7587 | 0.7950 | 0.7764 | precision recall f1-score support |
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LOC 0.85 0.86 0.86 1668 |
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MISC 0.56 0.67 0.61 702 |
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ORG 0.68 0.67 0.68 1661 |
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PER 0.83 0.91 0.87 1617 |
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micro avg 0.76 0.79 0.78 5648 |
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macro avg 0.73 0.78 0.75 5648 |
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weighted avg 0.76 0.79 0.78 5648 |
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### Framework versions |
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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 |
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