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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 |
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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 |
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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: 0.1837 |
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- Accuracy: 0.9548 |
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- Precision: 0.7491 |
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- Recall: 0.7882 |
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- F1: 0.7682 |
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- Classification Report: precision recall f1-score support |
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LOC 0.84 0.86 0.85 1668 |
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MISC 0.59 0.63 0.61 702 |
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ORG 0.64 0.67 0.66 1661 |
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PER 0.83 0.90 0.87 1617 |
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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 0.75 0.79 0.77 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: 3 |
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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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| 0.1443 | 1.0 | 434 | 0.1949 | 0.9462 | 0.6982 | 0.7466 | 0.7216 | precision recall f1-score support |
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LOC 0.83 0.80 0.81 1668 |
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MISC 0.59 0.62 0.60 702 |
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ORG 0.56 0.58 0.57 1661 |
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PER 0.75 0.92 0.83 1617 |
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micro avg 0.70 0.75 0.72 5648 |
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macro avg 0.68 0.73 0.70 5648 |
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weighted avg 0.70 0.75 0.72 5648 |
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| 0.071 | 2.0 | 868 | 0.1764 | 0.9551 | 0.7548 | 0.7702 | 0.7624 | precision recall f1-score support |
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LOC 0.81 0.88 0.84 1668 |
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MISC 0.59 0.64 0.61 702 |
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ORG 0.68 0.59 0.63 1661 |
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PER 0.83 0.90 0.86 1617 |
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micro avg 0.75 0.77 0.76 5648 |
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macro avg 0.73 0.75 0.74 5648 |
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weighted avg 0.75 0.77 0.76 5648 |
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| 0.0713 | 3.0 | 1302 | 0.1837 | 0.9548 | 0.7491 | 0.7882 | 0.7682 | precision recall f1-score support |
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LOC 0.84 0.86 0.85 1668 |
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MISC 0.59 0.63 0.61 702 |
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ORG 0.64 0.67 0.66 1661 |
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PER 0.83 0.90 0.87 1617 |
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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 0.75 0.79 0.77 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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