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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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- precision |
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- recall |
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- f1 |
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model-index: |
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- name: legal-base-bert-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-base-bert-NER |
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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 the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0195 |
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- Precision: 0.9678 |
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- Recall: 0.9748 |
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- F1: 0.9712 |
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- Classification Report: precision recall f1-score support |
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LOC 0.98 0.99 0.98 1837 |
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MISC 0.92 0.93 0.93 922 |
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ORG 0.96 0.97 0.96 1341 |
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PER 0.99 0.99 0.99 1842 |
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micro avg 0.97 0.97 0.97 5942 |
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macro avg 0.96 0.97 0.97 5942 |
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weighted avg 0.97 0.97 0.97 5942 |
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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: 5e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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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 | Precision | Recall | F1 | Classification Report | |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:| |
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| 0.0423 | 2.3 | 500 | 0.0195 | 0.9678 | 0.9748 | 0.9712 | precision recall f1-score support |
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LOC 0.98 0.99 0.98 1837 |
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MISC 0.92 0.93 0.93 922 |
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ORG 0.96 0.97 0.96 1341 |
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PER 0.99 0.99 0.99 1842 |
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micro avg 0.97 0.97 0.97 5942 |
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macro avg 0.96 0.97 0.97 5942 |
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weighted avg 0.97 0.97 0.97 5942 |
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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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