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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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