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--- |
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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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- accuracy |
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base_model: alexaapo/greek_legal_bert_v2 |
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model-index: |
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- name: greek_legal_bert_v2-finetuned-ner-V3 |
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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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# greek_legal_bert_v2-finetuned-ner-V3 |
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This model is a fine-tuned version of [alexaapo/greek_legal_bert_v2](https://huggingface.co/alexaapo/greek_legal_bert_v2) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0907 |
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- Precision: 0.9023 |
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- Recall: 0.9265 |
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- F1: 0.9142 |
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- Accuracy: 0.9828 |
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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: 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: 10 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
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| No log | 1.19 | 25 | 0.0661 | 0.8895 | 0.9229 | 0.9059 | 0.9813 | |
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| No log | 2.38 | 50 | 0.0820 | 0.9091 | 0.9319 | 0.9204 | 0.9838 | |
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| No log | 3.57 | 75 | 0.0791 | 0.8924 | 0.9211 | 0.9065 | 0.9825 | |
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| No log | 4.76 | 100 | 0.0824 | 0.8950 | 0.9319 | 0.9131 | 0.9841 | |
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| No log | 5.95 | 125 | 0.0820 | 0.8830 | 0.9194 | 0.9008 | 0.9812 | |
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| No log | 7.14 | 150 | 0.0862 | 0.9059 | 0.9319 | 0.9187 | 0.9817 | |
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| No log | 8.33 | 175 | 0.0915 | 0.9021 | 0.9247 | 0.9133 | 0.9826 | |
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| No log | 9.52 | 200 | 0.0905 | 0.9023 | 0.9265 | 0.9142 | 0.9828 | |
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### Framework versions |
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- Transformers 4.23.0 |
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- Pytorch 1.12.1+cu113 |
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- Datasets 2.5.2 |
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- Tokenizers 0.13.1 |
