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--- |
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language: el |
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license: gpl-3.0 |
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tags: |
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- generated_from_trainer |
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- roberta |
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- Greek |
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- ner |
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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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model-index: |
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- name: roberta-el-ner4 |
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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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# roberta-el-ner4 |
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This model is a fine-tuned version of [cvcio/roberta-el-news](https://huggingface.co/cvcio/roberta-el-news) on the [elNER](https://github.com/nmpartzio/elNER) dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0564 |
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- Precision: 0.9116 |
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- Recall: 0.9218 |
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- F1: 0.9167 |
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- Accuracy: 0.9883 |
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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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More information needed |
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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: 32 |
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- eval_batch_size: 32 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 128 |
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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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- lr_scheduler_warmup_ratio: 0.1 |
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- num_epochs: 60.0 |
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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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| 0.1723 | 1.87 | 250 | 0.0716 | 0.7937 | 0.8657 | 0.8281 | 0.9786 | |
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| 0.0372 | 3.73 | 500 | 0.0497 | 0.8587 | 0.9215 | 0.8890 | 0.9852 | |
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| 0.0243 | 5.6 | 750 | 0.0524 | 0.8746 | 0.9263 | 0.8997 | 0.9867 | |
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| 0.0114 | 7.46 | 1000 | 0.0564 | 0.9116 | 0.9218 | 0.9167 | 0.9883 | |
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| 0.0071 | 9.33 | 1250 | 0.0602 | 0.9019 | 0.9309 | 0.9161 | 0.9881 | |
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| 0.0037 | 11.19 | 1500 | 0.0667 | 0.9074 | 0.9306 | 0.9188 | 0.9885 | |
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| 0.003 | 13.06 | 1750 | 0.0688 | 0.9001 | 0.9306 | 0.9151 | 0.9883 | |
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| 0.0019 | 14.93 | 2000 | 0.0724 | 0.9082 | 0.9229 | 0.9155 | 0.9883 | |
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| 0.0022 | 16.79 | 2250 | 0.0745 | 0.9159 | 0.9169 | 0.9164 | 0.9878 | |
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| 0.0016 | 18.66 | 2500 | 0.0727 | 0.9068 | 0.9249 | 0.9158 | 0.9880 | |
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| 0.0018 | 20.52 | 2750 | 0.0732 | 0.9088 | 0.9272 | 0.9179 | 0.9887 | |
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| 0.0014 | 22.39 | 3000 | 0.0767 | 0.9017 | 0.9243 | 0.9129 | 0.9876 | |
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| 0.0012 | 24.25 | 3250 | 0.0745 | 0.9072 | 0.9206 | 0.9139 | 0.9882 | |
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| 0.0011 | 26.12 | 3500 | 0.0790 | 0.8995 | 0.9297 | 0.9144 | 0.9878 | |
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| 0.0008 | 27.99 | 3750 | 0.0786 | 0.9081 | 0.9275 | 0.9177 | 0.9883 | |
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| 0.0011 | 29.85 | 4000 | 0.0775 | 0.9091 | 0.9277 | 0.9183 | 0.9885 | |
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| 0.0011 | 31.72 | 4250 | 0.0851 | 0.9005 | 0.9269 | 0.9135 | 0.9879 | |
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| 0.0007 | 33.58 | 4500 | 0.0848 | 0.9041 | 0.9223 | 0.9131 | 0.9876 | |
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| 0.0006 | 35.45 | 4750 | 0.0842 | 0.9082 | 0.9263 | 0.9172 | 0.9881 | |
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| 0.0005 | 37.31 | 5000 | 0.0851 | 0.9085 | 0.9266 | 0.9175 | 0.9881 | |
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| 0.0004 | 39.18 | 5250 | 0.0878 | 0.9035 | 0.9272 | 0.9152 | 0.9879 | |
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| 0.0004 | 41.04 | 5500 | 0.0856 | 0.9091 | 0.9275 | 0.9182 | 0.9885 | |
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| 0.0004 | 42.91 | 5750 | 0.0870 | 0.9099 | 0.9255 | 0.9176 | 0.9884 | |
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| 0.0005 | 44.78 | 6000 | 0.0860 | 0.9010 | 0.9269 | 0.9138 | 0.9882 | |
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| 0.0004 | 46.64 | 6250 | 0.0851 | 0.9114 | 0.9246 | 0.9179 | 0.9884 | |
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| 0.0003 | 48.51 | 6500 | 0.0899 | 0.9058 | 0.9252 | 0.9154 | 0.9884 | |
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| 0.0002 | 50.37 | 6750 | 0.0898 | 0.9050 | 0.9294 | 0.9171 | 0.9882 | |
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| 0.0002 | 52.24 | 7000 | 0.0890 | 0.9104 | 0.9252 | 0.9177 | 0.9884 | |
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| 0.0002 | 54.1 | 7250 | 0.0898 | 0.9052 | 0.9260 | 0.9155 | 0.9879 | |
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| 0.0002 | 55.97 | 7500 | 0.0894 | 0.9080 | 0.9263 | 0.9171 | 0.9883 | |
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| 0.0001 | 57.84 | 7750 | 0.0910 | 0.9046 | 0.9277 | 0.9160 | 0.9883 | |
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| 0.0003 | 59.7 | 8000 | 0.0903 | 0.9041 | 0.9283 | 0.9161 | 0.9882 | |
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### Eval results |
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| | Precision | Recall | F1 | Accuracy | |
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|:----:|:---------:|:------:|:------:|:--------:| |
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| eval | 0.9116 | 0.9218 | 0.9167 | 0.9883 | |
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| test | 0.9022 | 0.9107 | 0.9064 | 0.9861 | |
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### Framework versions |
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- Transformers 4.29.2 |
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- Pytorch 1.13.1+cu117 |
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- Datasets 2.9.0 |
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- Tokenizers 0.13.2 |
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## Authors |
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Dimitris Papaevagelou - [@andefined](https://huggingface.co/andefined) |
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## About Us |
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[Civic Information Office](https://cvcio.org/) is a Non Profit Organization based in Athens, Greece focusing on creating technology and research products for the public interest. |
