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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-ner18 |
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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: 0.1380 |
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- Precision: 0.9138 |
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- Recall: 0.9289 |
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- F1: 0.9213 |
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- Accuracy: 0.9832 |
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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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| 0.4245 | 1.87 | 250 | 0.1622 | 0.7727 | 0.8096 | 0.7907 | 0.9597 | |
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| 0.0798 | 3.73 | 500 | 0.0841 | 0.8587 | 0.9005 | 0.8791 | 0.9776 | |
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| 0.0487 | 5.6 | 750 | 0.0812 | 0.8850 | 0.9140 | 0.8992 | 0.9806 | |
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| 0.0222 | 7.46 | 1000 | 0.0855 | 0.9001 | 0.9180 | 0.9089 | 0.9819 | |
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| 0.0141 | 9.33 | 1250 | 0.0903 | 0.9023 | 0.9230 | 0.9125 | 0.9827 | |
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| 0.0079 | 11.19 | 1500 | 0.1006 | 0.9067 | 0.9258 | 0.9161 | 0.9823 | |
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| 0.0063 | 13.06 | 1750 | 0.1020 | 0.9049 | 0.9296 | 0.9171 | 0.9826 | |
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| 0.0039 | 14.93 | 2000 | 0.1097 | 0.9078 | 0.9246 | 0.9161 | 0.9820 | |
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| 0.004 | 16.79 | 2250 | 0.1119 | 0.9084 | 0.9239 | 0.9161 | 0.9825 | |
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| 0.0024 | 18.66 | 2500 | 0.1166 | 0.9086 | 0.9268 | 0.9177 | 0.9828 | |
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| 0.0029 | 20.52 | 2750 | 0.1192 | 0.9106 | 0.9260 | 0.9182 | 0.9825 | |
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| 0.0023 | 22.39 | 3000 | 0.1161 | 0.9085 | 0.9284 | 0.9183 | 0.9829 | |
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| 0.0022 | 24.25 | 3250 | 0.1238 | 0.9078 | 0.9281 | 0.9178 | 0.9825 | |
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| 0.0021 | 26.12 | 3500 | 0.1232 | 0.9082 | 0.9239 | 0.9160 | 0.9821 | |
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| 0.0013 | 27.99 | 3750 | 0.1253 | 0.9050 | 0.9296 | 0.9172 | 0.9824 | |
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| 0.0012 | 29.85 | 4000 | 0.1247 | 0.9075 | 0.9284 | 0.9179 | 0.9827 | |
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| 0.0014 | 31.72 | 4250 | 0.1263 | 0.9063 | 0.9237 | 0.9149 | 0.9823 | |
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| 0.0012 | 33.58 | 4500 | 0.1295 | 0.9028 | 0.9272 | 0.9148 | 0.9827 | |
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| 0.001 | 35.45 | 4750 | 0.1341 | 0.9107 | 0.9305 | 0.9205 | 0.9831 | |
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| 0.001 | 37.31 | 5000 | 0.1296 | 0.9122 | 0.9298 | 0.9209 | 0.9833 | |
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| 0.0013 | 39.18 | 5250 | 0.1273 | 0.9058 | 0.9249 | 0.9153 | 0.9823 | |
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| 0.0007 | 41.04 | 5500 | 0.1296 | 0.9053 | 0.9261 | 0.9156 | 0.9824 | |
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| 0.0007 | 42.91 | 5750 | 0.1326 | 0.9083 | 0.9303 | 0.9192 | 0.9830 | |
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| 0.0006 | 44.78 | 6000 | 0.1328 | 0.9088 | 0.9270 | 0.9178 | 0.9828 | |
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| 0.0006 | 46.64 | 6250 | 0.1362 | 0.9103 | 0.9314 | 0.9207 | 0.9831 | |
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| 0.0004 | 48.51 | 6500 | 0.1351 | 0.9132 | 0.9288 | 0.9209 | 0.9830 | |
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| 0.0005 | 50.37 | 6750 | 0.1325 | 0.9138 | 0.9270 | 0.9204 | 0.9830 | |
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| 0.0005 | 52.24 | 7000 | 0.1330 | 0.9115 | 0.9272 | 0.9193 | 0.9832 | |
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| 0.0005 | 54.1 | 7250 | 0.1356 | 0.9119 | 0.9270 | 0.9194 | 0.9833 | |
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| 0.0004 | 55.97 | 7500 | 0.1367 | 0.9132 | 0.9274 | 0.9202 | 0.9832 | |
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| 0.0003 | 57.84 | 7750 | 0.1380 | 0.9141 | 0.9288 | 0.9214 | 0.9832 | |
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| 0.0004 | 59.7 | 8000 | 0.1380 | 0.9138 | 0.9289 | 0.9213 | 0.9832 | |
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### Eval results |
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| | Precision | Recall | F1 | Accuracy | |
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|:----:|:---------:|:------:|:------:|:--------:| |
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| eval | 0.9138 | 0.9289 | 0.9213 | 0.9832 | |
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| test | 0.9097 | 0.9232 | 0.9164 | 0.9808 | |
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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. |
