plt5-seq-clf-with-entities-updated-finetuned
This model is a fine-tuned version of allegro/plt5-small on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.9863
- Accuracy: {'accuracy': 0.6016129032258064}
- Recall: {'recall': 0.6016129032258064}
- F1: {'f1': 0.6090459454706235}
- Precision: {'precision': 0.6487538544674235}
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5.6e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Recall | F1 | Precision |
|---|---|---|---|---|---|---|---|
| 1.7248 | 1.0 | 718 | 1.6722 | {'accuracy': 0.47258064516129034} | {'recall': 0.47258064516129034} | {'f1': 0.30332120269936047} | {'precision': 0.2233324661810614} |
| 1.6984 | 2.0 | 1436 | 1.6216 | {'accuracy': 0.47258064516129034} | {'recall': 0.47258064516129034} | {'f1': 0.30332120269936047} | {'precision': 0.2233324661810614} |
| 1.6839 | 3.0 | 2154 | 1.6344 | {'accuracy': 0.47258064516129034} | {'recall': 0.47258064516129034} | {'f1': 0.30332120269936047} | {'precision': 0.2233324661810614} |
| 1.6882 | 4.0 | 2872 | 1.6261 | {'accuracy': 0.47258064516129034} | {'recall': 0.47258064516129034} | {'f1': 0.30332120269936047} | {'precision': 0.2233324661810614} |
| 1.68 | 5.0 | 3590 | 1.6223 | {'accuracy': 0.47258064516129034} | {'recall': 0.47258064516129034} | {'f1': 0.30332120269936047} | {'precision': 0.2233324661810614} |
| 1.6777 | 6.0 | 4308 | 1.6521 | {'accuracy': 0.4854838709677419} | {'recall': 0.4854838709677419} | {'f1': 0.36285141031634727} | {'precision': 0.30095036265938396} |
| 1.6681 | 7.0 | 5026 | 1.6165 | {'accuracy': 0.47096774193548385} | {'recall': 0.47096774193548385} | {'f1': 0.3142909197822259} | {'precision': 0.27758817356390014} |
| 1.6585 | 8.0 | 5744 | 1.5583 | {'accuracy': 0.47580645161290325} | {'recall': 0.47580645161290325} | {'f1': 0.3179514906044033} | {'precision': 0.274054689168981} |
| 1.6399 | 9.0 | 6462 | 1.6084 | {'accuracy': 0.3564516129032258} | {'recall': 0.3564516129032258} | {'f1': 0.30977675417942074} | {'precision': 0.3086887092441926} |
| 1.6158 | 10.0 | 7180 | 1.6613 | {'accuracy': 0.3225806451612903} | {'recall': 0.3225806451612903} | {'f1': 0.2777093706693196} | {'precision': 0.5070305497722745} |
| 1.5835 | 11.0 | 7898 | 1.6525 | {'accuracy': 0.3370967741935484} | {'recall': 0.3370967741935484} | {'f1': 0.2946753320835634} | {'precision': 0.4987499213117131} |
| 1.5443 | 12.0 | 8616 | 1.5433 | {'accuracy': 0.39838709677419354} | {'recall': 0.39838709677419354} | {'f1': 0.37257538542456536} | {'precision': 0.5472359482869795} |
| 1.4792 | 13.0 | 9334 | 1.4685 | {'accuracy': 0.4290322580645161} | {'recall': 0.4290322580645161} | {'f1': 0.3843028777529311} | {'precision': 0.5497170294652844} |
| 1.419 | 14.0 | 10052 | 1.5534 | {'accuracy': 0.4032258064516129} | {'recall': 0.4032258064516129} | {'f1': 0.35189485350144095} | {'precision': 0.5701307405449848} |
| 1.3881 | 15.0 | 10770 | 1.3641 | {'accuracy': 0.4790322580645161} | {'recall': 0.4790322580645161} | {'f1': 0.4461803399889066} | {'precision': 0.5258731490942117} |
| 1.3582 | 16.0 | 11488 | 1.3837 | {'accuracy': 0.43870967741935485} | {'recall': 0.43870967741935485} | {'f1': 0.3975785817347331} | {'precision': 0.5481481481481482} |
| 1.3074 | 17.0 | 12206 | 1.2409 | {'accuracy': 0.5177419354838709} | {'recall': 0.5177419354838709} | {'f1': 0.49737440159156987} | {'precision': 0.5439755251062998} |
| 1.2529 | 18.0 | 12924 | 1.2490 | {'accuracy': 0.5241935483870968} | {'recall': 0.5241935483870968} | {'f1': 0.5075488601971412} | {'precision': 0.5801964826379877} |
| 1.2223 | 19.0 | 13642 | 1.1680 | {'accuracy': 0.5435483870967742} | {'recall': 0.5435483870967742} | {'f1': 0.5172098120467532} | {'precision': 0.5483692723442298} |
| 1.1881 | 20.0 | 14360 | 1.1325 | {'accuracy': 0.5467741935483871} | {'recall': 0.5467741935483871} | {'f1': 0.528976565119481} | {'precision': 0.5918362760770626} |
| 1.1524 | 21.0 | 15078 | 1.1075 | {'accuracy': 0.5338709677419354} | {'recall': 0.5338709677419354} | {'f1': 0.5363641334830415} | {'precision': 0.6113524377471905} |
| 1.1307 | 22.0 | 15796 | 1.0685 | {'accuracy': 0.5612903225806452} | {'recall': 0.5612903225806452} | {'f1': 0.567131293394492} | {'precision': 0.6230821316117012} |
| 1.1198 | 23.0 | 16514 | 1.0978 | {'accuracy': 0.5564516129032258} | {'recall': 0.5564516129032258} | {'f1': 0.5596055517552543} | {'precision': 0.6285694241881432} |
| 1.0856 | 24.0 | 17232 | 1.0779 | {'accuracy': 0.5532258064516129} | {'recall': 0.5532258064516129} | {'f1': 0.5591833153283243} | {'precision': 0.6338935526492327} |
| 1.0829 | 25.0 | 17950 | 1.0175 | {'accuracy': 0.5903225806451613} | {'recall': 0.5903225806451613} | {'f1': 0.5964860501094582} | {'precision': 0.6422535611112073} |
| 1.0613 | 26.0 | 18668 | 1.0426 | {'accuracy': 0.567741935483871} | {'recall': 0.567741935483871} | {'f1': 0.5748961882147833} | {'precision': 0.6378855920377489} |
| 1.0363 | 27.0 | 19386 | 0.9920 | {'accuracy': 0.5935483870967742} | {'recall': 0.5935483870967742} | {'f1': 0.6001368374403852} | {'precision': 0.6385480642288512} |
| 1.0412 | 28.0 | 20104 | 1.0210 | {'accuracy': 0.5758064516129032} | {'recall': 0.5758064516129032} | {'f1': 0.5836230006413563} | {'precision': 0.6487093843541626} |
| 1.0256 | 29.0 | 20822 | 0.9992 | {'accuracy': 0.5870967741935483} | {'recall': 0.5870967741935483} | {'f1': 0.5944960724933464} | {'precision': 0.6439234847872369} |
| 1.0354 | 30.0 | 21540 | 0.9863 | {'accuracy': 0.6016129032258064} | {'recall': 0.6016129032258064} | {'f1': 0.6090459454706235} | {'precision': 0.6487538544674235} |
Framework versions
- Transformers 4.32.0
- Pytorch 2.0.1+cu117
- Datasets 2.14.4
- Tokenizers 0.13.3
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