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