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