Model save

README.md

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+---
+license: cc-by-4.0
+base_model: allegro/plt5-small
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+- recall
+- f1
+- precision
+model-index:
+- name: plt5-seq-clf-with-entities-updated-finetuned
+  results: []
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# plt5-seq-clf-with-entities-updated-finetuned
+
+This model is a fine-tuned version of [allegro/plt5-small](https://huggingface.co/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