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--- |
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license: mit |
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tags: |
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- generated_from_trainer |
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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: balanced-augmented-roberta-gest-pred-seqeval-partialmatch-2 |
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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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# balanced-augmented-roberta-gest-pred-seqeval-partialmatch-2 |
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This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.5211 |
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- Precision: 0.9245 |
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- Recall: 0.9214 |
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- F1: 0.9159 |
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- Accuracy: 0.9027 |
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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: 2e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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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: 20 |
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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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| 3.0873 | 1.0 | 52 | 2.5606 | 0.1508 | 0.1205 | 0.1095 | 0.3096 | |
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| 2.2599 | 2.0 | 104 | 1.8545 | 0.3409 | 0.3827 | 0.3343 | 0.5265 | |
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| 1.7149 | 3.0 | 156 | 1.4711 | 0.5470 | 0.5222 | 0.4715 | 0.6087 | |
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| 1.3056 | 4.0 | 208 | 1.0879 | 0.6500 | 0.6103 | 0.5886 | 0.6919 | |
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| 0.9978 | 5.0 | 260 | 1.0036 | 0.7039 | 0.6766 | 0.6497 | 0.7221 | |
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| 0.7532 | 6.0 | 312 | 0.7722 | 0.7356 | 0.7552 | 0.7286 | 0.7842 | |
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| 0.5945 | 7.0 | 364 | 0.6766 | 0.8316 | 0.7902 | 0.7790 | 0.8053 | |
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| 0.473 | 8.0 | 416 | 0.5994 | 0.8602 | 0.8248 | 0.8224 | 0.8406 | |
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| 0.3762 | 9.0 | 468 | 0.5572 | 0.8725 | 0.8743 | 0.8600 | 0.8593 | |
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| 0.2943 | 10.0 | 520 | 0.5767 | 0.8893 | 0.8714 | 0.8659 | 0.8593 | |
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| 0.251 | 11.0 | 572 | 0.5480 | 0.8892 | 0.8765 | 0.8667 | 0.8633 | |
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| 0.2074 | 12.0 | 624 | 0.5652 | 0.8960 | 0.8866 | 0.8757 | 0.8714 | |
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| 0.1714 | 13.0 | 676 | 0.5254 | 0.9172 | 0.9087 | 0.9019 | 0.8875 | |
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| 0.1523 | 14.0 | 728 | 0.5788 | 0.9217 | 0.8900 | 0.8918 | 0.8790 | |
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| 0.1309 | 15.0 | 780 | 0.5209 | 0.9205 | 0.9141 | 0.9080 | 0.8961 | |
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| 0.1187 | 16.0 | 832 | 0.5030 | 0.9163 | 0.9138 | 0.9073 | 0.8961 | |
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| 0.1065 | 17.0 | 884 | 0.5449 | 0.9278 | 0.9212 | 0.9153 | 0.8986 | |
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| 0.0923 | 18.0 | 936 | 0.4965 | 0.9214 | 0.9180 | 0.9135 | 0.9012 | |
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| 0.0894 | 19.0 | 988 | 0.5171 | 0.9236 | 0.9189 | 0.9148 | 0.9007 | |
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| 0.0869 | 20.0 | 1040 | 0.5211 | 0.9245 | 0.9214 | 0.9159 | 0.9027 | |
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### Framework versions |
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- Transformers 4.27.4 |
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- Pytorch 1.13.1+cu116 |
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- Datasets 2.11.0 |
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- Tokenizers 0.13.2 |
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