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update model card README.md
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README.md
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---
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tags:
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- generated_from_trainer
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model-index:
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- name: diff_based_error_tagger
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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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# diff_based_error_tagger
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This model is a fine-tuned version of [csebuetnlp/banglabert](https://huggingface.co/csebuetnlp/banglabert) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.0013
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- 5 Err Precision: 1.0
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- 5 Err Recall: 0.9706
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- 5 Err F1: 0.9851
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- 5 Err Number: 34
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- Precision: 0.9915
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- Recall: 0.9910
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- F1: 0.9913
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- Number: 9934
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- Err Precision: 0.9724
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- Err Recall: 0.9895
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- Err F1: 0.9809
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- Err Number: 285
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- Egin Err Precision: 0.9903
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- Egin Err Recall: 0.9964
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- Egin Err F1: 0.9934
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- Egin Err Number: 1126
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- El Err Precision: 0.9942
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- El Err Recall: 0.9971
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- El Err F1: 0.9957
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- El Err Number: 1380
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- Nd Err Precision: 0.9932
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- Nd Err Recall: 0.9907
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- Nd Err F1: 0.9920
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- Nd Err Number: 1188
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- Ne Word Err Precision: 0.9971
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- Ne Word Err Recall: 0.9947
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- Ne Word Err F1: 0.9959
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- Ne Word Err Number: 8247
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- Unc Insert Err Precision: 0.9956
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- Unc Insert Err Recall: 0.9967
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- Unc Insert Err F1: 0.9961
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- Unc Insert Err Number: 902
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- Micro Avg Precision: 0.9936
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- Micro Avg Recall: 0.9931
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- Micro Avg F1: 0.9934
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- Micro Avg Number: 23096
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- Macro Avg Precision: 0.9918
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- Macro Avg Recall: 0.9908
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- Macro Avg F1: 0.9913
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- Macro Avg Number: 23096
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- Weighted Avg Precision: 0.9936
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- Weighted Avg Recall: 0.9931
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- Weighted Avg F1: 0.9934
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- Weighted Avg Number: 23096
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- Overall Accuracy: 0.9994
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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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- gradient_accumulation_steps: 2
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- total_train_batch_size: 32
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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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- lr_scheduler_warmup_ratio: 0.1
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- num_epochs: 30.0
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | 5 Err Precision | 5 Err Recall | 5 Err F1 | 5 Err Number | Precision | Recall | F1 | Number | Err Precision | Err Recall | Err F1 | Err Number | Egin Err Precision | Egin Err Recall | Egin Err F1 | Egin Err Number | El Err Precision | El Err Recall | El Err F1 | El Err Number | Nd Err Precision | Nd Err Recall | Nd Err F1 | Nd Err Number | Ne Word Err Precision | Ne Word Err Recall | Ne Word Err F1 | Ne Word Err Number | Unc Insert Err Precision | Unc Insert Err Recall | Unc Insert Err F1 | Unc Insert Err Number | Micro Avg Precision | Micro Avg Recall | Micro Avg F1 | Micro Avg Number | Macro Avg Precision | Macro Avg Recall | Macro Avg F1 | Macro Avg Number | Weighted Avg Precision | Weighted Avg Recall | Weighted Avg F1 | Weighted Avg Number | Overall Accuracy |
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|:-------------:|:-----:|:-----:|:---------------:|:---------------:|:------------:|:--------:|:------------:|:-----------:|:--------:|:------:|:--------:|:--------------:|:-----------:|:-------:|:-----------:|:------------------:|:---------------:|:-----------:|:---------------:|:----------------:|:-------------:|:---------:|:-------------:|:----------------:|:-------------:|:---------:|:-------------:|:---------------------:|:------------------:|:--------------:|:------------------:|:------------------------:|:---------------------:|:-----------------:|:---------------------:|:-------------------:|:----------------:|:------------:|:----------------:|:-------------------:|:----------------:|:------------:|:----------------:|:----------------------:|:-------------------:|:---------------:|:-------------------:|:----------------:|
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| 0.7818 | 1.0 | 575 | 0.2832 | 0.0 | 0.0 | 0.0 | 34 | 0.2682 | 0.1068 | 0.1528 | 9934 | 0.0 | 0.0 | 0.0 | 285 | 0.0 | 0.0 | 0.0 | 1126 | 0.0 | 0.0 | 0.0 | 1380 | 0.0 | 0.0 | 0.0 | 1188 | 0.6340 | 0.3370 | 0.4401 | 8247 | 0.0 | 0.0 | 0.0 | 902 | 0.4605 | 0.1663 | 0.2443 | 23096 | 0.1128 | 0.0555 | 0.0741 | 23096 | 0.3418 | 0.1663 | 0.2228 | 23096 | 0.9313 |
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| 0.2475 | 2.0 | 1150 | 0.1883 | 0.0 | 0.0 | 0.0 | 34 | 0.3734 | 0.2400 | 0.2922 | 9934 | 0.0 | 0.0 | 0.0 | 285 | 0.7004 | 0.3011 | 0.4211 | 1126 | 0.875 | 0.1370 | 0.2368 | 1380 | 0.6521 | 0.4007 | 0.4964 | 1188 | 0.7050 | 0.5472 | 0.6162 | 8247 | 1.0 | 0.0011 | 0.0022 | 902 | 0.5558 | 0.3421 | 0.4236 | 23096 | 0.5382 | 0.2034 | 0.2581 | 23096 | 0.5714 | 0.3421 | 0.4060 | 23096 | 0.9455 |
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| 0.1897 | 3.0 | 1725 | 0.1407 | 0.0 | 0.0 | 0.0 | 34 | 0.4878 | 0.3798 | 0.4271 | 9934 | 0.0 | 0.0 | 0.0 | 285 | 0.6812 | 0.6377 | 0.6587 | 1126 | 0.8474 | 0.2616 | 0.3998 | 1380 | 0.6642 | 0.6145 | 0.6384 | 1188 | 0.7622 | 0.6830 | 0.7205 | 8247 | 0.5556 | 0.2106 | 0.3055 | 902 | 0.6320 | 0.4938 | 0.5544 | 23096 | 0.4998 | 0.3484 | 0.3937 | 23096 | 0.6217 | 0.4938 | 0.5417 | 23096 | 0.9567 |
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| 0.1506 | 4.0 | 2300 | 0.1015 | 0.0 | 0.0 | 0.0 | 34 | 0.6286 | 0.5423 | 0.5823 | 9934 | 0.8421 | 0.0561 | 0.1053 | 285 | 0.8435 | 0.7371 | 0.7867 | 1126 | 0.8640 | 0.4420 | 0.5849 | 1380 | 0.8267 | 0.7306 | 0.7757 | 1188 | 0.8228 | 0.8070 | 0.8148 | 8247 | 0.7046 | 0.2539 | 0.3733 | 902 | 0.7393 | 0.6319 | 0.6814 | 23096 | 0.6915 | 0.4461 | 0.5029 | 23096 | 0.7374 | 0.6319 | 0.6705 | 23096 | 0.9701 |
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| 0.1156 | 5.0 | 2875 | 0.0717 | 0.0 | 0.0 | 0.0 | 34 | 0.7391 | 0.6964 | 0.7171 | 9934 | 0.7792 | 0.2105 | 0.3315 | 285 | 0.8678 | 0.8393 | 0.8533 | 1126 | 0.7723 | 0.7790 | 0.7756 | 1380 | 0.8691 | 0.8157 | 0.8415 | 1188 | 0.8878 | 0.8694 | 0.8785 | 8247 | 0.7042 | 0.4302 | 0.5341 | 902 | 0.8091 | 0.7588 | 0.7831 | 23096 | 0.7024 | 0.5800 | 0.6164 | 23096 | 0.8052 | 0.7588 | 0.7783 | 23096 | 0.9797 |
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| 0.0872 | 6.0 | 3450 | 0.0485 | 0.0 | 0.0 | 0.0 | 34 | 0.8417 | 0.8257 | 0.8336 | 9934 | 0.8644 | 0.3579 | 0.5062 | 285 | 0.9104 | 0.8757 | 0.8927 | 1126 | 0.8719 | 0.8435 | 0.8575 | 1380 | 0.8859 | 0.8434 | 0.8642 | 1188 | 0.9185 | 0.9351 | 0.9268 | 8247 | 0.7744 | 0.7384 | 0.7560 | 902 | 0.8750 | 0.8588 | 0.8668 | 23096 | 0.7584 | 0.6775 | 0.7046 | 23096 | 0.8730 | 0.8588 | 0.8645 | 23096 | 0.9874 |
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| 0.0675 | 7.0 | 4025 | 0.0354 | 0.0 | 0.0 | 0.0 | 34 | 0.9029 | 0.8865 | 0.8946 | 9934 | 0.8846 | 0.4842 | 0.6259 | 285 | 0.8819 | 0.9023 | 0.8920 | 1126 | 0.9421 | 0.8601 | 0.8992 | 1380 | 0.8854 | 0.8586 | 0.8718 | 1188 | 0.9518 | 0.9523 | 0.9521 | 8247 | 0.8220 | 0.8603 | 0.8407 | 902 | 0.9174 | 0.9005 | 0.9089 | 23096 | 0.7838 | 0.7255 | 0.7470 | 23096 | 0.9161 | 0.9005 | 0.9074 | 23096 | 0.9914 |
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| 0.0532 | 8.0 | 4600 | 0.0257 | 0.0 | 0.0 | 0.0 | 34 | 0.9338 | 0.9137 | 0.9237 | 9934 | 0.8514 | 0.6632 | 0.7456 | 285 | 0.9321 | 0.9272 | 0.9297 | 1126 | 0.9607 | 0.8681 | 0.9121 | 1380 | 0.9100 | 0.8847 | 0.8971 | 1188 | 0.9697 | 0.9664 | 0.9681 | 8247 | 0.8899 | 0.9047 | 0.8972 | 902 | 0.9445 | 0.9242 | 0.9342 | 23096 | 0.8059 | 0.7660 | 0.7842 | 23096 | 0.9428 | 0.9242 | 0.9332 | 23096 | 0.9938 |
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| 0.0418 | 9.0 | 5175 | 0.0194 | 0.0 | 0.0 | 0.0 | 34 | 0.9550 | 0.9472 | 0.9511 | 9934 | 0.8765 | 0.7719 | 0.8209 | 285 | 0.9262 | 0.9361 | 0.9311 | 1126 | 0.9503 | 0.9283 | 0.9391 | 1380 | 0.9075 | 0.9007 | 0.9041 | 1188 | 0.9783 | 0.9762 | 0.9772 | 8247 | 0.9124 | 0.9579 | 0.9346 | 902 | 0.9566 | 0.9503 | 0.9535 | 23096 | 0.8133 | 0.8023 | 0.8073 | 23096 | 0.9552 | 0.9503 | 0.9527 | 23096 | 0.9955 |
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| 0.0329 | 10.0 | 5750 | 0.0151 | 0.0 | 0.0 | 0.0 | 34 | 0.9629 | 0.9588 | 0.9609 | 9934 | 0.7925 | 0.8842 | 0.8358 | 285 | 0.9405 | 0.9538 | 0.9471 | 1126 | 0.9744 | 0.9362 | 0.9549 | 1380 | 0.9233 | 0.9217 | 0.9225 | 1188 | 0.9809 | 0.9784 | 0.9797 | 8247 | 0.9369 | 0.9712 | 0.9537 | 902 | 0.9634 | 0.9605 | 0.9619 | 23096 | 0.8139 | 0.8255 | 0.8193 | 23096 | 0.9623 | 0.9605 | 0.9613 | 23096 | 0.9963 |
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| 0.027 | 11.0 | 6325 | 0.0129 | 0.0 | 0.0 | 0.0 | 34 | 0.9672 | 0.9620 | 0.9646 | 9934 | 0.8838 | 0.8807 | 0.8822 | 285 | 0.9627 | 0.9627 | 0.9627 | 1126 | 0.9769 | 0.95 | 0.9633 | 1380 | 0.9347 | 0.9394 | 0.9370 | 1188 | 0.9895 | 0.9791 | 0.9843 | 8247 | 0.8993 | 0.9800 | 0.9379 | 902 | 0.9698 | 0.9646 | 0.9672 | 23096 | 0.8268 | 0.8318 | 0.8290 | 23096 | 0.9687 | 0.9646 | 0.9666 | 23096 | 0.9969 |
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| 106 |
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| 0.0228 | 12.0 | 6900 | 0.0097 | 1.0 | 0.0294 | 0.0571 | 34 | 0.9776 | 0.9719 | 0.9748 | 9934 | 0.9049 | 0.9018 | 0.9033 | 285 | 0.9455 | 0.9707 | 0.9579 | 1126 | 0.9786 | 0.9616 | 0.9700 | 1380 | 0.9354 | 0.9512 | 0.9432 | 1188 | 0.9917 | 0.9870 | 0.9894 | 8247 | 0.9674 | 0.9856 | 0.9764 | 902 | 0.9776 | 0.9738 | 0.9757 | 23096 | 0.9626 | 0.8449 | 0.8465 | 23096 | 0.9777 | 0.9738 | 0.9751 | 23096 | 0.9976 |
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| 107 |
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| 0.0187 | 13.0 | 7475 | 0.0079 | 1.0 | 0.0588 | 0.1111 | 34 | 0.9804 | 0.9741 | 0.9773 | 9934 | 0.9343 | 0.8982 | 0.9159 | 285 | 0.9700 | 0.9751 | 0.9725 | 1126 | 0.9860 | 0.9688 | 0.9773 | 1380 | 0.9573 | 0.9621 | 0.9597 | 1188 | 0.9920 | 0.9886 | 0.9903 | 8247 | 0.9727 | 0.9867 | 0.9796 | 902 | 0.9823 | 0.9766 | 0.9795 | 23096 | 0.9741 | 0.8516 | 0.8605 | 23096 | 0.9823 | 0.9766 | 0.9788 | 23096 | 0.9981 |
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| 108 |
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| 0.0164 | 14.0 | 8050 | 0.0063 | 1.0 | 0.1176 | 0.2105 | 34 | 0.9849 | 0.9802 | 0.9825 | 9934 | 0.9519 | 0.9018 | 0.9261 | 285 | 0.9683 | 0.9760 | 0.9721 | 1126 | 0.9818 | 0.9797 | 0.9808 | 1380 | 0.9583 | 0.9663 | 0.9623 | 1188 | 0.9944 | 0.9897 | 0.9920 | 8247 | 0.9823 | 0.9856 | 0.9840 | 902 | 0.9854 | 0.9806 | 0.9830 | 23096 | 0.9777 | 0.8621 | 0.8763 | 23096 | 0.9855 | 0.9806 | 0.9825 | 23096 | 0.9984 |
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| 0.014 | 15.0 | 8625 | 0.0052 | 1.0 | 0.2941 | 0.4545 | 34 | 0.9866 | 0.9840 | 0.9853 | 9934 | 0.8984 | 0.9614 | 0.9288 | 285 | 0.9727 | 0.9822 | 0.9775 | 1126 | 0.9840 | 0.9819 | 0.9830 | 1380 | 0.9698 | 0.9731 | 0.9714 | 1188 | 0.9951 | 0.9909 | 0.9930 | 8247 | 0.9933 | 0.9889 | 0.9911 | 902 | 0.9870 | 0.9846 | 0.9858 | 23096 | 0.9750 | 0.8946 | 0.9106 | 23096 | 0.9871 | 0.9846 | 0.9856 | 23096 | 0.9986 |
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| 110 |
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| 0.0123 | 16.0 | 9200 | 0.0042 | 0.9375 | 0.4412 | 0.6 | 34 | 0.9878 | 0.9868 | 0.9873 | 9934 | 0.9386 | 0.9649 | 0.9516 | 285 | 0.9762 | 0.9840 | 0.9801 | 1126 | 0.9870 | 0.9891 | 0.9881 | 1380 | 0.9715 | 0.9764 | 0.9740 | 1188 | 0.9949 | 0.9935 | 0.9942 | 8247 | 0.9857 | 0.9945 | 0.9901 | 902 | 0.9881 | 0.9879 | 0.9880 | 23096 | 0.9724 | 0.9163 | 0.9332 | 23096 | 0.9881 | 0.9879 | 0.9879 | 23096 | 0.9989 |
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| 111 |
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| 0.0105 | 17.0 | 9775 | 0.0036 | 0.9048 | 0.5588 | 0.6909 | 34 | 0.9886 | 0.9877 | 0.9882 | 9934 | 0.9488 | 0.9754 | 0.9619 | 285 | 0.9840 | 0.9822 | 0.9831 | 1126 | 0.9870 | 0.9935 | 0.9902 | 1380 | 0.9806 | 0.9798 | 0.9802 | 1188 | 0.9945 | 0.9949 | 0.9947 | 8247 | 0.9933 | 0.9922 | 0.9928 | 902 | 0.9896 | 0.9893 | 0.9895 | 23096 | 0.9727 | 0.9331 | 0.9478 | 23096 | 0.9896 | 0.9893 | 0.9894 | 23096 | 0.9990 |
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| 112 |
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| 0.0093 | 18.0 | 10350 | 0.0032 | 1.0 | 0.5588 | 0.7170 | 34 | 0.9895 | 0.9900 | 0.9898 | 9934 | 0.9619 | 0.9754 | 0.9686 | 285 | 0.9798 | 0.9902 | 0.9850 | 1126 | 0.9913 | 0.9949 | 0.9931 | 1380 | 0.9759 | 0.9865 | 0.9812 | 1188 | 0.9949 | 0.9954 | 0.9952 | 8247 | 0.9933 | 0.9922 | 0.9928 | 902 | 0.9902 | 0.9913 | 0.9908 | 23096 | 0.9858 | 0.9355 | 0.9528 | 23096 | 0.9902 | 0.9913 | 0.9907 | 23096 | 0.9991 |
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| 113 |
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| 0.0077 | 19.0 | 10925 | 0.0027 | 0.96 | 0.7059 | 0.8136 | 34 | 0.9903 | 0.9898 | 0.9901 | 9934 | 0.9589 | 0.9825 | 0.9705 | 285 | 0.9894 | 0.9902 | 0.9898 | 1126 | 0.9928 | 0.9942 | 0.9935 | 1380 | 0.9841 | 0.9907 | 0.9874 | 1188 | 0.9956 | 0.9945 | 0.9951 | 8247 | 0.9901 | 0.9967 | 0.9934 | 902 | 0.9916 | 0.9916 | 0.9916 | 23096 | 0.9826 | 0.9556 | 0.9667 | 23096 | 0.9916 | 0.9916 | 0.9915 | 23096 | 0.9992 |
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| 114 |
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| 0.0072 | 20.0 | 11500 | 0.0024 | 1.0 | 0.7353 | 0.8475 | 34 | 0.9915 | 0.9903 | 0.9909 | 9934 | 0.9688 | 0.9789 | 0.9738 | 285 | 0.9833 | 0.9911 | 0.9872 | 1126 | 0.9921 | 0.9964 | 0.9942 | 1380 | 0.9874 | 0.9933 | 0.9903 | 1188 | 0.9960 | 0.9949 | 0.9955 | 8247 | 0.9967 | 0.9922 | 0.9944 | 902 | 0.9925 | 0.9921 | 0.9923 | 23096 | 0.9895 | 0.9591 | 0.9717 | 23096 | 0.9925 | 0.9921 | 0.9922 | 23096 | 0.9993 |
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| 115 |
+
| 0.0065 | 21.0 | 12075 | 0.0022 | 1.0 | 0.8529 | 0.9206 | 34 | 0.9908 | 0.9909 | 0.9909 | 9934 | 0.9756 | 0.9825 | 0.9790 | 285 | 0.9876 | 0.9938 | 0.9907 | 1126 | 0.9921 | 0.9949 | 0.9935 | 1380 | 0.9874 | 0.9933 | 0.9903 | 1188 | 0.9954 | 0.9954 | 0.9954 | 8247 | 0.9956 | 0.9933 | 0.9945 | 902 | 0.9922 | 0.9928 | 0.9925 | 23096 | 0.9906 | 0.9746 | 0.9819 | 23096 | 0.9922 | 0.9928 | 0.9925 | 23096 | 0.9993 |
|
| 116 |
+
| 0.0056 | 22.0 | 12650 | 0.0020 | 1.0 | 0.8235 | 0.9032 | 34 | 0.9909 | 0.9904 | 0.9907 | 9934 | 0.9691 | 0.9895 | 0.9792 | 285 | 0.9894 | 0.9938 | 0.9916 | 1126 | 0.9921 | 0.9964 | 0.9942 | 1380 | 0.9907 | 0.9899 | 0.9903 | 1188 | 0.9967 | 0.9947 | 0.9957 | 8247 | 0.9923 | 0.9956 | 0.9939 | 902 | 0.9928 | 0.9924 | 0.9926 | 23096 | 0.9901 | 0.9717 | 0.9798 | 23096 | 0.9928 | 0.9924 | 0.9926 | 23096 | 0.9993 |
|
| 117 |
+
| 0.005 | 23.0 | 13225 | 0.0018 | 1.0 | 0.8824 | 0.9375 | 34 | 0.9917 | 0.9911 | 0.9914 | 9934 | 0.9691 | 0.9895 | 0.9792 | 285 | 0.9894 | 0.9956 | 0.9925 | 1126 | 0.9914 | 0.9978 | 0.9946 | 1380 | 0.9899 | 0.9916 | 0.9907 | 1188 | 0.9976 | 0.9942 | 0.9959 | 8247 | 0.9934 | 0.9967 | 0.9950 | 902 | 0.9934 | 0.9929 | 0.9931 | 23096 | 0.9903 | 0.9798 | 0.9846 | 23096 | 0.9934 | 0.9929 | 0.9931 | 23096 | 0.9994 |
|
| 118 |
+
| 0.0044 | 24.0 | 13800 | 0.0017 | 1.0 | 0.9118 | 0.9538 | 34 | 0.9910 | 0.9915 | 0.9913 | 9934 | 0.9790 | 0.9825 | 0.9807 | 285 | 0.9894 | 0.9956 | 0.9925 | 1126 | 0.9921 | 0.9986 | 0.9953 | 1380 | 0.9908 | 0.9933 | 0.9920 | 1188 | 0.9960 | 0.9955 | 0.9958 | 8247 | 0.9945 | 0.9956 | 0.9950 | 902 | 0.9928 | 0.9936 | 0.9932 | 23096 | 0.9916 | 0.9830 | 0.9871 | 23096 | 0.9928 | 0.9936 | 0.9932 | 23096 | 0.9994 |
|
| 119 |
+
| 0.0042 | 25.0 | 14375 | 0.0016 | 1.0 | 0.9118 | 0.9538 | 34 | 0.9914 | 0.9910 | 0.9912 | 9934 | 0.9659 | 0.9930 | 0.9792 | 285 | 0.9911 | 0.9929 | 0.9920 | 1126 | 0.9957 | 0.9957 | 0.9957 | 1380 | 0.9933 | 0.9916 | 0.9924 | 1188 | 0.9968 | 0.9949 | 0.9959 | 8247 | 0.9956 | 0.9956 | 0.9956 | 902 | 0.9935 | 0.9929 | 0.9932 | 23096 | 0.9912 | 0.9833 | 0.9870 | 23096 | 0.9936 | 0.9929 | 0.9932 | 23096 | 0.9994 |
|
| 120 |
+
| 0.0037 | 26.0 | 14950 | 0.0014 | 1.0 | 0.9412 | 0.9697 | 34 | 0.9906 | 0.9920 | 0.9913 | 9934 | 0.9724 | 0.9895 | 0.9809 | 285 | 0.9903 | 0.9947 | 0.9925 | 1126 | 0.9935 | 0.9978 | 0.9957 | 1380 | 0.9932 | 0.9899 | 0.9916 | 1188 | 0.9958 | 0.9959 | 0.9958 | 8247 | 0.9934 | 0.9978 | 0.9956 | 902 | 0.9926 | 0.9939 | 0.9932 | 23096 | 0.9911 | 0.9873 | 0.9891 | 23096 | 0.9926 | 0.9939 | 0.9933 | 23096 | 0.9994 |
|
| 121 |
+
| 0.0034 | 27.0 | 15525 | 0.0014 | 1.0 | 0.9412 | 0.9697 | 34 | 0.9915 | 0.9907 | 0.9911 | 9934 | 0.9724 | 0.9895 | 0.9809 | 285 | 0.9911 | 0.9938 | 0.9925 | 1126 | 0.9957 | 0.9957 | 0.9957 | 1380 | 0.9916 | 0.9916 | 0.9916 | 1188 | 0.9965 | 0.9950 | 0.9958 | 8247 | 0.9945 | 0.9967 | 0.9956 | 902 | 0.9934 | 0.9929 | 0.9932 | 23096 | 0.9917 | 0.9868 | 0.9891 | 23096 | 0.9934 | 0.9929 | 0.9932 | 23096 | 0.9994 |
|
| 122 |
+
| 0.0034 | 28.0 | 16100 | 0.0014 | 1.0 | 0.9412 | 0.9697 | 34 | 0.9910 | 0.9915 | 0.9913 | 9934 | 0.9724 | 0.9895 | 0.9809 | 285 | 0.9903 | 0.9947 | 0.9925 | 1126 | 0.9942 | 0.9971 | 0.9957 | 1380 | 0.9924 | 0.9924 | 0.9924 | 1188 | 0.9964 | 0.9951 | 0.9958 | 8247 | 0.9945 | 0.9967 | 0.9956 | 902 | 0.9931 | 0.9935 | 0.9933 | 23096 | 0.9914 | 0.9873 | 0.9892 | 23096 | 0.9931 | 0.9935 | 0.9933 | 23096 | 0.9994 |
|
| 123 |
+
| 0.0032 | 29.0 | 16675 | 0.0013 | 1.0 | 0.9706 | 0.9851 | 34 | 0.9913 | 0.9914 | 0.9914 | 9934 | 0.9757 | 0.9860 | 0.9808 | 285 | 0.9903 | 0.9964 | 0.9934 | 1126 | 0.9942 | 0.9971 | 0.9957 | 1380 | 0.9941 | 0.9916 | 0.9928 | 1188 | 0.9962 | 0.9953 | 0.9958 | 8247 | 0.9967 | 0.9956 | 0.9961 | 902 | 0.9934 | 0.9935 | 0.9934 | 23096 | 0.9923 | 0.9905 | 0.9914 | 23096 | 0.9934 | 0.9935 | 0.9934 | 23096 | 0.9994 |
|
| 124 |
+
| 0.003 | 30.0 | 17250 | 0.0013 | 1.0 | 0.9706 | 0.9851 | 34 | 0.9915 | 0.9910 | 0.9913 | 9934 | 0.9724 | 0.9895 | 0.9809 | 285 | 0.9903 | 0.9964 | 0.9934 | 1126 | 0.9942 | 0.9971 | 0.9957 | 1380 | 0.9932 | 0.9907 | 0.9920 | 1188 | 0.9971 | 0.9947 | 0.9959 | 8247 | 0.9956 | 0.9967 | 0.9961 | 902 | 0.9936 | 0.9931 | 0.9934 | 23096 | 0.9918 | 0.9908 | 0.9913 | 23096 | 0.9936 | 0.9931 | 0.9934 | 23096 | 0.9994 |
|
| 125 |
+
|
| 126 |
+
|
| 127 |
+
### Framework versions
|
| 128 |
+
|
| 129 |
+
- Transformers 4.25.1
|
| 130 |
+
- Pytorch 1.13.1+cu117
|
| 131 |
+
- Datasets 2.9.0
|
| 132 |
+
- Tokenizers 0.13.2
|