| --- |
| tags: |
| - generated_from_trainer |
| model-index: |
| - name: output_diff_approach |
| 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. --> |
|
|
| # output_diff_approach |
|
|
| This model is a fine-tuned version of [csebuetnlp/banglabert](https://huggingface.co/csebuetnlp/banglabert) on an unknown dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.1603 |
| - 5 Err Precision: 0.0 |
| - 5 Err Recall: 0.0 |
| - 5 Err F1: 0.0 |
| - 5 Err Number: 34 |
| - Precision: 0.4328 |
| - Recall: 0.3244 |
| - F1: 0.3709 |
| - Number: 9934 |
| - Err Precision: 0.0 |
| - Err Recall: 0.0 |
| - Err F1: 0.0 |
| - Err Number: 285 |
| - Egin Err Precision: 0.7528 |
| - Egin Err Recall: 0.4192 |
| - Egin Err F1: 0.5385 |
| - Egin Err Number: 1126 |
| - El Err Precision: 0.7112 |
| - El Err Recall: 0.2891 |
| - El Err F1: 0.4111 |
| - El Err Number: 1380 |
| - Nd Err Precision: 0.6986 |
| - Nd Err Recall: 0.4487 |
| - Nd Err F1: 0.5464 |
| - Nd Err Number: 1188 |
| - Ne Word Err Precision: 0.7223 |
| - Ne Word Err Recall: 0.6297 |
| - Ne Word Err F1: 0.6728 |
| - Ne Word Err Number: 8247 |
| - Unc Insert Err Precision: 0.6140 |
| - Unc Insert Err Recall: 0.0776 |
| - Unc Insert Err F1: 0.1378 |
| - Unc Insert Err Number: 902 |
| - Micro Avg Precision: 0.5922 |
| - Micro Avg Recall: 0.4282 |
| - Micro Avg F1: 0.4970 |
| - Micro Avg Number: 23096 |
| - Macro Avg Precision: 0.4915 |
| - Macro Avg Recall: 0.2736 |
| - Macro Avg F1: 0.3347 |
| - Macro Avg Number: 23096 |
| - Weighted Avg Precision: 0.5832 |
| - Weighted Avg Recall: 0.4282 |
| - Weighted Avg F1: 0.4841 |
| - Weighted Avg Number: 23096 |
| - Overall Accuracy: 0.9514 |
|
|
| ## 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: 2e-05 |
| - train_batch_size: 16 |
| - eval_batch_size: 16 |
| - seed: 42 |
| - gradient_accumulation_steps: 2 |
| - total_train_batch_size: 32 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: linear |
| - lr_scheduler_warmup_ratio: 0.1 |
| - num_epochs: 2.0 |
|
|
| ### Training results |
|
|
| | 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 | |
| |:-------------:|:-----:|:----:|:---------------:|:---------------:|:------------:|:--------:|:------------:|:-----------:|:--------:|:------:|:--------:|:--------------:|:-----------:|:-------:|:-----------:|:------------------:|:---------------:|:-----------:|:---------------:|:----------------:|:-------------:|:---------:|:-------------:|:----------------:|:-------------:|:---------:|:-------------:|:---------------------:|:------------------:|:--------------:|:------------------:|:------------------------:|:---------------------:|:-----------------:|:---------------------:|:-------------------:|:----------------:|:------------:|:----------------:|:-------------------:|:----------------:|:------------:|:----------------:|:----------------------:|:-------------------:|:---------------:|:-------------------:|:----------------:| |
| | 0.3987 | 1.0 | 575 | 0.1930 | 0.0 | 0.0 | 0.0 | 34 | 0.3517 | 0.1737 | 0.2326 | 9934 | 0.0 | 0.0 | 0.0 | 285 | 0.8127 | 0.2389 | 0.3693 | 1126 | 0.8345 | 0.1681 | 0.2799 | 1380 | 0.6727 | 0.3460 | 0.4569 | 1188 | 0.7470 | 0.4215 | 0.5389 | 8247 | 0.25 | 0.0011 | 0.0022 | 902 | 0.5670 | 0.2648 | 0.3610 | 23096 | 0.4586 | 0.1687 | 0.2350 | 23096 | 0.5519 | 0.2648 | 0.3508 | 23096 | 0.9422 | |
| | 0.1861 | 2.0 | 1150 | 0.1603 | 0.0 | 0.0 | 0.0 | 34 | 0.4328 | 0.3244 | 0.3709 | 9934 | 0.0 | 0.0 | 0.0 | 285 | 0.7528 | 0.4192 | 0.5385 | 1126 | 0.7112 | 0.2891 | 0.4111 | 1380 | 0.6986 | 0.4487 | 0.5464 | 1188 | 0.7223 | 0.6297 | 0.6728 | 8247 | 0.6140 | 0.0776 | 0.1378 | 902 | 0.5922 | 0.4282 | 0.4970 | 23096 | 0.4915 | 0.2736 | 0.3347 | 23096 | 0.5832 | 0.4282 | 0.4841 | 23096 | 0.9514 | |
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| ### Framework versions |
|
|
| - Transformers 4.25.1 |
| - Pytorch 1.13.1+cu117 |
| - Datasets 2.9.0 |
| - Tokenizers 0.13.2 |
|
|
