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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: output_diff_approach |
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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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# output_diff_approach |
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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: 0.1603 |
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- 5 Err Precision: 0.0 |
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- 5 Err Recall: 0.0 |
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- 5 Err F1: 0.0 |
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- 5 Err Number: 34 |
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- Precision: 0.4328 |
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- Recall: 0.3244 |
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- F1: 0.3709 |
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- Number: 9934 |
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- Err Precision: 0.0 |
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- Err Recall: 0.0 |
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- Err F1: 0.0 |
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- Err Number: 285 |
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- Egin Err Precision: 0.7528 |
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- Egin Err Recall: 0.4192 |
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- Egin Err F1: 0.5385 |
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- Egin Err Number: 1126 |
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- El Err Precision: 0.7112 |
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- El Err Recall: 0.2891 |
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- El Err F1: 0.4111 |
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- El Err Number: 1380 |
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- Nd Err Precision: 0.6986 |
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- Nd Err Recall: 0.4487 |
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- Nd Err F1: 0.5464 |
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- Nd Err Number: 1188 |
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- Ne Word Err Precision: 0.7223 |
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- Ne Word Err Recall: 0.6297 |
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- Ne Word Err F1: 0.6728 |
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- Ne Word Err Number: 8247 |
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- Unc Insert Err Precision: 0.6140 |
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- Unc Insert Err Recall: 0.0776 |
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- Unc Insert Err F1: 0.1378 |
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- Unc Insert Err Number: 902 |
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- Micro Avg Precision: 0.5922 |
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- Micro Avg Recall: 0.4282 |
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- Micro Avg F1: 0.4970 |
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- Micro Avg Number: 23096 |
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- Macro Avg Precision: 0.4915 |
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- Macro Avg Recall: 0.2736 |
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- Macro Avg F1: 0.3347 |
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- Macro Avg Number: 23096 |
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- Weighted Avg Precision: 0.5832 |
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- Weighted Avg Recall: 0.4282 |
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- Weighted Avg F1: 0.4841 |
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- Weighted Avg Number: 23096 |
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- Overall Accuracy: 0.9514 |
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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: 2.0 |
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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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| 0.3987 | 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 | |
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| 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 |
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- Transformers 4.25.1 |
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- Pytorch 1.13.1+cu117 |
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- Datasets 2.9.0 |
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- Tokenizers 0.13.2 |
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