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output_diff_approach
This model is a fine-tuned version of 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 |
Framework versions
- Transformers 4.25.1
- Pytorch 1.13.1+cu117
- Datasets 2.9.0
- Tokenizers 0.13.2
