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outputs
This model is a fine-tuned version of csebuetnlp/banglabert on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.2073
- 5 Err Precision: 0.0
- 5 Err Recall: 0.0
- 5 Err F1: 0.0
- 5 Err Number: 34
- Precision: 0.3586
- Recall: 0.2192
- F1: 0.2721
- Number: 9934
- Err Precision: 0.0
- Err Recall: 0.0
- Err F1: 0.0
- Err Number: 285
- Egin Err Precision: 0.9184
- Egin Err Recall: 0.0400
- Egin Err F1: 0.0766
- Egin Err Number: 1126
- El Err Precision: 0.8718
- El Err Recall: 0.1478
- El Err F1: 0.2528
- El Err Number: 1380
- Nd Err Precision: 0.7453
- Nd Err Recall: 0.1995
- Nd Err F1: 0.3147
- Nd Err Number: 1188
- Ne Word Err Precision: 0.6677
- Ne Word Err Recall: 0.5206
- Ne Word Err F1: 0.5850
- Ne Word Err Number: 8247
- Unc Insert Err Precision: 1.0
- Unc Insert Err Recall: 0.0011
- Unc Insert Err F1: 0.0022
- Unc Insert Err Number: 902
- Micro Avg Precision: 0.5309
- Micro Avg Recall: 0.3013
- Micro Avg F1: 0.3844
- Micro Avg Number: 23096
- Macro Avg Precision: 0.5702
- Macro Avg Recall: 0.1410
- Macro Avg F1: 0.1879
- Macro Avg Number: 23096
- Weighted Avg Precision: 0.5669
- Weighted Avg Recall: 0.3013
- Weighted Avg F1: 0.3611
- Weighted Avg Number: 23096
- Overall Accuracy: 0.9419
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: 1.0
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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.3677 | 1.0 | 575 | 0.2073 | 0.0 | 0.0 | 0.0 | 34 | 0.3586 | 0.2192 | 0.2721 | 9934 | 0.0 | 0.0 | 0.0 | 285 | 0.9184 | 0.0400 | 0.0766 | 1126 | 0.8718 | 0.1478 | 0.2528 | 1380 | 0.7453 | 0.1995 | 0.3147 | 1188 | 0.6677 | 0.5206 | 0.5850 | 8247 | 1.0 | 0.0011 | 0.0022 | 902 | 0.5309 | 0.3013 | 0.3844 | 23096 | 0.5702 | 0.1410 | 0.1879 | 23096 | 0.5669 | 0.3013 | 0.3611 | 23096 | 0.9419 |
Framework versions
- Transformers 4.25.1
- Pytorch 1.13.1+cu117
- Datasets 2.9.0
- Tokenizers 0.13.2
