update model card README.md

README.md

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+---
+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           |
+
+
+### Framework versions
+
+- Transformers 4.25.1
+- Pytorch 1.13.1+cu117
+- Datasets 2.9.0
+- Tokenizers 0.13.2