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# diff_based_error_tagger

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: 0.0009
- 5 Err Precision: 0.9667
- 5 Err Recall: 1.0
- 5 Err F1: 0.9831
- 5 Err Number: 29
-   Precision: 0.9922
-   Recall: 0.9916
-   F1: 0.9919
-   Number: 9932
-  Err Precision: 0.9695
-  Err Recall: 1.0
-  Err F1: 0.9845
-  Err Number: 286
- Egin Err Precision: 0.9938
- Egin Err Recall: 0.9964
- Egin Err F1: 0.9951
- Egin Err Number: 1126
- El Err Precision: 0.9957
- El Err Recall: 0.9942
- El Err F1: 0.9949
- El Err Number: 1384
- Nd Err Precision: 0.9932
- Nd Err Recall: 0.9941
- Nd Err F1: 0.9937
- Nd Err Number: 1183
- Ne Word Err Precision: 0.9978
- Ne Word Err Recall: 0.9942
- Ne Word Err F1: 0.9960
- Ne Word Err Number: 8248
- Unc Insert Err Precision: 0.9956
- Unc Insert Err Recall: 0.9978
- Unc Insert Err F1: 0.9967
- Unc Insert Err Number: 903
- Micro Avg Precision: 0.9944
- Micro Avg Recall: 0.9934
- Micro Avg F1: 0.9939
- Micro Avg Number: 23091
- Macro Avg Precision: 0.9881
- Macro Avg Recall: 0.9960
- Macro Avg F1: 0.9920
- Macro Avg Number: 23091
- Weighted Avg Precision: 0.9944
- Weighted Avg Recall: 0.9934
- Weighted Avg F1: 0.9939
- Weighted Avg Number: 23091
- Overall Accuracy: 0.9994

## 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: 40.0

### 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 |
|:-------------:|:-----:|:-----:|:---------------:|:---------------:|:------------:|:--------:|:------------:|:-----------:|:--------:|:------:|:--------:|:--------------:|:-----------:|:-------:|:-----------:|:------------------:|:---------------:|:-----------:|:---------------:|:----------------:|:-------------:|:---------:|:-------------:|:----------------:|:-------------:|:---------:|:-------------:|:---------------------:|:------------------:|:--------------:|:------------------:|:------------------------:|:---------------------:|:-----------------:|:---------------------:|:-------------------:|:----------------:|:------------:|:----------------:|:-------------------:|:----------------:|:------------:|:----------------:|:----------------------:|:-------------------:|:---------------:|:-------------------:|:----------------:|
| 0.8517        | 1.0   | 575   | 0.2995          | 0.0             | 0.0          | 0.0      | 29           | 0.2579      | 0.0859   | 0.1289 | 9932     | 0.0            | 0.0         | 0.0     | 286         | 0.0                | 0.0             | 0.0         | 1126            | 0.0              | 0.0           | 0.0       | 1384          | 0.0              | 0.0           | 0.0       | 1183          | 0.6479                | 0.2836             | 0.3945         | 8248               | 0.0                      | 0.0                   | 0.0               | 903                   | 0.4615              | 0.1382           | 0.2127       | 23091            | 0.1132              | 0.0462           | 0.0654       | 23091            | 0.3424                 | 0.1382              | 0.1963          | 23091               | 0.9291           |
| 0.2635        | 2.0   | 1150  | 0.2054          | 0.0             | 0.0          | 0.0      | 29           | 0.3578      | 0.2270   | 0.2778 | 9932     | 0.0            | 0.0         | 0.0     | 286         | 0.8148             | 0.0195          | 0.0382      | 1126            | 0.8571           | 0.0650        | 0.1209    | 1384          | 0.7344           | 0.2384        | 0.3599    | 1183          | 0.6604                | 0.5507             | 0.6006         | 8248               | 0.0                      | 0.0                   | 0.0               | 903                   | 0.5250              | 0.3114           | 0.3910       | 23091            | 0.4281              | 0.1376           | 0.1747       | 23091            | 0.5185                 | 0.3114              | 0.3616          | 23091               | 0.9422           |
| 0.2027        | 3.0   | 1725  | 0.1546          | 0.0             | 0.0          | 0.0      | 29           | 0.4421      | 0.3362   | 0.3819 | 9932     | 0.0            | 0.0         | 0.0     | 286         | 0.6649             | 0.5639          | 0.6103      | 1126            | 0.8              | 0.2168        | 0.3411    | 1384          | 0.6406           | 0.5740        | 0.6054    | 1183          | 0.7271                | 0.6519             | 0.6875         | 8248               | 0.6094                   | 0.0864                | 0.1513            | 903                   | 0.5959              | 0.4507           | 0.5133       | 23091            | 0.4855              | 0.3036           | 0.3472       | 23091            | 0.5869                 | 0.4507              | 0.4970          | 23091               | 0.9526           |
| 0.1655        | 4.0   | 2300  | 0.1197          | 0.0             | 0.0          | 0.0      | 29           | 0.5546      | 0.4326   | 0.4861 | 9932     | 1.0            | 0.0315      | 0.0610  | 286         | 0.8037             | 0.6874          | 0.7410      | 1126            | 0.8553           | 0.3374        | 0.4839    | 1384          | 0.8139           | 0.6653        | 0.7321    | 1183          | 0.8075                | 0.7180             | 0.7601         | 8248               | 0.6805                   | 0.1816                | 0.2867            | 903                   | 0.6974              | 0.5379           | 0.6073       | 23091            | 0.6894              | 0.3817           | 0.4439       | 23091            | 0.6981                 | 0.5379              | 0.5952          | 23091               | 0.9636           |
| 0.1321        | 5.0   | 2875  | 0.0841          | 0.0             | 0.0          | 0.0      | 29           | 0.6868      | 0.6291   | 0.6567 | 9932     | 0.8431         | 0.1503      | 0.2552  | 286         | 0.8635             | 0.7869          | 0.8234      | 1126            | 0.7739           | 0.7197        | 0.7458    | 1384          | 0.8690           | 0.7625        | 0.8122    | 1183          | 0.8646                | 0.8339             | 0.8490         | 8248               | 0.6517                   | 0.3378                | 0.4449            | 903                   | 0.7771              | 0.7041           | 0.7388       | 23091            | 0.6941              | 0.5275           | 0.5734       | 23091            | 0.7732                 | 0.7041              | 0.7327          | 23091               | 0.9756           |
| 0.0998        | 6.0   | 3450  | 0.0578          | 0.0             | 0.0          | 0.0      | 29           | 0.8054      | 0.7739   | 0.7893 | 9932     | 0.8182         | 0.2832      | 0.4208  | 286         | 0.8971             | 0.8597          | 0.8780      | 1126            | 0.8313           | 0.8259        | 0.8286    | 1384          | 0.8867           | 0.8335        | 0.8593    | 1183          | 0.9104                | 0.9089             | 0.9097         | 8248               | 0.7280                   | 0.6224                | 0.6710            | 903                   | 0.8518              | 0.8195           | 0.8353       | 23091            | 0.7346              | 0.6384           | 0.6696       | 23091            | 0.8492                 | 0.8195              | 0.8324          | 23091               | 0.9845           |
| 0.0768        | 7.0   | 4025  | 0.0410          | 0.0             | 0.0          | 0.0      | 29           | 0.8790      | 0.8519   | 0.8652 | 9932     | 0.8889         | 0.3916      | 0.5437  | 286         | 0.9032             | 0.9032          | 0.9032      | 1126            | 0.9332           | 0.8374        | 0.8827    | 1384          | 0.8774           | 0.8588        | 0.8680    | 1183          | 0.9427                | 0.9377             | 0.9402         | 8248               | 0.7850                   | 0.7885                | 0.7867            | 903                   | 0.9027              | 0.8753           | 0.8888       | 23091            | 0.7762              | 0.6961           | 0.7237       | 23091            | 0.9014                 | 0.8753              | 0.8869          | 23091               | 0.9897           |
| 0.0601        | 8.0   | 4600  | 0.0294          | 0.0             | 0.0          | 0.0      | 29           | 0.9161      | 0.8936   | 0.9047 | 9932     | 0.8775         | 0.6259      | 0.7306  | 286         | 0.9336             | 0.9245          | 0.9290      | 1126            | 0.9555           | 0.8526        | 0.9011    | 1384          | 0.9115           | 0.8791        | 0.8950    | 1183          | 0.9606                | 0.9630             | 0.9618         | 8248               | 0.8757                   | 0.8505                | 0.8629            | 903                   | 0.9333              | 0.9106           | 0.9218       | 23091            | 0.8038              | 0.7487           | 0.7731       | 23091            | 0.9317                 | 0.9106              | 0.9206          | 23091               | 0.9928           |
| 0.0465        | 9.0   | 5175  | 0.0233          | 0.0             | 0.0          | 0.0      | 29           | 0.9420      | 0.9258   | 0.9338 | 9932     | 0.8583         | 0.7413      | 0.7955  | 286         | 0.9158             | 0.9369          | 0.9263      | 1126            | 0.9421           | 0.9162        | 0.9289    | 1384          | 0.8985           | 0.8977        | 0.8981    | 1183          | 0.9781                | 0.9622             | 0.9701         | 8248               | 0.8934                   | 0.9280                | 0.9104            | 903                   | 0.9484              | 0.9340           | 0.9411       | 23091            | 0.8035              | 0.7885           | 0.7954       | 23091            | 0.9473                 | 0.9340              | 0.9405          | 23091               | 0.9944           |
| 0.037         | 10.0  | 5750  | 0.0167          | 0.0             | 0.0          | 0.0      | 29           | 0.9539      | 0.9528   | 0.9534 | 9932     | 0.8418         | 0.8741      | 0.8576  | 286         | 0.9517             | 0.9449          | 0.9483      | 1126            | 0.9699           | 0.9321        | 0.9506    | 1384          | 0.9330           | 0.9298        | 0.9314    | 1183          | 0.9726                | 0.9787             | 0.9756         | 8248               | 0.9411                   | 0.9557                | 0.9484            | 903                   | 0.9585              | 0.9572           | 0.9578       | 23091            | 0.8205              | 0.8210           | 0.8207       | 23091            | 0.9573                 | 0.9572              | 0.9572          | 23091               | 0.9960           |
| 0.0295        | 11.0  | 6325  | 0.0141          | 0.0             | 0.0          | 0.0      | 29           | 0.9551      | 0.9578   | 0.9565 | 9932     | 0.8571         | 0.9021      | 0.8790  | 286         | 0.9607             | 0.9547          | 0.9577      | 1126            | 0.9791           | 0.9473        | 0.9629    | 1384          | 0.9367           | 0.9374        | 0.9371    | 1183          | 0.9868                | 0.9807             | 0.9838         | 8248               | 0.8456                   | 0.9767                | 0.9065            | 903                   | 0.9609              | 0.9630           | 0.9619       | 23091            | 0.8151              | 0.8321           | 0.8229       | 23091            | 0.9605                 | 0.9630              | 0.9616          | 23091               | 0.9964           |
| 0.0249        | 12.0  | 6900  | 0.0102          | 1.0             | 0.0690       | 0.1290   | 29           | 0.9775      | 0.9723   | 0.9749 | 9932     | 0.9231         | 0.8811      | 0.9016  | 286         | 0.9453             | 0.9671          | 0.9561      | 1126            | 0.9708           | 0.9624        | 0.9666    | 1384          | 0.9456           | 0.9544        | 0.9499    | 1183          | 0.9896                | 0.9850             | 0.9873         | 8248               | 0.9671                   | 0.9779                | 0.9725            | 903                   | 0.9771              | 0.9730           | 0.9751       | 23091            | 0.9649              | 0.8461           | 0.8547       | 23091            | 0.9772                 | 0.9730              | 0.9746          | 23091               | 0.9975           |
| 0.0203        | 13.0  | 7475  | 0.0084          | 1.0             | 0.1379       | 0.2424   | 29           | 0.9787      | 0.9723   | 0.9755 | 9932     | 0.9357         | 0.9161      | 0.9258  | 286         | 0.9733             | 0.9716          | 0.9724      | 1126            | 0.9904           | 0.9646        | 0.9773    | 1384          | 0.9574           | 0.9687        | 0.9630    | 1183          | 0.9924                | 0.9879             | 0.9902         | 8248               | 0.9757                   | 0.9767                | 0.9762            | 903                   | 0.9823              | 0.9756           | 0.9789       | 23091            | 0.9755              | 0.8620           | 0.8779       | 23091            | 0.9823                 | 0.9756              | 0.9785          | 23091               | 0.9980           |
| 0.0181        | 14.0  | 8050  | 0.0066          | 1.0             | 0.2069       | 0.3429   | 29           | 0.9827      | 0.9785   | 0.9806 | 9932     | 0.9627         | 0.9021      | 0.9314  | 286         | 0.9743             | 0.9760          | 0.9752      | 1126            | 0.9804           | 0.9776        | 0.9790    | 1384          | 0.9662           | 0.9653        | 0.9658    | 1183          | 0.9934                | 0.9905             | 0.9920         | 8248               | 0.9738                   | 0.9889                | 0.9813            | 903                   | 0.9846              | 0.9804           | 0.9825       | 23091            | 0.9792              | 0.8732           | 0.8935       | 23091            | 0.9846                 | 0.9804              | 0.9822          | 23091               | 0.9983           |
| 0.0149        | 15.0  | 8625  | 0.0060          | 1.0             | 0.3448       | 0.5128   | 29           | 0.9842      | 0.9783   | 0.9812 | 9932     | 0.9416         | 0.9580      | 0.9497  | 286         | 0.9744             | 0.9822          | 0.9783      | 1126            | 0.9883           | 0.9776        | 0.9829    | 1384          | 0.9748           | 0.9806        | 0.9777    | 1183          | 0.9957                | 0.9871             | 0.9914         | 8248               | 0.9824                   | 0.9900                | 0.9862            | 903                   | 0.9870              | 0.9811           | 0.9840       | 23091            | 0.9802              | 0.8998           | 0.9200       | 23091            | 0.9870                 | 0.9811              | 0.9839          | 23091               | 0.9985           |
| 0.0128        | 16.0  | 9200  | 0.0041          | 1.0             | 0.4828       | 0.6512   | 29           | 0.9874      | 0.9854   | 0.9864 | 9932     | 0.9618         | 0.9685      | 0.9652  | 286         | 0.9832             | 0.9885          | 0.9858      | 1126            | 0.9898           | 0.9848        | 0.9873    | 1384          | 0.9789           | 0.9822        | 0.9806    | 1183          | 0.9951                | 0.9928             | 0.9940         | 8248               | 0.9879                   | 0.9945                | 0.9912            | 903                   | 0.9894              | 0.9875           | 0.9884       | 23091            | 0.9855              | 0.9224           | 0.9427       | 23091            | 0.9894                 | 0.9875              | 0.9883          | 23091               | 0.9989           |
| 0.0109        | 17.0  | 9775  | 0.0038          | 1.0             | 0.6552       | 0.7917   | 29           | 0.9880      | 0.9858   | 0.9869 | 9932     | 0.9516         | 0.9615      | 0.9565  | 286         | 0.9876             | 0.9876          | 0.9876      | 1126            | 0.9877           | 0.9892        | 0.9884    | 1384          | 0.9772           | 0.9789        | 0.9780    | 1183          | 0.9947                | 0.9932             | 0.9939         | 8248               | 0.9944                   | 0.9911                | 0.9928            | 903                   | 0.9896              | 0.9879           | 0.9887       | 23091            | 0.9851              | 0.9428           | 0.9595       | 23091            | 0.9896                 | 0.9879              | 0.9887          | 23091               | 0.9989           |
| 0.0104        | 18.0  | 10350 | 0.0033          | 1.0             | 0.6207       | 0.7660   | 29           | 0.9885      | 0.9872   | 0.9879 | 9932     | 0.9561         | 0.9895      | 0.9725  | 286         | 0.9763             | 0.9893          | 0.9828      | 1126            | 0.9899           | 0.9921        | 0.9910    | 1384          | 0.9783           | 0.9890        | 0.9836    | 1183          | 0.9953                | 0.9941             | 0.9947         | 8248               | 0.9945                   | 0.9934                | 0.9939            | 903                   | 0.9897              | 0.9900           | 0.9898       | 23091            | 0.9849              | 0.9444           | 0.9590       | 23091            | 0.9897                 | 0.9900              | 0.9898          | 23091               | 0.9990           |
| 0.009         | 19.0  | 10925 | 0.0026          | 0.9565          | 0.7586       | 0.8462   | 29           | 0.9903      | 0.9898   | 0.9901 | 9932     | 0.9690         | 0.9825      | 0.9757  | 286         | 0.9902             | 0.9876          | 0.9889      | 1126            | 0.9942           | 0.9899        | 0.9920    | 1384          | 0.9890           | 0.9899        | 0.9894    | 1183          | 0.9952                | 0.9954             | 0.9953         | 8248               | 0.9934                   | 0.9956                | 0.9945            | 903                   | 0.9920              | 0.9916           | 0.9918       | 23091            | 0.9847              | 0.9612           | 0.9715       | 23091            | 0.9920                 | 0.9916              | 0.9918          | 23091               | 0.9992           |
| 0.0077        | 20.0  | 11500 | 0.0024          | 1.0             | 0.8966       | 0.9455   | 29           | 0.9913      | 0.9906   | 0.9910 | 9932     | 0.9530         | 0.9930      | 0.9726  | 286         | 0.9885             | 0.9938          | 0.9911      | 1126            | 0.9942           | 0.9949        | 0.9946    | 1384          | 0.9874           | 0.9915        | 0.9895    | 1183          | 0.9976                | 0.9931             | 0.9953         | 8248               | 0.9956                   | 0.9956                | 0.9956            | 903                   | 0.9931              | 0.9921           | 0.9926       | 23091            | 0.9885              | 0.9811           | 0.9844       | 23091            | 0.9931                 | 0.9921              | 0.9926          | 23091               | 0.9993           |
| 0.0068        | 21.0  | 12075 | 0.0023          | 1.0             | 0.8966       | 0.9455   | 29           | 0.9911      | 0.9895   | 0.9903 | 9932     | 0.9823         | 0.9720      | 0.9772  | 286         | 0.9868             | 0.9947          | 0.9907      | 1126            | 0.9957           | 0.9928        | 0.9942    | 1384          | 0.9858           | 0.9941        | 0.9899    | 1183          | 0.9966                | 0.9939             | 0.9953         | 8248               | 0.9967                   | 0.9934                | 0.9950            | 903                   | 0.9930              | 0.9916           | 0.9923       | 23091            | 0.9919              | 0.9784           | 0.9848       | 23091            | 0.9930                 | 0.9916              | 0.9923          | 23091               | 0.9993           |
| 0.0062        | 22.0  | 12650 | 0.0019          | 1.0             | 0.8966       | 0.9455   | 29           | 0.9913      | 0.9914   | 0.9914 | 9932     | 0.9758         | 0.9860      | 0.9809  | 286         | 0.9894             | 0.9911          | 0.9902      | 1126            | 0.9942           | 0.9935        | 0.9939    | 1384          | 0.9890           | 0.9907        | 0.9899    | 1183          | 0.9960                | 0.9956             | 0.9958         | 8248               | 0.9956                   | 0.9956                | 0.9956            | 903                   | 0.9929              | 0.9930           | 0.9930       | 23091            | 0.9914              | 0.9801           | 0.9854       | 23091            | 0.9929                 | 0.9930              | 0.9930          | 23091               | 0.9993           |
| 0.0055        | 23.0  | 13225 | 0.0018          | 1.0             | 0.9310       | 0.9643   | 29           | 0.9923      | 0.9911   | 0.9917 | 9932     | 0.9758         | 0.9860      | 0.9809  | 286         | 0.9902             | 0.9911          | 0.9907      | 1126            | 0.9942           | 0.9949        | 0.9946    | 1384          | 0.9882           | 0.9932        | 0.9907    | 1183          | 0.9967                | 0.9943             | 0.9955         | 8248               | 0.9967                   | 0.9945                | 0.9956            | 903                   | 0.9937              | 0.9926           | 0.9931       | 23091            | 0.9918              | 0.9845           | 0.9880       | 23091            | 0.9937                 | 0.9926              | 0.9931          | 23091               | 0.9994           |
| 0.0053        | 24.0  | 13800 | 0.0015          | 1.0             | 0.9310       | 0.9643   | 29           | 0.9922      | 0.9916   | 0.9919 | 9932     | 0.9860         | 0.9825      | 0.9842  | 286         | 0.9903             | 0.9929          | 0.9916      | 1126            | 0.9942           | 0.9957        | 0.9949    | 1384          | 0.9899           | 0.9924        | 0.9911    | 1183          | 0.9959                | 0.9958             | 0.9958         | 8248               | 0.9967                   | 0.9945                | 0.9956            | 903                   | 0.9935              | 0.9934           | 0.9935       | 23091            | 0.9931              | 0.9845           | 0.9887       | 23091            | 0.9935                 | 0.9934              | 0.9935          | 23091               | 0.9994           |
| 0.0048        | 25.0  | 14375 | 0.0015          | 0.9667          | 1.0          | 0.9831   | 29           | 0.9916      | 0.9915   | 0.9916 | 9932     | 0.9758         | 0.9860      | 0.9809  | 286         | 0.9912             | 0.9956          | 0.9934      | 1126            | 0.9928           | 0.9971        | 0.9950    | 1384          | 0.9891           | 0.9949        | 0.9920    | 1183          | 0.9967                | 0.9941             | 0.9954         | 8248               | 0.9967                   | 0.9967                | 0.9967            | 903                   | 0.9933              | 0.9933           | 0.9933       | 23091            | 0.9876              | 0.9945           | 0.9910       | 23091            | 0.9933                 | 0.9933              | 0.9933          | 23091               | 0.9994           |
| 0.0039        | 26.0  | 14950 | 0.0013          | 0.9667          | 1.0          | 0.9831   | 29           | 0.9920      | 0.9908   | 0.9914 | 9932     | 0.9792         | 0.9895      | 0.9843  | 286         | 0.9912             | 0.9956          | 0.9934      | 1126            | 0.9971           | 0.9928        | 0.9949    | 1384          | 0.9916           | 0.9932        | 0.9924    | 1183          | 0.9966                | 0.9952             | 0.9959         | 8248               | 0.9967                   | 0.9967                | 0.9967            | 903                   | 0.9939              | 0.9931           | 0.9935       | 23091            | 0.9889              | 0.9942           | 0.9915       | 23091            | 0.9939                 | 0.9931              | 0.9935          | 23091               | 0.9994           |
| 0.0039        | 27.0  | 15525 | 0.0013          | 1.0             | 0.9310       | 0.9643   | 29           | 0.9912      | 0.9921   | 0.9917 | 9932     | 0.9726         | 0.9930      | 0.9827  | 286         | 0.9929             | 0.9973          | 0.9951      | 1126            | 0.9978           | 0.9921        | 0.9949    | 1384          | 0.9907           | 0.9949        | 0.9928    | 1183          | 0.9949                | 0.9964             | 0.9956         | 8248               | 0.9923                   | 0.9989                | 0.9956            | 903                   | 0.9928              | 0.9942           | 0.9935       | 23091            | 0.9916              | 0.9870           | 0.9891       | 23091            | 0.9928                 | 0.9942              | 0.9935          | 23091               | 0.9994           |
| 0.0037        | 28.0  | 16100 | 0.0013          | 1.0             | 0.9655       | 0.9825   | 29           | 0.9925      | 0.9915   | 0.9920 | 9932     | 0.9826         | 0.9860      | 0.9843  | 286         | 0.9929             | 0.9956          | 0.9942      | 1126            | 0.9942           | 0.9957        | 0.9949    | 1384          | 0.9924           | 0.9949        | 0.9937    | 1183          | 0.9982                | 0.9936             | 0.9959         | 8248               | 0.9956                   | 0.9978                | 0.9967            | 903                   | 0.9947              | 0.9930           | 0.9938       | 23091            | 0.9936              | 0.9901           | 0.9918       | 23091            | 0.9947                 | 0.9930              | 0.9938          | 23091               | 0.9994           |
| 0.0034        | 29.0  | 16675 | 0.0012          | 1.0             | 0.9655       | 0.9825   | 29           | 0.9918      | 0.9919   | 0.9919 | 9932     | 0.9726         | 0.9930      | 0.9827  | 286         | 0.9964             | 0.9938          | 0.9951      | 1126            | 0.9957           | 0.9942        | 0.9949    | 1384          | 0.9949           | 0.9924        | 0.9937    | 1183          | 0.9965                | 0.9950             | 0.9958         | 8248               | 0.9956                   | 0.9978                | 0.9967            | 903                   | 0.9940              | 0.9935           | 0.9938       | 23091            | 0.9929              | 0.9905           | 0.9916       | 23091            | 0.9940                 | 0.9935              | 0.9938          | 23091               | 0.9994           |
| 0.0031        | 30.0  | 17250 | 0.0011          | 0.9667          | 1.0          | 0.9831   | 29           | 0.9909      | 0.9920   | 0.9915 | 9932     | 0.9792         | 0.9895      | 0.9843  | 286         | 0.9956             | 0.9947          | 0.9951      | 1126            | 0.9942           | 0.9957        | 0.9949    | 1384          | 0.9949           | 0.9932        | 0.9941    | 1183          | 0.9956                | 0.9962             | 0.9959         | 8248               | 0.9989                   | 0.9945                | 0.9967            | 903                   | 0.9934              | 0.9940           | 0.9937       | 23091            | 0.9895              | 0.9945           | 0.9920       | 23091            | 0.9934                 | 0.9940              | 0.9937          | 23091               | 0.9994           |
| 0.0026        | 31.0  | 17825 | 0.0011          | 0.9667          | 1.0          | 0.9831   | 29           | 0.9910      | 0.9918   | 0.9914 | 9932     | 0.9727         | 0.9965      | 0.9845  | 286         | 0.9938             | 0.9964          | 0.9951      | 1126            | 0.9928           | 0.9971        | 0.9950    | 1384          | 0.9932           | 0.9924        | 0.9928    | 1183          | 0.9964                | 0.9953             | 0.9958         | 8248               | 0.9956                   | 0.9978                | 0.9967            | 903                   | 0.9932              | 0.9939           | 0.9936       | 23091            | 0.9878              | 0.9959           | 0.9918       | 23091            | 0.9932                 | 0.9939              | 0.9936          | 23091               | 0.9994           |
| 0.0025        | 32.0  | 18400 | 0.0011          | 1.0             | 0.9655       | 0.9825   | 29           | 0.9914      | 0.9920   | 0.9917 | 9932     | 0.9727         | 0.9965      | 0.9845  | 286         | 0.9973             | 0.9929          | 0.9951      | 1126            | 0.9900           | 1.0           | 0.9950    | 1384          | 0.9932           | 0.9924        | 0.9928    | 1183          | 0.9972                | 0.9948             | 0.9960         | 8248               | 0.9945                   | 0.9989                | 0.9967            | 903                   | 0.9937              | 0.9939           | 0.9938       | 23091            | 0.9920              | 0.9916           | 0.9918       | 23091            | 0.9937                 | 0.9939              | 0.9938          | 23091               | 0.9994           |
| 0.0025        | 33.0  | 18975 | 0.0010          | 1.0             | 0.9655       | 0.9825   | 29           | 0.9918      | 0.9922   | 0.9920 | 9932     | 0.9694         | 0.9965      | 0.9828  | 286         | 0.9973             | 0.9929          | 0.9951      | 1126            | 0.9928           | 0.9971        | 0.9950    | 1384          | 0.9932           | 0.9932        | 0.9932    | 1183          | 0.9966                | 0.9952             | 0.9959         | 8248               | 0.9967                   | 0.9967                | 0.9967            | 903                   | 0.9939              | 0.9939           | 0.9939       | 23091            | 0.9922              | 0.9912           | 0.9916       | 23091            | 0.9939                 | 0.9939              | 0.9939          | 23091               | 0.9994           |
| 0.0023        | 34.0  | 19550 | 0.0010          | 0.9667          | 1.0          | 0.9831   | 29           | 0.9914      | 0.9920   | 0.9917 | 9932     | 0.9662         | 1.0         | 0.9828  | 286         | 0.9947             | 0.9956          | 0.9951      | 1126            | 0.9964           | 0.9935        | 0.9949    | 1384          | 0.9932           | 0.9924        | 0.9928    | 1183          | 0.9964                | 0.9954             | 0.9959         | 8248               | 0.9956                   | 0.9978                | 0.9967            | 903                   | 0.9935              | 0.9939           | 0.9937       | 23091            | 0.9876              | 0.9958           | 0.9916       | 23091            | 0.9936                 | 0.9939              | 0.9937          | 23091               | 0.9994           |
| 0.0023        | 35.0  | 20125 | 0.0010          | 0.9667          | 1.0          | 0.9831   | 29           | 0.9924      | 0.9918   | 0.9921 | 9932     | 0.9662         | 1.0         | 0.9828  | 286         | 0.9929             | 0.9973          | 0.9951      | 1126            | 0.9964           | 0.9935        | 0.9949    | 1384          | 0.9916           | 0.9958        | 0.9937    | 1183          | 0.9970                | 0.9945             | 0.9958         | 8248               | 0.9967                   | 0.9967                | 0.9967            | 903                   | 0.9941              | 0.9937           | 0.9939       | 23091            | 0.9875              | 0.9962           | 0.9918       | 23091            | 0.9941                 | 0.9937              | 0.9939          | 23091               | 0.9994           |
| 0.002         | 36.0  | 20700 | 0.0010          | 0.9667          | 1.0          | 0.9831   | 29           | 0.9923      | 0.9915   | 0.9919 | 9932     | 0.9695         | 1.0         | 0.9845  | 286         | 0.9947             | 0.9956          | 0.9951      | 1126            | 0.9957           | 0.9942        | 0.9949    | 1384          | 0.9924           | 0.9949        | 0.9937    | 1183          | 0.9979                | 0.9941             | 0.9960         | 8248               | 0.9956                   | 0.9978                | 0.9967            | 903                   | 0.9945              | 0.9933           | 0.9939       | 23091            | 0.9881              | 0.9960           | 0.9920       | 23091            | 0.9945                 | 0.9933              | 0.9939          | 23091               | 0.9994           |
| 0.0018        | 37.0  | 21275 | 0.0009          | 0.9667          | 1.0          | 0.9831   | 29           | 0.9915      | 0.9919   | 0.9917 | 9932     | 0.9792         | 0.9895      | 0.9843  | 286         | 0.9938             | 0.9964          | 0.9951      | 1126            | 0.9964           | 0.9935        | 0.9949    | 1384          | 0.9924           | 0.9949        | 0.9937    | 1183          | 0.9965                | 0.9955             | 0.9960         | 8248               | 0.9967                   | 0.9967                | 0.9967            | 903                   | 0.9938              | 0.9939           | 0.9938       | 23091            | 0.9891              | 0.9948           | 0.9919       | 23091            | 0.9938                 | 0.9939              | 0.9938          | 23091               | 0.9994           |
| 0.002         | 38.0  | 21850 | 0.0009          | 0.9667          | 1.0          | 0.9831   | 29           | 0.9920      | 0.9914   | 0.9917 | 9932     | 0.9727         | 0.9965      | 0.9845  | 286         | 0.9956             | 0.9947          | 0.9951      | 1126            | 0.9949           | 0.9949        | 0.9949    | 1384          | 0.9924           | 0.9949        | 0.9937    | 1183          | 0.9979                | 0.9938             | 0.9959         | 8248               | 0.9956                   | 0.9978                | 0.9967            | 903                   | 0.9944              | 0.9932           | 0.9938       | 23091            | 0.9885              | 0.9955           | 0.9919       | 23091            | 0.9944                 | 0.9932              | 0.9938          | 23091               | 0.9994           |
| 0.0018        | 39.0  | 22425 | 0.0009          | 0.9667          | 1.0          | 0.9831   | 29           | 0.9924      | 0.9914   | 0.9919 | 9932     | 0.9695         | 1.0         | 0.9845  | 286         | 0.9947             | 0.9956          | 0.9951      | 1126            | 0.9957           | 0.9942        | 0.9949    | 1384          | 0.9941           | 0.9932        | 0.9937    | 1183          | 0.9978                | 0.9942             | 0.9960         | 8248               | 0.9956                   | 0.9978                | 0.9967            | 903                   | 0.9945              | 0.9932           | 0.9939       | 23091            | 0.9883              | 0.9958           | 0.9920       | 23091            | 0.9946                 | 0.9932              | 0.9939          | 23091               | 0.9994           |
| 0.0017        | 40.0  | 23000 | 0.0009          | 0.9667          | 1.0          | 0.9831   | 29           | 0.9922      | 0.9916   | 0.9919 | 9932     | 0.9695         | 1.0         | 0.9845  | 286         | 0.9938             | 0.9964          | 0.9951      | 1126            | 0.9957           | 0.9942        | 0.9949    | 1384          | 0.9932           | 0.9941        | 0.9937    | 1183          | 0.9978                | 0.9942             | 0.9960         | 8248               | 0.9956                   | 0.9978                | 0.9967            | 903                   | 0.9944              | 0.9934           | 0.9939       | 23091            | 0.9881              | 0.9960           | 0.9920       | 23091            | 0.9944                 | 0.9934              | 0.9939          | 23091               | 0.9994           |


### Framework versions

- Transformers 4.25.1
- Pytorch 1.13.1+cu117
- Datasets 2.9.0
- Tokenizers 0.13.2

{"tags": ["generated_from_trainer"], "model-index": [{"name": "diff_based_error_tagger", "results": []}]}
transformers pytorch tensorboard electra token-classification generated_from_trainer autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #electra #token-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us 

diff\_based\_error\_tagger
==========================


This model is a fine-tuned version of csebuetnlp/banglabert on an unknown dataset.
It achieves the following results on the evaluation set:


* Loss: 0.0009
* 5 Err Precision: 0.9667
* 5 Err Recall: 1.0
* 5 Err F1: 0.9831
* 5 Err Number: 29
* Precision: 0.9922
* Recall: 0.9916
* F1: 0.9919
* Number: 9932
* Err Precision: 0.9695
* Err Recall: 1.0
* Err F1: 0.9845
* Err Number: 286
* Egin Err Precision: 0.9938
* Egin Err Recall: 0.9964
* Egin Err F1: 0.9951
* Egin Err Number: 1126
* El Err Precision: 0.9957
* El Err Recall: 0.9942
* El Err F1: 0.9949
* El Err Number: 1384
* Nd Err Precision: 0.9932
* Nd Err Recall: 0.9941
* Nd Err F1: 0.9937
* Nd Err Number: 1183
* Ne Word Err Precision: 0.9978
* Ne Word Err Recall: 0.9942
* Ne Word Err F1: 0.9960
* Ne Word Err Number: 8248
* Unc Insert Err Precision: 0.9956
* Unc Insert Err Recall: 0.9978
* Unc Insert Err F1: 0.9967
* Unc Insert Err Number: 903
* Micro Avg Precision: 0.9944
* Micro Avg Recall: 0.9934
* Micro Avg F1: 0.9939
* Micro Avg Number: 23091
* Macro Avg Precision: 0.9881
* Macro Avg Recall: 0.9960
* Macro Avg F1: 0.9920
* Macro Avg Number: 23091
* Weighted Avg Precision: 0.9944
* Weighted Avg Recall: 0.9934
* Weighted Avg F1: 0.9939
* Weighted Avg Number: 23091
* Overall Accuracy: 0.9994


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: 40.0


### Training results



### Framework versions


* Transformers 4.25.1
* Pytorch 1.13.1+cu117
* Datasets 2.9.0
* Tokenizers 0.13.2



### 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: 40.0 ### Training results ### Framework versions


* Transformers 4.25.1
* Pytorch 1.13.1+cu117
* Datasets 2.9.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #electra #token-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us 
 ### 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: 40.0 ### Training results ### Framework versions


* Transformers 4.25.1
* Pytorch 1.13.1+cu117
* Datasets 2.9.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #electra #token-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us 
### 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: 40.0### Training results### Framework versions


* Transformers 4.25.1
* Pytorch 1.13.1+cu117
* Datasets 2.9.0
* Tokenizers 0.13.2