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# 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

{"tags": ["generated_from_trainer"], "model-index": [{"name": "output_diff_approach", "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 

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



### 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: 2.0 ### 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: 2.0 ### 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: 2.0### Training results### Framework versions


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