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# FineTuning_Method_2_SC

This model is a fine-tuned version of [rafsankabir/Pretrained_E13_Method2](https://huggingface.co/rafsankabir/Pretrained_E13_Method2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.3223
- Accuracy: 0.6790
- F1 Macro: 0.6487

## 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 40
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy | F1 Macro |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|
| No log        | 0.32  | 500   | 1.0745          | 0.3976   | 0.1896   |
| 1.0543        | 0.64  | 1000  | 0.9059          | 0.5967   | 0.4614   |
| 1.0543        | 0.95  | 1500  | 0.8259          | 0.6414   | 0.5633   |
| 0.8389        | 1.27  | 2000  | 0.8177          | 0.6394   | 0.5715   |
| 0.8389        | 1.59  | 2500  | 0.8269          | 0.6356   | 0.5724   |
| 0.7713        | 1.91  | 3000  | 0.7916          | 0.6631   | 0.6238   |
| 0.7713        | 2.23  | 3500  | 0.7996          | 0.6745   | 0.6155   |
| 0.6734        | 2.54  | 4000  | 0.7921          | 0.6624   | 0.6307   |
| 0.6734        | 2.86  | 4500  | 0.7743          | 0.6726   | 0.6459   |
| 0.6309        | 3.18  | 5000  | 0.8343          | 0.6803   | 0.6382   |
| 0.6309        | 3.5   | 5500  | 0.8233          | 0.6784   | 0.6390   |
| 0.5582        | 3.82  | 6000  | 0.8678          | 0.6631   | 0.6273   |
| 0.5582        | 4.13  | 6500  | 0.8621          | 0.6758   | 0.6368   |
| 0.4988        | 4.45  | 7000  | 0.9389          | 0.6720   | 0.6386   |
| 0.4988        | 4.77  | 7500  | 0.9067          | 0.6918   | 0.6505   |
| 0.4885        | 5.09  | 8000  | 0.9116          | 0.6937   | 0.6583   |
| 0.4885        | 5.41  | 8500  | 1.0357          | 0.6822   | 0.6459   |
| 0.427         | 5.73  | 9000  | 0.9428          | 0.6847   | 0.6479   |
| 0.427         | 6.04  | 9500  | 1.0233          | 0.6752   | 0.6531   |
| 0.4034        | 6.36  | 10000 | 1.1578          | 0.6835   | 0.6515   |
| 0.4034        | 6.68  | 10500 | 1.1870          | 0.6790   | 0.6545   |
| 0.4053        | 7.0   | 11000 | 1.0370          | 0.7007   | 0.6651   |
| 0.4053        | 7.32  | 11500 | 1.2087          | 0.6822   | 0.6497   |
| 0.3545        | 7.63  | 12000 | 1.2255          | 0.6847   | 0.6605   |
| 0.3545        | 7.95  | 12500 | 1.2710          | 0.6905   | 0.6609   |
| 0.3437        | 8.27  | 13000 | 1.3646          | 0.6918   | 0.6618   |
| 0.3437        | 8.59  | 13500 | 1.3767          | 0.6879   | 0.6563   |
| 0.3407        | 8.91  | 14000 | 1.2705          | 0.6796   | 0.6506   |
| 0.3407        | 9.22  | 14500 | 1.4605          | 0.6803   | 0.6496   |
| 0.2876        | 9.54  | 15000 | 1.4202          | 0.6860   | 0.6555   |
| 0.2876        | 9.86  | 15500 | 1.4151          | 0.6847   | 0.6517   |
| 0.3035        | 10.18 | 16000 | 1.4536          | 0.6713   | 0.6514   |
| 0.3035        | 10.5  | 16500 | 1.4806          | 0.6828   | 0.6469   |
| 0.2733        | 10.81 | 17000 | 1.4596          | 0.6899   | 0.6552   |
| 0.2733        | 11.13 | 17500 | 1.6183          | 0.6886   | 0.6557   |
| 0.2562        | 11.45 | 18000 | 1.6054          | 0.6771   | 0.6591   |
| 0.2562        | 11.77 | 18500 | 1.5966          | 0.6701   | 0.6503   |
| 0.2582        | 12.09 | 19000 | 1.5659          | 0.6822   | 0.6531   |
| 0.2582        | 12.4  | 19500 | 1.6146          | 0.6867   | 0.6575   |
| 0.2368        | 12.72 | 20000 | 1.6207          | 0.6899   | 0.6629   |
| 0.2368        | 13.04 | 20500 | 1.5220          | 0.6918   | 0.6640   |
| 0.245         | 13.36 | 21000 | 1.6572          | 0.6720   | 0.6489   |
| 0.245         | 13.68 | 21500 | 1.6443          | 0.6860   | 0.6590   |
| 0.2226        | 13.99 | 22000 | 1.6238          | 0.6847   | 0.6589   |
| 0.2226        | 14.31 | 22500 | 1.7241          | 0.6777   | 0.6521   |
| 0.2117        | 14.63 | 23000 | 1.6134          | 0.6867   | 0.6580   |
| 0.2117        | 14.95 | 23500 | 1.6723          | 0.6911   | 0.6618   |
| 0.2056        | 15.27 | 24000 | 1.6257          | 0.6892   | 0.6529   |
| 0.2056        | 15.59 | 24500 | 1.7072          | 0.6796   | 0.6531   |
| 0.1859        | 15.9  | 25000 | 1.7174          | 0.6771   | 0.6554   |
| 0.1859        | 16.22 | 25500 | 1.6951          | 0.6879   | 0.6555   |
| 0.1725        | 16.54 | 26000 | 1.7240          | 0.6905   | 0.6632   |
| 0.1725        | 16.86 | 26500 | 1.7126          | 0.6879   | 0.6608   |
| 0.1817        | 17.18 | 27000 | 1.7949          | 0.6847   | 0.6520   |
| 0.1817        | 17.49 | 27500 | 1.7694          | 0.6911   | 0.6622   |
| 0.1617        | 17.81 | 28000 | 1.7891          | 0.6828   | 0.6527   |
| 0.1617        | 18.13 | 28500 | 1.7860          | 0.6790   | 0.6526   |
| 0.1628        | 18.45 | 29000 | 1.8127          | 0.6867   | 0.6605   |
| 0.1628        | 18.77 | 29500 | 1.7317          | 0.6892   | 0.6610   |
| 0.1736        | 19.08 | 30000 | 1.7273          | 0.6899   | 0.6569   |
| 0.1736        | 19.4  | 30500 | 1.7853          | 0.6854   | 0.6584   |
| 0.1441        | 19.72 | 31000 | 1.7866          | 0.6918   | 0.6624   |
| 0.1441        | 20.04 | 31500 | 1.7842          | 0.6873   | 0.6580   |
| 0.1392        | 20.36 | 32000 | 1.8669          | 0.6860   | 0.6597   |
| 0.1392        | 20.67 | 32500 | 1.8392          | 0.6899   | 0.6639   |
| 0.159         | 20.99 | 33000 | 1.8412          | 0.6784   | 0.6552   |
| 0.159         | 21.31 | 33500 | 1.8673          | 0.6854   | 0.6584   |
| 0.1275        | 21.63 | 34000 | 1.8622          | 0.6854   | 0.6571   |
| 0.1275        | 21.95 | 34500 | 1.8622          | 0.6796   | 0.6583   |
| 0.1216        | 22.26 | 35000 | 1.9509          | 0.6854   | 0.6604   |
| 0.1216        | 22.58 | 35500 | 1.9425          | 0.6809   | 0.6550   |
| 0.1351        | 22.9  | 36000 | 1.9496          | 0.6784   | 0.6559   |
| 0.1351        | 23.22 | 36500 | 1.9685          | 0.6847   | 0.6582   |
| 0.1221        | 23.54 | 37000 | 1.9112          | 0.6911   | 0.6642   |
| 0.1221        | 23.85 | 37500 | 1.9341          | 0.6726   | 0.6526   |
| 0.1155        | 24.17 | 38000 | 1.9573          | 0.6899   | 0.6614   |
| 0.1155        | 24.49 | 38500 | 1.9853          | 0.6873   | 0.6580   |
| 0.1139        | 24.81 | 39000 | 1.9915          | 0.6790   | 0.6533   |
| 0.1139        | 25.13 | 39500 | 1.9997          | 0.6796   | 0.6539   |
| 0.1166        | 25.45 | 40000 | 1.9994          | 0.6847   | 0.6592   |
| 0.1166        | 25.76 | 40500 | 1.9848          | 0.6745   | 0.6513   |
| 0.1128        | 26.08 | 41000 | 2.0095          | 0.6867   | 0.6578   |
| 0.1128        | 26.4  | 41500 | 2.0585          | 0.6822   | 0.6547   |
| 0.1048        | 26.72 | 42000 | 2.0293          | 0.6777   | 0.6510   |
| 0.1048        | 27.04 | 42500 | 2.0797          | 0.6758   | 0.6512   |
| 0.1           | 27.35 | 43000 | 2.1162          | 0.6822   | 0.6544   |
| 0.1           | 27.67 | 43500 | 2.0569          | 0.6835   | 0.6538   |
| 0.1106        | 27.99 | 44000 | 2.0991          | 0.6828   | 0.6565   |
| 0.1106        | 28.31 | 44500 | 2.0976          | 0.6841   | 0.6563   |
| 0.0886        | 28.63 | 45000 | 2.1305          | 0.6854   | 0.6532   |
| 0.0886        | 28.94 | 45500 | 2.1015          | 0.6867   | 0.6564   |
| 0.1027        | 29.26 | 46000 | 2.1105          | 0.6867   | 0.6559   |
| 0.1027        | 29.58 | 46500 | 2.1396          | 0.6765   | 0.6499   |
| 0.1057        | 29.9  | 47000 | 2.1237          | 0.6790   | 0.6501   |
| 0.1057        | 30.22 | 47500 | 2.1849          | 0.6790   | 0.6518   |
| 0.0876        | 30.53 | 48000 | 2.1346          | 0.6841   | 0.6533   |
| 0.0876        | 30.85 | 48500 | 2.1441          | 0.6828   | 0.6540   |
| 0.0856        | 31.17 | 49000 | 2.1528          | 0.6911   | 0.6600   |
| 0.0856        | 31.49 | 49500 | 2.1725          | 0.6847   | 0.6509   |
| 0.0869        | 31.81 | 50000 | 2.2085          | 0.6771   | 0.6503   |
| 0.0869        | 32.12 | 50500 | 2.2606          | 0.6688   | 0.6434   |
| 0.0848        | 32.44 | 51000 | 2.2510          | 0.6745   | 0.6451   |
| 0.0848        | 32.76 | 51500 | 2.2528          | 0.6739   | 0.6496   |
| 0.0816        | 33.08 | 52000 | 2.2532          | 0.6758   | 0.6503   |
| 0.0816        | 33.4  | 52500 | 2.2356          | 0.6803   | 0.6500   |
| 0.0793        | 33.72 | 53000 | 2.2579          | 0.6745   | 0.6483   |
| 0.0793        | 34.03 | 53500 | 2.2126          | 0.6816   | 0.6520   |
| 0.0767        | 34.35 | 54000 | 2.2504          | 0.6803   | 0.6497   |
| 0.0767        | 34.67 | 54500 | 2.2601          | 0.6803   | 0.6524   |
| 0.0844        | 34.99 | 55000 | 2.2785          | 0.6733   | 0.6470   |
| 0.0844        | 35.31 | 55500 | 2.2756          | 0.6784   | 0.6520   |
| 0.0755        | 35.62 | 56000 | 2.2813          | 0.6816   | 0.6542   |
| 0.0755        | 35.94 | 56500 | 2.2752          | 0.6803   | 0.6518   |
| 0.077         | 36.26 | 57000 | 2.2815          | 0.6796   | 0.6518   |
| 0.077         | 36.58 | 57500 | 2.2861          | 0.6803   | 0.6514   |
| 0.0752        | 36.9  | 58000 | 2.2929          | 0.6771   | 0.6505   |
| 0.0752        | 37.21 | 58500 | 2.2859          | 0.6816   | 0.6537   |
| 0.0698        | 37.53 | 59000 | 2.3117          | 0.6796   | 0.6525   |
| 0.0698        | 37.85 | 59500 | 2.3038          | 0.6816   | 0.6511   |
| 0.0613        | 38.17 | 60000 | 2.3176          | 0.6765   | 0.6477   |
| 0.0613        | 38.49 | 60500 | 2.3131          | 0.6796   | 0.6493   |
| 0.0706        | 38.8  | 61000 | 2.3161          | 0.6777   | 0.6477   |
| 0.0706        | 39.12 | 61500 | 2.3127          | 0.6784   | 0.6484   |
| 0.0678        | 39.44 | 62000 | 2.3174          | 0.6765   | 0.6467   |
| 0.0678        | 39.76 | 62500 | 2.3223          | 0.6790   | 0.6487   |


### Framework versions

- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3

{"tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "FineTuning_Method_2_SC", "results": []}]}
transformers pytorch tensorboard bert text-classification generated_from_trainer autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us 

FineTuning\_Method\_2\_SC
=========================


This model is a fine-tuned version of rafsankabir/Pretrained\_E13\_Method2 on an unknown dataset.
It achieves the following results on the evaluation set:


* Loss: 2.3223
* Accuracy: 0.6790
* F1 Macro: 0.6487


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: 5e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 40
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.29.2
* Pytorch 2.0.1+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 40
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.29.2
* Pytorch 2.0.1+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 40
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.29.2
* Pytorch 2.0.1+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3