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# Fine_Tuning_SC_Method_2_Epoch_13B

This model is a fine-tuned version of [rafsankabir/Pretrained_E13B_Method2](https://huggingface.co/rafsankabir/Pretrained_E13B_Method2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4244
- Accuracy: 0.6873
- F1 Macro: 0.6544

## 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: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- 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        | 1.27  | 500   | 1.0673          | 0.3976   | 0.1896   |
| 1.0138        | 2.54  | 1000  | 0.8217          | 0.6331   | 0.5569   |
| 1.0138        | 3.82  | 1500  | 0.7889          | 0.6662   | 0.6049   |
| 0.7305        | 5.09  | 2000  | 0.7821          | 0.6765   | 0.6382   |
| 0.7305        | 6.36  | 2500  | 0.7867          | 0.6918   | 0.6457   |
| 0.5856        | 7.63  | 3000  | 0.8236          | 0.6892   | 0.6623   |
| 0.5856        | 8.91  | 3500  | 0.8490          | 0.6835   | 0.6551   |
| 0.4723        | 10.18 | 4000  | 0.9057          | 0.6854   | 0.6533   |
| 0.4723        | 11.45 | 4500  | 0.9237          | 0.6796   | 0.6455   |
| 0.3896        | 12.72 | 5000  | 0.9814          | 0.6879   | 0.6499   |
| 0.3896        | 13.99 | 5500  | 0.9984          | 0.6745   | 0.6487   |
| 0.3299        | 15.27 | 6000  | 1.0226          | 0.6822   | 0.6545   |
| 0.3299        | 16.54 | 6500  | 1.0579          | 0.6758   | 0.6485   |
| 0.2783        | 17.81 | 7000  | 1.0932          | 0.6796   | 0.6487   |
| 0.2783        | 19.08 | 7500  | 1.1047          | 0.6950   | 0.6609   |
| 0.2455        | 20.36 | 8000  | 1.1643          | 0.6860   | 0.6559   |
| 0.2455        | 21.63 | 8500  | 1.1953          | 0.6841   | 0.6548   |
| 0.2181        | 22.9  | 9000  | 1.2043          | 0.6835   | 0.6516   |
| 0.2181        | 24.17 | 9500  | 1.2603          | 0.6867   | 0.6502   |
| 0.1894        | 25.45 | 10000 | 1.2652          | 0.6860   | 0.6552   |
| 0.1894        | 26.72 | 10500 | 1.2860          | 0.6790   | 0.6474   |
| 0.1757        | 27.99 | 11000 | 1.2892          | 0.6854   | 0.6541   |
| 0.1757        | 29.26 | 11500 | 1.3400          | 0.6803   | 0.6496   |
| 0.1599        | 30.53 | 12000 | 1.3630          | 0.6828   | 0.6493   |
| 0.1599        | 31.81 | 12500 | 1.3688          | 0.6854   | 0.6538   |
| 0.1531        | 33.08 | 13000 | 1.3962          | 0.6854   | 0.6534   |
| 0.1531        | 34.35 | 13500 | 1.4021          | 0.6841   | 0.6523   |
| 0.1452        | 35.62 | 14000 | 1.4029          | 0.6847   | 0.6524   |
| 0.1452        | 36.9  | 14500 | 1.4130          | 0.6886   | 0.6562   |
| 0.1391        | 38.17 | 15000 | 1.4203          | 0.6879   | 0.6553   |
| 0.1391        | 39.44 | 15500 | 1.4244          | 0.6873   | 0.6544   |


### 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": "Fine_Tuning_SC_Method_2_Epoch_13B", "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 

Fine\_Tuning\_SC\_Method\_2\_Epoch\_13B
=======================================


This model is a fine-tuned version of rafsankabir/Pretrained\_E13B\_Method2 on an unknown dataset.
It achieves the following results on the evaluation set:


* Loss: 1.4244
* Accuracy: 0.6873
* F1 Macro: 0.6544


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: 3e-05
* train\_batch\_size: 32
* eval\_batch\_size: 32
* 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: 3e-05
* train\_batch\_size: 32
* eval\_batch\_size: 32
* 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: 3e-05
* train\_batch\_size: 32
* eval\_batch\_size: 32
* 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