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# Hinton_SC_BS32_LR3e5

This model is a fine-tuned version of [rafsankabir/Pretrained_Final_E6](https://huggingface.co/rafsankabir/Pretrained_Final_E6) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4069
- Accuracy: 0.6790
- F1 Macro: 0.6473

## 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   | 1.0674          | 0.3976   | 0.1896   |
| 1.0108        | 2.54  | 1000  | 0.8270          | 0.6426   | 0.5565   |
| 1.0108        | 3.82  | 1500  | 0.8016          | 0.6522   | 0.5753   |
| 0.7423        | 5.09  | 2000  | 0.7922          | 0.6611   | 0.6099   |
| 0.7423        | 6.36  | 2500  | 0.8057          | 0.6726   | 0.6155   |
| 0.6098        | 7.63  | 3000  | 0.8303          | 0.6860   | 0.6456   |
| 0.6098        | 8.91  | 3500  | 0.8322          | 0.6847   | 0.6481   |
| 0.5049        | 10.18 | 4000  | 0.8775          | 0.6994   | 0.6603   |
| 0.5049        | 11.45 | 4500  | 0.9122          | 0.6956   | 0.6510   |
| 0.4132        | 12.72 | 5000  | 0.9451          | 0.6879   | 0.6564   |
| 0.4132        | 13.99 | 5500  | 0.9600          | 0.6809   | 0.6433   |
| 0.3571        | 15.27 | 6000  | 1.0050          | 0.6854   | 0.6515   |
| 0.3571        | 16.54 | 6500  | 1.0671          | 0.6847   | 0.6496   |
| 0.2952        | 17.81 | 7000  | 1.0836          | 0.6873   | 0.6525   |
| 0.2952        | 19.08 | 7500  | 1.0993          | 0.6873   | 0.6558   |
| 0.2577        | 20.36 | 8000  | 1.1465          | 0.6924   | 0.6613   |
| 0.2577        | 21.63 | 8500  | 1.2137          | 0.6828   | 0.6541   |
| 0.2314        | 22.9  | 9000  | 1.1916          | 0.6924   | 0.6610   |
| 0.2314        | 24.17 | 9500  | 1.2445          | 0.6860   | 0.6525   |
| 0.2044        | 25.45 | 10000 | 1.2564          | 0.6867   | 0.6554   |
| 0.2044        | 26.72 | 10500 | 1.2770          | 0.6828   | 0.6509   |
| 0.1899        | 27.99 | 11000 | 1.3005          | 0.6854   | 0.6553   |
| 0.1899        | 29.26 | 11500 | 1.3149          | 0.6816   | 0.6519   |
| 0.1777        | 30.53 | 12000 | 1.3320          | 0.6835   | 0.6512   |
| 0.1777        | 31.81 | 12500 | 1.3456          | 0.6847   | 0.6538   |
| 0.1652        | 33.08 | 13000 | 1.3620          | 0.6796   | 0.6486   |
| 0.1652        | 34.35 | 13500 | 1.3808          | 0.6796   | 0.6500   |
| 0.1544        | 35.62 | 14000 | 1.3878          | 0.6841   | 0.6533   |
| 0.1544        | 36.9  | 14500 | 1.3989          | 0.6790   | 0.6490   |
| 0.1521        | 38.17 | 15000 | 1.4031          | 0.6822   | 0.6501   |
| 0.1521        | 39.44 | 15500 | 1.4069          | 0.6790   | 0.6473   |


### 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": "Hinton_SC_BS32_LR3e5", "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 

Hinton\_SC\_BS32\_LR3e5
=======================


This model is a fine-tuned version of rafsankabir/Pretrained\_Final\_E6 on an unknown dataset.
It achieves the following results on the evaluation set:


* Loss: 1.4069
* Accuracy: 0.6790
* F1 Macro: 0.6473


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