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# BertWhatCommitPreprocessed

This model is a fine-tuned version of [prajjwal1/bert-small](https://huggingface.co/prajjwal1/bert-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3631
- Accuracy: 0.9152

## 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: 1e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 200

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 38   | 0.5383          | 0.7333   |
| No log        | 2.0   | 76   | 0.4130          | 0.8485   |
| No log        | 3.0   | 114  | 0.3096          | 0.8727   |
| No log        | 4.0   | 152  | 0.3140          | 0.8788   |
| No log        | 5.0   | 190  | 0.2983          | 0.8970   |
| No log        | 6.0   | 228  | 0.3019          | 0.8848   |
| No log        | 7.0   | 266  | 0.3235          | 0.9030   |
| No log        | 8.0   | 304  | 0.3571          | 0.8970   |
| No log        | 9.0   | 342  | 0.3457          | 0.8970   |
| No log        | 10.0  | 380  | 0.3340          | 0.8909   |
| No log        | 11.0  | 418  | 0.3378          | 0.9091   |
| No log        | 12.0  | 456  | 0.3389          | 0.9091   |
| No log        | 13.0  | 494  | 0.3753          | 0.9030   |
| 0.2144        | 14.0  | 532  | 0.3492          | 0.9152   |
| 0.2144        | 15.0  | 570  | 0.3631          | 0.9152   |


### Framework versions

- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3

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

BertWhatCommitPreprocessed
==========================


This model is a fine-tuned version of prajjwal1/bert-small on an unknown dataset.
It achieves the following results on the evaluation set:


* Loss: 0.3631
* Accuracy: 0.9152


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: 1e-05
* train\_batch\_size: 64
* eval\_batch\_size: 64
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 200


### Training results



### Framework versions


* Transformers 4.28.0
* Pytorch 2.0.1+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-05
* train\_batch\_size: 64
* eval\_batch\_size: 64
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 200 ### Training results ### Framework versions


* Transformers 4.28.0
* Pytorch 2.0.1+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-05
* train\_batch\_size: 64
* eval\_batch\_size: 64
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 200 ### Training results ### Framework versions


* Transformers 4.28.0
* Pytorch 2.0.1+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3