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# git-base-pokemon

This model is a fine-tuned version of [microsoft/git-base](https://huggingface.co/microsoft/git-base) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0407
- Wer Score: 2.1746

## 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: 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
- num_epochs: 50
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer Score |
|:-------------:|:-----:|:----:|:---------------:|:---------:|
| 7.3192        | 2.13  | 50   | 4.4908          | 21.3506   |
| 2.2486        | 4.26  | 100  | 0.3526          | 10.3803   |
| 0.1047        | 6.38  | 150  | 0.0321          | 0.3635    |
| 0.0222        | 8.51  | 200  | 0.0298          | 0.6636    |
| 0.0145        | 10.64 | 250  | 0.0286          | 2.1759    |
| 0.0081        | 12.77 | 300  | 0.0321          | 2.9690    |
| 0.004         | 14.89 | 350  | 0.0340          | 2.2962    |
| 0.002         | 17.02 | 400  | 0.0356          | 2.1837    |
| 0.0012        | 19.15 | 450  | 0.0370          | 3.1501    |
| 0.0009        | 21.28 | 500  | 0.0379          | 2.5821    |
| 0.0007        | 23.4  | 550  | 0.0382          | 2.7995    |
| 0.0006        | 25.53 | 600  | 0.0386          | 2.8318    |
| 0.0006        | 27.66 | 650  | 0.0387          | 2.4541    |
| 0.0006        | 29.79 | 700  | 0.0390          | 2.6404    |
| 0.0006        | 31.91 | 750  | 0.0395          | 2.5614    |
| 0.0006        | 34.04 | 800  | 0.0395          | 2.5317    |
| 0.0006        | 36.17 | 850  | 0.0399          | 2.5886    |
| 0.0006        | 38.3  | 900  | 0.0403          | 2.3829    |
| 0.0006        | 40.43 | 950  | 0.0404          | 2.2937    |
| 0.0006        | 42.55 | 1000 | 0.0404          | 2.2173    |
| 0.0006        | 44.68 | 1050 | 0.0406          | 2.1617    |
| 0.0006        | 46.81 | 1100 | 0.0406          | 2.1669    |
| 0.0006        | 48.94 | 1150 | 0.0407          | 2.1746    |


### Framework versions

- Transformers 4.27.4
- Pytorch 1.13.1
- Datasets 2.11.0
- Tokenizers 0.13.3

{"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "model-index": [{"name": "git-base-pokemon", "results": []}]}
transformers pytorch tensorboard git text-generation generated_from_trainer dataset:imagefolder license:mit autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #git #text-generation #generated_from_trainer #dataset-imagefolder #license-mit #autotrain_compatible #endpoints_compatible #region-us 

git-base-pokemon
================


This model is a fine-tuned version of microsoft/git-base on the imagefolder dataset.
It achieves the following results on the evaluation set:


* Loss: 0.0407
* Wer Score: 2.1746


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: 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
* num\_epochs: 50
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.27.4
* Pytorch 1.13.1
* Datasets 2.11.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-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
* num\_epochs: 50
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.27.4
* Pytorch 1.13.1
* Datasets 2.11.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #git #text-generation #generated_from_trainer #dataset-imagefolder #license-mit #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-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
* num\_epochs: 50
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.27.4
* Pytorch 1.13.1
* Datasets 2.11.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #git #text-generation #generated_from_trainer #dataset-imagefolder #license-mit #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-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
* num\_epochs: 50
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.27.4
* Pytorch 1.13.1
* Datasets 2.11.0
* Tokenizers 0.13.3