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# git-base-textvqa

This model is a fine-tuned version of [microsoft/git-base-textvqa](https://huggingface.co/microsoft/git-base-textvqa) on the textvqa dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0472

## 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: 4
- eval_batch_size: 3
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.9764        | 0.2   | 500  | 0.0499          |
| 0.0524        | 0.4   | 1000 | 0.0492          |
| 0.0525        | 0.6   | 1500 | 0.0494          |
| 0.0531        | 0.8   | 2000 | 0.0480          |
| 0.0515        | 1.0   | 2500 | 0.0477          |
| 0.0473        | 1.2   | 3000 | 0.0483          |
| 0.0479        | 1.4   | 3500 | 0.0477          |
| 0.0473        | 1.6   | 4000 | 0.0476          |
| 0.0486        | 1.8   | 4500 | 0.0472          |
| 0.0471        | 2.0   | 5000 | 0.0473          |
| 0.0454        | 2.2   | 5500 | 0.0473          |
| 0.0452        | 2.4   | 6000 | 0.0476          |
| 0.0438        | 2.6   | 6500 | 0.0475          |
| 0.0463        | 2.8   | 7000 | 0.0474          |
| 0.0449        | 3.0   | 7500 | 0.0472          |


### Framework versions

- Transformers 4.28.0
- Pytorch 2.0.0
- Datasets 2.12.0
- Tokenizers 0.13.3

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

git-base-textvqa
================


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


* Loss: 0.0472


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: 4
* eval\_batch\_size: 3
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.28.0
* Pytorch 2.0.0
* Datasets 2.12.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 4
* eval\_batch\_size: 3
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.28.0
* Pytorch 2.0.0
* Datasets 2.12.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #git #text-generation #generated_from_trainer #dataset-textvqa #license-mit #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 4
* eval\_batch\_size: 3
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.28.0
* Pytorch 2.0.0
* Datasets 2.12.0
* Tokenizers 0.13.3