---
license: mit
base_model: microsoft/git-base
tags:
- generated_from_trainer
model-index:
- name: git-base-floors615images
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# git-base-floors615images

This model is a fine-tuned version of [microsoft/git-base](https://huggingface.co/microsoft/git-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0139
- Wer Score: 0.5837

## 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: 4
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|
| 6.955         | 0.72  | 50   | 4.0586          | 1.6636    |
| 1.911         | 1.44  | 100  | 0.2120          | 3.1636    |
| 0.0574        | 2.16  | 150  | 0.0130          | 4.5642    |
| 0.0118        | 2.88  | 200  | 0.0126          | 2.8213    |
| 0.0099        | 3.6   | 250  | 0.0095          | 2.2693    |
| 0.0082        | 4.32  | 300  | 0.0089          | 5.0186    |
| 0.0077        | 5.04  | 350  | 0.0088          | 5.3560    |
| 0.0068        | 5.76  | 400  | 0.0091          | 2.8026    |
| 0.0064        | 6.47  | 450  | 0.0082          | 3.4589    |
| 0.0063        | 7.19  | 500  | 0.0086          | 4.6660    |
| 0.006         | 7.91  | 550  | 0.0082          | 4.8546    |
| 0.0056        | 8.63  | 600  | 0.0085          | 3.1033    |
| 0.0062        | 9.35  | 650  | 0.0087          | 4.0475    |
| 0.0057        | 10.07 | 700  | 0.0086          | 5.4452    |
| 0.0054        | 10.79 | 750  | 0.0084          | 4.8614    |
| 0.0056        | 11.51 | 800  | 0.0084          | 5.4922    |
| 0.005         | 12.23 | 850  | 0.0084          | 5.4324    |
| 0.0054        | 12.95 | 900  | 0.0083          | 5.6768    |
| 0.0051        | 13.67 | 950  | 0.0086          | 5.1234    |
| 0.0052        | 14.39 | 1000 | 0.0091          | 6.0069    |
| 0.0053        | 15.11 | 1050 | 0.0085          | 5.0867    |
| 0.0049        | 15.83 | 1100 | 0.0092          | 5.4711    |
| 0.0051        | 16.55 | 1150 | 0.0087          | 5.7106    |
| 0.0048        | 17.27 | 1200 | 0.0086          | 4.0867    |
| 0.0047        | 17.99 | 1250 | 0.0087          | 5.0113    |
| 0.0048        | 18.71 | 1300 | 0.0088          | 5.9662    |
| 0.0046        | 19.42 | 1350 | 0.0091          | 4.5553    |
| 0.005         | 20.14 | 1400 | 0.0088          | 6.0235    |
| 0.0054        | 20.86 | 1450 | 0.0085          | 6.0230    |
| 0.005         | 21.58 | 1500 | 0.0088          | 6.0230    |
| 0.0052        | 22.3  | 1550 | 0.0084          | 6.0186    |
| 0.0049        | 23.02 | 1600 | 0.0093          | 3.9496    |
| 0.005         | 23.74 | 1650 | 0.0089          | 3.9275    |
| 0.0046        | 24.46 | 1700 | 0.0085          | 4.7311    |
| 0.0049        | 25.18 | 1750 | 0.0089          | 5.2463    |
| 0.0045        | 25.9  | 1800 | 0.0088          | 3.8296    |
| 0.0044        | 26.62 | 1850 | 0.0088          | 4.5010    |
| 0.0043        | 27.34 | 1900 | 0.0090          | 2.7282    |
| 0.0045        | 28.06 | 1950 | 0.0089          | 3.4383    |
| 0.0038        | 28.78 | 2000 | 0.0094          | 1.3452    |
| 0.0042        | 29.5  | 2050 | 0.0089          | 3.1126    |
| 0.0039        | 30.22 | 2100 | 0.0092          | 2.4177    |
| 0.0042        | 30.94 | 2150 | 0.0092          | 2.7204    |
| 0.0037        | 31.65 | 2200 | 0.0092          | 2.6229    |
| 0.0039        | 32.37 | 2250 | 0.0095          | 2.3673    |
| 0.0038        | 33.09 | 2300 | 0.0102          | 1.4265    |
| 0.0034        | 33.81 | 2350 | 0.0098          | 2.4819    |
| 0.0032        | 34.53 | 2400 | 0.0110          | 1.9114    |
| 0.0034        | 35.25 | 2450 | 0.0111          | 1.4285    |
| 0.0031        | 35.97 | 2500 | 0.0108          | 2.0573    |
| 0.0027        | 36.69 | 2550 | 0.0109          | 1.6538    |
| 0.0028        | 37.41 | 2600 | 0.0114          | 1.9789    |
| 0.0029        | 38.13 | 2650 | 0.0108          | 2.1675    |
| 0.0026        | 38.85 | 2700 | 0.0112          | 2.3497    |
| 0.0026        | 39.57 | 2750 | 0.0124          | 2.1665    |
| 0.0026        | 40.29 | 2800 | 0.0119          | 1.4652    |
| 0.0024        | 41.01 | 2850 | 0.0119          | 2.0720    |
| 0.0022        | 41.73 | 2900 | 0.0122          | 1.2238    |
| 0.0022        | 42.45 | 2950 | 0.0128          | 1.5343    |
| 0.0021        | 43.17 | 3000 | 0.0121          | 1.3310    |
| 0.0019        | 43.88 | 3050 | 0.0131          | 1.3859    |
| 0.0018        | 44.6  | 3100 | 0.0131          | 0.8487    |
| 0.0017        | 45.32 | 3150 | 0.0133          | 0.9540    |
| 0.0016        | 46.04 | 3200 | 0.0131          | 1.0446    |
| 0.0014        | 46.76 | 3250 | 0.0135          | 0.6459    |
| 0.0014        | 47.48 | 3300 | 0.0137          | 0.6562    |
| 0.0013        | 48.2  | 3350 | 0.0135          | 0.5901    |
| 0.0012        | 48.92 | 3400 | 0.0139          | 0.5857    |
| 0.0012        | 49.64 | 3450 | 0.0139          | 0.5837    |


### Framework versions

- Transformers 4.38.2
- Pytorch 2.0.1
- Datasets 2.18.0
- Tokenizers 0.15.2