---
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: 0.0971
- Wer Score: 4.3530

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|
| 7.0263        | 0.72  | 50   | 4.1468          | 2.9871    |
| 1.9906        | 1.45  | 100  | 0.2971          | 0.4260    |
| 0.1401        | 2.17  | 150  | 0.0827          | 2.2259    |
| 0.0772        | 2.9   | 200  | 0.0704          | 3.9564    |
| 0.0567        | 3.62  | 250  | 0.0677          | 3.6360    |
| 0.0501        | 4.35  | 300  | 0.0678          | 3.2940    |
| 0.0443        | 5.07  | 350  | 0.0664          | 3.3094    |
| 0.0386        | 5.8   | 400  | 0.0666          | 3.0884    |
| 0.035         | 6.52  | 450  | 0.0681          | 3.3818    |
| 0.0329        | 7.25  | 500  | 0.0667          | 3.2474    |
| 0.0297        | 7.97  | 550  | 0.0681          | 3.6648    |
| 0.0271        | 8.7   | 600  | 0.0700          | 3.9405    |
| 0.0257        | 9.42  | 650  | 0.0697          | 3.0393    |
| 0.0243        | 10.14 | 700  | 0.0701          | 3.5973    |
| 0.0233        | 10.87 | 750  | 0.0695          | 3.0565    |
| 0.0213        | 11.59 | 800  | 0.0721          | 3.6980    |
| 0.0203        | 12.32 | 850  | 0.0730          | 3.8883    |
| 0.0206        | 13.04 | 900  | 0.0718          | 4.9104    |
| 0.0187        | 13.77 | 950  | 0.0729          | 4.4850    |
| 0.0192        | 14.49 | 1000 | 0.0743          | 4.6366    |
| 0.0184        | 15.22 | 1050 | 0.0729          | 4.9141    |
| 0.0169        | 15.94 | 1100 | 0.0754          | 4.6446    |
| 0.0167        | 16.67 | 1150 | 0.0769          | 4.8232    |
| 0.0162        | 17.39 | 1200 | 0.0766          | 4.7569    |
| 0.0165        | 18.12 | 1250 | 0.0768          | 4.3266    |
| 0.0157        | 18.84 | 1300 | 0.0776          | 4.1375    |
| 0.0144        | 19.57 | 1350 | 0.0778          | 3.9724    |
| 0.015         | 20.29 | 1400 | 0.0790          | 4.7041    |
| 0.0146        | 21.01 | 1450 | 0.0780          | 4.5003    |
| 0.0137        | 21.74 | 1500 | 0.0794          | 4.2167    |
| 0.0141        | 22.46 | 1550 | 0.0792          | 4.4856    |
| 0.0137        | 23.19 | 1600 | 0.0805          | 4.2634    |
| 0.0137        | 23.91 | 1650 | 0.0817          | 4.4162    |
| 0.0127        | 24.64 | 1700 | 0.0804          | 4.0319    |
| 0.0127        | 25.36 | 1750 | 0.0829          | 4.3628    |
| 0.013         | 26.09 | 1800 | 0.0826          | 4.4211    |
| 0.0121        | 26.81 | 1850 | 0.0823          | 4.8932    |
| 0.0119        | 27.54 | 1900 | 0.0835          | 4.6636    |
| 0.012         | 28.26 | 1950 | 0.0842          | 3.8926    |
| 0.0118        | 28.99 | 2000 | 0.0844          | 3.9994    |
| 0.011         | 29.71 | 2050 | 0.0833          | 4.1743    |
| 0.0109        | 30.43 | 2100 | 0.0864          | 4.4217    |
| 0.0108        | 31.16 | 2150 | 0.0851          | 4.8029    |
| 0.0103        | 31.88 | 2200 | 0.0855          | 4.0694    |
| 0.01          | 32.61 | 2250 | 0.0871          | 4.3198    |
| 0.0102        | 33.33 | 2300 | 0.0863          | 4.4082    |
| 0.0099        | 34.06 | 2350 | 0.0871          | 4.2112    |
| 0.0094        | 34.78 | 2400 | 0.0872          | 4.1774    |
| 0.0092        | 35.51 | 2450 | 0.0887          | 3.9742    |
| 0.009         | 36.23 | 2500 | 0.0882          | 4.1958    |
| 0.0088        | 36.96 | 2550 | 0.0893          | 4.2591    |
| 0.0084        | 37.68 | 2600 | 0.0885          | 4.2983    |
| 0.0079        | 38.41 | 2650 | 0.0894          | 4.6550    |
| 0.008         | 39.13 | 2700 | 0.0904          | 4.1277    |
| 0.0076        | 39.86 | 2750 | 0.0908          | 3.6771    |
| 0.0072        | 40.58 | 2800 | 0.0912          | 4.1252    |
| 0.0072        | 41.3  | 2850 | 0.0908          | 4.5660    |
| 0.0069        | 42.03 | 2900 | 0.0917          | 3.9441    |
| 0.0062        | 42.75 | 2950 | 0.0924          | 4.2259    |
| 0.006         | 43.48 | 3000 | 0.0924          | 4.2167    |
| 0.0059        | 44.2  | 3050 | 0.0937          | 4.6047    |
| 0.0055        | 44.93 | 3100 | 0.0945          | 4.4408    |
| 0.0048        | 45.65 | 3150 | 0.0950          | 3.9871    |
| 0.0048        | 46.38 | 3200 | 0.0952          | 4.2259    |
| 0.0046        | 47.1  | 3250 | 0.0962          | 4.2204    |
| 0.0042        | 47.83 | 3300 | 0.0963          | 4.2750    |
| 0.0037        | 48.55 | 3350 | 0.0971          | 4.1891    |
| 0.0039        | 49.28 | 3400 | 0.0970          | 4.3100    |
| 0.0036        | 50.0  | 3450 | 0.0971          | 4.3530    |


### Framework versions

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