Downloads · 30 days
4
9% of all-time downloads
TrOCR-SIN-DeiT-Handwritten-Beam10
kavg/TrOCR-SIN-DeiT-Handwritten-Beam10
TrOCR-SIN-DeiT-Handwritten-Beam10 is a image-text-to-text model from kavg. Use it for the image-text-to-text task on the model card, and read the license before you ship it in a product. It is set up for transformers.
should probably proofread and complete it, then remove this comment. --
All-time downloads
43
Public
Parameters
241M
963 MB on disk
Likes
0
Public
Repo makeup
Click a slice to open those files.
.safetensors963 MB · 100%
At a glance
- Task
- Image-Text-to-Text
- Library
- transformers
- Model type
- vision-encoder-decoder
- Access
- Public
- Created
- Apr 26, 2024
- Updated
- Apr 26, 2024
- SHA
- 9d1af47b
Try a prompt
Base models
Model card
From the Hugging Face model README
<!-- 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. -->
TrOCR-SIN-DeiT-Handwritten-Beam10
This model is a fine-tuned version of kavg/TrOCR-SIN-DeiT on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 2.2754
- Cer: 0.5246
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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 2400
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Cer | Validation Loss |
|---|---|---|---|---|
| 0.9957 | 1.75 | 100 | 0.6176 | 1.6796 |
| 0.0678 | 3.51 | 200 | 0.5996 | 1.7777 |
| 0.1315 | 5.26 | 300 | 0.6794 | 2.1444 |
| 0.0668 | 7.02 | 400 | 0.6363 | 2.0162 |
| 0.0656 | 8.77 | 500 | 0.6046 | 1.9573 |
| 0.0612 | 10.53 | 600 | 0.6330 | 1.9388 |
| 0.0454 | 12.28 | 700 | 0.6679 | 3.0649 |
| 0.004 | 14.04 | 800 | 0.5814 | 2.0252 |
| 0.0034 | 15.79 | 900 | 0.5492 | 2.0399 |
| 0.0336 | 17.54 | 1000 | 0.6041 | 2.9769 |
| 0.0135 | 19.3 | 1100 | 0.5742 | 1.9405 |
| 0.0012 | 21.05 | 1200 | 0.5959 | 2.5722 |
| 0.0143 | 22.81 | 1300 | 0.5527 | 2.0862 |
| 0.0018 | 24.56 | 1400 | 0.5764 | 2.4146 |
| 0.0064 | 26.32 | 1500 | 0.5647 | 2.0710 |
| 0.0006 | 28.07 | 1600 | 0.5472 | 2.1849 |
| 0.0004 | 29.82 | 1700 | 0.5547 | 2.4497 |
| 0.0001 | 31.58 | 1800 | 0.5430 | 2.0830 |
| 0.0215 | 33.33 | 1900 | 0.5560 | 2.5979 |
| 0.0 | 35.09 | 2000 | 0.5525 | 2.4792 |
| 0.0 | 36.84 | 2100 | 0.5428 | 2.4779 |
| 0.0 | 38.6 | 2200 | 0.5438 | 2.7873 |
| 0.0 | 40.35 | 2300 | 0.5552 | 2.9236 |
| 0.0 | 42.11 | 2400 | 0.5246 | 2.2754 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.1
- Task
- Image-Text-to-Text
- Library
- transformers
- Type
- vision-encoder-decoder
- Created
- Apr 26, 2024
- Updated
- Apr 26, 2024