Downloads · 30 days
3
7% of all-time downloads
florence-2
Tuan-Bao/florence-2
florence-2 is a machine learning model from Tuan-Bao. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as mit.
should probably proofread and complete it, then remove this comment. --
All-time downloads
43
Public
Parameters
202M
809 MB on disk
Likes
0
Public
Repo makeup
Click a slice to open those files.
.safetensors809 MB · 99%
Parameter types
How the weights are stored.
F32202M · 100%
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. -->
donut-finetuned-v2.1
This model is a fine-tuned version of naver-clova-ix/donut-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0396
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: 2e-05
- train_batch_size: 2
- eval_batch_size: 1
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 4.2166 | 0.0394 | 200 | 2.8622 |
| 1.7778 | 0.0788 | 400 | 1.6184 |
| 1.3467 | 0.1182 | 600 | 1.0477 |
| 0.6550 | 0.1577 | 800 | 0.6482 |
| 0.5164 | 0.1971 | 1000 | 0.4542 |
| 0.3735 | 0.2365 | 1200 | 0.3639 |
| 0.4138 | 0.2759 | 1400 | 0.2679 |
| 0.2386 | 0.3153 | 1600 | 0.2156 |
| 0.2029 | 0.3547 | 1800 | 0.1628 |
| 0.2021 | 0.3942 | 2000 | 0.1289 |
| 0.1504 | 0.4336 | 2200 | 0.1080 |
| 0.1357 | 0.4730 | 2400 | 0.0904 |
| 0.0719 | 0.5124 | 2600 | 0.0798 |
| 0.0880 | 0.5518 | 2800 | 0.0715 |
| 0.0867 | 0.5912 | 3000 | 0.0616 |
| 0.0563 | 0.6307 | 3200 | 0.0601 |
| 0.1239 | 0.6701 | 3400 | 0.0550 |
| 0.0483 | 0.7095 | 3600 | 0.0513 |
| 0.0445 | 0.7489 | 3800 | 0.0480 |
| 0.0259 | 0.7883 | 4000 | 0.0469 |
| 0.0223 | 0.8277 | 4200 | 0.0448 |
| 0.0665 | 0.8672 | 4400 | 0.0426 |
| 0.0254 | 0.9066 | 4600 | 0.0413 |
| 0.0205 | 0.9460 | 4800 | 0.0404 |
| 0.0353 | 0.9854 | 5000 | 0.0396 |
Framework versions
- Transformers 5.0.0
- Pytorch 2.10.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.2
- Library
- transformers
- Type
- vision-encoder-decoder
- License
- mit
- Created
- May 21, 2026
- Updated
- May 21, 2026