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APNR-Braincore-V2
Sans1807/APNR-Braincore-V2
APNR-Braincore-V2 is a image-text-to-text model from Sans1807. 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. --
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At a glance
- Task
- Image-Text-to-Text
- Library
- transformers
- Model type
- vision-encoder-decoder
- Access
- Public
- Created
- Oct 9, 2024
- Updated
- Oct 9, 2024
- SHA
- 004a8435
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. -->
microsoft/trocr-base-str
This model is a fine-tuned version of microsoft/trocr-base-str on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0856
- Cer: 0.0098
- Wer: 0.0573
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: 1e-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
- num_epochs: 20
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Cer | Wer |
|---|---|---|---|---|---|
| 1.6623 | 1.0 | 217 | 0.4722 | 0.0574 | 0.2340 |
| 0.4122 | 2.0 | 434 | 0.3248 | 0.0378 | 0.1585 |
| 0.1077 | 3.0 | 651 | 0.0898 | 0.0132 | 0.0722 |
| 0.047 | 4.0 | 868 | 0.0848 | 0.0114 | 0.0614 |
| 0.0304 | 5.0 | 1085 | 0.0836 | 0.0122 | 0.0634 |
| 0.0224 | 6.0 | 1302 | 0.0891 | 0.0104 | 0.0566 |
| 0.0154 | 7.0 | 1519 | 0.0873 | 0.0107 | 0.0587 |
| 0.0137 | 8.0 | 1736 | 0.0852 | 0.0102 | 0.0560 |
| 0.0121 | 9.0 | 1953 | 0.0883 | 0.0107 | 0.0634 |
| 0.0095 | 10.0 | 2170 | 0.0829 | 0.0092 | 0.0526 |
| 0.0068 | 11.0 | 2387 | 0.0851 | 0.0091 | 0.0519 |
| 0.0075 | 12.0 | 2604 | 0.0831 | 0.0102 | 0.0600 |
| 0.0055 | 13.0 | 2821 | 0.0824 | 0.0098 | 0.0580 |
| 0.0048 | 14.0 | 3038 | 0.0821 | 0.0099 | 0.0587 |
| 0.0023 | 15.0 | 3255 | 0.0873 | 0.0096 | 0.0553 |
| 0.0018 | 16.0 | 3472 | 0.0835 | 0.0102 | 0.0593 |
| 0.0016 | 17.0 | 3689 | 0.0888 | 0.0100 | 0.0600 |
| 0.0034 | 18.0 | 3906 | 0.0853 | 0.0094 | 0.0553 |
| 0.001 | 19.0 | 4123 | 0.0857 | 0.0096 | 0.0566 |
| 0.0013 | 20.0 | 4340 | 0.0856 | 0.0098 | 0.0573 |
Framework versions
- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 2.17.0
- Tokenizers 0.19.1
- Task
- Image-Text-to-Text
- Library
- transformers
- Type
- vision-encoder-decoder
- Created
- Oct 9, 2024
- Updated
- Oct 9, 2024