metadata
library_name: transformers
license: mit
base_model: microsoft/Florence-2-base-ft
tags:
- generated_from_trainer
model-index:
- name: Florence-2-FT-JP-OCR
results: []
datasets:
- EtashGuha/JapaneseDocQA
Florence-2-FT-JP-OCR
This model is a fine-tuned version of microsoft/Florence-2-base-ft on EtashGuha/JapaneseDocQA dataset. It achieves the following results on the evaluation set:
- Loss: 2.5827
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-06
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 10
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 14.3012 | 0.9662 | 100 | 3.1360 |
| 12.8837 | 1.9275 | 200 | 2.9369 |
| 12.1693 | 2.8889 | 300 | 2.7978 |
| 11.6903 | 3.8502 | 400 | 2.7166 |
| 11.3443 | 4.8116 | 500 | 2.6688 |
| 11.1585 | 5.7729 | 600 | 2.6340 |
| 10.9682 | 6.7343 | 700 | 2.6140 |
| 10.9556 | 7.6957 | 800 | 2.5960 |
| 10.7355 | 8.6570 | 900 | 2.5882 |
| 10.6794 | 9.6184 | 1000 | 2.5827 |
Framework versions
- Transformers 4.48.0.dev0
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0