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---
base_model: facebook/nougat-base
library_name: transformers
license: cc-by-4.0
tags:
- generated_from_trainer
model-index:
- name: dhivehi-nougat-base
results: []
datasets:
- alakxender/dhivehi-image-text
language:
- dv
---
# DHIVEHI NOUGAT BASE (IMAGE-TO-TEXT)
This model is a fine-tuned version of [facebook/nougat-base](https://huggingface.co/facebook/nougat-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0142
## Model description
Finetuned dhivehi on text-image dataset, config all
## Usage
```python
from PIL import Image
import torch
from transformers import NougatProcessor, VisionEncoderDecoderModel
from pathlib import Path
# Load the model and processor
processor = NougatProcessor.from_pretrained("alakxender/dhivehi-nougat-base")
model = VisionEncoderDecoderModel.from_pretrained(
"alakxender/dhivehi-nougat-base",
torch_dtype=torch.bfloat16, # Optional: Load the model with BF16 data type for faster inference and lower memory usage
attn_implementation={ # Optional: Specify the attention kernel implementations for different parts of the model
"decoder": "flash_attention_2", # Use FlashAttention-2 for the decoder for improved performance
"encoder": "eager" # Use the default ("eager") attention implementation for the encoder
}
)
device = "cuda" if torch.cuda.is_available() else "cpu"
model.to(device)
context_length = 128
def predict(img_path):
# Ensure image is in RGB format
image = Image.open(img_path).convert("RGB")
pixel_values = processor(image, return_tensors="pt").pixel_values.to(torch.bfloat16)
# generate prediction
outputs = model.generate(
pixel_values.to(device),
min_length=1,
max_new_tokens=context_length,
repetition_penalty=1.5,
bad_words_ids=[[processor.tokenizer.unk_token_id]],
eos_token_id=processor.tokenizer.eos_token_id,
)
page_sequence = processor.batch_decode(outputs, skip_special_tokens=True)[0]
return page_sequence
print(predict("DV01-04_31.jpg"))
```
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 3
- eval_batch_size: 3
- seed: 42
- gradient_accumulation_steps: 6
- total_train_batch_size: 18
- 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: 100
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 6.4404 | 0.0057 | 100 | 1.0417 |
| 5.7761 | 0.0114 | 200 | 0.9055 |
| 5.1723 | 0.0171 | 300 | 0.8193 |
| 4.8315 | 0.0228 | 400 | 0.7661 |
| 4.4217 | 0.0285 | 500 | 0.7232 |
| 3.9861 | 0.0342 | 600 | 0.6724 |
| 3.7268 | 0.0400 | 700 | 0.5966 |
| 3.5393 | 0.0457 | 800 | 0.5337 |
| 2.8666 | 0.0514 | 900 | 0.4108 |
| 2.0269 | 0.0571 | 1000 | 0.2803 |
| 1.4121 | 0.0628 | 1100 | 0.1904 |
| 1.0161 | 0.0685 | 1200 | 0.1351 |
| 0.867 | 0.0742 | 1300 | 0.1130 |
| 0.7506 | 0.0799 | 1400 | 0.0950 |
| 0.5764 | 0.0856 | 1500 | 0.0801 |
| 0.5123 | 0.0913 | 1600 | 0.0716 |
| 0.558 | 0.0970 | 1700 | 0.0650 |
| 0.5242 | 0.1027 | 1800 | 0.0616 |
| 0.4229 | 0.1084 | 1900 | 0.0556 |
| 0.3721 | 0.1142 | 2000 | 0.0545 |
| 0.3388 | 0.1199 | 2100 | 0.0519 |
| 0.4042 | 0.1256 | 2200 | 0.0499 |
| 0.3593 | 0.1313 | 2300 | 0.0449 |
| 0.3837 | 0.1370 | 2400 | 0.0421 |
| 0.3291 | 0.1427 | 2500 | 0.0407 |
| 0.3092 | 0.1484 | 2600 | 0.0388 |
| 0.2762 | 0.1541 | 2700 | 0.0380 |
| 0.3073 | 0.1598 | 2800 | 0.0422 |
| 0.2577 | 0.1655 | 2900 | 0.0340 |
| 0.2596 | 0.1712 | 3000 | 0.0331 |
| 0.3397 | 0.1769 | 3100 | 0.0328 |
| 0.3019 | 0.1826 | 3200 | 0.0307 |
| 0.2522 | 0.1884 | 3300 | 0.0314 |
| 0.2546 | 0.1941 | 3400 | 0.0289 |
| 0.1972 | 0.1998 | 3500 | 0.0282 |
| 0.2231 | 0.2055 | 3600 | 0.0300 |
| 0.2342 | 0.2112 | 3700 | 0.0278 |
| 0.2152 | 0.2169 | 3800 | 0.0276 |
| 0.2059 | 0.2226 | 3900 | 0.0260 |
| 0.2165 | 0.2283 | 4000 | 0.0257 |
| 0.1919 | 0.2340 | 4100 | 0.0253 |
| 0.1608 | 0.2397 | 4200 | 0.0244 |
| 0.1673 | 0.2454 | 4300 | 0.0242 |
| 0.2004 | 0.2511 | 4400 | 0.0248 |
| 0.2277 | 0.2568 | 4500 | 0.0230 |
| 0.1831 | 0.2625 | 4600 | 0.0228 |
| 0.1905 | 0.2683 | 4700 | 0.0221 |
| 0.0996 | 0.2740 | 4800 | 0.0215 |
| 0.1596 | 0.2797 | 4900 | 0.0213 |
| 0.168 | 0.2854 | 5000 | 0.0208 |
| 0.2119 | 0.2911 | 5100 | 0.0215 |
| 0.1436 | 0.2968 | 5200 | 0.0202 |
| 0.1656 | 0.3025 | 5300 | 0.0202 |
| 0.1183 | 0.3082 | 5400 | 0.0194 |
| 0.1397 | 0.3139 | 5500 | 0.0202 |
| 0.1248 | 0.3196 | 5600 | 0.0191 |
| 0.1202 | 0.3253 | 5700 | 0.0191 |
| 0.1175 | 0.3310 | 5800 | 0.0207 |
| 0.1427 | 0.3367 | 5900 | 0.0183 |
| 0.1487 | 0.3425 | 6000 | 0.0178 |
| 0.1597 | 0.3482 | 6100 | 0.0174 |
| 0.1363 | 0.3539 | 6200 | 0.0172 |
| 0.1266 | 0.3596 | 6300 | 0.0171 |
| 0.1288 | 0.3653 | 6400 | 0.0170 |
| 0.1202 | 0.3710 | 6500 | 0.0170 |
| 0.1174 | 0.3767 | 6600 | 0.0164 |
| 0.1334 | 0.3824 | 6700 | 0.0168 |
| 0.1627 | 0.3881 | 6800 | 0.0164 |
| 0.0982 | 0.3938 | 6900 | 0.0161 |
| 0.1038 | 0.3995 | 7000 | 0.0160 |
| 0.1523 | 0.4052 | 7100 | 0.0160 |
| 0.1337 | 0.4109 | 7200 | 0.0157 |
| 0.2063 | 0.4167 | 7300 | 0.0153 |
| 0.1476 | 0.4224 | 7400 | 0.0156 |
| 0.0838 | 0.4281 | 7500 | 0.0150 |
| 0.082 | 0.4338 | 7600 | 0.0158 |
| 0.1269 | 0.4395 | 7700 | 0.0159 |
| 0.1168 | 0.4452 | 7800 | 0.0147 |
| 0.1024 | 0.4509 | 7900 | 0.0147 |
| 0.1138 | 0.4566 | 8000 | 0.0145 |
| 0.1188 | 0.4623 | 8100 | 0.0146 |
| 0.0881 | 0.4680 | 8200 | 0.0142 |
| 0.0752 | 0.4737 | 8300 | 0.0138 |
| 0.1165 | 0.4794 | 8400 | 0.0141 |
| 0.1017 | 0.4851 | 8500 | 0.0137 |
| 0.0971 | 0.4909 | 8600 | 0.0135 |
| 0.135 | 0.4966 | 8700 | 0.0136 |
| 0.0732 | 0.5023 | 8800 | 0.0137 |
| 0.1217 | 0.5080 | 8900 | 0.0142 |
### Framework versions
- Transformers 4.47.0
- Pytorch 2.6.0+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0 |