Image-Text-to-Text
PEFT
Safetensors
Turkish
paligemma
lora
document-question-answering
visual-question-answering
turkish
tr-docvqa
Instructions to use Ethosoft/trdocvqa-paligemma-3b-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Ethosoft/trdocvqa-paligemma-3b-lora with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("google/paligemma-3b-pt-224") model = PeftModel.from_pretrained(base_model, "Ethosoft/trdocvqa-paligemma-3b-lora") - Notebooks
- Google Colab
- Kaggle
| model,metric,value,ci95_low,ci95_high,bootstrap_iters | |
| donut-tr,EM,0.015,0.01,0.0205,1000 | |
| donut-tr,Normalized EM,0.0215,0.015,0.0285,1000 | |
| donut-tr,ANLS,0.04740478199269954,0.039238324767232484,0.05648583138346346,1000 | |
| donut-tr,Token F1,0.043477974802974814,0.03678377733377733,0.05140066461316464,1000 | |
| paligemma-3b-lora,EM,0.7205,0.701,0.7405,1000 | |
| paligemma-3b-lora,Normalized EM,0.7205,0.701,0.7405,1000 | |
| paligemma-3b-lora,ANLS,0.8744819224833323,0.8615901361915446,0.8872022218772235,1000 | |
| paligemma-3b-lora,Token F1,0.7293814941766181,0.7112880116959064,0.7499092836257306,1000 | |
| pix2struct-docvqa-zeroshot,EM,0.2105,0.192,0.229,1000 | |
| pix2struct-docvqa-zeroshot,Normalized EM,0.313,0.291,0.334,1000 | |
| pix2struct-docvqa-zeroshot,ANLS,0.5537995436963891,0.5343424548480816,0.5736398189606926,1000 | |
| pix2struct-docvqa-zeroshot,Token F1,0.3538515446971328,0.3327193722943723,0.3736668044373925,1000 | |