Image-to-Text
PEFT
Safetensors
Portuguese
English
vision-language
table-extraction
scientific-figures
markdown-table
qwen2.5-vl
lora
icdar-metric-loss
Instructions to use lucasoc/sci-image-models with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use lucasoc/sci-image-models with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-VL-3B-Instruct") model = PeftModel.from_pretrained(base_model, "lucasoc/sci-image-models") - Notebooks
- Google Colab
- Kaggle
File size: 537 Bytes
48dc456 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | {
"valid_table": 0.9973190348525469,
"exact_match": 0.002680965147453083,
"edit_similarity": 0.38106812583065297,
"rouge_l": 0.384215920331165,
"rouge_1": 0.4313245229429092,
"rouge_2": 0.23438600558595007,
"bleu_4": 0.19265916634631913,
"cell_precision": 0.47449411624740195,
"cell_recall": 0.6043179468792442,
"cell_f1": 0.43105424843380974,
"cell_rmse": 1.2001784360768568,
"cell_rne": 0.004605617228839706,
"teds": 0.43956215186081643,
"icdar_rms": 0.602554720605129,
"icdar_score": 0.5210584362329728
} |