Mit1208/Florence-2-DocLayNet
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Model Details
Model Description
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by: [Mit Patel]
- Shared by [optional]: [Mit Patel]
- Finetuned from model [optional]: https://huggingface.co/microsoft/Florence-2-base-ft
Recommendations
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
How to Get Started with the Model
Use the code below to get started with the model.
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Training Details
Training Data
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Inference Procedure
!pip install -qU transformers
!pip install -qU accelerate bitsandbytes einops flash_attn timm
!pip install -q datasets
from PIL import Image
import requests
import torch
from transformers import AutoProcessor, AutoModelForVision2Seq, BitsAndBytesConfig, TrainingArguments, AutoModelForCausalLM
import requests
import re
from transformers import AutoConfig, AutoProcessor, AutoModelForCausalLM
base_model = AutoModelForCausalLM.from_pretrained("microsoft/Florence-2-base-ft", trust_remote_code=True,)
processor = AutoProcessor.from_pretrained("microsoft/Florence-2-base-ft", trust_remote_code=True,)
model = AutoModelForCausalLM.from_pretrained("Mit1208/Florence-2-DocLayNet", trust_remote_code=True, config = base_model.config)
def run_example(task_prompt, image, text_input=None):
if text_input is None:
prompt = task_prompt
else:
prompt = task_prompt + text_input
print(prompt)
inputs = processor(text=prompt, images=image, return_tensors="pt").to(device)
generated_ids = model.generate(
input_ids=inputs["input_ids"],
pixel_values=inputs["pixel_values"],
max_new_tokens=1024,
early_stopping=False,
do_sample=False,
num_beams=3,
)
generated_text = processor.batch_decode(generated_ids, skip_special_tokens=False)[0]
print(generated_text)
parsed_answer = processor.post_process_generation(
generated_text,
task=task_prompt,
image_size=(image.width, image.height)
)
return parsed_answer
from PIL import Image
import requests
image = Image.open('form-1.png').convert('RGB')
task_prompt = '<OD>'
results = run_example(task_prompt, example['image'].resize(size=(1000, 1000)))
print(results)
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