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---
library_name: transformers
license: mit
datasets:
- pierreguillou/DocLayNet-small
language:
- en
pipeline_tag: image-text-to-text
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
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
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Inference Procedure
```python
!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)
```
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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