DREX-062225-exp

The DREX-062225-exp (Document Retrieval and Extraction eXpert) model is a specialized fine-tuned version of docscopeOCR-7B-050425-exp, optimized for Document Retrieval, Content Extraction, and Analysis Recognition. Built on top of the Qwen2.5-VL architecture, this model enhances document comprehension capabilities with focused training on the Opendoc2-Analysis-Recognition dataset for superior document analysis and information extraction tasks.

DREX: Document Retrieval and Extraction eXpert [ experimental ]

Key Enhancements

• Advanced Document Retrieval: Specialized capabilities for locating and retrieving specific information from complex document structures and layouts.

• Enhanced Content Extraction: Optimized for extracting structured data, key information, and relevant content from diverse document types including reports, forms, and technical documentation.

• Superior Analysis Recognition: Fine-tuned recognition abilities for document analysis tasks, pattern identification, and contextual understanding of document hierarchies.

• Inherited OCR Excellence: Maintains all advanced OCR capabilities from the base docscopeOCR model including mathematical LaTeX formatting and multi-language support.

• Document-Centric Understanding: Specialized training for understanding document relationships, cross-references, and contextual dependencies within complex document sets.

---

Markdown (.MD) - Inference

---

---

Quick Start with Transformers

from transformers import Qwen2_5_VLForConditionalGeneration, AutoTokenizer, AutoProcessor
from qwen_vl_utils import process_vision_info

model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
    "prithivMLmods/DREX-062225-exp", torch_dtype="auto", device_map="auto"
)

processor = AutoProcessor.from_pretrained("prithivMLmods/DREX-062225-exp")

messages = [
    {
        "role": "user",
        "content": [
            {
                "type": "image",
                "image": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg",
            },
            {"type": "text", "text": "Extract and analyze the key information from this document."},
        ],
    }
]

text = processor.apply_chat_template(
    messages, tokenize=False, add_generation_prompt=True
)
image_inputs, video_inputs = process_vision_info(messages)
inputs = processor(
    text=[text],
    images=image_inputs,
    videos=video_inputs,
    padding=True,
    return_tensors="pt",
)
inputs = inputs.to("cuda")

generated_ids = model.generate(**inputs, max_new_tokens=128)
generated_ids_trimmed = [
    out_ids[len(in_ids):] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
]
output_text = processor.batch_decode(
    generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
)
print(output_text)

---

Training Details

Parameter	Value
Dataset	Opendoc2-Analysis-Recognition
Dataset Size	6,910 samples
Base Model	docscopeOCR-7B-050425-exp
Model Architecture	Qwen2_5_VLForConditionalGeneration
Hardware	2 × A40 (19 vCPUs)
Total Disk	280,000 MB
Training Time	3,407 seconds (~0.95 hours)
Warmup Steps	250
Precision	bfloat16

This model builds upon the robust foundation of docscopeOCR-7B-050425-exp with specialized training for document retrieval and extraction tasks.

Intended Use

This model is specifically designed for:

• Document Retrieval: Efficiently locating specific information within large document collections and complex layouts.
• Content Extraction: Precise extraction of structured data, tables, forms, and key information from various document types.
• Analysis Recognition: Advanced recognition and analysis of document patterns, structures, and contextual relationships.
• Enterprise Document Processing: Automated processing of business documents, reports, contracts, and administrative forms.
• Research Document Analysis: Academic paper analysis, citation extraction, and research document comprehension.
• Regulatory Compliance: Processing of compliance documents, regulatory filings, and standardized reporting formats.

Limitations

• Inherits computational requirements from the base docscopeOCR model, requiring substantial resources for optimal performance.
• Performance may vary on document types significantly different from the Opendoc2-Analysis-Recognition training dataset.
• May show reduced accuracy on extremely specialized or domain-specific document formats not covered in training.
• Long document processing requires adequate memory allocation and may not be suitable for real-time streaming applications.
• Optimal performance depends on proper visual token configuration and input preprocessing.

References

• Base Model: docscopeOCR-7B-050425-exp https://huggingface.co/prithivMLmods/docscopeOCR-7B-050425-exp

• DocVLM: Make Your VLM an Efficient Reader https://arxiv.org/pdf/2412.08746v1

• YaRN: Efficient Context Window Extension of Large Language Models
  https://arxiv.org/pdf/2309.00071

• Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution
  https://arxiv.org/pdf/2409.12191

• Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond
  https://arxiv.org/pdf/2308.12966

• A Comprehensive and Challenging OCR Benchmark for Evaluating Large Multimodal Models in Literacy https://arxiv.org/pdf/2412.02210