EraX VL 7B V1
EraX VL 7B V1 is a powerful multimodal model for OCR and VQA tasks, with a strong focus on Vietnamese language support. What makes this model stand out is its ability to perform multi-turn Q&A with impressive reasoning capabilities, thanks to its 7+ billion parameters. But what does this mean for you? It means you can use EraX VL 7B V1 to extract information from documents, answer complex questions, and even generate text based on images. With its robust performance in various languages, this model is particularly useful for applications in hospitals, clinics, and insurance companies. So, how does it work? EraX VL 7B V1 is built on the Qwen/Qwen2-VL-7B-Instruct model, which has been fine-tuned to enhance its performance. The result is a model that can handle a wide range of tasks, from OCR to VQA, with high accuracy and speed. But don't just take our word for it - check out the examples to see EraX VL 7B V1 in action.
Table of Contents
Model Overview
The EraX-VL-7B-V1 model is a robust multimodal AI model that excels in various languages, with a particular focus on Vietnamese. It’s designed for OCR (optical character recognition) and VQA (visual question-answering) tasks.
What makes it special?
- Multimodal capabilities: It can handle both text and images, making it a versatile tool for various applications.
- Strong recognition capabilities: It can accurately recognize text in various documents, including medical forms, invoices, and bills of sale.
- Reasoning skills: It can perform multi-turn Q&A with good reasoning, thanks to its large size of over
7Bparameters.
Where can it be applied?
- Hospitals and clinics: It can help with document processing and patient data management.
- Insurance companies: It can assist with claims processing and document verification.
- Other applications: It can be used in various industries where document processing and visual question-answering are crucial.
Capabilities
Key Strengths
- Multilingual capabilities: Primarily supports Vietnamese, but also has multilingual capabilities.
- High precision: Excels in recognizing text from various documents, including medical forms, invoices, bills of sale, quotes, and medical records.
- Good reasoning: Capable of multi-turn Q&A with good reasoning.
Unique Features
- Multimodal LLM-based model: Not a typical OCR-only tool, but a multimodal model that can handle various tasks.
- 7+ billion parameters: A large model that enables it to perform complex tasks.
Example Use Cases
- Extracting text from medical records: EraX-VL-7B-V1 can extract text from medical records with high precision, making it useful for hospitals, clinics, and insurance companies.
- Answering questions about images: The model can answer questions about images, including multi-turn Q&A, making it useful for applications that require image analysis.
- Generating captions for images: EraX-VL-7B-V1 can generate captions for images, including handwritten text, making it useful for applications that require image description.
Performance
The EraX-VL-7B-V1 model showcases remarkable performance in various tasks, including OCR (optical character recognition) and VQA (visual question-answering).
Speed
How fast can EraX-VL-7B-V1 process images and documents? With its robust architecture, it can quickly recognize characters, extract information, and answer questions.
Accuracy
EraX-VL-7B-V1 boasts high accuracy in recognizing characters and extracting information from documents. Its performance is particularly impressive in Vietnamese, with a strong ability to understand nuances and complexities of the language.
Efficiency
EraX-VL-7B-V1 is not just fast and accurate; it’s also efficient. With its multimodal architecture, it can handle a wide range of tasks, from OCR to VQA, without requiring significant computational resources.
Comparison to Other Models
How does EraX-VL-7B-V1 compare to other models? While ==Other Models== may excel in specific tasks, EraX-VL-7B-V1 offers a unique combination of speed, accuracy, and efficiency that makes it an excellent choice for a wide range of applications.
Limitations
EraX-VL-7B-V1 is a powerful multimodal model, but it’s not perfect. Let’s explore some of its limitations.
Language Limitations
While EraX-VL-7B-V1 excels in Vietnamese, its performance may vary in other languages. This is because it was primarily trained on Vietnamese data, which might not be sufficient for other languages.
Domain Knowledge Limitations
EraX-VL-7B-V1 has been fine-tuned for specific tasks like OCR and VQA, but its knowledge in other domains might be limited. For example, it might not perform well in tasks that require specialized knowledge in medicine, law, or finance.
Format
EraX-VL-7B-V1 is a multimodal model that excels in OCR (optical character recognition) and VQA (visual question-answering) tasks. It supports multiple languages, with a primary focus on Vietnamese.
Architecture
The model is built on a multimodal transformer architecture with over 7B parameters. It’s designed to handle various types of input, including images and text.
Data Formats
EraX-VL-7B-V1 accepts input in the following formats:
- Images (e.g.,
JPEG,PNG) - PDF documents
- Text sequences (e.g.,
JSON)
Input Requirements
To use EraX-VL-7B-V1 effectively, you’ll need to carefully craft your prompts depending on the task at hand. Here are some examples:
- For OCR tasks, provide a clear image of the text you want to recognize.
- For VQA tasks, provide a relevant image and a specific question about the image.
Output
The model’s output will vary depending on the task. For example:
- OCR tasks will return the recognized text in a structured format (e.g.,
JSON). - VQA tasks will return a relevant answer to the question.
Special Requirements
EraX-VL-7B-V1 is not a typical OCR-only tool, but rather a multimodal LLM-based model. This means you may need to adjust your input and output handling accordingly.
Example Code
Here’s an example of how to handle input and output for EraX-VL-7B-V1:
// OCR example
{
"document": {
"header": {
"title": "GIẤY HẸN KHÁM LẠI",
"organization": "SỞ Y TẾ NGHỆ AN\\nBỆNH VIỆN UNG BƯỚU NGHỆ AN",
"address": "Võ Thị Sáu, Thủy Tùng - TP Vinh - Nghệ An"
},
"patient_info": {
"name": "NGUYỄN THỊ LUÂN",
"date_of_birth": "03/07/1976",
"gender": "40",
"address": "Xã Nghĩa Khánh-Huyện Nghĩa Đàn-Nghệ An",
"medical_card_number": "CN 3 40 40 168 60413",
"registration_date": "16/12/2016",
"admission_date": "Từ 01/03/2016",
"diagnosis": "C20-Bướu ac trực tràng",
"revisit_date": "17/01/2017"
},
"administrative_details": {
"department": "Trung tâm điều trị ung bướu",
"revisit_instruction": "vào ngày 17/01/2017, hoặc đến hết kỳ thời gian nếu nước ngoài hẹn khám lại nếu có dấu hiệu (triệu chứng)",
"note": "nếu KCB ban đầu: Trạm y tế xã Nghĩa Khánh",
"signature": "Trưởng khoa",
"doctor_signature": "Lâm Nguyễn Khang",
"revisiting_date_confirmation": "Ngày 16 tháng 12 năm 2016",
"confirmation_signature": "Bác sĩ điều trị",
"physician_signature": "Nguyễn Văn Việt"
}
}
}
This example shows how to structure the input for an OCR task. The output will be the recognized text in a similar format.
