Nuextract 2.0 8B

🚀 NuExtract 2.0 8B by NuMind

NuExtract 2.0是專門為結構化信息提取任務訓練的一系列模型。它支持多模態輸入，並且具備多語言處理能力。我們提供了幾種不同規模的版本，所有版本均基於QwenVL系列的預訓練模型。

🚀 快速開始

模型選擇

我們提供了不同大小的模型版本，具體信息如下：

⚠️ 重要提示

NuExtract-2.0-2B基於Qwen2-VL而非Qwen2.5-VL，因為最小的Qwen2.5-VL模型（3B）具有更嚴格的非商業許可證。因此，我們將NuExtract-2.0-2B作為可用於商業用途的小模型選項。

✨ 主要特性

• 多模態支持：支持文本和圖像的多模態輸入。
• 多語言處理：具備多語言處理能力。
• 多種輸出類型：支持多種輸出類型，如字符串、整數、日期等。

📦 安裝指南

使用前請確保已經安裝了transformers庫。以下是使用transformers加載模型的示例代碼：

import torch
from transformers import AutoProcessor, AutoModelForVision2Seq

model_name = "numind/NuExtract-2.0-2B"
# model_name = "numind/NuExtract-2.0-8B"

model = AutoModelForVision2Seq.from_pretrained(model_name, 
                                               trust_remote_code=True, 
                                               torch_dtype=torch.bfloat16,
                                               attn_implementation="flash_attention_2",
                                               device_map="auto")
processor = AutoProcessor.from_pretrained(model_name, 
                                          trust_remote_code=True, 
                                          padding_side='left',
                                          use_fast=True)

# You can set min_pixels and max_pixels according to your needs, such as a token range of 256-1280, to balance performance and cost.
# min_pixels = 256*28*28
# max_pixels = 1280*28*28
# processor = AutoProcessor.from_pretrained(model_name, min_pixels=min_pixels, max_pixels=max_pixels)

💻 使用示例

基礎用法

以下是一個從文本中提取姓名的基礎示例：

template = """{"names": ["string"]}"""
document = "John went to the restaurant with Mary. James went to the cinema."

# prepare the user message content
messages = [{"role": "user", "content": document}]
text = processor.tokenizer.apply_chat_template(
    messages,
    template=template, # template is specified here
    tokenize=False,
    add_generation_prompt=True,
)

print(text)
""""<|im_start|>user
# Template:
{"names": ["string"]}
# Context:
John went to the restaurant with Mary. James went to the cinema.<|im_end|> 
<|im_start|>assistant"""

image_inputs = process_all_vision_info(messages)
inputs = processor(
    text=[text],
    images=image_inputs,
    padding=True,
    return_tensors="pt",
).to("cuda")

# we choose greedy sampling here, which works well for most information extraction tasks
generation_config = {"do_sample": False, "num_beams": 1, "max_new_tokens": 2048}

# Inference: Generation of the output
generated_ids = model.generate(
    **inputs,
    **generation_config
)
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)
# ['{"names": ["John", "Mary", "James"]}']

高級用法

上下文示例

有時候模型可能無法達到我們期望的效果，因為任務具有挑戰性或存在一定的歧義。在這種情況下，提供“上下文示例”可以幫助模型更好地理解任務。以下是一個使用上下文示例的示例：

template = """{"names": ["string"]}"""
document = "John went to the restaurant with Mary. James went to the cinema."
examples = [
    {
        "input": "Stephen is the manager at Susan's store.",
        "output": """{"names": ["-STEPHEN-", "-SUSAN-"]}"""
    }
]

messages = [{"role": "user", "content": document}]
text = processor.tokenizer.apply_chat_template(
    messages,
    template=template,
    examples=examples, # examples provided here
    tokenize=False,
    add_generation_prompt=True,
)

image_inputs = process_all_vision_info(messages, examples)
inputs = processor(
    text=[text],
    images=image_inputs,
    padding=True,
    return_tensors="pt",
).to("cuda")

# we choose greedy sampling here, which works well for most information extraction tasks
generation_config = {"do_sample": False, "num_beams": 1, "max_new_tokens": 2048}

# Inference: Generation of the output
generated_ids = model.generate(
    **inputs,
    **generation_config
)
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)
# ['{"names": ["-JOHN-", "-MARY-", "-JAMES-"]}']

圖像輸入

如果要給模型提供圖像輸入，只需將消息內容指定為一個描述所需圖像文件的字典，而不是字符串。以下是一個使用圖像輸入的示例：

template = """{"store": "verbatim-string"}"""
document = {"type": "image", "image": "file://1.jpg"}

messages = [{"role": "user", "content": [document]}]
text = processor.tokenizer.apply_chat_template(
    messages,
    template=template,
    tokenize=False,
    add_generation_prompt=True,
)

image_inputs = process_all_vision_info(messages)
inputs = processor(
    text=[text],
    images=image_inputs,
    padding=True,
    return_tensors="pt",
).to("cuda")

generation_config = {"do_sample": False, "num_beams": 1, "max_new_tokens": 2048}

# Inference: Generation of the output
generated_ids = model.generate(
    **inputs,
    **generation_config
)
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)
# ['{"store": "Trader Joe\'s"}']

批量推理

以下是一個批量推理的示例：

inputs = [
    # image input with no ICL examples
    {
        "document": {"type": "image", "image": "file://0.jpg"},
        "template": """{"store_name": "verbatim-string"}""",
    },
    # image input with 1 ICL example
    {
        "document": {"type": "image", "image": "file://0.jpg"},
        "template": """{"store_name": "verbatim-string"}""",
        "examples": [
            {
                "input": {"type": "image", "image": "file://1.jpg"},
                "output": """{"store_name": "Trader Joe's"}""",
            }
        ],
    },
    # text input with no ICL examples
    {
        "document": {"type": "text", "text": "John went to the restaurant with Mary. James went to the cinema."},
        "template": """{"names": ["string"]}""",
    },
    # text input with ICL example
    {
        "document": {"type": "text", "text": "John went to the restaurant with Mary. James went to the cinema."},
        "template": """{"names": ["string"]}""",
        "examples": [
            {
                "input": "Stephen is the manager at Susan's store.",
                "output": """{"names": ["STEPHEN", "SUSAN"]}"""
            }
        ],
    },
]

# messages should be a list of lists for batch processing
messages = [
    [
        {
            "role": "user",
            "content": [x['document']],
        }
    ]
    for x in inputs
]

# apply chat template to each example individually
texts = [
    processor.tokenizer.apply_chat_template(
        messages[i],  # Now this is a list containing one message
        template=x['template'],
        examples=x.get('examples', None),
        tokenize=False, 
        add_generation_prompt=True)
    for i, x in enumerate(inputs)
]

image_inputs = process_all_vision_info(messages, [x.get('examples') for x in inputs])
inputs = processor(
    text=texts,
    images=image_inputs,
    padding=True,
    return_tensors="pt",
).to("cuda")

generation_config = {"do_sample": False, "num_beams": 1, "max_new_tokens": 2048}

# Batch Inference
generated_ids = model.generate(**inputs, **generation_config)
generated_ids_trimmed = [
    out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
]
output_texts = processor.batch_decode(
    generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
)
for y in output_texts:
    print(y)
# {"store_name": "WAL-MART"}
# {"store_name": "Walmart"}
# {"names": ["John", "Mary", "James"]}
# {"names": ["JOHN", "MARY", "JAMES"]}

模板生成

如果想將現有的其他格式的模式文件（如XML、YAML等）轉換為NuExtract模板，或者從自然語言描述生成模板，NuExtract 2.0模型可以自動為你生成。以下是兩個示例：

# convert XML into a NuExtract template
xml_template = """<SportResult>
    <Date></Date>
    <Sport></Sport>
    <Venue></Venue>
    <HomeTeam></HomeTeam>
    <AwayTeam></AwayTeam>
    <HomeScore></HomeScore>
    <AwayScore></AwayScore>
    <TopScorer></TopScorer>
</SportResult>"""

messages = [
        {
            "role": "user",
            "content": [{"type": "text", "text": xml_template}],
        }
    ]

text = processor.apply_chat_template(
    messages, tokenize=False, add_generation_prompt=True,
)

image_inputs = process_all_vision_info(messages)
inputs = processor(
    text=[text],
    images=image_inputs,
    padding=True,
    return_tensors="pt",
).to("cuda")

generated_ids = model.generate(
    **inputs,
    **generation_config
)
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[0])
# {
#     "Date": "date-time",
#     "Sport": "verbatim-string",
#     "Venue": "verbatim-string",
#     "HomeTeam": "verbatim-string",
#     "AwayTeam": "verbatim-string",
#     "HomeScore": "integer",
#     "AwayScore": "integer",
#     "TopScorer": "verbatim-string"
# }

# generate a template from natural language description
description = "I would like to extract important details from the contract."

messages = [
        {
            "role": "user",
            "content": [{"type": "text", "text": description}],
        }
    ]

text = processor.apply_chat_template(
    messages, tokenize=False, add_generation_prompt=True,
)

image_inputs = process_all_vision_info(messages)
inputs = processor(
    text=[text],
    images=image_inputs,
    padding=True,
    return_tensors="pt",
).to("cuda")

generated_ids = model.generate(
    **inputs,
    **generation_config
)
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[0])
# {
#     "Contract": {
#         "Title": "verbatim-string",
#         "Description": "verbatim-string",
#         "Terms": [
#             {
#                 "Term": "verbatim-string",
#                 "Description": "verbatim-string"
#             }
#         ],
#         "Date": "date-time",
#         "Signatory": "verbatim-string"
#     }
# }

📚 詳細文檔

模板支持類型

使用模型時，需要提供一個輸入文本/圖像和一個描述需要提取信息的JSON模板。模板應該是一個JSON對象，指定字段名及其預期類型。支持的類型包括：

• verbatim-string - 指示模型提取輸入中逐字存在的文本。
• string - 一個通用的字符串字段，可以包含釋義/抽象內容。
• integer - 一個整數。
• number - 一個整數或小數。
• date-time - ISO格式的日期。
• 上述任何類型的數組（例如 ["string"]）
• enum - 從一組可能的答案中選擇（在模板中表示為選項數組，例如 ["yes", "no", "maybe"]）。
• multi-label - 一個可以有多個可能答案的枚舉（在模板中表示為雙重包裝的數組，例如 [["A", "B", "C"]]）。

如果模型沒有為某個字段識別到相關信息，它將返回 null 或 []（對於數組和多標籤）。

以下是一個示例模板：

{
  "first_name": "verbatim-string",
  "last_name": "verbatim-string",
  "description": "string",
  "age": "integer",
  "gpa": "number",
  "birth_date": "date-time",
  "nationality": ["France", "England", "Japan", "USA", "China"],
  "languages_spoken": [["English", "French", "Japanese", "Mandarin", "Spanish"]]
}

一個示例輸出：

{
  "first_name": "Susan",
  "last_name": "Smith",
  "description": "A student studying computer science.",
  "age": 20,
  "gpa": 3.7,
  "birth_date": "2005-03-01",
  "nationality": "England",
  "languages_spoken": ["English", "French"]
}

💡 使用建議

建議在使用NuExtract時將溫度設置為0或非常接近0。一些推理框架（如Ollama）默認使用0.7的溫度，這可能不適用於許多提取任務。

🔧 技術細節

圖像輸入數據處理函數

以下是一個處理圖像輸入數據加載的函數：

def process_all_vision_info(messages, examples=None):
    """
    Process vision information from both messages and in-context examples, supporting batch processing.
    
    Args:
        messages: List of message dictionaries (single input) OR list of message lists (batch input)
        examples: Optional list of example dictionaries (single input) OR list of example lists (batch)
    
    Returns:
        A flat list of all images in the correct order:
        - For single input: example images followed by message images
        - For batch input: interleaved as (item1 examples, item1 input, item2 examples, item2 input, etc.)
        - Returns None if no images were found
    """
    from qwen_vl_utils import process_vision_info, fetch_image
    
    # Helper function to extract images from examples
    def extract_example_images(example_item):
        if not example_item:
            return []
            
        # Handle both list of examples and single example
        examples_to_process = example_item if isinstance(example_item, list) else [example_item]
        images = []
        
        for example in examples_to_process:
            if isinstance(example.get('input'), dict) and example['input'].get('type') == 'image':
                images.append(fetch_image(example['input']))
                
        return images
    
    # Normalize inputs to always be batched format
    is_batch = messages and isinstance(messages[0], list)
    messages_batch = messages if is_batch else [messages]
    is_batch_examples = examples and isinstance(examples, list) and (isinstance(examples[0], list) or examples[0] is None)
    examples_batch = examples if is_batch_examples else ([examples] if examples is not None else None)
    
    # Ensure examples batch matches messages batch if provided
    if examples and len(examples_batch) != len(messages_batch):
        if not is_batch and len(examples_batch) == 1:
            # Single example set for a single input is fine
            pass
        else:
            raise ValueError("Examples batch length must match messages batch length")
    
    # Process all inputs, maintaining correct order
    all_images = []
    for i, message_group in enumerate(messages_batch):
        # Get example images for this input
        if examples and i < len(examples_batch):
            input_example_images = extract_example_images(examples_batch[i])
            all_images.extend(input_example_images)
        
        # Get message images for this input
        input_message_images = process_vision_info(message_group)[0] or []
        all_images.extend(input_message_images)
    
    return all_images if all_images else None

📄 許可證

本項目採用MIT許可證。

微調與部署

微調

你可以在GitHub倉庫的cookbooks文件夾中找到微調教程筆記本。

vLLM部署

運行以下命令以提供一個與OpenAI兼容的API：

vllm serve numind/NuExtract-2.0-8B --trust_remote_code --limit-mm-per-prompt image=6 --chat-template-content-format openai

如果你遇到內存問題，請相應地設置--max-model-len。

以下是向模型發送請求的示例代碼：

import json
from openai import OpenAI

openai_api_key = "EMPTY"
openai_api_base = "http://localhost:8000/v1"

client = OpenAI(
    api_key=openai_api_key,
    base_url=openai_api_base,
)

chat_response = client.chat.completions.create(
    model="numind/NuExtract-2.0-8B",
    temperature=0,
    messages=[
        {
            "role": "user", 
            "content": [{"type": "text", "text": "Yesterday I went shopping at Bunnings"}],
        },
    ],
    extra_body={
        "chat_template_kwargs": {
            "template": json.dumps(json.loads("""{\"store\": \"verbatim-string\"}"""), indent=4)
        },
    }
)
print("Chat response:", chat_response)

對於圖像輸入，請求結構如下所示。確保在 "content" 中按圖像在提示中出現的順序排列（即任何上下文示例在主輸入之前）。

import base64

def encode_image(image_path):
    """
    Encode the image file to base64 string
    """
    with open(image_path, "rb") as image_file:
        return base64.b64encode(image_file.read()).decode('utf-8')

base64_image = encode_image("0.jpg")
base64_image2 = encode_image("1.jpg")

chat_response = client.chat.completions.create(
    model="numind/NuExtract-2.0-8B",
    temperature=0,
    messages=[
        {
            "role": "user", 
            "content": [
                {"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{base64_image}"}}, # first ICL example image