Document Intelligence 自定义模型不适用

Question

我是编码新手，并尝试在 python 文件中使用我的自定义提取模板模型来阅读更多文档。我使用 Document Intelligence Studio 制作了模型，应用了我自己的标签，对其进行了训练和测试，它似乎工作得很好。我尝试使用模型 ID 使用 python 应用程序从本地文件夹中读取 pdf，但它没有分析带有模型标签的文档。相反，它似乎使用了我不想要的自动标记，因为那时我会使用示例模型。是我的代码有问题吗？或者也许是别的什么？就是这样（我主要使用了Document Intelligence Studio提供的代码，也许我应该改变它？）：

endpoint = "<myendpoint>"
key = "<mykey>"

model_id = "<my-custom-model-id>"
formUrl = "<pdf-url>"

document_analysis_client = DocumentAnalysisClient(
    endpoint=endpoint, credential=AzureKeyCredential(key)
)

# Initialize the DocumentAnalysisClient with endpoint and key
document_analysis_client = DocumentAnalysisClient(
        endpoint=endpoint, credential=AzureKeyCredential(key)
)

# Make sure your document's type is included in the list of document types the custom model can analyze
poller = document_analysis_client.begin_analyze_document_from_url(model_id, formUrl)
result = poller.result()

for idx, document in enumerate(result.documents):
    print("--------Analyzing document #{}--------".format(idx + 1))
    print("Document has type {}".format(document.doc_type))
    print("Document has confidence {}".format(document.confidence))
    print("Document was analyzed by model with ID {}".format(result.model_id))
    for name, field in document.fields.items():
        field_value = field.value if field.value else field.content
        print("......found field of type '{}' with value '{}' and with confidence {}".format(field.value_type, field_value, field.confidence))


# iterate over tables, lines, and selection marks on each page
for page in result.pages:
    print("\nLines found on page {}".format(page.page_number))
    for line in page.lines:
        print("...Line '{}'".format(line.content.encode('utf-8')))
    for word in page.words:
        print(
            "...Word '{}' has a confidence of {}".format(
                word.content.encode('utf-8'), word.confidence
            )
        )
    for selection_mark in page.selection_marks:
        print(
            "...Selection mark is '{}' and has a confidence of {}".format(
                selection_mark.state, selection_mark.confidence
            )
        )

for i, table in enumerate(result.tables):
    print("\nTable {} can be found on page:".format(i + 1))
    for region in table.bounding_regions:
        print("...{}".format(i + 1, region.page_number))
    for cell in table.cells:
        print(
            "...Cell[{}][{}] has content '{}'".format(
                cell.row_index, cell.column_index, cell.content.encode('utf-8')
            )
        )
print("-----------------------------------")

我尝试更改 Document Intelligence Studio 上的模型类型，但没有任何帮助，所以我认为它必须对我的代码做一些事情......也许我必须指定标签？但不知道...

Answer 1

使用特定标签训练自定义模型，它应该能够处理所训练的数据类型。检查 Document Intelligence Studio 中的标签与正在分析的 PDF 的数据结构是否匹配。

我在 Document Intelligence Studio 中训练了一个自定义模型来识别这些键值对。

• 在代码中添加标签，以从文档分析结果中提取并打印这些对。

代码：

import os
from azure.core.credentials import AzureKeyCredential
from azure.ai.formrecognizer import DocumentAnalysisClient

# Replace with your endpoint and key
endpoint = "https://doc inexxx.cognitiveservices.azure.com/"
key = "699a1e68fxxxxxxxxxxxx683a44d"
model_id = "testmodel2"

# Initialize the client
client = DocumentAnalysisClient(endpoint=endpoint, credential=AzureKeyCredential(key))

# Function to analyze a document using the custom model
def analyze_document(file_path):
    with open(file_path, "rb") as f:
        poller = client.begin_analyze_document(model_id=model_id, document=f)
        result = poller.result()

    print("Full Analysis Result:")
    print(result)
    
    for page in result.pages:
        print(f"Page number: {page.page_number}")
        
        if hasattr(page, 'key_value_pairs'):
            print("Key-Value Pairs:")
            for kv_pair in page.key_value_pairs:
                key = kv_pair.key.content if kv_pair.key else "None"
                value = kv_pair.value.content if kv_pair.value else "None"
                print(f"  Key: {key}, Value: {value}")

        if hasattr(page, 'lines'):
            print("Lines:")
            for line in page.lines:
                print(f"  {line.content}")

        if hasattr(page, 'words'):
            print("Words:")
            for word in page.words:
                print(f"  Word: {word.content}, Confidence: {word.confidence}")

        if hasattr(page, 'selection_marks'):
            print("Selection Marks:")
            for selection_mark in page.selection_marks:
                print(f"  Selection Mark: {selection_mark.state}, Confidence: {selection_mark.confidence}")

        if hasattr(page, 'tables'):
            print("Tables:")
            for table in page.tables:
                for cell in table.cells:
                    print(f"  Cell[{cell.row_index}][{cell.column_index}]: {cell.content}")

# Path to your single PDF file
pdf_path = r"C:\Users\v-chikkams\Downloads\Get_Started_With_Smallpdf.pdf"

# Analyze the document
print(f"Analyzing document: {pdf_path}")
analyze_document(pdf_path)

定制训练模型：

输出：