本文為英文版的機器翻譯版本，如內容有任何歧義或不一致之處，概以英文版為準。

# 非同步分析任務的輸出
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分析任務完成後，會將結果存放在您在請求中指定的 S3 儲存貯體中。

## 文字輸入的輸出
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對於任意格式的文字輸入文件 （多類別或多標籤），任務輸出包含名為 的單一檔案`output.tar.gz`。這是壓縮的封存檔案，其中包含具有輸出的文字檔案。

**多類別輸出**

當您使用以多類別模式訓練的分類器時，結果會顯示 `classes`。這些`classes`都是在訓練您的分類器時用來建立一組類別的類別。

如需這些輸出欄位的詳細資訊，請參閱《*Amazon Comprehend API 參考*》中的 [ClassifyDocument](https://docs.aws.amazon.com/comprehend/latest/APIReference/API_ClassifyDocument.html)。

下列範例使用以下互斥類別。

```
DOCUMENTARY
SCIENCE_FICTION
ROMANTIC_COMEDY
SERIOUS_DRAMA
OTHER
```

如果您的輸入資料格式是每行一個文件，輸出檔案會包含輸入中每行一行。每一行都包含檔案名稱、以零為基礎的輸入行編號，以及文件中找到的類別。最後，Amazon Comprehend 確信個別執行個體已正確分類。

例如：

```
{"File": "file1.txt", "Line": "0", "Classes": [{"Name": "Documentary", "Score": 0.8642}, {"Name": "Other", "Score": 0.0381}, {"Name": "Serious_Drama", "Score": 0.0372}]}
{"File": "file1.txt", "Line": "1", "Classes": [{"Name": "Science_Fiction", "Score": 0.5}, {"Name": "Science_Fiction", "Score": 0.0381}, {"Name": "Science_Fiction", "Score": 0.0372}]}
{"File": "file2.txt", "Line": "2", "Classes": [{"Name": "Documentary", "Score": 0.1}, {"Name": "Documentary", "Score": 0.0381}, {"Name": "Documentary", "Score": 0.0372}]}
{"File": "file2.txt", "Line": "3", "Classes": [{"Name": "Serious_Drama", "Score": 0.3141}, {"Name": "Other", "Score": 0.0381}, {"Name": "Other", "Score": 0.0372}]}
```

如果您的輸入資料格式是每個檔案一個文件，輸出檔案會包含每個文件一行。每一行都有檔案名稱，以及在文件中找到的類別。其結尾是 Amazon Comprehend 準確分類個別執行個體的可信度。

例如：

```
{"File": "file0.txt", "Classes": [{"Name": "Documentary", "Score": 0.8642}, {"Name": "Other", "Score": 0.0381}, {"Name": "Serious_Drama", "Score": 0.0372}]}
{"File": "file1.txt", "Classes": [{"Name": "Science_Fiction", "Score": 0.5}, {"Name": "Science_Fiction", "Score": 0.0381}, {"Name": "Science_Fiction", "Score": 0.0372}]}
{"File": "file2.txt", "Classes": [{"Name": "Documentary", "Score": 0.1}, {"Name": "Documentary", "Score": 0.0381}, {"Name": "Domentary", "Score": 0.0372}]}
{"File": "file3.txt", "Classes": [{"Name": "Serious_Drama", "Score": 0.3141}, {"Name": "Other", "Score": 0.0381}, {"Name": "Other", "Score": 0.0372}]}
```

**多標籤輸出**

當您使用以多標籤模式訓練的分類器時，結果會顯示 `labels`。這些都是`labels`在訓練您的分類器時用來建立一組類別的標籤。

下列範例使用這些唯一標籤。

```
SCIENCE_FICTION
ACTION
DRAMA
COMEDY
ROMANCE
```

如果您的輸入資料格式是每行一個文件，輸出檔案會包含輸入中每行一行。每一行都包含檔案名稱、以零為基礎的輸入行編號，以及文件中找到的類別。最後，Amazon Comprehend 確信個別執行個體已正確分類。

例如：

```
{"File": "file1.txt", "Line": "0", "Labels": [{"Name": "Action", "Score": 0.8642}, {"Name": "Drama", "Score": 0.650}, {"Name": "Science Fiction", "Score": 0.0372}]}
{"File": "file1.txt", "Line": "1", "Labels": [{"Name": "Comedy", "Score": 0.5}, {"Name": "Action", "Score": 0.0381}, {"Name": "Drama", "Score": 0.0372}]}
{"File": "file1.txt", "Line": "2", "Labels": [{"Name": "Action", "Score": 0.9934}, {"Name": "Drama", "Score": 0.0381}, {"Name": "Action", "Score": 0.0372}]}
{"File": "file1.txt", "Line": "3", "Labels": [{"Name": "Romance", "Score": 0.9845}, {"Name": "Comedy", "Score": 0.8756}, {"Name": "Drama", "Score": 0.7723}, {"Name": "Science_Fiction", "Score": 0.6157}]}
```

如果您的輸入資料格式是每個檔案一個文件，輸出檔案會包含每個文件一行。每一行都有檔案名稱，以及在文件中找到的類別。其結尾是 Amazon Comprehend 準確分類個別執行個體的可信度。

例如：

```
{"File": "file0.txt", "Labels": [{"Name": "Action", "Score": 0.8642}, {"Name": "Drama", "Score": 0.650}, {"Name": "Science Fiction", "Score": 0.0372}]}
{"File": "file1.txt", "Labels": [{"Name": "Comedy", "Score": 0.5}, {"Name": "Action", "Score": 0.0381}, {"Name": "Drama", "Score": 0.0372}]}
{"File": "file2.txt", "Labels": [{"Name": "Action", "Score": 0.9934}, {"Name": "Drama", "Score": 0.0381}, {"Name": "Action", "Score": 0.0372}]}
{"File": "file3.txt”, "Labels": [{"Name": "Romance", "Score": 0.9845}, {"Name": "Comedy", "Score": 0.8756}, {"Name": "Drama", "Score": 0.7723}, {"Name": "Science_Fiction", "Score": 0.6157}]}
```

## 半結構化輸入文件的輸出
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對於半結構化輸入文件，輸出可以包含下列其他欄位：
+ DocumentMetadata – 文件的擷取資訊。中繼資料包含文件中的頁面清單，其中包含從每個頁面擷取的字元數。如果請求包含 `Byte` 參數，則此欄位會出現在回應中。
+ DocumentType – 輸入文件中每個頁面的文件類型。如果請求包含 `Byte` 參數，則此欄位會出現在回應中。
+ 錯誤 – 系統在處理輸入文件時偵測到的頁面層級錯誤。如果系統沒有發生錯誤，則此欄位為空白。

如需這些輸出欄位的詳細資訊，請參閱《*Amazon Comprehend API 參考*》中的 [ClassifyDocument](https://docs.aws.amazon.com/comprehend/latest/APIReference/API_ClassifyDocument.html)。

下列範例顯示兩頁掃描 PDF 檔案的輸出。

```
[{ #First page output
    "Classes": [
        {
            "Name": "__label__2 ",
            "Score": 0.9993996620178223
        },
        {
            "Name": "__label__3 ",
            "Score": 0.0004330444789957255
        }
    ],
    "DocumentMetadata": {
        "PageNumber": 1,
        "Pages": 2
    },
    "DocumentType": "ScannedPDF",
    "File": "file.pdf",
    "Version": "VERSION_NUMBER"
},
#Second page output
{
    "Classes": [
        {
            "Name": "__label__2 ",
            "Score": 0.9993996620178223
        },
        {
            "Name": "__label__3 ",
            "Score": 0.0004330444789957255
        }
    ],
    "DocumentMetadata": {
        "PageNumber": 2,
        "Pages": 2
    },
    "DocumentType": "ScannedPDF",
    "File": "file.pdf",
    "Version": "VERSION_NUMBER" 
}]
```