搭配 REST API 和 SynapseML 在 Fabric 中使用預先建置的文字分析 (預覽)
重要
這項功能處於預覽狀態。
文字分析是一種 Azure AI 服務，使您能夠使用自然語言處理 (NLP) 功能執行文字採礦和文字分析。
本教學課程示範如何搭配 RESTful API 在 Fabric 中使用文字分析：
- 在句子或文件層級偵測情感標籤。
- 識別指定文字輸入的語言。
- 從文字中擷取關鍵片語。
- 識別文字中的不同實體，並將它們分類成預先定義的類別或類型。
必要條件
# Get workload endpoints and access token
from synapse.ml.mlflow import get_mlflow_env_config
import json
mlflow_env_configs = get_mlflow_env_config()
access_token = access_token = mlflow_env_configs.driver_aad_token
prebuilt_AI_base_host = mlflow_env_configs.workload_endpoint + "cognitive/textanalytics/"
print("Workload endpoint for AI service: \n" + prebuilt_AI_base_host)
service_url = prebuilt_AI_base_host + "language/:analyze-text?api-version=2022-05-01"
# Make a RESful request to AI service
post_headers = {
"Content-Type" : "application/json",
"Authorization" : "Bearer {}".format(access_token)
}
def printresponse(response):
print(f"HTTP {response.status_code}")
if response.status_code == 200:
try:
result = response.json()
print(json.dumps(result, indent=2, ensure_ascii=False))
except:
print(f"pasre error {response.content}")
else:
print(response.headers)
print(f"error message: {response.content}")
情感分析
情感分析功能提供在句子或文件層級偵測情感標籤 (例如「負面」、「中性」和「正面」) 和信賴分數的一種方法。 此功能也會為每份文件和其中的句子傳回 0 到 1 之間的信賴分數，以表示正面、中性和負面情感。 如需啟用的語言清單，請參閱情感分析和意見挖掘語言支援。
import requests
from pprint import pprint
import uuid
post_body = {
"kind": "SentimentAnalysis",
"parameters": {
"modelVersion": "latest",
"opinionMining": "True"
},
"analysisInput":{
"documents":[
{
"id":"1",
"language":"en",
"text": "The food and service were unacceptable. The concierge was nice, however."
}
]
}
}
post_headers["x-ms-workload-resource-moniker"] = str(uuid.uuid1())
response = requests.post(service_url, json=post_body, headers=post_headers)
# Output all information of the request process
printresponse(response)
輸出
HTTP 200
{
"kind": "SentimentAnalysisResults",
"results": {
"documents": [
{
"id": "1",
"sentiment": "mixed",
"confidenceScores": {
"positive": 0.43,
"neutral": 0.04,
"negative": 0.53
},
"sentences": [
{
"sentiment": "negative",
"confidenceScores": {
"positive": 0.0,
"neutral": 0.01,
"negative": 0.99
},
"offset": 0,
"length": 40,
"text": "The food and service were unacceptable. ",
"targets": [
{
"sentiment": "negative",
"confidenceScores": {
"positive": 0.01,
"negative": 0.99
},
"offset": 4,
"length": 4,
"text": "food",
"relations": [
{
"relationType": "assessment",
"ref": "#/documents/0/sentences/0/assessments/0"
}
]
},
{
"sentiment": "negative",
"confidenceScores": {
"positive": 0.01,
"negative": 0.99
},
"offset": 13,
"length": 7,
"text": "service",
"relations": [
{
"relationType": "assessment",
"ref": "#/documents/0/sentences/0/assessments/0"
}
]
}
],
"assessments": [
{
"sentiment": "negative",
"confidenceScores": {
"positive": 0.01,
"negative": 0.99
},
"offset": 26,
"length": 12,
"text": "unacceptable",
"isNegated": false
}
]
},
{
"sentiment": "positive",
"confidenceScores": {
"positive": 0.86,
"neutral": 0.08,
"negative": 0.07
},
"offset": 40,
"length": 32,
"text": "The concierge was nice, however.",
"targets": [
{
"sentiment": "positive",
"confidenceScores": {
"positive": 1.0,
"negative": 0.0
},
"offset": 44,
"length": 9,
"text": "concierge",
"relations": [
{
"relationType": "assessment",
"ref": "#/documents/0/sentences/1/assessments/0"
}
]
}
],
"assessments": [
{
"sentiment": "positive",
"confidenceScores": {
"positive": 1.0,
"negative": 0.0
},
"offset": 58,
"length": 4,
"text": "nice",
"isNegated": false
}
]
}
],
"warnings": []
}
],
"errors": [],
"modelVersion": "2022-11-01"
}
}
語言偵測器
語言偵測器會針對每份文件評估文字輸入，並傳回語言識別碼，其中含有指出分析強度的分數。 此功能很適合用於收集未知語言任意文字的內容存放區。 如需啟用的語言清單，請參閱支援的語言偵測語言。
post_body = {
"kind": "LanguageDetection",
"parameters": {
"modelVersion": "latest"
},
"analysisInput":{
"documents":[
{
"id":"1",
"text": "This is a document written in English."
}
]
}
}
post_headers["x-ms-workload-resource-moniker"] = str(uuid.uuid1())
response = requests.post(service_url, json=post_body, headers=post_headers)
# Output all information of the request process
printresponse(response)
輸出
HTTP 200
{
"kind": "LanguageDetectionResults",
"results": {
"documents": [
{
"id": "1",
"detectedLanguage": {
"name": "English",
"iso6391Name": "en",
"confidenceScore": 0.99
},
"warnings": []
}
],
"errors": [],
"modelVersion": "2022-10-01"
}
}
關鍵片語擷取器
關鍵片語擷取會評估非結構化的文字，並傳回關鍵片語的清單。 此功能在您需要快速識別文件集合中的要點時相當有用。 如需啟用的語言清單，請參閱支援關鍵片語擷取的語言。
post_body = {
"kind": "KeyPhraseExtraction",
"parameters": {
"modelVersion": "latest"
},
"analysisInput":{
"documents":[
{
"id":"1",
"language":"en",
"text": "Dr. Smith has a very modern medical office, and she has great staff."
}
]
}
}
post_headers["x-ms-workload-resource-moniker"] = str(uuid.uuid1())
response = requests.post(service_url, json=post_body, headers=post_headers)
# Output all information of the request process
printresponse(response)
輸出
HTTP 200
{
"kind": "KeyPhraseExtractionResults",
"results": {
"documents": [
{
"id": "1",
"keyPhrases": [
"modern medical office",
"Dr. Smith",
"great staff"
],
"warnings": []
}
],
"errors": [],
"modelVersion": "2022-10-01"
}
}
具名實體辨識 (NER)
具名實體辨識 (NER) 能夠識別文字中的不同實體，並將它們分類成預先定義的類別或類型，例如：人員、位置、事件、產品和組織。 如需支援語言的清單，請參閱 NER 語言支援。
post_body = {
"kind": "EntityRecognition",
"parameters": {
"modelVersion": "latest"
},
"analysisInput":{
"documents":[
{
"id":"1",
"language": "en",
"text": "I had a wonderful trip to Seattle last week."
}
]
}
}
post_headers["x-ms-workload-resource-moniker"] = str(uuid.uuid1())
response = requests.post(service_url, json=post_body, headers=post_headers)
# Output all information of the request process
printresponse(response)
輸出
HTTP 200
{
"kind": "EntityRecognitionResults",
"results": {
"documents": [
{
"id": "1",
"entities": [
{
"text": "trip",
"category": "Event",
"offset": 18,
"length": 4,
"confidenceScore": 0.74
},
{
"text": "Seattle",
"category": "Location",
"subcategory": "GPE",
"offset": 26,
"length": 7,
"confidenceScore": 1.0
},
{
"text": "last week",
"category": "DateTime",
"subcategory": "DateRange",
"offset": 34,
"length": 9,
"confidenceScore": 0.8
}
],
"warnings": []
}
],
"errors": [],
"modelVersion": "2021-06-01"
}
}