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# coding=utf-8
# ------------------------------------
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
# ------------------------------------
"""
FILE: sample_get_model_evaluation_results.py
DESCRIPTION:
This sample demonstrates how to retrieve detailed evaluation results (per utterance)
for a trained model in a Conversation Authoring project.
USAGE:
python sample_get_model_evaluation_results.py
REQUIRED ENV VARS (for AAD / DefaultAzureCredential):
AZURE_CONVERSATIONS_AUTHORING_ENDPOINT
AZURE_CLIENT_ID
AZURE_TENANT_ID
AZURE_CLIENT_SECRET
NOTE:
If you want to use AzureKeyCredential instead, set:
- AZURE_CONVERSATIONS_AUTHORING_ENDPOINT
- AZURE_CONVERSATIONS_AUTHORING_KEY
OPTIONAL ENV VARS:
PROJECT_NAME # defaults to "<project-name>"
TRAINED_MODEL # defaults to "<trained-model-label>"
"""
# [START conversation_authoring_get_model_evaluation_results]
import os
from azure.identity import DefaultAzureCredential
from azure.ai.language.conversations.authoring import ConversationAuthoringClient
def sample_get_model_evaluation_results():
# settings
endpoint = os.environ["AZURE_CONVERSATIONS_AUTHORING_ENDPOINT"]
project_name = os.environ.get("PROJECT_NAME", "<project-name>")
trained_model_label = os.environ.get("TRAINED_MODEL", "<trained-model-label>")
# create a client with AAD
credential = DefaultAzureCredential()
client = ConversationAuthoringClient(endpoint, credential=credential)
project_client = client.get_project_client(project_name)
# fetch paged evaluation results
results = project_client.trained_model.get_model_evaluation_results(
trained_model_label=trained_model_label, string_index_type="Utf16CodeUnit"
)
print("=== Model Evaluation Results ===")
seen = 0
for r in results:
seen += 1
print(f"Text: {r.text}")
print(f"Language: {r.language}")
if r.intents_result:
print(f"Expected Intent: {r.intents_result.expected_intent}")
print(f"Predicted Intent: {r.intents_result.predicted_intent}")
if r.entities_result:
expected_entities = r.entities_result.expected_entities or []
print("Expected Entities:")
for ent in expected_entities:
print(f" - Category: {ent.category}, Offset: {ent.offset}, Length: {ent.length}")
predicted_entities = r.entities_result.predicted_entities or []
print("Predicted Entities:")
for ent in predicted_entities:
print(f" - Category: {ent.category}, Offset: {ent.offset}, Length: {ent.length}")
print()
print(f"Total Results: {seen}")
# [END conversation_authoring_get_model_evaluation_results]
def main():
sample_get_model_evaluation_results()
if __name__ == "__main__":
main()
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