This shows two capabilities of Kiri: zero-shot classification, and sentiment detection.
Zero-shot classification is categorizing into a group of labels that were never seen during training.
Sentiment detection... detects text sentiment.
A full list of sentiments is availble in the README -- only a few are noted in this example.
"""
# Common labels for e.g. an e-commerce store's emails
labels = ["Returns", "Promotional", "Technical Issues", "Product Inquiries", "Shipping Questions", "Other"]
# Negative sentiment, give special attention to these.
negative_sentiments = ["annoyance", "disapproval", "disappointment", "anger", "disgust"]
kiri = Kiri()
kiri.classify("This is just to get rid of the example message before printing", ["test"])
# Print example, just to display local results
print("Inbox")
print("==================")
for email in emails:
classification_results = kiri.classify(email, labels)
label = max(classification_results, key=classification_results.get)
emote = kiri.emotion(email)
high_priority = any([e in emote for e in negative_sentiments])
print(f"Category: {label}")
if high_priority:
print("\033[91mPRIORITY\033[0m")
print(f"{email[:85]}...")
def test_classify():
kiri = Kiri(local=True)
out = kiri.classify(text, ["interests", "alcoholism"])
assert isinstance(out, dict)