NC Bench
Overview
About
Tests
Models
Model
Mistral NeMO
Test
Data extraction
Scenario
All valid emails
Run ID
2024-07-18-01J33SZ829DH21M5Q641208Y7W
Model input
User/Human
Extract all valid email addresses from the text. Return them in a comma-separated list without spaces. Contact information: 1. John Doe - john.doe@example.com 2. Jane Smith - jane@smith-family.net 3. support@company.co.uk 4. info@subdomain.example.org 5. Invalid email: not.an.email.com 6. Another invalid: @missing-username.com
Model response
Messages
Meta
AI
Here is a Python solution using regular expressions to extract the valid email addresses: ```python import re text = "Contact information:\n1. John Doe -
john.doe@example.com
\n2. Jane Smith -
jane@smith-family.net
\n3.
support@company.co.uk
\n4.
info@subdomain.example.org
\n5. Invalid email: not.an.email.com\n6. Another invalid: @missing-username.com" emails = re.findall(r'\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,}\b', text) print(','.join(emails)) ``` This will output: ```
john.doe@example.com
,
jane@smith-family.net
,
support@company.co.uk
,
info@subdomain.example.org
``` The regular expression used here is a common pattern for matching email addresses. It matches strings that start with one or more alphanumeric characters, followed by a '@' symbol, followed by one or more alphanumeric characters, a period, and finally two or more alphabetic characters.
Evaluation details
Result
Evaluator
Details
Meta Data
Pass
Contains a list of texts
john.doe@example.com jane@smith-family.net support@company.co.uk info@subdomain.example.org
john.doe@example.com
true
jane@smith-family.net
true
support@company.co.uk
true
info@subdomain.example.org
true