NC Bench
Overview
About
Tests
Models
Model
Mistral Nemo 12B Celeste
Test
Data extraction
Scenario
All valid emails
Run ID
2024-08-06-01J4MXTNN2ZHZ9T9Y7Y7QA5R20
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
To extract all valid email addresses from the given text and return them in a comma-separated list without spaces, you can use regular expressions to find the email addresses and then concatenate them into a single string. Here's the Python code to accomplish this: ```python import re text = """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""" email_pattern = r'\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,7}\b' valid_emails = re.findall(email_pattern, text) emails_list = ','.join(valid_emails) emails_list ``` Output: ```
john.doe@example.com
,
jane@smith-family.net
,
support@company.co.uk
,
info@subdomain.example.org
``` Explanation: 1. We import the `re` module for working with regular expressions. 2. We define the `text` variable containing the contact information text. 3. We define the `email_pattern` variable using a regular expression to match valid email addresses: - `\b`: Matches the position where a word starts - `[A-Za-z0-9._%+-]+`: Matches one or more characters that are alphanumeric, dot, underscore, percent, plus, or hyphen - `@`: Matches the '@' symbol - `[A-Za-z0-9.-]+`: Matches one or more characters that are alphanumeric, dot, or hyphen - `\.`: Matches a dot ('.') - `[A-Z|a-z]{2,7}`: Matches 2 to 7 characters that are uppercase letters (case-insensitive) - `\b`: Matches the position where a word ends 4. We use `re.findall()` to find all occurrences of the `email_pattern` in the `text` variable and store the results in the `valid_emails` list. 5. We use `join()` to concatenate the elements of the `valid_emails` list into a single string, separated by commas and without spaces, and store the result in the `emails_list` variable. 6. Finally, we print the `emails_list` output. Note: This regular expression pattern assumes a basic format for valid email addresses and may not cover all possible variations of valid email formats. You can adjust the pattern as needed for your specific requirements.
id
"gen-aEVynqP7flCAHVc3omPdOul06Ty2"
total_cost
0.000975
upstream_id
"cmpl-ef3039fec8644958a6eefbb2c6feec43"
created_at
"2024-08-06T22:44:25.847846+00:00"
Tuesday, August 6th, 2024 at 10:44:25 PM GMT+00:00
model
"nothingiisreal/mn-celeste-12b"
app_id
182717
streamed
false
cancelled
false
provider_name
"Infermatic"
latency
8278
moderation_latency
(null)
generation_time
0
finish_reason
"stop"
tokens_prompt
88
tokens_completion
533
native_tokens_prompt
95
native_tokens_completion
555
num_media_prompt
(null)
num_media_completion
(null)
origin
"https://ncbench.com/"
usage
0.000975
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