NC Bench
Overview
About
Tests
Models
Model
EVA Qwen 2.5 14B
Test
Data extraction
Scenario
All valid emails
Run ID
2024-10-01-01J94QZB2C60QQCSKSXDM45WBX
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
````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 """ emails = re.findall(r'\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,}\b', text) email_list = ','.join(emails) print(email_list) ```` This Python script uses regular expressions to extract email addresses from the given text. Here's how it works: 1. The `text` variable contains the input string with a list of contact information, including valid and invalid email addresses. 2. The script uses the `re.findall()` function to search for all email addresses within the `text` string. 3. The regular expression pattern `\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,}\b` is used to match the email addresses. Let's break down the pattern: - `\b`: Matches a word boundary to ensure the email address stands alone. - `[A-Za-z0-9._%+-]+`: Matches the username part of the email address, which can contain letters (both uppercase and lowercase), digits, dots, underscores, percent signs, plus signs, or hyphens. - `@`: Matches the "@" symbol. - `[A-Za-z0-9.-]+`: Matches the domain name part of the email address, which can contain letters, digits, dots, or hyphens. - `\.`: Matches the dot (.) before the domain extension. - `[A-Z|a-z]{2,}`: Matches the domain extension, which consists of at least two letters (uppercase or lowercase). 4. The `re.findall()` function returns a list of all email addresses found in the `text`. 5. The script then uses the `','.join()` method on the `emails` list to join all email addresses together into a single string, with each email separated by a comma, without spaces. 6. Finally, the script prints the resulting email list. For the given input text, the output will be: ```
john.doe@example.com
,
jane@smith-family.net
,
support@company.co.uk
,
info@subdomain.example.org
``` The script successfully extracts the valid email addresses and returns them as a comma-separated list without spaces.
id
"gen-1727809957-VjsxaSY7qMx7L8kv60ex"
total_cost
0.00029425
upstream_id
"chatcmpl-26f0f6f8-ffac-456d-886f-ddd4fe1f56b1"
created_at
"2024-10-01T19:13:04.13998+00:00"
Tuesday, October 1st, 2024 at 7:13:04 PM GMT+00:00
model
"eva-unit-01/eva-qwen-2.5-14b"
app_id
182717
streamed
true
cancelled
false
provider_name
"Infermatic"
latency
55
moderation_latency
(null)
generation_time
26574
finish_reason
"stop"
tokens_prompt
88
tokens_completion
544
native_tokens_prompt
89
native_tokens_completion
544
native_tokens_reasoning
(null)
num_media_prompt
(null)
num_media_completion
(null)
origin
"https://ncbench.com/"
usage
0.00029425
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