File size: 2,083 Bytes
ead4ce0
 
271a204
2aee76f
271a204
c5a7c61
 
 
b1219f8
c5a7c61
 
 
b2b1b50
c5a7c61
 
 
 
 
 
 
b2b1b50
ead4ce0
56be032
666b816
da983ec
666b816
579bf4e
da983ec
 
 
c6efe2d
da983ec
174b18a
 
 
 
 
 
 
 
 
 
ce5c5f5
e64e3af
 
8aeece9
 
e64e3af
 
 
 
 
ce5c5f5
7b2bd3e
 
 
 
aa8d662
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
---
license: bigscience-openrail-m
pipeline_tag: text-classification

widget:
  - example_title: "Commercial"
    text: "custom sports jerseys"
  - example_title: "Non-Commercial"
    text: "health tips"
  - example_title: "Informational"
    text: "is cycling healthy"
  - example_title: "Navigational"
    text: "owayo login page"
  - example_title: "Transactional"
    text: "buy custom sport jerseys"
  - example_title: "Commercial Investigation"
    text: "owayo custom jerseys reviews"
  - example_title: "Local"
    text: "cycling shop in brisbane"
  - example_title: "Entertainment"
    text: "funny cycling videos"
---
Multi-label binary sequence classification model developed by [Dejan Marketing](https://dejanmarketing.com/).

The model is designed to be deployed in an automated pipeline capable of classifying search query intent for thousands (or even millions) of search queries from common data sources such as Google Search Console, SEMRush, Ahrefs, Moz, Majestic and Google Ads.

This is a small demo model which may occassionally misclasify some queries. In a typical commercial project, a larger model is deployed for the task, and in special cases, a domain-specific model is developed for the client.

# Engage Our Team
Interested in using this in an automated pipeline for bulk query processing?

Please [book an appointment](https://dejanmarketing.com/conference/) to discuss your needs.

# Base Model

albert/albert-base-v2

# Output

A list of binary classes (0,1) for 10 classification labels.

## Labels

    LABEL_0: 'Commercial'
    LABEL_1: 'Non-Commercial'
    LABEL_2: 'Branded' # Needs-further fine-tuning.
    LABEL_3: 'Non-Branded' # Needs-further fine-tuning.
    LABEL_4: 'Informational'
    LABEL_5: 'Navigational'
    LABEL_6: 'Transactional'
    LABEL_7: 'Commercial Investigation'
    LABEL_8: 'Local'
    LABEL_9: 'Entertainment'

# Sources of Training Data

## Owayo:
- [USA](https://www.owayo.com/), [Australia](https://www.owayo.com.au/), [Germany](https://www.owayo.de/), [UK](https://www.owayo.co.uk/), [Canada](https://www.owayo.ca/)