aendra.dev
Automated labeller that gives classifies news content using a fine-tuned BERT model
1export enum LABELS {
2 LABEL_0 = "arts & culture",
3 LABEL_1 = "arts & culture",
4 LABEL_2 = "black voices - UNUSED",
5 LABEL_3 = "business",
6 LABEL_4 = "college",
7 LABEL_5 = "comedy",
8 LABEL_6 = "crime",
9 LABEL_7 = "culture & arts",
10 LABEL_8 = "education",
11 LABEL_9 = "entertainment",
12 LABEL_10 = "environment",
13 LABEL_11 = "content intended for people over 50 years of age",
14 LABEL_12 = "food & drink",
15 LABEL_13 = "good news",
16 LABEL_14 = "environment",
17 LABEL_15 = "healthy living",
18 LABEL_16 = "home & living",
19 LABEL_17 = "impact - UNUSED",
20 LABEL_18 = "latino voices - UNUSED",
21 LABEL_19 = "media",
22 LABEL_20 = "money",
23 LABEL_21 = "parenting",
24 LABEL_22 = "parenting",
25 LABEL_23 = "politics",
26 LABEL_24 = "queer voices - UNUSED",
27 LABEL_25 = "religion",
28 LABEL_26 = "science",
29 LABEL_27 = "sports",
30 LABEL_28 = "style & beauty",
31 LABEL_29 = "style & beauty",
32 LABEL_30 = "taste - UNUSED",
33 LABEL_31 = "tech",
34 LABEL_32 = "world news",
35 LABEL_33 = "travel",
36 LABEL_34 = "u.s. news",
37 LABEL_35 = "weddings",
38 LABEL_36 = "weird news",
39 LABEL_37 = "wellness",
40 LABEL_38 = "women's issues - UNUSED",
41 LABEL_39 = "world news",
42 LABEL_40 = "world news",
43}