Instructions to use vietdata/vietnamese-content-cls with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vietdata/vietnamese-content-cls with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="vietdata/vietnamese-content-cls")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("vietdata/vietnamese-content-cls") model = AutoModelForSequenceClassification.from_pretrained("vietdata/vietnamese-content-cls") - Notebooks
- Google Colab
- Kaggle
BERT-base for news genre classification
This is a fine-tuned version of NlpHUST/vibert4news-base-cased model for news genre classification. The training data is 3864 vietnamese news summaries in WikiMulti dataset.
Labels:
0-> Agriculture, food, and drink
1-> Albums
2-> Architecture
3-> Art
4-> Biology and medicine
5-> Chemistry and materials science
6-> Classical compositions
7-> Computing and engineering
8-> Earth science
9-> Film
10-> Geography
11-> Language and literature
12-> Mathematics and mathematicians
13-> Media and drama
14-> Other music articles
15-> Philosophy
16-> Physics and astronomy
17-> Places
18-> Religion
19-> Royalty, nobility, and heraldry
20-> Songs
21-> Television
22-> Transport
23-> World history
24->Armies and military units
25->Baseball
26->Basketball
27->Battles, exercises, and conflicts
28->Culture, sociology, and psychology
29->Economics and business
30->Education
31->Football
32->Hockey
33->Law
34->Magazines and print journalism
35->Military aircraft
36->Military decorations and memorials
37->Military people
38->Motorsport
39->Multi-sport event
40->Other sports
41->Politics and government
42->Pro wrestling
43->Recreation
44->Video games
45->Warships and naval units
46->Weapons, equipment, and buildings
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