metadata

library_name: transformers
tags: []
pipeline_tag: fill-mask
widget:
  - text: shop làm ăn như cái <mask>
  - text: hag từ Quảng <mask> kực nét
  - text: Set xinh quá, <mask> bèo nhèo
  - text: ăn nói xà <mask>

5CD-AI/visobert-14gb-corpus

Overview

We continually pretrain uitnlp/visobert on a merged 14GB dataset, the training dataset includes:

Internal data (100M comments and 15M posts on Facebook)
UIT data, which is used to pretrain uitnlp/visobert
MC4 ecommerce

Here are the results on 4 downstream tasks on Vietnamese social media texts, including Emotion Recognition(UIT-VSMEC), Hate Speech Detection(UIT-HSD), Spam Reviews Detection(ViSpamReviews), Hate Speech Spans Detection(ViHOS):

Model	Avg	Emotion Recognition			Hate Speech Detection			Spam Reviews Detection			Hate Speech Spans Detection
Model	Avg	Acc	WF1	MF1	Acc	WF1	MF1	Acc	WF1	MF1	Acc	WF1	MF1
viBERT	78.16	61.91	61.98	59.7	85.34	85.01	62.07	89.93	89.79	76.8	90.42	90.45	84.55
vELECTRA	79.23	64.79	64.71	61.95	86.96	86.37	63.95	89.83	89.68	76.23	90.59	90.58	85.12
PhoBERT-Base	79.3	63.49	63.36	61.41	87.12	86.81	65.01	89.83	89.75	76.18	91.32	91.38	85.92
PhoBERT-Large	79.82	64.71	64.66	62.55	87.32	86.98	65.14	90.12	90.03	76.88	91.44	91.46	86.56
ViSoBERT	81.58	68.1	68.37	65.88	88.51	88.31	68.77	90.99	90.92	79.06	91.62	91.57	86.8
visobert-14gb-corpus	82.2	68.69	68.75	66.03	88.79	88.6	69.57	91.02	90.88	77.13	93.69	93.63	89.66

Usage (HuggingFace Transformers)

Install transformers package:

pip install transformers

Then you can use this model for fill-mask task like this:

from transformers import pipeline

model_path = "5CD-AI/visobert-14gb-corpus"
mask_filler = pipeline("fill-mask", model_path)

mask_filler("ăn nói xà <mask>", top_k=10)