VietJobs is the first large-scale, publicly available corpus of Vietnamese job advertisements, comprising 48,092 postings and over 15 million words collected from all 34 provinces and municipalities across Vietnam. The dataset provides extensive linguistic and structured information, including job titles, categories, salaries, skills, and employment conditions, covering 16 occupational domains and multiple employment types (full-time, part-time, and internship). Designed to support research in natural language processing and labour market analytics, VietJobs captures substantial linguistic, regional, and socio-economic diversity. We benchmark several generative large language models (LLMs) on two core tasks: job category classification and salary estimation. Instruction-tuned models such as Qwen2.5-7B-Instruct and Llama-SEA-LION-v3-8B-IT demonstrate notable gains under few-shot and fine-tuned settings, while highlighting challenges in multilingual and Vietnamese-specific modelling for structured labour market prediction. VietJobs establishes a new benchmark for Vietnamese NLP and offers a valuable foundation for future research on recruitment language, socio-economic representation, and AI-driven labour market analysis. All code and resources are available at: https://github.com/VinNLP/VietJobs.
Figures & tables
Statistic
Value
General Information
Total job postings
48,092
Total tokens (words)
15,429,581
Average tokens per posting
321 (mean)
Vocabulary size
78,002
Proportion of English tokens
0.32% (49,375 words)
Table 1: Summary statistics of the VietJobs dataset.
Job Category (translated to English)
Ad Count
Business, Sales & Customer Service
8,276
Manufacturing, Manual Labour & Mechanics
6,407
Marketing, Communications, Advertising & Content
5,950
Finance, Accounting, Banking & Insurance
5,530
Tourism, Hospitality & Services
4,243
Design, Arts, Entertainment, Media & Journalism
3,465
Table 2: Distribution of job advertisements across the 16 consolidated occupational categories.
Figure 1: The distribution of Minimum and Maximum Salaries (extreme outliers are removed for better visualisation)
LLM
Job Classification
Salary Estimation
Acc
Macro F1
RMSE
R 2
Zero-shot
Qwen2.5-7B-Instruct
0.31
0.32
14.06
-0.46
Llama-3.1-8B-Instruct
0.2
0.2
16.01
-0.87
Ministral-8B-Instruct-2410
0.19
0.16
40.75
-13.12
Llama-SEA-LION-v3-8B-IT
0.26
0.29
11.72
0.07
Table 3: Performance comparison of various large language models (LLMs) across 2 tasks: Job Classification and Salary Estimation
LLM
VietJobs
Vietnam Jobs Dataset
Both
RMSE
R 2
RMSE
R 2
RMSE
R 2
Base Model
Qwen2.5-7B-Instruct
14.06
-0.46
16.19
-0.31
15.61
-0.34
Llama-3.1-8B-Instruct
16.01
-0.87
1319.94
-7744.05
1092.21
-6031.91
Ministral-8B-Instruct-2410
40.75
-13.12
2600.09
-30621.95
2190.20
-25135.32
Llama-SEA-LION-v3-8B-IT
11.72
0.07
15.78
-0.05
14.61
-0.02
Table 4: Performance comparison of LLMs in Salary Estimation with different settings