ViLegalQwen2.5-1.5B-Base
ViLegalQwen2.5-1.5B-Base is a decoder-only language model for Vietnamese legal text understanding, part of the ViLegalLM suite. It is continually pretrained from Qwen2.5-1.5B on a newly curated 16GB Vietnamese legal corpus. ViLegalQwen2.5-1.5B-Base achieves state-of-the-art results among 1.5B-scale models across Vietnamese legal downstream tasks including Question Answering, Natural Language Inference, and Syllogism Reasoning.
Paper: ViLegalLM: Language Models for Vietnamese Legal Text — ACL 2026
Resources: GitHub | ViLegalBERT | ViLegalQwen3-1.7B-Base
How to Use
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("ntphuc149/ViLegalQwen2.5-1.5B-Base")
model = AutoModelForCausalLM.from_pretrained("ntphuc149/ViLegalQwen2.5-1.5B-Base")
Note: This is a base (pretrained) model, not an instruction-tuned model. We do not recommend using base language models for conversations. Instead, you can apply post-training, e.g., SFT, RLHF, continued pretraining, etc., on this model.
Model Summary
Summary for ViLegalQwen2.5-1.5B-Base checkpoint (click to expand)
| Attribute | Value |
|---|---|
| Architecture | Qwen2 (decoder-only, causal LM) |
| Parameters | 1.54B |
| Base model | Qwen2.5-1.5B |
| Max sequence length | 2048 tokens |
| Tokenizer | Qwen2 tokenizer |
| Training objective | Causal Language Modeling (CLM) |
| Training domain | Vietnamese legal text |
| Precision | BF16 |
Evaluation Results
ViLegalQwen2.5-1.5B-Base achieves state-of-the-art results among 1.5B-scale models across all evaluated Vietnamese legal benchmarks. Bold = best in the 1.5B parameter group. Italic = closed-source model scores.
Question Answering (click to expand)
True/False — ALQAC-TF
| Model | Pre | Rec | F1 |
|---|---|---|---|
| Qwen2-1.5B | 85.05 | 86.84 | 85.94 |
| Qwen2.5-1.5B | 74.47 | 92.11 | 83.34 |
| ViLegalQwen2.5-1.5B-Base | 87.31 | 90.53 | 88.89 |
| gpt-4o-mini (0-shot) | 89.86 | 97.89 | 93.70 |
Multiple Choice — ALQAC-MCQ & VLSP-MCQ-LK
| Model | Pre_mac | Rec_mac | F1_mac |
|---|---|---|---|
| ALQAC-MCQ | |||
| Qwen2-1.5B | 82.19 | 81.05 | 81.42 |
| Qwen2.5-1.5B | 84.80 | 84.05 | 84.37 |
| ViLegalQwen2.5-1.5B-Base | 85.66 | 84.53 | 84.96 |
| gpt-4o-mini (0-shot) | 90.83 | 91.58 | 91.15 |
| VLSP-MCQ-LK | |||
| Qwen2-1.5B | 68.02 | 53.62 | 58.15 |
| Qwen2.5-1.5B | 65.05 | 52.34 | 56.54 |
| ViLegalQwen2.5-1.5B-Base | 65.24 | 54.54 | 58.39 |
| gpt-4o-mini (0-shot) | 69.05 | 51.16 | 58.17 |
Abstractive QA — ViBidLQA-AQA
| Model | ROUGE-L | BLEU-4 | BS-F1 |
|---|---|---|---|
| Qwen2-1.5B | 72.44 | 49.00 | 89.68 |
| Qwen2.5-1.5B | 73.02 | 49.12 | 89.74 |
| ViLegalQwen2.5-1.5B-Base | 73.45 | 49.90 | 89.91 |
| gpt-4o-mini (0-shot) | 67.06 | 40.46 | 86.85 |
Natural Language Inference (click to expand)
NLI — VLSP-NLI
| Model | Precision | Recall | F1 |
|---|---|---|---|
| Qwen2-1.5B | 92.86 | 86.67 | 89.66 |
| Qwen2.5-1.5B | 100.00 | 80.00 | 88.89 |
| ViLegalQwen2.5-1.5B-Base | 84.90 | 100.00 | 91.84 |
| gpt-4o-mini (0-shot) | 100.00 | 86.67 | 92.86 |
Syllogism Reasoning (click to expand)
Syllogism Reasoning — VLSP-Syllogism
| Model | BS-F1 | LLM-Judge |
|---|---|---|
| Qwen2-1.5B | 76.19 | 0.2639 |
| Qwen2.5-1.5B | 76.89 | 0.2656 |
| ViLegalQwen2.5-1.5B-Base | 76.63 | 0.2674 |
| gpt-4o-mini (0-shot) | 78.63 | 0.5069 |
Also in ViLegalLM
| Model | Architecture | Params | Context |
|---|---|---|---|
| ViLegalBERT | Encoder-only | 135M | 256 |
| ViLegalQwen2.5-1.5B-Base (this model) | Decoder-only | 1.54B | 2,048 |
| ViLegalQwen3-1.7B-Base | Decoder-only | 1.72B | 4,096 |
Limitations and Biases
- Domain scope: Trained exclusively on Vietnamese legal texts; may not generalize to other legal systems or jurisdictions.
- Base model only: Not instruction-tuned; outputs may be incomplete or incoherent without task-specific fine-tuning.
- Temporal bias: Legal corpus reflects Vietnamese law as of the collection date; model outputs may not reflect recent legislative changes.
- Inherited biases: May reflect biases present in the source legal corpora, including regional variations in legal practice and domain coverage imbalances.
- Not a legal authority: Model outputs should never be used as definitive legal interpretations without expert validation.
Intended Use
Intended for:
- Further fine-tuning on Vietnamese legal downstream tasks (QA, NLI, reasoning)
- Vietnamese legal text generation and completion
- Research on Vietnamese legal NLP and continual pretraining
Not intended for:
- Direct conversational or instruction-following use without fine-tuning
- Replacing professional legal counsel or human judgment in legal decision-making
- Providing legal advice without expert validation
- Legal systems outside Vietnam without careful domain adaptation
Citation
If you use ViLegalQwen2.5-1.5B-Base, please cite our paper:
<!-- ViLegalLM citation — available soon -->
License
This model is derived from Qwen2.5-1.5B which is licensed under Apache-2.0. ViLegalQwen2.5-1.5B-Base is released under the same Apache-2.0 license.
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