This is a question-answering model fine-tuned on Vietnamese language datasets, utilizing the Qwen/Qwen2.5-1.5B-Instruct base model. The model is designed to handle complex instructions and provide accurate, context-aware answers in Vietnamese. It has been fine-tuned on datasets such as AIForge/arcee-evol-messages and AIForge/evolved-instructions-gemini, making it suitable for advanced conversational tasks.
Model Details
Model Description
Developed by: [More Information Needed]
Funded by: [More Information Needed]
Shared by: [More Information Needed]
Model Type: Transformer-based Question-Answering
Language(s): Vietnamese (vi)
License: [More Information Needed]
Finetuned From: Qwen/Qwen2.5-1.5B-Instruct
Model Sources
Repository: [More Information Needed]
Paper: [More Information Needed]
Demo: [More Information Needed]
Uses
Direct Use
The model can be used directly for question-answering tasks in Vietnamese, particularly in customer service, educational tools, or virtual assistants.
Downstream Use
Fine-tuning the model for specific domains such as legal, healthcare, or technical support to improve domain-specific question answering.
Out-of-Scope Use
The model should not be used for generating harmful, biased, or offensive content. It is not intended for decision-making in critical applications without human oversight.
Bias, Risks, and Limitations
While fine-tuned for Vietnamese, the model may still reflect biases present in its training data. Users should exercise caution when using it in sensitive or high-stakes scenarios.
Recommendations
Regular audits of the model’s output for bias or inappropriate content.
Clear communication to users regarding the model’s limitations.
How to Get Started with the Model
Training Details
Training Data
The model was fine-tuned on:
Datasets:
AIForge/arcee-evol-messages
AIForge/evolved-instructions-gemini
These datasets include diverse conversational and instructional data tailored for Vietnamese NLP tasks.
Training Procedure
Preprocessing: Text normalization, tokenization, and Vietnamese-specific preprocessing.
Training Regime: Mixed precision training (e.g., fp16) for efficiency.
Hyperparameters: [More Information Needed]
Speeds, Sizes, Times
Checkpoint Size: [More Information Needed]
Training Time: [More Information Needed]
Evaluation
Testing Data, Factors & Metrics
Testing Data
Evaluation was conducted using unseen subsets of the training datasets.
Factors
Performance was assessed across various subdomains to evaluate the model’s robustness.
Metrics
Standard metrics such as F1 score and exact match (EM) were used for evaluation.
Results
F1 Score: [More Information Needed]
Exact Match: [More Information Needed]
Summary
The model performs well on most Vietnamese question-answering tasks, though further evaluation and tuning may be required for specialized domains.
