library_name: transformers
datasets:
- AIForge/arcee-evol-messages
- AIForge/evolved-instructions-gemini
language:
- vi
base_model:
- Qwen/Qwen2.5-1.5B-Instruct
pipeline_tag: question-answering
Model Card for Model ID
Model Summary
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.
Environmental Impact
The environmental impact of training the model can be estimated using tools like the Machine Learning Impact Calculator:
- Hardware Type: [More Information Needed]
- Hours Used: [More Information Needed]
- Cloud Provider: [More Information Needed]
- Compute Region: [More Information Needed]
- Carbon Emitted: [More Information Needed]
Technical Specifications
Model Architecture and Objective
- Architecture: Transformer-based architecture with 1.5 billion parameters.
- Objective: Instruction-tuned for contextual understanding and accurate response generation.
Compute Infrastructure
- Hardware: [More Information Needed]
- Software: Hugging Face Transformers library.
Citation
BibTeX:
[More Information Needed]
APA:
[More Information Needed]
Glossary
- Transformer: A deep learning architecture that uses self-attention mechanisms.
- Question-Answering (QA): A task where the model provides answers based on given questions and context.
More Information
For further details, contact [More Information Needed].