README.md

---
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](https://mlco2.github.io/impact#compute):

- **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:**

```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].