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--- |
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library_name: transformers |
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datasets: |
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- AIForge/arcee-evol-messages |
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- AIForge/evolved-instructions-gemini |
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language: |
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- vi |
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base_model: |
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- Qwen/Qwen2.5-1.5B-Instruct |
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pipeline_tag: question-answering |
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--- |
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# Model Card for Model ID |
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## Model Summary |
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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. |
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## Model Details |
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### Model Description |
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- **Developed by:** [More Information Needed] |
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- **Funded by:** [More Information Needed] |
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- **Shared by:** [More Information Needed] |
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- **Model Type:** Transformer-based Question-Answering |
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- **Language(s):** Vietnamese (vi) |
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- **License:** [More Information Needed] |
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- **Finetuned From:** Qwen/Qwen2.5-1.5B-Instruct |
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### Model Sources |
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- **Repository:** [More Information Needed] |
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- **Paper:** [More Information Needed] |
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- **Demo:** [More Information Needed] |
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## Uses |
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### Direct Use |
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The model can be used directly for question-answering tasks in Vietnamese, particularly in customer service, educational tools, or virtual assistants. |
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### Downstream Use |
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Fine-tuning the model for specific domains such as legal, healthcare, or technical support to improve domain-specific question answering. |
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### Out-of-Scope Use |
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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. |
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## Bias, Risks, and Limitations |
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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. |
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### Recommendations |
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- Regular audits of the model’s output for bias or inappropriate content. |
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- Clear communication to users regarding the model’s limitations. |
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## How to Get Started with the Model |
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## Training Details |
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### Training Data |
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The model was fine-tuned on: |
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- **Datasets:** |
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- AIForge/arcee-evol-messages |
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- AIForge/evolved-instructions-gemini |
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These datasets include diverse conversational and instructional data tailored for Vietnamese NLP tasks. |
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### Training Procedure |
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- **Preprocessing:** Text normalization, tokenization, and Vietnamese-specific preprocessing. |
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- **Training Regime:** Mixed precision training (e.g., fp16) for efficiency. |
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- **Hyperparameters:** [More Information Needed] |
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### Speeds, Sizes, Times |
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- **Checkpoint Size:** [More Information Needed] |
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- **Training Time:** [More Information Needed] |
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## Evaluation |
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### Testing Data, Factors & Metrics |
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#### Testing Data |
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Evaluation was conducted using unseen subsets of the training datasets. |
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#### Factors |
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Performance was assessed across various subdomains to evaluate the model’s robustness. |
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#### Metrics |
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Standard metrics such as F1 score and exact match (EM) were used for evaluation. |
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### Results |
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- **F1 Score:** [More Information Needed] |
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- **Exact Match:** [More Information Needed] |
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#### Summary |
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The model performs well on most Vietnamese question-answering tasks, though further evaluation and tuning may be required for specialized domains. |
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## Environmental Impact |
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The environmental impact of training the model can be estimated using tools like the [Machine Learning Impact Calculator](https://mlco2.github.io/impact#compute): |
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- **Hardware Type:** [More Information Needed] |
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- **Hours Used:** [More Information Needed] |
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- **Cloud Provider:** [More Information Needed] |
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- **Compute Region:** [More Information Needed] |
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- **Carbon Emitted:** [More Information Needed] |
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## Technical Specifications |
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### Model Architecture and Objective |
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- **Architecture:** Transformer-based architecture with 1.5 billion parameters. |
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- **Objective:** Instruction-tuned for contextual understanding and accurate response generation. |
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### Compute Infrastructure |
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- **Hardware:** [More Information Needed] |
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- **Software:** Hugging Face Transformers library. |
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## Citation |
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**BibTeX:** |
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```bibtex |
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[More Information Needed] |
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``` |
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**APA:** |
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[More Information Needed] |
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## Glossary |
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- **Transformer:** A deep learning architecture that uses self-attention mechanisms. |
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- **Question-Answering (QA):** A task where the model provides answers based on given questions and context. |
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## More Information |
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For further details, contact [More Information Needed]. |
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