Update README.md

README.md

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----
-license: mit
----
+---
+license: mit
+language:
+- en
+base_model:
+- openai-community/gpt2
+pipeline_tag: text-generation
+library_name: transformers
+---
+# GPT2 Student Advisor
+
+## Model Description
+
+The **GPT2 Student Advisor** is a fine-tuned version of GPT-2 aimed at generating personalized academic suggestions for students based on their individual profiles. The model analyzes various factors such as study hours, attendance, parental involvement, sleep patterns, and more to provide tailored advice that can help improve their academic performance and overall well-being.
+
+This model was trained using the **Student Performance Factors** dataset, which contains a variety of student attributes and corresponding suggestions to improve their academic outcomes. The model uses GPT-2’s language generation capabilities to create human-like advisory responses.
+
+### Model architecture
+- **Base model**: GPT-2
+- **Fine-tuned**: Yes (on student profile and suggestion generation)
+
+## Intended Use
+
+This model can be used as a conversational tool for students, teachers, or counselors to guide students toward better academic and personal practices. It is designed to:
+- Generate personalized suggestions for students based on their profiles.
+- Provide actionable advice to improve academic performance, motivation, and well-being.
+  
+### Use Cases:
+- **Student advisory systems**: As a chatbot providing automated guidance to students.
+- **Educational platforms**: Offering personalized insights and tips.
+- **Counseling assistance**: Helping school counselors or tutors to get quick suggestions.
+
+## Training Data
+
+The model was fine-tuned on the **Student Performance Factors** dataset. The dataset includes features such as:
+- Hours Studied
+- Attendance
+- Parental Involvement
+- Sleep Hours
+- Motivation Level
+- Physical Activity
+- Internet Access
+- And more...
+
+Each student's profile was paired with a list of suggestions based on their individual data. These suggestions were used as target outputs for the fine-tuning process.
+
+### Example Input:
+```
+Student Profile:
+- Hours Studied per week: 5
+- Attendance: 60%
+- Parental Involvement: Low
+- Access to Resources: Medium
+- Extracurricular Activities: No
+- Sleep Hours per night: 6
+- Previous Scores: 70
+- Motivation Level: Low
+- Internet Access: No
+- Tutoring Sessions per month: 0
+- Family Income: Low
+- Teacher Quality: Medium
+- School Type: Public
+- Peer Influence: Negative
+- Physical Activity per week: 1 hour
+- Learning Disabilities: Yes
+- Parental Education Level: High School
+- Distance from Home: Far
+- Gender: Male
+```
+
+### Example Output:
+```
+Suggestions:
+- Consider increasing your study hours.
+- Improve your class attendance.
+- Seek more support from your parents.
+- Ensure you get enough sleep each night.
+- Find ways to boost your motivation.
+- Find ways to access the internet for study resources.
+- Consider seeking help for your learning disabilities.
+- Engage in more physical activities for better health.
+```
+
+## Training Procedure
+
+### Training Details
+- **Batch size**: 8 (with gradient accumulation steps of 2 for an effective batch size of 16).
+- **Epochs**: 3
+- **Learning rate**: 5e-5
+- **Optimizer**: AdamW
+- **Weight decay**: 0.01
+- **Mixed precision**: Enabled on GPU via `fp16` for faster training.
+- **Evaluation strategy**: Performed at the end of each epoch with the best model saved based on lowest loss.
+
+### Environment
+- **Hardware**: Trained on an NVIDIA GPU.
+- **Software**: Used the `transformers` library from Hugging Face with `PyTorch` backend.
+
+## Performance
+
+The model was evaluated using the following metrics:
+- **Loss**: Minimized using causal language modeling, with padding ignored during loss calculation.
+- **Epoch validation**: Best model was selected based on lowest validation loss.
+
+## Limitations
+
+- **Domain-specific**: This model is trained on student profiles and may not generalize well to other types of input.
+- **Sensitive to input format**: For optimal results, the student profile should follow a consistent format.
+
+## Ethical Considerations
+
+This model was trained using a dataset that assumes certain relationships between academic performance factors and suggestions. Users should be aware that real-life factors affecting student performance can be complex and multifaceted. This model should be used as a supplementary tool and not as a replacement for professional counseling or personalized human feedback.
+
+## How to Use
+
+```python
+from transformers import GPT2LMHeadModel, GPT2Tokenizer
+
+# Load the fine-tuned model and tokenizer
+model = GPT2LMHeadModel.from_pretrained("LyubomirT/gpt2-student-suggester")
+tokenizer = GPT2Tokenizer.from_pretrained("LyubomirT/gpt2-student-suggester")
+
+# Define a student profile
+student_profile = """
+Student Profile:
+- Hours Studied per week: 5
+- Attendance: 60%
+- Parental Involvement: Low
+- Sleep Hours per night: 6
+- Motivation Level: Low
+"""
+
+# Tokenize the input and generate suggestions
+inputs = tokenizer.encode(student_profile, return_tensors="pt")
+outputs = model.generate(inputs, max_length=600, num_beams=5, early_stopping=True)
+suggestions = tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+print("Generated Suggestions:")
+print(suggestions)
+```
+
+## License
+
+This model is released under the MIT license. Please check [Hugging Face's Model Licensing guidelines](https://huggingface.co/docs/hub/model-repositories#license-a-model) for more information.