|
|
--- |
|
|
pipeline_tag: text-generation |
|
|
license: mit |
|
|
datasets: |
|
|
- data.jsonl |
|
|
language: |
|
|
- en |
|
|
base_model: |
|
|
- EleutherAI/gpt-neo-125m |
|
|
new_version: v1.0 |
|
|
library_name: transformers |
|
|
tags: |
|
|
- GPT-Neo |
|
|
- Fine-Tuned |
|
|
- Chatbot |
|
|
- Text Generation |
|
|
- Abhinav Academy |
|
|
- NLP |
|
|
--- |
|
|
# Abhinav Academy Chatbot |
|
|
|
|
|
A fine-tuned language model designed to answer questions about Abhinav Academy's courses, facilities, and services. This model is based on EleutherAI's GPT-Neo 1.3B and has been specifically trained to provide accurate information about the institution. |
|
|
|
|
|
## Model Details |
|
|
|
|
|
- **Base Model**: EleutherAI/gpt-neo-1.3B |
|
|
- **Training Dataset**: Custom dataset of question-answer pairs about Abhinav Academy |
|
|
- **Task**: Instruction-following for educational institution information |
|
|
- **Primary Use Case**: Answering student and parent queries about Abhinav Academy |
|
|
|
|
|
## Use Cases |
|
|
|
|
|
This model is designed to: |
|
|
|
|
|
- Answer questions about course offerings (JEE, NEET, MHT-CET preparation) |
|
|
- Provide information about faculty and facilities |
|
|
- Explain admission requirements and processes |
|
|
- Share details about extracurricular activities |
|
|
- Address queries about fees, scholarships, and logistical details |
|
|
|
|
|
## Example Usage |
|
|
|
|
|
```python |
|
|
from transformers import AutoTokenizer, AutoModelForCausalLM |
|
|
|
|
|
# Load model and tokenizer |
|
|
model_name = "accesscreate012/abhinav-chatbot" |
|
|
tokenizer = AutoTokenizer.from_pretrained(model_name) |
|
|
model = AutoModelForCausalLM.from_pretrained(model_name) |
|
|
|
|
|
# Function to generate responses |
|
|
def generate_response(instruction, max_new_tokens=150): |
|
|
prompt = f"Instruction: {instruction}\nResponse:" |
|
|
|
|
|
# Tokenize the input |
|
|
inputs = tokenizer(prompt, return_tensors="pt") |
|
|
|
|
|
# Generate with improved parameters |
|
|
outputs = model.generate( |
|
|
**inputs, |
|
|
max_new_tokens=max_new_tokens, |
|
|
num_beams=5, |
|
|
no_repeat_ngram_size=3, |
|
|
early_stopping=True, |
|
|
top_p=0.92, |
|
|
temperature=0.7, |
|
|
do_sample=True, |
|
|
pad_token_id=tokenizer.eos_token_id |
|
|
) |
|
|
|
|
|
# Process and clean the output |
|
|
generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True) |
|
|
|
|
|
# Extract only the response part |
|
|
if "Response:" in generated_text: |
|
|
response = generated_text.split("Response:")[1].strip() |
|
|
else: |
|
|
response = generated_text.replace(prompt, "").strip() |
|
|
|
|
|
return response |
|
|
|
|
|
# Example queries |
|
|
queries = [ |
|
|
"What courses do you offer for JEE preparation?", |
|
|
"Tell me about your NEET coaching program", |
|
|
"Where is Abhinav Academy located?", |
|
|
"Do you have hostel facilities?" |
|
|
] |
|
|
|
|
|
# Generate and print responses |
|
|
for query in queries: |
|
|
print(f"Q: {query}") |
|
|
print(f"A: {generate_response(query)}") |
|
|
print() |
|
|
``` |
|
|
|
|
|
## Sample Interactions |
|
|
|
|
|
**Q: What courses does Abhinav Academy offer?** |
|
|
A: We offer MHT-CET, JEE, NEET preparation, as well as supplementary courses like English Communication, Personality Development, and Foreign Languages (German/French). |
|
|
|
|
|
**Q: Does Abhinav Academy offer NEET coaching?** |
|
|
A: Yes, we offer NEET coaching with expert faculty and structured study materials. Our 24-month NEET Preparation program emphasizes NCERT-based learning and practical lab sessions for Biology, Physics, and Chemistry. |
|
|
|
|
|
**Q: What are the admission requirements for Abhinav Academy?** |
|
|
A: Admission requirements vary by program. Generally, students must pass an entrance test and interview. Competitive courses require strong academics. |
|
|
|
|
|
## Limitations |
|
|
|
|
|
- The model is specialized for information about Abhinav Academy only |
|
|
- It may not provide accurate information about other educational institutions |
|
|
- Responses are based on training data and may not reflect real-time changes to curriculum or policies |
|
|
- The model should be regularly updated as the institution's offerings evolve |
|
|
|
|
|
## Training Methodology |
|
|
|
|
|
This model was fine-tuned using a supervised approach on a custom dataset containing question-answer pairs about Abhinav Academy. The training process focused on: |
|
|
|
|
|
- Preserving factual accuracy about course offerings |
|
|
- Maintaining consistent formatting and tone |
|
|
- Optimizing for natural-sounding responses |
|
|
- Handling variations in question phrasing |
|
|
|
|
|
## Integration |
|
|
|
|
|
This model can be integrated into: |
|
|
|
|
|
- The academy's official website as a chat assistant |
|
|
- Mobile applications for student support |
|
|
- SMS or WhatsApp-based query systems |
|
|
- Internal student support systems |
|
|
|
|
|
## License |
|
|
|
|
|
This model is provided for educational purposes. Please contact Abhinav Academy for commercial use. |
|
|
|
|
|
## Contact |
|
|
|
|
|
For questions or feedback about this model, please reach out to [CONTACT_EMAIL]. |
