---
library_name: transformers
license: apache-2.0
language:
- en
base_model:
- openai-community/gpt2-medium
---

# MORI AI

## Model Details

### Model Description

MORI (Machine-Oriented Responsive Intelligence) is a transformer-based conversational AI developed as part of the research project:

**"Design and Implementation of an NLP-Driven Intelligent Web Companion for Real-Time User Interaction."**

This version of MORI is specifically designed to assist students, faculty, and stakeholders of ICCT Colleges – San Mateo Campus by providing information related to academic programs, enrollment procedures, registrar services, school policies, and general campus inquiries.

The model focuses on answering institution-related questions and providing guidance based on publicly available academic and administrative information.

**Developed by:** ShikoShin
**Model Type:** Transformer-Based Conversational AI
**Language(s):** English
**License:** Apache 2.0
**Finetuned from:** DialoGPT-Large

---

# Model Sources

**Institution:** ICCT Colleges

**Research Project:** Design and Implementation of an NLP-Driven Intelligent Web Companion for Real-Time User Interaction

---

# Intended Use

## Primary Use Cases

MORI is intended to assist users with:

* Academic program inquiries
* Course information
* Admission requirements
* Enrollment procedures
* Registrar-related concerns
* School policies and guidelines
* Frequently asked questions about ICCT Colleges – San Mateo Campus

## Educational Support

The model may be integrated into:

* Student assistance platforms
* School websites
* Mobile applications
* Information kiosks
* Academic support systems

---

# Out-of-Scope Use

MORI is not intended for:

* Medical advice
* Legal advice
* Financial consulting
* Psychological counseling
* Emergency response
* Safety-critical decision making

Users should consult official ICCT personnel for authoritative decisions regarding academic records, enrollment status, and institutional policies.

---

# Training Data

## Dataset Description

MORI was trained using a curated dataset consisting of institution-specific information related to:

* Academic courses and programs
* Enrollment workflows
* Admission requirements
* Registrar procedures
* Frequently asked student inquiries
* Publicly available institutional information

### Data Privacy Statement

No sensitive personal information was used during training.

The dataset does **not** contain:

* Student records
* Grades or transcripts
* Personal identification information
* Financial records
* Medical information
* Confidential institutional documents

Training data was limited to educational and administrative information intended for public dissemination.

---

# Limitations

MORI may:

* Generate incorrect responses
* Provide outdated information if institutional policies change
* Misinterpret ambiguous questions
* Require human verification for official transactions

The model should be treated as an informational assistant rather than an official source of record.

---

# How to Get Started

```python
from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "ShikoShin/DialoGPT-Large-MORIAI"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

prompt = "How do I enroll at ICCT San Mateo?"
inputs = tokenizer(prompt, return_tensors="pt")

outputs = model.generate(
    **inputs,
    max_new_tokens=100,
    do_sample=True,
    temperature=0.8
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

# Training Procedure

## Preprocessing

The dataset underwent:

* Data cleaning
* Duplicate removal
* Text normalization
* Formatting into question-answer pairs
* Tokenization

## Framework

* Python
* PyTorch
* Hugging Face Transformers
* Hugging Face Tokenizers

---

# Evaluation

## Evaluation Criteria

The model was evaluated based on:

* Response relevance
* Institutional information accuracy
* Conversational consistency
* User query understanding

## Results

MORI demonstrated the ability to answer common student and administrative questions related to ICCT Colleges – San Mateo Campus while maintaining conversational coherence.

---

# Technical Specifications

## Architecture

Transformer-based autoregressive language model fine-tuned from DialoGPT-Large.

### Objective

Generate contextually appropriate responses to institution-related inquiries while maintaining conversational flow.

---

# Citation

### APA

ShikoShin. (2026). MORI: Intelligent Academic Assistant for ICCT Colleges – San Mateo Campus. Hugging Face.

### BibTeX

@misc{mori2026,
author = {ShikoShin},
title = {MORI: Intelligent Academic Assistant for ICCT Colleges -- San Mateo Campus},
year = {2026},
publisher = {Hugging Face}
}

---

# Authors

ShikoShin

---

# Contact

For questions, feedback, or collaboration opportunities, please contact the repository owner through Hugging Face.