ChisomChibuike

amazon-rating-sentiment

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Model Details

Developed by: Chisom Chibuike
Model Type: Large Language Model (Autoregressive Transformer), fine-tuned via Parameter-Efficient Fine-Tuning (PEFT/QLoRA).
Base Model: Llama 3.1 8B Instruct
Language(s): English (primarily e-commerce review vernacular)
License: Meta Llama 3.1 Community License
Finetuning Date: May 2026

Intended Use

Primary Use Case

Task A (Persona-to-Review Mapping): Predicting a user's likely numerical rating, and review text for a specific item, given their structured behavioral persona profile.
Simulation & Testing: Enabling e-commerce platforms to simulate how different customer cohorts might react to new products or inventory changes.

Out-of-Scope Uses

Generating fake reviews for real products to manipulate commercial ratings.
Deploying without safety filtering to interact directly with end-users without monitoring for hallucinations.

Training Data & Inputs

The model was fine-tuned on a merged dataset consisting of product reviews (amazon review) and generated user persona profiles (the user persona profile was generated with the aid of an LLM, using the user information from amazon review database). see here for dataset.

Input Context:
- persona: A structured JSON object containing a user's values, writing style markers, common complaints, and rating distribution.
- title: The textual headline of the product being review.
Target Outputs:
- average_rating: Numerical target (1.0 to 5.0).
- text: The detailed review body reflecting the persona's tone, detail level, and vocabulary markers.

Evaluation Metrics

During training and validation, model performance was tracked using the following metrics:

Table with columns: Component, Metric, Target Objective
Component	Metric	Target Objective
Rating Prediction	Mean Absolute Error (MAE)	Minimize deviation from true `average_rating`
Text Generation	ROUGE-L	Capture style matching and alignment with persona markers

Limitations & Biases

Here are the honest limitation of what we currently have.

Critical Caveat: The model's predictive power is strictly bounded by the data present in the training set (heavily weighted toward categories like All Beauty and Skincare in the initial samples).

Domain Bias: Performance may degrade significantly if evaluated on items outside the primary training categories (e.g., items like Electronics or Automotive parts ).
Cold Start Problem: The model requires a rich, established persona profile. If a persona contains minimal evidence or empty arrays, prediction accuracy drops.
Style Homogenization: The model may default to generic "helpful reviewer" archetypes if the unique writing style signals in the input persona are weak.

See this link for github repository.

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Model Details

Model Provider

ChisomChibuike

Model Tree

Base

meta-llama/Llama-3.2-1B-Instruct

Adapter

this model

Input Modalities

Text

Output Modalities

Text

Supported Functionality

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Model Details

Developed by: Chisom Chibuike
Model Type: Large Language Model (Autoregressive Transformer), fine-tuned via Parameter-Efficient Fine-Tuning (PEFT/QLoRA).
Base Model: Llama 3.1 8B Instruct
Language(s): English (primarily e-commerce review vernacular)
License: Meta Llama 3.1 Community License
Finetuning Date: May 2026

Intended Use

Primary Use Case

Task A (Persona-to-Review Mapping): Predicting a user's likely numerical rating, and review text for a specific item, given their structured behavioral persona profile.
Simulation & Testing: Enabling e-commerce platforms to simulate how different customer cohorts might react to new products or inventory changes.

Out-of-Scope Uses

Generating fake reviews for real products to manipulate commercial ratings.
Deploying without safety filtering to interact directly with end-users without monitoring for hallucinations.

Training Data & Inputs

Input Context:
- persona: A structured JSON object containing a user's values, writing style markers, common complaints, and rating distribution.
- title: The textual headline of the product being review.
Target Outputs:
- average_rating: Numerical target (1.0 to 5.0).
- text: The detailed review body reflecting the persona's tone, detail level, and vocabulary markers.

Evaluation Metrics

During training and validation, model performance was tracked using the following metrics:

Table with columns: Component, Metric, Target Objective
Component	Metric	Target Objective
Rating Prediction	Mean Absolute Error (MAE)	Minimize deviation from true `average_rating`
Text Generation	ROUGE-L	Capture style matching and alignment with persona markers

Limitations & Biases

Here are the honest limitation of what we currently have.

Critical Caveat: The model's predictive power is strictly bounded by the data present in the training set (heavily weighted toward categories like All Beauty and Skincare in the initial samples).

Domain Bias: Performance may degrade significantly if evaluated on items outside the primary training categories (e.g., items like Electronics or Automotive parts ).
Cold Start Problem: The model requires a rich, established persona profile. If a persona contains minimal evidence or empty arrays, prediction accuracy drops.
Style Homogenization: The model may default to generic "helpful reviewer" archetypes if the unique writing style signals in the input persona are weak.

See this link for github repository.

amazon-rating-sentiment

README

Model Details

Intended Use

Primary Use Case

Out-of-Scope Uses

Training Data & Inputs

Evaluation Metrics

Limitations & Biases

Explore FriendliAI today

README

Model Details

Intended Use

Primary Use Case

Out-of-Scope Uses

Training Data & Inputs

Evaluation Metrics

Limitations & Biases