LLama3-reviewClassifier-Lora-weights

Model Card for Model ID

<!-- Provide a quick summary of what the model is/does. -->

This is a Llama 3:8b based fine-tuned model used for for classifying reviews as useful for improvement of an application or not. A review was considered useful if it contained something useful for the further development or improvement of the application, that is, a user story, bug report, or other specific problems that users have. It does not include general complaints such as “I hate this app”, or encouragements such as “I like your payment feature”, even if these encouragements are directed at a specific feature of the app.

The idea is to use this model as part of a pipeline, where you classify reviews as 'useful' or not, and then you can further tag the useful reviews according to your need and store them. This allows your organisation to send the right reviews to different departments in your organisation to be handled, for example feature requests and bug reports. This model outperformed Chat-bison for this classification task if you weigh the result higher for recall. It had an F1-weighted score of 0.90 for the 550 reviews it was tested against, with an F1-score of 0.92 for the negative class and F1-score of 0.87 for the positive class.

The model is fine-tuned to answers "True" or "False" for inputted reviews. It was trained using unsloth AI, based on Llama-3-8b-bnb-4bit, also using TRL SFTTrainer for 10 epochs with a validation loss of 0.0235.

It was trained on real reviews from the Google Play store using the following prompt:

""" You are an AI trained to classify user reviews for a company called Kivra, which offers a digital mailbox service featuring invoice payment, digital signing, mail scanning, simple bookkeeping, and digital receipts.

Your task is to analyze each user review and determine whether it contains useful information such as feature requests, bug reports, or other valuable feedback that can aid the developers in improving the service. You will classify the review as 'True' if it contains such useful information, and 'False' if it does not.

Classification Guidelines:

• Classify a review as 'True' if it suggests a new feature, reports a bug, complains in a way that highlights a problem, or provides any other information that could lead to an improvement of the service.
• Classify as 'False' if the review only praises the service, comments on its current state without constructive feedback, or is irrelevant to service development.

Example Classifications:

• Review: "It's difficult to increase text size" Classification: True Reason: The review suggests a feature to increase text size, which is useful feedback for enhancing readability.

• Review: "This is the best app to pay bills" Classification: False Reason: The review praises the service but lacks suggestions for improvement or reports of issues.

• Review: "The app crashes every time I try to upload a document." Classification: True Reason: The review reports a specific bug, providing critical information for troubleshooting and improvement."""

Model Details

Model Description

<!-- Provide a longer summary of what this model is. -->

This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.

• Developed by: [More Information Needed]
• Funded by [optional]: [More Information Needed]
• Shared by [optional]: [More Information Needed]
• Model type: [More Information Needed]
• Language(s) (NLP): [More Information Needed]
• License: [More Information Needed]
• Finetuned from model [optional]: [More Information Needed]

Model Sources [optional]

<!-- Provide the basic links for the model. -->

• Repository: [More Information Needed]
• Paper [optional]: [More Information Needed]
• Demo [optional]: [More Information Needed]

Uses

<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->

Direct Use

<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->

[More Information Needed]

Downstream Use [optional]

<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->

[More Information Needed]

Out-of-Scope Use

<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->

[More Information Needed]

Bias, Risks, and Limitations

<!-- This section is meant to convey both technical and sociotechnical limitations. -->

[More Information Needed]

Recommendations

<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

How to Get Started with the Model

Use the code below to get started with the model.

[More Information Needed]

Training Details

Training Data

<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->

[More Information Needed]

Training Procedure

<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->

Preprocessing [optional]

[More Information Needed]

Training Hyperparameters

• Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->

Speeds, Sizes, Times [optional]

<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->

[More Information Needed]

Evaluation

<!-- This section describes the evaluation protocols and provides the results. -->

Testing Data, Factors & Metrics

Testing Data

<!-- This should link to a Dataset Card if possible. -->

[More Information Needed]

Factors

<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->

[More Information Needed]

Metrics

<!-- These are the evaluation metrics being used, ideally with a description of why. -->

[More Information Needed]

Results

[More Information Needed]

Summary

Model Examination [optional]

<!-- Relevant interpretability work for the model goes here -->

[More Information Needed]

Environmental Impact

<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

• Hardware Type: [More Information Needed]
• Hours used: [More Information Needed]
• Cloud Provider: [More Information Needed]
• Compute Region: [More Information Needed]
• Carbon Emitted: [More Information Needed]

Technical Specifications [optional]

Model Architecture and Objective

[More Information Needed]

Compute Infrastructure

[More Information Needed]

Hardware

[More Information Needed]

Software

[More Information Needed]

Citation [optional]

<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->

BibTeX:

[More Information Needed]

APA:

[More Information Needed]

Glossary [optional]

<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->

[More Information Needed]

More Information [optional]

[More Information Needed]

Model Card Authors [optional]

[More Information Needed]

Model Card Contact

[More Information Needed]