nikhilkulkarni1755/resume-model-finetuned

T5 Base Finetuned for Resume Optimization

This model is a fine-tuned version of google/t5-base specifically designed to transform informal technical achievement descriptions into professional, well-structured resume bullet points.

Model Details

This T5-based model converts casual descriptions of technical projects and achievements into polished resume bullets that follow a consistent professional format. It's particularly effective for STEM fields including software engineering, data science, DevOps, embedded systems, and more.

Model Description

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: Nikhil Kulkarni
• Model type: Text Generation
• Language(s) (NLP): Python
• License: Apache 2.0
• Finetuned from model Google T5:

Model Sources [optional]

• Repository: https://github.com/nikhilkulkarni1755/finetuned-t5-tokenizer

Uses

Useful to people frequently updating their profile. Instead of finetuning a mainstream model everytime, give just the project name, tech stack used, features implemented and some impacts of the project. This model will convert into the standard format 'To {take specific action}, built {project name} with {technologies}. Implemented {features}, resulting in {impact}'

Direct Use

[More Information Needed]

Downstream Use [optional]

[More Information Needed]

Out-of-Scope Use

[More Information Needed]

Bias, Risks, and Limitations

[More Information Needed]

Recommendations

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

[More Information Needed]

Training Procedure

Preprocessing [optional]

[More Information Needed]

Training Hyperparameters

• Training regime: [More Information Needed]

Speeds, Sizes, Times [optional]

[More Information Needed]

Evaluation

Testing Data, Factors & Metrics

Testing Data

[More Information Needed]

Factors

[More Information Needed]

Metrics

[More Information Needed]

Results

[More Information Needed]

Summary

Model Examination [optional]

[More Information Needed]

Environmental Impact

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]

BibTeX:

[More Information Needed]

APA:

[More Information Needed]

Glossary [optional]

[More Information Needed]

More Information [optional]

[More Information Needed]

Model Card Authors [optional]

[More Information Needed]

Model Card Contact

[More Information Needed]