GitHub Tag Generator with T5 + PEFT (LoRA)
This model is a fine-tuned version of t5-small
using Parameter-Efficient Fine-Tuning (PEFT) with LoRA adapters. It is trained to generate relevant, deduplicated tags based on natural language descriptions of GitHub repositories. The goal is to assist in automatic tagging for improved search, discoverability, and categorization of repositories.
Model Details
Model Description
This model is part of a lightweight, end-to-end pipeline for automatic tag generation. It takes a short GitHub repo summary as input and returns a comma-separated list of tags. The model was fine-tuned using the PEFT library with LoRA to optimize only a small subset of parameters for efficiency and portability.
- Model type: Seq2Seq text generation (T5)
- Language(s): English
- License: Apache 2.0
- Fine-tuned from model:
t5-small
on Hugging Face - LoRA Adapter Configuration:
r=16
,alpha=32
,dropout=0.05
, target modules: ["q", "v"]
Model Sources
- Dataset: zamal/github-meta-data
- Model Repo: zamal/github-tag-generatorr
- Training Notebook: GitHub Tag Generator Notebook
Uses
Direct Use
The model can be used directly via the ๐ค Transformers pipeline
or with generate()
for:
- Auto-tagging GitHub repos based on descriptions
- Enhancing search filters in dev tools
- Bootstrapping tags for new AI project listings
Example:
from transformers import pipeline
tag_generator = pipeline("text2text-generation", model="zamal/github-tag-generatorr")
text = "Looking for repos that show real-world AI use cases with open-source tools"
tags = tag_generator(text)[0]["generated_text"]
print(tags) # e.g. ai, ml, open-source, examples
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Base model
google-t5/t5-small