---
license: apache-2.0
language:
- en
base_model:
- Salesforce/codet5-small
pipeline_tag: text-generation
tags:
- code
- commit
- gitdiff
datasets:
- seniruk/git-diff_to_commit_msg_large
---

# Hi, I’m Seniru Epasinghe 👋

I’m an AI undergraduate and an AI enthusiast, working on machine learning projects and open-source contributions.  
I enjoy exploring AI pipelines, natural language processing, and building tools that make development easier.

---

## 🌐 Connect with me

[![Hugging Face](https://img.shields.io/badge/Hugging%20Face-seniruk-orange?logo=huggingface&logoColor=white)](https://huggingface.co/seniruk)   
[![Medium](https://img.shields.io/badge/Medium-seniruk_epasinghe-black?logo=medium&logoColor=white)](https://medium.com/@senirukepasinghe)   
[![LinkedIn](https://img.shields.io/badge/LinkedIn-seniru_epasinghe-blue?logo=linkedin&logoColor=white)](https://www.linkedin.com/in/seniru-epasinghe-b34b86232/)   
[![GitHub](https://img.shields.io/badge/GitHub-seth2k2-181717?logo=github&logoColor=white)](https://github.com/seth2k2)


## Fintuned Salesforce/codet5-small base model using 1000000 rows of data with git commits of different types of random languages for 5 epochs
  - [dataset huggingface link](https://huggingface.co/datasets/seniruk/git-diff_to_commit_msg_large)
  - [dataset kaggle link](https://www.kaggle.com/datasets/seniruepasinghe/git-diff-to-commit-msg-large)

## Took a total of 10 hours in a gpu of RTX 4060TI 16GB VRAM.
## Use the below instructions for inference
## Modules required- transformers,pytorch,CUDA
```
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

Load the correct CodeT5 tokenizer and model
model_name = "Salesforce/codet5-small"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)

#Example Git diff input
git_diff = """
diff --git a/example.py b/example.py
index 3b18e12..b3f7e54 100644
--- a/example.py
+++ b/example.py
@@ -1,5 +1,6 @@
-def greet():
-    print("Hello, world!")
+def greet_user(name):
+    print(f"Hello, {name}!")

-def farewell():
-    print("Goodbye!")
+def farewell_user(name):
+    print(f"Goodbye, {name}!")
"""

#keep the instruction unchanged, becus the model was trained on this static instruction 
instruction = "Generate a commit message based on the following Git diff:\n\n"

task_input = instruction + git_diff

# Tokenize the input
inputs = tokenizer(
    task_input,
    max_length=512,  # Truncate if necessary
    truncation=True,
    padding="max_length",
    return_tensors="pt"
)

# Generate commit message
outputs = model.generate(
    inputs["input_ids"],
    max_length=50,
    num_beams=5,       # Use beam search
    temperature=0.9,   # Adds controlled randomness
    top_p=0.9,         # Nucleus sampling
    early_stopping=True
)

# Decode the generated commit message
commit_message = tokenizer.decode(outputs[0], skip_special_tokens=True)

# Print the result
print("Generated Commit Message:")
print(commit_message)
```