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license: cc-by-sa-4.0
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# DeepSeek-Coder-1.3B – Clean DSC Model (DSCc)
This repository hosts **DSCc**, a fine-tuned version of **DeepSeek-Coder-1.3B** trained for **Python function generation** from docstrings and function signatures, using a *cleaned* subset of The Stack.
The model is part of the study:
> **Quality In, Quality Out: Investigating Training Data’s Role in AI Code Generation**
> 33rd IEEE/ACM International Conference on Program Comprehension (ICPC 2025)
DSCc is specifically trained on a **Semgrep-filtered dataset** that removes many low-quality and syntactically incorrect functions, allowing us to study how training data quality impacts code generation performance.
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## Model description
- **Base model:** DeepSeek-Coder-1.3B (Python-focused code LLM)
- **Task:** Python code generation
- **Input:** Python function **docstring + signature**
- **Output:** The corresponding **function body** in Python
In our experiments, the model is conditioned on a prompt consisting of:
- A natural-language docstring describing the function behavior
- The Python function signature
and is then asked to generate the rest of the function body.
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## What does the model do?
The model generates **Python functions** that implement the behavior described in the docstring and implied by the signature. Typical use cases:
- Synthesizing a function implementation from a high-level description
- Suggesting implementations for partially specified functions
- Exploring how training data quality affects generated code (correctness, style, quality issues)
### “Clean” training set (for DSCc)
The initial training set contains ~4.4M pairs. To construct the **clean dataset**:
- We run **Semgrep** (static analysis) on all training functions.
- Semgrep detects:
- Low-quality patterns
- Potentially problematic constructs
- Syntactically incorrect functions
- All flagged low-quality / invalid functions are removed.
This yields:
- **`clean_training_set.json` — ~4.2M pairs**
- Derived from The Stack
- But with many quality issues and syntax errors filtered out.
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