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Remove benchmark results from README
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README.md

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[![GitHub forks](https://img.shields.io/github/forks/jorge-martinez-gil/small-code-models?style=social)](https://github.com/jorge-martinez-gil/small-code-models/network/members)
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[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/jorge-martinez-gil/small-code-models/blob/main/notebooks/quick_start.ipynb)
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[![DOI](https://img.shields.io/badge/DOI-10.48550%2FarXiv.2506.10995-blue)](https://doi.org/10.48550/arXiv.2506.10995)
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[![CI](https://github.com/jorge-martinez-gil/small-code-models/actions/workflows/ci.yml/badge.svg)](https://github.com/jorge-martinez-gil/small-code-models/actions/workflows/ci.yml)
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**TL;DR:** This repository is the official, reproducible benchmark suite for the paper *Evaluating Small-Scale Code Models for Code Clone Detection*, with unified scripts to train and evaluate six compact code models across five clone-detection datasets.
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| UniXCoder | ~200 M | Unified Enc-Dec | `microsoft/unixcoder-base` |
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| Salesforce CodeT5 | 220 M | Encoder-Decoder | `Salesforce/codet5-base` |
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## Benchmark Results
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The following values are **representative values — see paper for exact figures**.
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### BigCloneBench (BCB)
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| Model | Precision | Recall | F1 |
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|---|---|---|---|
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| CodeBERT | 0.91 | 0.89 | 0.90 |
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| GraphCodeBERT | 0.92 | 0.90 | 0.91 |
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| PLBART | 0.87 | 0.85 | 0.86 |
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| PolyCoder | 0.82 | 0.80 | 0.81 |
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| UniXCoder | 0.90 | 0.88 | 0.89 |
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| CodeT5 | 0.88 | 0.86 | 0.87 |
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### POJ104
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| Model | Precision | Recall | F1 |
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|---|---|---|---|
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| CodeBERT | 0.89 | 0.87 | 0.88 |
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| GraphCodeBERT | 0.90 | 0.88 | 0.89 |
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| PLBART | 0.84 | 0.83 | 0.84 |
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| PolyCoder | 0.79 | 0.77 | 0.78 |
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| UniXCoder | 0.88 | 0.86 | 0.87 |
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| CodeT5 | 0.85 | 0.84 | 0.85 |
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### GCJ
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| Model | Precision | Recall | F1 |
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|---|---|---|---|
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| CodeBERT | 0.87 | 0.85 | 0.86 |
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| GraphCodeBERT | 0.88 | 0.86 | 0.87 |
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| PLBART | 0.82 | 0.80 | 0.81 |
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| PolyCoder | 0.76 | 0.74 | 0.75 |
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| UniXCoder | 0.86 | 0.84 | 0.85 |
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| CodeT5 | 0.83 | 0.81 | 0.82 |
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### Karnalim
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| Model | Precision | Recall | F1 |
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|---|---|---|---|
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| CodeBERT | 0.85 | 0.83 | 0.84 |
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| GraphCodeBERT | 0.86 | 0.84 | 0.85 |
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| PLBART | 0.80 | 0.78 | 0.79 |
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| PolyCoder | 0.73 | 0.71 | 0.72 |
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| UniXCoder | 0.84 | 0.82 | 0.83 |
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| CodeT5 | 0.81 | 0.79 | 0.80 |
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### PoolC
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| Model | Precision | Recall | F1 |
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|---|---|---|---|
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| CodeBERT | 0.88 | 0.86 | 0.87 |
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| GraphCodeBERT | 0.89 | 0.87 | 0.88 |
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| PLBART | 0.83 | 0.81 | 0.82 |
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| PolyCoder | 0.77 | 0.75 | 0.76 |
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| UniXCoder | 0.87 | 0.85 | 0.86 |
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| CodeT5 | 0.84 | 0.82 | 0.83 |
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Exact figures are reported in Table 2 of the <a href="https://doi.org/10.48550/arXiv.2506.10995">paper</a>. Run the scripts to reproduce.
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## Quick Start
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