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  <p>This organization is dedicated to language models for code generation. In particular CodeParrot is a GPT-2 model trained to generate Python code.</p>
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  <h2>Table of contents:</h2>
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  <ul>
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- <li>Interactive blog: <a href="https://huggingface.co/spaces/loubnabnl/code-generation-models">Code generation with 🤗</a>, where we compare different code models and explain how they are trained and evaluated.</li>
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- <li>Spaces: code generation with: <a href="https://huggingface.co/codeparrot/codeparrot">CodeParrot</a> (1.5B), <a href="https://huggingface.co/facebook/incoder-6B">InCoder</a> (6B) and <a href="https://github.com/salesforce/CodeGen">CodeGen</a> (6B)</li>
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  <li>Models: CodeParrot (1.5B) and CodeParrot-small (110M), each repo has different ongoing experiments in the branches.</li>
 
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  <li>Datasets:<ul>
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  <li><a href="https://huggingface.co/datasets/codeparrot/codeparrot-clean">codeparrot-clean</a>, dataset on which we trained and evaluated CodeParrot, the splits are available under <a href="https://huggingface.co/datasets/codeparrot/codeparrot-clean-train">codeparrot-clean-train</a> and <a href="https://huggingface.co/datasets/codeparrot/codeparrot-clean-valid">codeparrot-clean-valid</a>.</li>
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  <li>A more filtered version of codeparrot-clean under <a href="https://huggingface.co/datasets/codeparrot/codeparrot-train-more-filtering">codeparrot-train-more-filtering</a> and <a href="https://huggingface.co/datasets/codeparrot/codeparrot-valid-more-filtering">codeparrot-train-more-filtering</a>.</li>
 
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  <p>This organization is dedicated to language models for code generation. In particular CodeParrot is a GPT-2 model trained to generate Python code.</p>
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  <h2>Table of contents:</h2>
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  <ul>
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+
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+ <li>
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+ <p>
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+ Interactive blog where we compare different code models and explain how they are trained and evaluated: <a
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+ href="https://huggingface.co/spaces/loubnabnl/code-generation-models"
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+ class="underline">Code generation with 🤗</a
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+ >
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+ </p>
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+ </li>
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+
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+ <li>
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+ <p>
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+ Spaces: code generation with: <a href="https://huggingface.co/codeparrot/codeparrot">CodeParrot</a> (1.5B), <a href="https://huggingface.co/facebook/incoder-6B">InCoder</a> (6B) and <a href="https://github.com/salesforce/CodeGen">CodeGen</a> (6B)
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+ </p>
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+ </li>
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+
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  <li>Models: CodeParrot (1.5B) and CodeParrot-small (110M), each repo has different ongoing experiments in the branches.</li>
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+
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  <li>Datasets:<ul>
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  <li><a href="https://huggingface.co/datasets/codeparrot/codeparrot-clean">codeparrot-clean</a>, dataset on which we trained and evaluated CodeParrot, the splits are available under <a href="https://huggingface.co/datasets/codeparrot/codeparrot-clean-train">codeparrot-clean-train</a> and <a href="https://huggingface.co/datasets/codeparrot/codeparrot-clean-valid">codeparrot-clean-valid</a>.</li>
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  <li>A more filtered version of codeparrot-clean under <a href="https://huggingface.co/datasets/codeparrot/codeparrot-train-more-filtering">codeparrot-train-more-filtering</a> and <a href="https://huggingface.co/datasets/codeparrot/codeparrot-valid-more-filtering">codeparrot-train-more-filtering</a>.</li>