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  fifty-three thousand samples of code, annotated with input and output types as well as any dependencies or libraries required for their use. completely anonymized variables. i anonymized all the variables to sort of low-pass a bunch of high frequency features... a model of the space should be smoother to navigate. what ever you think i'm doing with this it's cooler than that.
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  550k files from the stack -> 68k cleanly typed functions w/ dependencies -> variable names anonymized -> 53k de-duplicated w minhash+lsh<br>
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- licenses: check https://huggingface.co/datasets/bigcode/the-stack
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  <details>
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  <summary>filtering libraries:</summary>
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  use the following: <code>dataset.filter(lambda x: False not in [label in allowed_set for label in x['lib_used']])</code><br>
 
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  fifty-three thousand samples of code, annotated with input and output types as well as any dependencies or libraries required for their use. completely anonymized variables. i anonymized all the variables to sort of low-pass a bunch of high frequency features... a model of the space should be smoother to navigate. what ever you think i'm doing with this it's cooler than that.
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  550k files from the stack -> 68k cleanly typed functions w/ dependencies -> variable names anonymized -> 53k de-duplicated w minhash+lsh<br>
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+ licenses: check https://hf.co/datasets/bigcode/the-stack
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  <details>
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  <summary>filtering libraries:</summary>
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  use the following: <code>dataset.filter(lambda x: False not in [label in allowed_set for label in x['lib_used']])</code><br>