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SWE-bench_bm25_13K

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princeton-nlp commited on Nov 16, 2023

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@@ -60,7 +60,7 @@ SWE-bench is a dataset that tests systems’ ability to solve GitHub issues auto
 The dataset was released as part of [SWE-bench: Can Language Models Resolve Real-World GitHub Issues?](https://arxiv.org/abs/2310.06770)
-This dataset `SWE-bench_bm25_13K` includes a formatting of each instance using Pyserini's BM25 retrieval as described in the paper. The context limit size is 13,000 `cl100k_base` tokens from the [`tiktoken`](https://github.com/openai/tiktoken) tokenization package used for OpenAI models.
 The `text` column can be used directly with LMs to generate patch files.
 Models are instructed to generate [`patch`](https://en.wikipedia.org/wiki/Patch_(Unix)) formatted file using the following template:
 ```diff

 The dataset was released as part of [SWE-bench: Can Language Models Resolve Real-World GitHub Issues?](https://arxiv.org/abs/2310.06770)
+This dataset `SWE-bench_bm25_13K` includes a formatting of each instance using Pyserini's BM25 retrieval as described in the paper. The code context size limit is 13,000 `cl100k_base` tokens from the [`tiktoken`](https://github.com/openai/tiktoken) tokenization package used for OpenAI models.
 The `text` column can be used directly with LMs to generate patch files.
 Models are instructed to generate [`patch`](https://en.wikipedia.org/wiki/Patch_(Unix)) formatted file using the following template:
 ```diff