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---
dataset_info:
features:
- name: problem_id
dtype: string
- name: source
dtype: string
- name: task_type
dtype: string
- name: in_source_id
dtype: string
- name: prompt
dtype: string
- name: golden_standard_solution
dtype: string
- name: verification_info
dtype: string
- name: metadata
dtype: string
splits:
- name: train
num_bytes: 6988481341
num_examples: 69752
download_size: 2821986433
dataset_size: 6988481341
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
```python
ds_up = ds_debug.map(lambda x, idx: {"problem_id": f"swe_fixer_{idx}"}, with_indices=True)
ds_up = ds_up.map(lambda x: {"source": "internlm/SWE-Fixer-Train-Editing-CoT-70K", "task_type": "swe_fixer"})
num_proc = 16
# Function to format code files for display
def format_files(files):
formatted = ""
for file_info in files:
formatted += f"## `{file_info['file']}`\n```\n{file_info['file content']}\n```\n\n"
return formatted
ds_up = ds_up.map(lambda x: {"in_source_id": x["instance_id"]}, num_proc=num_proc)
# Format the prompt using the template
ds_up = ds_up.map(lambda x: {
"prompt": prompt_template.format(
issue_description=x['input']['input']['issue'],
files=format_files(x['input']['input']['files to be modified'])
)
}, num_proc=num_proc)
# Format the golden_standard_solution properly - use repr() to ensure it's a valid Python literal
ds_up = ds_up.map(lambda x: {"golden_standard_solution": repr({
"edited code": x["output"]["edited code"]
})}, num_proc=num_proc)
ds_up = ds_up.map(lambda x: {"verification_info": repr({
"input": x["input"]["input"],
"output": x["output"]
})}, num_proc=num_proc)
# Format the metadata as a string representation of a dictionary
ds_up = ds_up.map(lambda x: {"metadata": repr({
# "input": x["input"]["input"]
})}, num_proc=num_proc)
ds_up = ds_up.select_columns(["problem_id", "source", "task_type", "in_source_id", "prompt", "golden_standard_solution", "verification_info", "metadata"])
``` |