dataset_info:
features:
- name: data_source
dtype: string
- name: prompt
list:
- name: content
dtype: string
- name: role
dtype: string
- name: ability
dtype: string
- name: reward_model
struct:
- name: ground_truth
dtype: 'null'
- name: style
dtype: string
- name: extra_info
struct:
- name: split
dtype: string
- name: tools_kwargs
struct:
- name: difficulty
dtype: int64
- name: idx
dtype: int64
- name: item
struct:
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struct:
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dtype: bool
- name: class_code
dtype: string
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dtype: string
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- name: class_loc
dtype: int64
- name: class_name
dtype: string
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dtype: string
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- name: class_without_method
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- name: file_path
dtype: string
- name: file_without_method
dtype: string
- name: idx
dtype: int64
- name: line_numbers
dtype: string
- name: method
struct:
- name: body
dtype: string
- name: declaration
dtype: string
- name: description
dtype: string
- name: global_method_body_index
list: int64
- name: global_method_declaration_index
list: int64
- name: method_body_index
list: int64
- name: method_declaration_index
list: int64
- name: name
dtype: string
- name: num_lines
dtype: int64
- name: num_lines_quartile
dtype: int64
- name: method_count
dtype: int64
- name: qwen3-8b_pass_repair
dtype: bool
- name: qwen3-8b_pass_single
dtype: bool
- name: raw_doc
dtype: string
- name: raw_file_content
dtype: string
- name: split
dtype: string
- name: tests
struct:
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list: string
- name: methods
list: string
- name: modules
list: string
- name: test_output
dtype: string
- name: time_cat
dtype: float64
- name: time_edit
dtype: float64
- name: time_reset
dtype: float64
- name: time_reset_norm
dtype: float64
- name: time_test
dtype: float64
- name: time_test_norm
dtype: float64
- name: time_total
dtype: float64
- name: agent_name
dtype: string
splits:
- name: train
num_bytes: 232713631
num_examples: 1364
- name: test
num_bytes: 14568916
num_examples: 100
download_size: 247410221
dataset_size: 247282547
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
JetBrains-Research/django_method_gen is a code generation benchmark built from the Django codebase.
This dataset is used in the example of IDEGym usage for VERL-based RL training (see this repo).
Each example is a task to regenerate a single Python method that has been cut from its class. The dataset provides the surrounding class code, file imports, and docstrings as context. Reward is rule-based: the agent's submission is evaluated by running the original unit tests.
The dataset follows the VERL multi-turn format: each row contains a prompt (chat-style system + user messages), an agent_name field ("idegym_django"), and an extra_info blob carrying the raw task data passed to the IDEGym server — including the method body to recover, file context, and test metadata. There are 1,364 training examples and 100 test examples, spanning four difficulty levels.