mirror of
https://github.com/hanzoai/open-instruct.git
synced 2026-10-30 18:26:32 +00:00
74069db498
* Reorganize the data preparation scripts for tulu v1 and v2. * Minor improvement * Remove open_platypus_commercial subset from Daring-Anteater * Use hard-coded examples repo. * Fix some bugs. * Add OpenMathInstruct. * Add a few more v3.5.x SFT mix ablations for the cleaner datasets. * More experiments on mixes. * help merge * prep for merge * reapply changes * fix naming --------- Co-authored-by: Nathan Lambert <[email protected]>
79 lines
2.8 KiB
Python
79 lines
2.8 KiB
Python
import argparse
|
|
import os
|
|
from collections import defaultdict
|
|
from typing import List, Optional
|
|
|
|
from scripts.data.sft.utils import convert_sft_dataset
|
|
|
|
|
|
if __name__ == "__main__":
|
|
parser = argparse.ArgumentParser(
|
|
description="Process no_robots dataset and optionally upload to Hugging Face Hub."
|
|
)
|
|
parser.add_argument(
|
|
"--push_to_hub",
|
|
action="store_true",
|
|
help="Upload the dataset to Hugging Face Hub",
|
|
)
|
|
parser.add_argument(
|
|
"--hf_entity",
|
|
type=str,
|
|
default=None,
|
|
help="Hugging Face organization to upload to (if not provided, uploads to user's account)",
|
|
)
|
|
parser.add_argument(
|
|
"--converted_dataset_name",
|
|
type=str,
|
|
default="no_robots_converted",
|
|
help="Name of the converted dataset on Hugging Face Hub.",
|
|
)
|
|
parser.add_argument(
|
|
"--local_save_dir",
|
|
type=str,
|
|
default=None,
|
|
help="Local directory to save the dataset (if not provided, does not save locally)",
|
|
)
|
|
parser.add_argument(
|
|
"--apply_keyword_filters",
|
|
action="store_true",
|
|
help=(
|
|
"Apply keyword filters to the dataset. "
|
|
"Currently filters out conversations with OpenAI, ChatGPT, BingChat, etc."
|
|
),
|
|
)
|
|
parser.add_argument(
|
|
"--apply_empty_message_filters",
|
|
action="store_true",
|
|
help="Apply empty message filters to the dataset.",
|
|
)
|
|
args = parser.parse_args()
|
|
|
|
readme_content = (
|
|
"This is a converted version of the no_robots dataset into Tulu SFT training format.\n\n"
|
|
"The conversion script can be found in our "
|
|
"[open-instruct](https://github.com/allenai/open-instruct/blob/main/scripts/data/sft/no_robots.py) repo.\n"
|
|
"The conversion took the following parameters:\n"
|
|
f"- apply_keyword_filters: {args.apply_keyword_filters}\n"
|
|
f"- apply_empty_message_filters: {args.apply_empty_message_filters}\n"
|
|
f"- push_to_hub: {args.push_to_hub}\n"
|
|
f"- hf_entity: {args.hf_entity}\n"
|
|
f"- converted_dataset_name: {args.converted_dataset_name}\n"
|
|
f"- local_save_dir: {args.local_save_dir}\n\n"
|
|
"Please refer to the [original dataset](https://huggingface.co/datasets/HuggingFaceH4/no_robots) "
|
|
"for more information about this dataset and the license."
|
|
)
|
|
|
|
convert_sft_dataset(
|
|
ds=None,
|
|
hf_dataset_id="HuggingFaceH4/no_robots",
|
|
convert_fn=None,
|
|
apply_keyword_filters=args.apply_keyword_filters,
|
|
apply_empty_message_filters=args.apply_empty_message_filters,
|
|
push_to_hub=args.push_to_hub,
|
|
hf_entity=args.hf_entity,
|
|
converted_dataset_name=args.converted_dataset_name,
|
|
local_save_dir=args.local_save_dir,
|
|
readme_content=readme_content,
|
|
)
|
|
|