Spaces:
Paused
Paused
File size: 5,527 Bytes
2f5127c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 |
# Copyright 2020-2025 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from dataclasses import dataclass, field
from typing import Optional
from datasets import load_dataset
from huggingface_hub import ModelCard
from transformers import HfArgumentParser
@dataclass
class ScriptArguments:
r"""
Arguments for the script.
Args:
model_name (`str`, *optional*, defaults to `"gpt-3.5-turbo"`):
Language model to target. Possible values are:
aspect (`str`, *optional*, defaults to `"helpfulness"`):
Aspect to target.
push_to_hub (`bool`, *optional*, defaults to `False`):
Whether to push the dataset to the Hugging Face Hub.
repo_id (`str`, *optional*, defaults to `"trl-lib/ultrafeedback-gpt-3.5-turbo-helpfulness"`):
Hugging Face repository ID to push the dataset to.
dataset_num_proc (`int` or `None`, *optional*, defaults to `None`):
Number of workers to use for dataset processing.
"""
model_name: str = field(
default="gpt-3.5-turbo",
metadata={
"help": "Language model to target.",
"choices": [
"alpaca-7b",
"bard",
"falcon-40b-instruct",
"gpt-3.5-turbo",
"gpt-4",
"llama-2-13b-chat",
"llama-2-70b-chat",
"llama-2-7b-chat",
"mpt-30b-chat",
"pythia-12b",
"starchat",
"ultralm-13b",
"ultralm-65b",
"vicuna-33b",
"wizardlm-13b",
"wizardlm-70b",
"wizardlm-7b",
],
},
)
aspect: str = field(
default="helpfulness",
metadata={
"help": "Aspect to target. Possible values are: 'helpfulness' (default), 'honesty', "
"'instruction-following', 'truthfulness'.",
"choices": ["helpfulness", "honesty", "instruction-following", "truthfulness"],
},
)
push_to_hub: bool = field(
default=False,
metadata={"help": "Whether to push the dataset to the Hugging Face Hub."},
)
repo_id: str = field(
default="trl-lib/ultrafeedback-gpt-3.5-turbo-helpfulness",
metadata={"help": "Hugging Face repository ID to push the dataset to."},
)
dataset_num_proc: Optional[int] = field(
default=None,
metadata={"help": "Number of workers to use for dataset processing."},
)
def to_unpaired_preference(example, model_name, aspect):
prompt = [{"role": "user", "content": example["instruction"]}]
model_index = example["models"].index(model_name)
response_content = example["completions"][model_index]["response"]
completion = [{"role": "assistant", "content": response_content}]
score = int(example["completions"][model_index]["annotations"][aspect]["Rating"])
label = score >= 5
return {"prompt": prompt, "completion": completion, "label": label}
model_card = ModelCard("""
---
tags: [trl]
---
# UltraFeedback GPT-3.5-Turbo Helpfulness Dataset
## Summary
The UltraFeedback GPT-3.5-Turbo Helpfulness dataset contains processed user-assistant interactions filtered for helpfulness, derived from the [openbmb/UltraFeedback](https://huggingface.co/datasets/openbmb/UltraFeedback) dataset. It is designed for fine-tuning and evaluating models in alignment tasks.
## Data Structure
- **Format**: [Conversational](https://huggingface.co/docs/trl/main/dataset_formats#conversational)
- **Type**: [Unpaired preference](https://huggingface.co/docs/trl/main/dataset_formats#unpaired-preference)
Column:
- `"prompt"`: The input question or instruction provided to the model.
- `"completion"`: The model's response to the prompt.
- `"label"`: A binary value indicating whether the response is sufficiently helpful.
## Generation script
The script used to generate this dataset can be found [here](https://github.com/huggingface/trl/blob/main/examples/datasets/ultrafeedback.py).
""")
if __name__ == "__main__":
parser = HfArgumentParser(ScriptArguments)
script_args = parser.parse_args_into_dataclasses()[0]
dataset = load_dataset("openbmb/UltraFeedback", split="train")
dataset = dataset.filter(
lambda example: script_args.model_name in example["models"],
batched=False,
num_proc=script_args.dataset_num_proc,
)
dataset = dataset.map(
to_unpaired_preference,
remove_columns=["source", "instruction", "models", "completions", "correct_answers", "incorrect_answers"],
fn_kwargs={"model_name": script_args.model_name, "aspect": script_args.aspect},
num_proc=script_args.dataset_num_proc,
)
dataset = dataset.train_test_split(test_size=0.05, seed=42)
if script_args.push_to_hub:
dataset.push_to_hub(script_args.repo_id)
model_card.push_to_hub(script_args.repo_id, repo_type="dataset")
|