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# 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 features, load_dataset
from huggingface_hub import ModelCard
from transformers import HfArgumentParser
@dataclass
class ScriptArguments:
r"""
Arguments for the script.
Args:
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/rlaif-v"`):
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.
"""
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/rlaif-v",
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_conversational(example):
"""
Convert prompt from "xxx" to [{"role": "user", "content": [{"type": "image"}, {"type": "text", "text": "xxx"}]}]
and chosen and rejected from "xxx" to [{"role": "assistant", "content": [{"type": "text", "text": "xxx"}]}].
Images are wrapped into a list.
"""
prompt = [{"role": "user", "content": [{"type": "image"}, {"type": "text", "text": example["question"]}]}]
chosen = [{"role": "assistant", "content": [{"type": "text", "text": example["chosen"]}]}]
rejected = [{"role": "assistant", "content": [{"type": "text", "text": example["rejected"]}]}]
return {"prompt": prompt, "images": [example["image"]], "chosen": chosen, "rejected": rejected}
model_card = ModelCard("""
---
tags: [trl]
---
# RLAIF-V Dataset
## Summary
The RLAIF-V dataset is a processed version of the [openbmb/RLAIF-V-Dataset](https://huggingface.co/datasets/openbmb/RLAIF-V-Dataset#dataset-card-for-rlaif-v-dataset), specifically curated to train vision-language models using the [TRL library](https://github.com/huggingface/trl) for preference learning tasks. It contains 83,132 high-quality comparison pairs, each comprising an image and two textual descriptions: one preferred and one rejected. This dataset enables models to learn human preferences in visual contexts, enhancing their ability to generate and evaluate image captions.
## Data Structure
- **Format**: [Conversational](https://huggingface.co/docs/trl/main/dataset_formats#conversational)
- **Type**: [Preference](https://huggingface.co/docs/trl/main/dataset_formats#preference)
Columns:
- `"prompt"`: The task related to the image.
- `"images"`: The image.
- `"chosen"`: The preferred answer.
- `"rejected"`: An alternative answer that was not preferred.
This structure allows models to learn to prefer the _chosen_ response over the _rejected_ one, thereby aligning with human preferences in visual tasks.
## Generation script
The script used to generate this dataset can be found [here](https://github.com/huggingface/trl/blob/main/examples/datasets/rlaif-v.py).
""")
if __name__ == "__main__":
parser = HfArgumentParser(ScriptArguments)
script_args = parser.parse_args_into_dataclasses()[0]
dataset = load_dataset("openbmb/RLAIF-V-Dataset", split="train")
dataset = dataset.map(
to_conversational,
num_proc=script_args.dataset_num_proc,
remove_columns=dataset.column_names,
writer_batch_size=128,
)
# Cast the images to Sequence[Image] to avoid bytes format
f = dataset.features
f["images"] = features.Sequence(features.Image(decode=True))
dataset = dataset.cast(f)
dataset = dataset.train_test_split(test_size=0.01, writer_batch_size=128)
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")
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