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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 load_dataset
from huggingface_hub import ModelCard
from transformers import AutoTokenizer, 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/lm-human-preferences-sentiment"`):
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/lm-human-preferences-sentiment",
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_prompt_completion(example, tokenizer):
prompt = tokenizer.decode(example["query"]).strip()
best_idx = example["best"]
chosen = tokenizer.decode(example[f"sample{best_idx}"])
for rejected_idx in range(4): # take the first rejected sample that is different from the chosen one
rejected = tokenizer.decode(example[f"sample{rejected_idx}"])
if chosen != rejected:
break
assert chosen != rejected
return {"prompt": prompt, "chosen": chosen, "rejected": rejected}
model_card = ModelCard("""
---
tags: [trl]
---
# LM-Human-Preferences-Sentiment Dataset
## Summary
The LM-Human-Preferences-Sentiment dataset is a processed subset of [OpenAI's LM-Human-Preferences](https://github.com/openai/lm-human-preferences), focusing specifically on sentiment analysis tasks. It contains pairs of text samples, each labeled as either "chosen" or "rejected," based on human preferences regarding the sentiment conveyed in the text. This dataset enables models to learn human preferences in sentiment expression, enhancing their ability to generate and evaluate text with desired emotional tones.
## Data Structure
- **Format**: [Standard](https://huggingface.co/docs/trl/main/dataset_formats#standard)
- **Type**: [Preference](https://huggingface.co/docs/trl/main/dataset_formats#preference)
Columns:
- `"prompt"`: The text sample.
- `"chosen"`: A version of the text that conveys the desired sentiment.
- `"rejected"`: A version of the text that does not convey the desired sentiment.
This structure allows models to learn to prefer the _chosen_ response over the _rejected_ one, thereby aligning with human preferences in sentiment expression.
## Generation script
The script used to generate this dataset can be found [here](https://github.com/huggingface/trl/blob/main/examples/datasets/lm-human-preferences-sentiment.py).
""")
if __name__ == "__main__":
parser = HfArgumentParser(ScriptArguments)
script_args = parser.parse_args_into_dataclasses()[0]
dataset = load_dataset(
"json",
data_files="https://openaipublic.blob.core.windows.net/lm-human-preferences/labels/sentiment/offline_5k.json",
split="train",
)
dataset = dataset.map(
to_prompt_completion,
num_proc=script_args.dataset_num_proc,
remove_columns=["query", "sample0", "sample1", "sample2", "sample3", "best"],
fn_kwargs={"tokenizer": AutoTokenizer.from_pretrained("gpt2")},
)
# train_size taken from https://github.com/openai/lm-human-preferences/blob/cbfd210bb8b08f6bc5c26878c10984b90f516c66/launch.py#L70)
dataset = dataset.train_test_split(train_size=4992)
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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