lcalvobartolome/proxann_data
PROXANN Data PROXANN Data provides the corpora used for training and evaluating topic models inPROXANN: Use-Oriented Evaluations of Topic Models and Document Clustering(Hoyle et al., ACL 2025). This repository contains two dataset — Bills and Wiki — each with training (with contextualized embeddings) and test (metadata-only) splits. Structure Split File Rows Description bills_train bills_train.metadata.embeddings.jsonl.all-MiniLM-L6-v2.parquet 32,661… See the full description on the dataset page: https://huggingface.co/datasets/lcalvobartolome/proxann_data.
PROXANN Data
PROXANN Data provides the corpora used for training and evaluating topic models in [PROXANN: Use-Oriented Evaluations of Topic Models and Document Clustering](https://aclanthology.org/2025.acl-long.772/) (Hoyle et al., ACL 2025).
This repository contains two dataset — Bills and Wiki — each with training (with contextualized embeddings) and test (metadata-only) splits.
Structure
Columns
Bills (bills_train / bills_test)
Wiki (wiki_train / wiki_test)
Vocabularies
The dataset includes the 15k-token vocabularies used during preprocessing and model training. Each file is a JSON mapping of token -> integer index (0–14,999).
Usage Example
The dataset contains four Parquet files:
bills_trainbills_testwiki_trainwiki_test
Because the Bills and Wiki splits use different schemas, you should load each split directly from its Parquet file using the generic parquet loader from 🤗 Datasets:
from datasets import load_dataset
# ------------------------------
# Bills Dataset
# ------------------------------
bills_train = load_dataset(
"parquet",
data_files={
"train": "hf://datasets/lcalvobartolome/proxann_data@main/"
"bills_train.metadata.embeddings.jsonl.all-MiniLM-L6-v2.parquet"
},
split="train",
)
print("Bills train size:", len(bills_train)) # 32661
bills_test = load_dataset(
"parquet",
data_files={
"test": "hf://datasets/lcalvobartolome/proxann_data@main/"
"bills_test.metadata.parquet"
},
split="test",
)
print("Bills test size:", len(bills_test)) # 15242
# ------------------------------
# Wiki Dataset
# ------------------------------
wiki_train = load_dataset(
"parquet",
data_files={
"train": "hf://datasets/lcalvobartolome/proxann_data@main/"
"wiki_train.metadata.embeddings.jsonl.all-MiniLM-L6-v2.parquet"
},
split="train",
)
print("Wiki train size:", len(wiki_train)) # 14290
wiki_test = load_dataset(
"parquet",
data_files={
"test": "hf://datasets/lcalvobartolome/proxann_data@main/"
"wiki_test.metadata.parquet"
},
split="test",
)
print("Wiki test size:", len(wiki_test))Related Resources
- PROXANN GitHub Repository
- Are Neural Topic Models Broken? (Hoyle et al., 2022)
- Bills Dataset — Adler & Wilkerson (2008)
- WikiText Dataset — Merity et al. (2017)
License & Attribution
Released under the MIT License. Text content derives from Wikipedia (Merity et al. (2017)) and the Congressional Bills Project (Adler & Wilkerson, 2008). Please provide attribution when reusing these materials.
Citation
If you use this dataset, please cite:
@inproceedings{hoyle-etal-2025-proxann,
title = "{P}rox{A}nn: Use-Oriented Evaluations of Topic Models and Document Clustering",
author = "Hoyle, Alexander Miserlis and
Calvo-Bartolom{\'e}, Lorena and
Boyd-Graber, Jordan Lee and
Resnik, Philip",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.acl-long.772/",
doi = "10.18653/v1/2025.acl-long.772",
pages = "15872--15897",
ISBN = "979-8-89176-251-0",
abstract = "Topic models and document-clustering evaluations either use automated metrics that align poorly with human preferences, or require expert labels that are intractable to scale. We design a scalable human evaluation protocol and a corresponding automated approximation that reflect practitioners' real-world usage of models. Annotators{---}or an LLM-based proxy{---}review text items assigned to a topic or cluster, infer a category for the group, then apply that category to other documents. Using this protocol, we collect extensive crowdworker annotations of outputs from a diverse set of topic models on two datasets. We then use these annotations to validate automated proxies, finding that the best LLM proxy is statistically indistinguishable from a human annotator and can therefore serve as a reasonable substitute in automated evaluations."
}