Duplicate from lcalvobartolome/proxann_data

Co-authored-by: Lorena Calvo Bartolomé <lcalvobartolome@users.noreply.huggingface.co>

.gitattributes

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+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+\# filter=lfs diff=lfs merge=lfs -text
+adjust filter=lfs diff=lfs merge=lfs -text
+as filter=lfs diff=lfs merge=lfs -text
+needed filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.jsonl filter=lfs diff=lfs merge=lfs -text
+README.md !text !filter !merge !diff

README.md

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+---
+configs:
+- config_name: default
+  data_files:
+  - split: bills_train
+    path: bills_train.metadata.embeddings.jsonl.all-MiniLM-L6-v2.parquet
+  - split: bills_test
+    path: bills_test.metadata.parquet
+  - split: wiki_train
+    path: wiki_train.metadata.embeddings.jsonl.all-MiniLM-L6-v2.parquet
+  - split: wiki_test
+    path: wiki_test.metadata.parquet
+datasets:
+- lcalvobartolome/proxann_data
+language:
+- en
+license: mit
+pretty_name: PROXANN Data
+size_categories:
+- 10K<n<100K
+tags:
+- parquet
+- text
+- topic-modeling
+- bills
+- proxann
+- english
+---
+
+# 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
+
+| Split | File | Rows | Description |
+| ------|------|------:|-------------|
+| `bills_train` | `bills_train.metadata.embeddings.jsonl.all-MiniLM-L6-v2.parquet` | 32,661 | Congressional bills with summaries, topics, and 384-dim embeddings. |
+| `bills_test`  | `bills_test.metadata.parquet` | 15,242 | Bills test split without embeddings (metadata only). |
+| `wiki_train`  | `wiki_train.metadata.embeddings.jsonl.all-MiniLM-L6-v2.parquet` | 14,290 | Wikipedia articles with categories and 384-dim embeddings. |
+| `wiki_test`   | `wiki_test.metadata.parquet` | 8,024 | Wikipedia test split without embeddings (metadata only). |
+
+---
+
+## Columns
+
+### Bills (`bills_train` / `bills_test`)
+
+| Column | Type | Description |
+| ------- | ---- | ----------- |
+| `id` | string | Unique identifier. |
+| `summary` | string | Short summary of the bill. |
+| `topic` | string | Primary topic label. |
+| `subtopic` | string | Secondary topic label. |
+| `subjects_top_term` | string | Top subject term for the bill. |
+| `date` | string | Document date (ISO-8601 format). |
+| `tokenized_text` | list[string] | Preprocessed tokens from Hoyle et al. (2022), 15 k vocabulary. |
+| `embeddings` | list[float] (384) | Sentence embedding (MiniLM-L6-v2). *Absent in test split.* |
+
+### Wiki (`wiki_train` / `wiki_test`)
+
+| Column | Type | Description |
+| ------- | ---- | ----------- |
+| `id` | string | Unique identifier. |
+| `text` | string | Article text (raw or normalized). |
+| `supercategory` | string | High-level category. |
+| `category` | string | Primary category. |
+| `subcategory` | string | Secondary category. |
+| `page_name` | string | Wikipedia page title. |
+| `tokenized_text` | list[string] | Preprocessed tokens from Hoyle et al. (2022), 15 k vocabulary. |
+| `embeddings` | list[float] (384) | Sentence embedding (MiniLM-L6-v2). *Absent in test split.* |
+
+
+## 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).
+
+| File | Description |
+|------|-------------|
+| `data_with_embeddings/vocabs/bills_vocab.json` | Vocabulary for the Bills corpus. Keys are tokens, values are integer indices. |
+| `data_with_embeddings/vocabs/wiki_vocab.json`  | Vocabulary for the Wiki corpus. Keys are tokens, values are integer indices. |
+
+## Usage Example
+The dataset contains four Parquet files:
+
+- `bills_train`  
+- `bills_test`  
+- `wiki_train`  
+- `wiki_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:
+
+```python
+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](https://github.com/ahoho/proxann)
+* [Are Neural Topic Models Broken? (Hoyle et al., 2022)](https://aclanthology.org/2022.findings-emnlp.390/)
+* [Bills Dataset — Adler & Wilkerson (2008)](http://www.congressionalbills.org)
+* [WikiText Dataset — Merity et al. (2017)](https://arxiv.org/abs/1609.07843)
+
+---
+
+## 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:
+
+```bibtex
+@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."
+}
+```

bills_test.metadata.parquet

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metadata.json

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+{
+  "name": "proxann_data",
+  "version": "1.1.0",
+  "license": "mit",
+  "language": "en",
+  "pretty_name": "PROXANN Data",
+  "description": "Data used for training and evaluation in the 'PROXANN: Use-Oriented Evaluations of Topic Models and Document Clustering' paper. The files contain the original metadata from Merity et al. (2017) (Wiki) and Adler & Wilkerson (2008) (Bills). The preprocessed version (tokenized_text) comes from Hoyle et al. (2022), using their 15,000-word vocabulary version. Contextualized embeddings were generated using the all-MiniLM-L6-v2 model, with code available in the PROXANN repository.",
+  "size_categories": ["10K<n<100K"],
+  "splits": {
+    "bills_train": {
+      "filename": "bills_train.metadata.embeddings.jsonl.all-MiniLM-L6-v2.parquet",
+      "num_rows": 32661,
+      "description": "Congressional bills (Adler & Wilkerson, 2008) with summaries, topics, and 384-dim MiniLM embeddings."
+    },
+    "bills_test": {
+      "filename": "bills_test.metadata.parquet",
+      "num_rows": 15242,
+      "description": "Bills test split without embeddings (metadata only)."
+    },
+    "wiki_train": {
+      "filename": "wiki_train.metadata.embeddings.jsonl.all-MiniLM-L6-v2.parquet",
+      "num_rows": 14290,
+      "description": "Wikipedia articles (Merity et al., 2017) with categories and 384-dim MiniLM embeddings."
+    },
+    "wiki_test": {
+      "filename": "wiki_test.metadata.parquet",
+      "num_rows": 8024,
+      "description": "Wikipedia test split without embeddings (metadata only)."
+    }
+  },
+  "schemas": {
+    "bills_train": {
+      "columns": {
+        "id": { "type": "string", "description": "Unique identifier." },
+        "summary": { "type": "string", "description": "Short summary of the bill." },
+        "topic": { "type": "string", "description": "Primary topic label." },
+        "subtopic": { "type": "string", "description": "Secondary topic label." },
+        "subjects_top_term": { "type": "string", "description": "Top subject term for the bill." },
+        "date": { "type": "string", "description": "Document date (ISO-8601 format)." },
+        "tokenized_text": { "type": "list[string]", "description": "Preprocessed tokens from Hoyle et al. (2022), 15k vocabulary." },
+        "embeddings": { "type": "list[float]", "length": 384, "description": "Sentence embedding (MiniLM-L6-v2). Absent in test split." }
+      }
+    },
+    "bills_test": {
+      "columns": {
+        "id": { "type": "string", "description": "Unique identifier." },
+        "summary": { "type": "string", "description": "Short summary of the bill." },
+        "topic": { "type": "string", "description": "Primary topic label." },
+        "subtopic": { "type": "string", "description": "Secondary topic label." },
+        "subjects_top_term": { "type": "string", "description": "Top subject term for the bill." },
+        "date": { "type": "string", "description": "Document date (ISO-8601 format)." },
+        "tokenized_text": { "type": "list[string]", "description": "Preprocessed tokens from Hoyle et al. (2022), 15k vocabulary." }
+      }
+    },
+    "wiki_train": {
+      "columns": {
+        "id": { "type": "string", "description": "Unique identifier." },
+        "text": { "type": "string", "description": "Article text (raw or normalized)." },
+        "supercategory": { "type": "string", "description": "High-level category." },
+        "category": { "type": "string", "description": "Primary category." },
+        "subcategory": { "type": "string", "description": "Secondary category." },
+        "page_name": { "type": "string", "description": "Wikipedia page title." },
+        "tokenized_text": { "type": "list[string]", "description": "Preprocessed tokens from Hoyle et al. (2022), 15k vocabulary." },
+        "embeddings": { "type": "list[float]", "length": 384, "description": "Sentence embedding (MiniLM-L6-v2). Absent in test split." }
+      }
+    },
+    "wiki_test": {
+      "columns": {
+        "id": { "type": "string", "description": "Unique identifier." },
+        "text": { "type": "string", "description": "Article text (raw or normalized)." },
+        "supercategory": { "type": "string", "description": "High-level category." },
+        "category": { "type": "string", "description": "Primary category." },
+        "subcategory": { "type": "string", "description": "Secondary category." },
+        "page_name": { "type": "string", "description": "Wikipedia page title." },
+        "tokenized_text": { "type": "list[string]", "description": "Preprocessed tokens from Hoyle et al. (2022), 15k vocabulary." }
+      }
+    }
+  },
+  "resources": {
+    "vocabularies": [
+      {
+        "filename": "data_with_embeddings/vocabs/bills_vocab.json",
+        "description": "Vocabulary for the Bills corpus."
+      },
+      {
+        "filename": "data_with_embeddings/vocabs/wiki_vocab.json",
+        "description": "Vocabulary for the Wiki corpus."
+      }
+    ]
+  },
+  "data_stats": {
+    "table": [
+      { "split": "bills_train", "file": "bills_train.metadata.embeddings.jsonl.all-MiniLM-L6-v2.parquet", "rows": 32661, "description": "Congressional bills with summaries, topics, and 384-dim embeddings." },
+      { "split": "bills_test", "file": "bills_test.metadata.parquet", "rows": 15242, "description": "Bills test split without embeddings (metadata only)." },
+      { "split": "wiki_train", "file": "wiki_train.metadata.embeddings.jsonl.all-MiniLM-L6-v2.parquet", "rows": 14290, "description": "Wikipedia articles with categories and 384-dim embeddings." },
+      { "split": "wiki_test", "file": "wiki_test.metadata.parquet", "rows": 8024, "description": "Wikipedia test split without embeddings (metadata only)." }
+    ]
+  },
+  "creator": "Alexander Miserlis Hoyle, Lorena Calvo-Bartolomé, Jordan Boyd-Graber, Philip Resnik",
+  "source": {
+    "provider": "Wikipedia",
+    "type": "paper",
+    "note": "We use the curated versions from 'Are Neural Topic Models Broken?' (Hoyle et al., 2022).",
+    "repository": "https://github.com/ahoho/topics"
+  },
+  "dataset_type": "text",
+  "tags": ["parquet", "text", "topic-modeling", "bills", "proxann", "english", "vocabulary"],
+  "task_categories": ["unsupervised-learning", "topic-modeling", "clustering"],
+  "citation": "@inproceedings{hoyle-etal-2025-proxann, title = {PROXANN: Use-Oriented Evaluations of Topic Models and Document Clustering}, author = {Hoyle, Alexander Miserlis and Calvo-Bartolomé, Lorena and Boyd-Graber, Jordan Lee and Resnik, Philip}, booktitle = {Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)}, 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} }"
+}

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