---
license: cc0-1.0
task_categories:
  - text-classification
task_ids:
  - multi-label-classification
tags:
  - extreme-multi-label
  - pubmed
  - mesh
  - biomedical
  - nlp
language:
  - en
pretty_name: "PubMed MultiLabel Text Classification (MeSH)"
size_categories:
  - 10K<n<100K
---

# PubMed MultiLabel Text Classification (MeSH)

A dataset of **50,000 PubMed biomedical articles**, each manually annotated
by domain experts with **MeSH (Medical Subject Headings)** labels. With
**21,918 unique labels** and a mean of ~12.7 labels per document, this is a
densely-labeled extreme multi-label classification benchmark.

## Dataset Description

| Property | Value |
|---|---|
| Train examples | 40,000 |
| Test examples | 10,000 |
| Total unique MeSH labels | 21,918 |
| Mean labels per document | ~12.7 |
| Median labels per document | 12 |
| Max labels per document | 46 |

### Label distribution

| Docs per label | # Labels | % of total |
|---|---|---|
| 1 | 3,990 | 18.2% |
| 2–5 | 7,020 | 32.0% |
| 6–10 | 3,412 | 15.6% |
| 11–50 | 5,518 | 25.2% |
| 51–100 | 1,068 | 4.9% |
| 101+ | 910 | 4.2% |

### MeSH Root Categories

Each label belongs to one or more MeSH root categories. The dataset includes
binary indicator columns for the 14 root categories:

| Code | Root Category |
|---|---|
| A | Anatomy |
| B | Organisms |
| C | Diseases |
| D | Chemicals and Drugs |
| E | Analytical, Diagnostic and Therapeutic Techniques, and Equipment |
| F | Psychiatry and Psychology |
| G | Phenomena and Processes |
| H | Disciplines and Occupations |
| I | Anthropology, Education, Sociology, and Social Phenomena |
| J | Technology, Industry, and Agriculture |
| L | Information Science |
| M | Named Groups |
| N | Health Care |
| Z | Geographicals |

## Fields

| Field | Type | Description |
|---|---|---|
| `pmid` | string | PubMed article ID |
| `title` | string | Article title |
| `abstract` | string | Article abstract text |
| `label_ids` | list[int] | MeSH label indices (into the 21,918-label vocabulary) |
| `label_names` | list[string] | Human-readable MeSH label names |
| `mesh_roots` | dict | Binary flags `{"A": 0/1, ..., "Z": 0/1}` for root categories |

## Additional files

- **`label_vocab.json`** — ordered list of all 21,918 MeSH label names
  (index = label ID)
- **`label_metadata.jsonl`** — full label metadata including MeSH tree IDs
  and root categories for hierarchical classification research

## Splits

An 80/20 random split with seed 42 (no predefined split exists in the
original data).

## Usage

```python
from datasets import load_dataset

ds = load_dataset("Tellurio/PubMed-MultiLabel-MeSH")

example = ds["train"][0]
print(example["title"])
print(example["label_names"])  # e.g. ["Humans", "Female", "DNA Probes, HPV", ...]
print(example["label_ids"])    # e.g. [5, 2, 0, ...]
print(example["mesh_roots"])   # e.g. {"A": 0, "B": 1, "C": 1, ...}
```

### Loading label metadata for hierarchical / zero-shot approaches

Each of the 21,918 MeSH labels has associated tree IDs and root categories
stored in `label_metadata.jsonl`.

```python
import json
from huggingface_hub import hf_hub_download

path = hf_hub_download(
    repo_id="Tellurio/PubMed-MultiLabel-MeSH",
    filename="label_metadata.jsonl",
    repo_type="dataset",
)

labels = []
with open(path) as f:
    for line in f:
        labels.append(json.loads(line))

# Example label entry
print(labels[0])
# {"id": 0, "label": "DNA Probes, HPV", "mesh_tree_ids": ["D13.444...", ...], "mesh_roots": ["Chemicals and Drugs [D]"]}
```

## Source

Originally from Kaggle:
[PubMed MultiLabel Text Classification Dataset MeSH](https://www.kaggle.com/datasets/owaiskhan9654/pubmed-multilabel-text-classification)
by Owais Ahmad.

## Citation

```bibtex
@misc{pubmed_multilabel_mesh,
  author = {Owais Ahmad},
  title = {PubMed MultiLabel Text Classification Dataset MeSH},
  year = {2022},
  publisher = {Kaggle},
  url = {https://www.kaggle.com/datasets/owaiskhan9654/pubmed-multilabel-text-classification}
}
```

## License

CC0: Public Domain