---
license: mit
task_categories:
- time-series-forecasting
tags:
- tsfile
- timeseries
- time-series
- huggingface
- trending
- papers
- machine-learning
- research
- format:tsfile
pretty_name: Hugging Face Top Trending Papers (TsFile)
size_categories:
- n<1K
---

# Hugging Face Top Trending Papers (TsFile)

Apache TsFile version of [`ronantakizawa/huggingface-top-papers`](https://huggingface.co/datasets/ronantakizawa/huggingface-top-papers).

## Overview

Ranked dataset of the most trending papers on Hugging Face Daily Papers in 2025
(Jan–Nov), based on weighted scoring of their trending appearances — which AI/ML
research papers gained the most community attention and sustained visibility.
663 ranked papers, sourced from Wayback Machine snapshots of the Daily Papers
page.

## Schema (TsFile structure)

- **Time** (INT64) — a synthetic 0..N-1 row index (this is a single ranked
  snapshot with no wall-clock timestamp, so the sequential row number is used as
  the TsFile time axis).
- **name** (FIELD, STRING) — the paper title.
- **rank** (FIELD, INT64) — the overall rank (1..663).
- **times_trended** (FIELD, INT64) — number of trending appearances.
- **best_rank** (FIELD, INT64) — best rank achieved.
- **avg_rank** / **median_rank** (FIELD, FLOAT/INT64).
- **publish_date** (FIELD, STRING) — the paper's publish date.
- **max_upvotes** / **max_github_stars** (FIELD, INT64).

## Usage

Install the Apache TsFile Python SDK (`pip install tsfile`) and read a converted file:

```python
from pathlib import Path
from tsfile import TsFileReader

path = Path("huggingface_top_papers.tsfile")
with TsFileReader(str(path)) as reader:
    schemas = reader.get_all_table_schemas()
    print("tables:", list(schemas))
    table_name = next(iter(schemas))
    table = schemas[table_name]
    columns = [column.get_column_name() for column in table.get_columns()]
    print("columns:", columns)
    field_names = [
        column.get_column_name()
        for column in table.get_columns()
        if column.get_column_name() not in {"Time", "time"}
    ]
    if field_names:
        with reader.query_table(table_name, field_names[:3], batch_size=1024) as result:
            batch = result.read_arrow_batch()
            if batch is not None:
                print(batch.to_pandas().head())
```

## Source & license

- Original dataset: https://huggingface.co/datasets/ronantakizawa/huggingface-top-papers
- Author / publisher: ronantakizawa
- License: MIT