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language: en
tags:
- huggingface-hub
- leaderboard
- ecosystem
- landscape
---
# 🗺️ HF Landscape Study Data
Parquet crawl data powering [HF Landscape](https://huggingface.co/spaces/ranjithraj/hf-landscape) — a leaderboard and ecosystem stats dashboard for the [Hugging Face Hub](https://huggingface.co).
## What's Inside
Six Parquet files covering the full Hub at crawl time, generated from a DuckDB database:
| File | Records | Description |
| ---------------------- | ------- | ----------------------------------------------------------------------------------------------------------------------------------------------------- |
| `models.parquet` | ~2.9M | Every model: downloads (30d + all-time), likes, task, library, params, license, language, country, entity type, trending score, modality, size bucket |
| `datasets.parquet` | ~955K | Every dataset: downloads, likes, trending score, task categories, license, country, entity type |
| `spaces.parquet` | ~1.4M | Every space: likes, trending score, SDK, country, entity type |
| `collections.parquet` | ~217K | Every collection: upvotes, item count, country, entity type |
| `entities.parquet` | ~1.4M | Per-entity aggregation: one row per author with rolled-up counts across all repo types |
| `entities_all.parquet` | ~5.5M | All repos (models + datasets + spaces + collections) in one file, with a `repo_type` field to filter by type |
## Schema
### models.parquet
| Field | Type | Description |
| -------------- | -------------- | ------------------------------------------------------------------------------------------------------------------ |
| `id` | string | Repo ID (`org/name`) |
| `author` | string | Organization or user |
| `dl30` | double | Rolling 30-day downloads |
| `dlAll` | double | All-time downloads |
| `likes` | double | Total likes |
| `task` | string? | Pipeline tag (e.g. `text-generation`) |
| `params` | double? | Parameter count (from safetensors metadata) |
| `langs` | list\<string\> | Language tags |
| `createdAt` | timestamp | Creation timestamp |
| `license` | string? | License identifier |
| `baseModel` | string? | Base model name |
| `baseRelation` | string? | Relation to base (`quantized`, `adapter`, `finetune`, …) |
| `library` | string? | Framework (e.g. `transformers`) |
| `lastModified` | timestamp | Last commit timestamp |
| `gated` | string? | Whether the repo is gated |
| `trending` | double | Trending score |
| `modality` | string? | Input modality (`nlp`, `cv`, `multimodal`, `audio`, …) |
| `sizeBucket` | string? | Parameter count bucket (`<5M`, `5M–100M`, `100M–500M`, `0.5B–1B`, `1B–5B`, `5B–15B`, `15B–70B`, `70B+`, `unknown`) |
| `country` | string | Country code from hand-annotated entity map (`-` = unmapped) |
| `entityType` | string | `company`, `community`, `individual`, or `unknown` |
### datasets.parquet
| Field | Type | Description |
| ---------------- | -------------- | ------------------------ |
| `id` | string | Repo ID |
| `author` | string | Owner |
| `dl30` | double | Rolling 30-day downloads |
| `dlAll` | double | All-time downloads |
| `likes` | double | Total likes |
| `trending` | double | Trending score |
| `taskCategories` | list\<string\> | Task categories |
| `license` | string? | License identifier |
| `createdAt` | timestamp | Creation timestamp |
| `country` | string | Country code |
| `entityType` | string | Entity type |
### spaces.parquet
| Field | Type | Description |
| ------------ | --------- | -------------------------------------------------------- |
| `id` | string | Repo ID |
| `author` | string | Owner |
| `likes` | double | Total likes |
| `trending` | double | Trending score |
| `sdk` | string? | Space SDK (`docker`, `static`, `gradio`, `streamlit`, …) |
| `createdAt` | timestamp | Creation timestamp |
| `country` | string | Country code |
| `entityType` | string | Entity type |
### collections.parquet
| Field | Type | Description |
| ------------- | --------- | --------------------- |
| `slug` | string | Collection slug |
| `owner` | string | Owner username |
| `title` | string | Collection title |
| `upvotes` | double | Total upvotes |
| `itemCount` | double | Number of items |
| `lastUpdated` | timestamp | Last update timestamp |
| `country` | string | Country code |
| `entityType` | string | Entity type |
### entities.parquet
Per-entity aggregation — one row per author with rolled-up stats across all repo types.
| Field | Type | Description |
| -------------------- | ------ | ----------------------------------------- |
| `name` | string | Author / org name |
| `country` | string | Country code |
| `type` | string | Entity type |
| `models` | int | Number of models |
| `datasets` | int | Number of datasets |
| `spaces` | int | Number of spaces |
| `collections` | int | Number of collections |
| `downloads_all_time` | double | Total all-time downloads across all repos |
| `downloads_30d` | double | Total 30-day downloads across all repos |
| `likes` | double | Total likes across all repos |
| `upvotes` | double | Total collection upvotes |
### entities_all.parquet
All repos in one file. Each record carries the full fields of its source type plus a `repo_type` discriminator.
| Field | Type | Description |
| ------------------------------------------------------ | ------ | -------------------------------------------- |
| `repo_type` | string | `model`, `dataset`, `space`, or `collection` |
| _(+ all fields from the matching source schema above)_ | | |
## Country Attribution
Country and entity type are from a hand-maintained map ([`data/spotlight.json`](https://github.com/ranjithraj/hf-landscape/blob/main/data/spotlight.json)) applied at crawl time. The Hub API exposes no location field, so attribution is inferred from public signals — not a verified fact. Only ~1,200 mapped entities carry a country; the rest are `-` (unknown).
## Usage
With [Polars](https://pola.rs/) (recommended):
```python
import polars as pl
df = pl.read_parquet("models.parquet")
top = df.sort("dl30", descending=True).head(10).select("id", "dl30", "dlAll", "likes")
print(top)
```
Filter entities by type:
```python
import polars as pl
df = pl.read_parquet("entities_all.parquet")
datasets = df.filter(pl.col("repo_type") == "dataset")
print(datasets.sort("dlAll", descending=True).head(10).select("id", "author", "dlAll"))
```
Query list columns (e.g. find models with a specific language):
```python
import polars as pl
df = pl.read_parquet("models.parquet")
hindi = df.filter(pl.col("langs").list.contains("en"))
print(f"English models: {len(hindi):,}")
```
With [DuckDB](https://duckdb.org/):
```python
import duckdb
con = duckdb.connect()
df = con.execute("SELECT * FROM 'models.parquet' WHERE task = 'text-generation' LIMIT 10").fetchdf()
print(df)
```
## Regeneration
Data is crawled weekly from the Hugging Face Hub API and exported as Parquet via the [HF Landscape CLI](https://github.com/ranjithraj/hf-landscape):
```bash
npm link # install hf-study CLI
hf-study # full crawl + aggregation
npm run db:build # export Parquet files
```
## Citation
```bibtex
@dataset{hf_landscape_2026,
title = {HF Landscape Study Data},
author = {Ranjith Raj},
year = {2026},
url = {https://huggingface.co/datasets/ranjithraj/hf-landscape-study-data}
}
```
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