--- language: en tags: - huggingface-hub - leaderboard - ecosystem - landscape --- # πŸ—ΊοΈ HF Landscape Study Data Parquet crawl data powering [HF Landscape](https://huggingface.co/spaces/ranjithraj/hf-landscape) β€” 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\ | 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\ | 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 β€” 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 β€” 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} } ```