القائمة
Search and discover ML models, datasets, and Spaces on Hugging Face
يُحدَّد Prompt الذي يراجع المصدر أولًا بشكل افتراضي. يمكنك التبديل إلى أمر مباشر أو تنزيل نسخة محلية.
اقرأ SKILL.md وأي ملفات مرافقة يعرضها SkillsMP قبل أن تقرر التثبيت.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
يتجاوز الأمر المباشر Prompt المخصّص للمراجعة. افحص المصدر قبل تشغيله.
npx skills add https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills --skill huggingface-apiيبقى الأمر في سطر واحد. مرّر أفقيًا لمراجعته كاملًا قبل النسخ.
تفضّل نسخة محلية؟ نزّل الملفات المتاحة حاليًا لدى SkillsMP.
Route empirical-research requests through the Auto-Empirical Research Skills catalog when this whole repository is installed as one skill in Codex, CodeBuddy, Claude Code, or another IDE. Use to choose and load the right vendored AERS skill for causal inference, econometrics, replication, data acquisition, manuscript writing, peer review and referee responses, citation checking, de-AIGC editing, or full empirical-paper workflows without reading the entire repository at once.
中英双语学术降 AIGC / bilingual academic de-AIGC skill. Removes AI-generated writing signatures from empirical papers in economics, management, and the social sciences — in both English and Chinese. Covers Turnitin AI, GPTZero, Originality.ai on the English side and 知网 AMLC, 万方, 维普 on the Chinese side. Uses a six-step loop (intake → audit → claim-evidence check → differentiated rewrite → five-dimension self-score → cold-reader recheck) with two pattern libraries (22 English + 17 Chinese patterns), section-by-section strategies for empirical papers, and hard protections that keep every number, coefficient, and citation intact.
Use when a research task needs reproducible Kaggle discovery, metadata inspection, bounded public-data downloads, competition or kernel discovery, model discovery, or an explicitly approved Kaggle write/delete operation through the official CLI.
استنادا إلى تصنيف SOC المهني
عرض SKILL.md
| name | huggingface-api |
| description | Search and discover ML models, datasets, and Spaces on Hugging Face |
| metadata | {"openclaw":{"emoji":"🤗","category":"domains","subcategory":"ai-ml","keywords":["Hugging Face","ML models","datasets","model hub","transformers","NLP","computer vision"],"source":"https://huggingface.co/docs/hub/api"}} |
The Hugging Face Hub is the largest open-source ML ecosystem, hosting over 1 million models, 200,000+ datasets, and 400,000+ Spaces (demo apps). The Hub API at https://huggingface.co/api provides programmatic access to search, discover, and retrieve metadata for all public resources without authentication.
For academic researchers, the Hub API enables systematic model selection for benchmarking, dataset discovery for experiments, tracking community adoption metrics (downloads, likes), and building reproducible ML pipelines that reference specific model revisions by SHA.
Read endpoints require no authentication. All search and metadata queries work without a token.
For write operations (uploading models, creating repos), set a User Access Token:
export HF_TOKEN="hf_..."
# Pass via header:
curl -H "Authorization: Bearer $HF_TOKEN" https://huggingface.co/api/...
Generate tokens at: https://huggingface.co/settings/tokens
GET https://huggingface.co/api/models?search={query}&limit={n}&sort={field}&direction={-1|1}
Parameters: search (query string), limit (max results), sort (field: downloads, likes, lastModified, trending), direction (-1 descending, 1 ascending), filter (pipeline tag like text-classification), author (org/user filter), library (e.g. transformers, pytorch)
Example -- top 2 models for "bert" by downloads:
curl -s "https://huggingface.co/api/models?search=bert&limit=2&sort=downloads&direction=-1"
[
{
"id": "google-bert/bert-base-uncased",
"likes": 2587,
"downloads": 71053483,
"pipeline_tag": "fill-mask",
"library_name": "transformers",
"tags": ["transformers","pytorch","tf","jax","bert","fill-mask","en",
"dataset:bookcorpus","dataset:wikipedia","arxiv:1810.04805",
"license:apache-2.0"]
},
{
"id": "google-bert/bert-base-multilingual-uncased",
"likes": 153
GET https://huggingface.co/api/models/{owner}/{model_name}
Returns full metadata including config.architectures, cardData (license, datasets, language), siblings (file listing), sha (exact revision), and lastModified.
curl -s "https://huggingface.co/api/models/google-bert/bert-base-uncased"
Key fields in response:
{
"id": "google-bert/bert-base-uncased",
"sha": "86b5e0934494bd15c9632b12f734a8a67f723594",
"lastModified": "2024-02-19T11:06:12.000Z",
"downloads": 71053483,
"config": { "architectures": ["BertForMaskedLM"], "model_type": "bert" },
"cardData": { "language": "en", "license": "apache-2.0",
"datasets": ["bookcorpus","wikipedia"] }
}
GET https://huggingface.co/api/datasets?search={query}&limit={n}
Parameters: search, limit, sort, direction, author, filter (task tag like question-answering)
curl -s "https://huggingface.co/api/datasets?search=squad&limit=2"
[
{
"id": "rajpurkar/squad_v2",
"likes": 242,
"downloads": 36017,
"description": "Stanford Question Answering Dataset (SQuAD)...",
"tags": ["task_categories:question-answering","language:en",
"license:cc-by-sa-4.0","size_categories:100K<n<1M",
"arxiv:1806.03822"]
}
]
GET https://huggingface.co/api/datasets/{owner}/{dataset_name}
curl -s "https://huggingface.co/api/datasets/rajpurkar/squad_v2"
Returns cardData with structured metadata (task categories, languages, license, size), description, paperswithcode_id for cross-referencing, and tags with arXiv paper IDs.
GET https://huggingface.co/api/spaces?search={query}&limit={n}
curl -s "https://huggingface.co/api/spaces?search=chatbot&limit=2"
[
{
"id": "21Hg/chatbot",
"likes": 5,
"sdk": "docker",
"tags": ["docker","streamlit","region:us"]
},
{
"id": "lmarena-ai/chatbot-arena",
"likes": 234,
"sdk": "static"
}
]
Combine filters via query params to narrow results:
# PyTorch text-generation models with 1000+ likes
curl -s "https://huggingface.co/api/models?filter=text-generation&library=pytorch&sort=likes&direction=-1&limit=5"
# Datasets for NER tasks in Chinese
curl -s "https://huggingface.co/api/datasets?filter=token-classification&language=zh&limit=10"
# Gradio Spaces sorted by trending
curl -s "https://huggingface.co/api/spaces?filter=gradio&sort=trending&direction=-1&limit=5"
limit parameter to avoid fetching thousands of results; cache responses locally for batch analysistext-classification, token-classification, summarization) and sort by downloads to find community-validated baselinestask_categories, language, and size_categories tags to find training data matching your experimental requirementssha field from model details -- load exact revisions with revision="86b5e093..." in transformersarxiv: tags from model/dataset metadata to trace foundational papersimport requests
# Search for top text-classification models
resp = requests.get("https://huggingface.co/api/models", params={
"filter": "text-classification",
"sort": "downloads",
"direction": -1,
"limit": 10
})
models = resp.json()
for m in models:
print(f"{m['id']:50s} downloads={m.get('downloads',0):>12,}")
# Get specific model metadata
detail = requests.get("https://huggingface.co/api/models/google-bert/bert-base-uncased").json()
print(f"SHA: {detail['sha']}")
print(f"License: {detail['cardData'].get('license')}")
from huggingface_hub import HfApi
api = HfApi()
# Search models (returns ModelInfo objects)
models = api.list_models(search="bert", sort="downloads", direction=-1, limit=5)
for m in models:
print(f"{m.id} downloads={m.downloads}")
# Get full model info
info = api.model_info("google-bert/bert-base-uncased")
print(f"Pipeline: {info.pipeline_tag}, SHA: {info.sha}")
# Search datasets
datasets = api.list_datasets(search="squad", sort="downloads", direction=-1, limit=5)
for d in datasets:
print(f"{d.id} downloads={d.downloads}")
# List Spaces
spaces = api.list_spaces(search="chatbot", limit=5)
for s in spaces:
print(f"{s.id} sdk={s.sdk}")