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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "ed2ddd96-57f3-452e-9d28-e44654edbb65",
"metadata": {},
"outputs": [],
"source": [
"from huggingface_hub import DatasetFilter, list_datasets, HfApi, ModelFilter, DatasetSearchArguments\n",
"from pathlib import Path\n",
"from dotenv import load_dotenv\n",
"import os\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "c45ae63c-4e02-47e3-a3e9-895a7bc2702d",
"metadata": {},
"outputs": [],
"source": [
"if Path(\".env\").is_file():\n",
" load_dotenv(\".env\")\n",
"\n",
"auth_token = os.getenv(\"HF_HUB_TOKEN\")"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "23e088a3-276a-45bf-9373-4dfe934b5556",
"metadata": {},
"outputs": [],
"source": [
"filt = DatasetFilter(benchmark=\"raft\")\n",
"submissions = list_datasets(filter=filt, full=True, use_auth_token=auth_token)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "641c4060",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[0;31mSignature:\u001b[0m\n",
"\u001b[0mlist_datasets\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
"\u001b[0;34m\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
"\u001b[0;34m\u001b[0m \u001b[0mfilter\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mUnion\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mhuggingface_hub\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mutils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mendpoint_helpers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDatasetFilter\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mIterable\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mNoneType\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
"\u001b[0;34m\u001b[0m \u001b[0mauthor\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mUnion\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mNoneType\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
"\u001b[0;34m\u001b[0m \u001b[0msearch\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mUnion\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mNoneType\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
"\u001b[0;34m\u001b[0m \u001b[0msort\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mUnion\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mLiteral\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'lastModified'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mNoneType\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
"\u001b[0;34m\u001b[0m \u001b[0mdirection\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mUnion\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mLiteral\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mNoneType\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
"\u001b[0;34m\u001b[0m \u001b[0mlimit\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mUnion\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mNoneType\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
"\u001b[0;34m\u001b[0m \u001b[0mcardData\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mUnion\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mbool\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mNoneType\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
"\u001b[0;34m\u001b[0m \u001b[0mfull\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mUnion\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mbool\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mNoneType\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
"\u001b[0;34m\u001b[0m \u001b[0muse_auth_token\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mUnion\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mNoneType\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
"\u001b[0;34m\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mList\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mhuggingface_hub\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhf_api\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDatasetInfo\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mDocstring:\u001b[0m\n",
"Get the public list of all the datasets on huggingface.co\n",
"\n",
"Args:\n",
" filter ([`DatasetFilter`] or `str` or `Iterable`, *optional*):\n",
" A string or [`DatasetFilter`] which can be used to identify\n",
" datasets on the hub.\n",
" author (`str`, *optional*):\n",
" A string which identify the author of the returned models\n",
" search (`str`, *optional*):\n",
" A string that will be contained in the returned models.\n",
" sort (`Literal[\"lastModified\"]` or `str`, *optional*):\n",
" The key with which to sort the resulting datasets. Possible\n",
" values are the properties of the [`huggingface_hub.hf_api.DatasetInfo`] class.\n",
" direction (`Literal[-1]` or `int`, *optional*):\n",
" Direction in which to sort. The value `-1` sorts by descending\n",
" order while all other values sort by ascending order.\n",
" limit (`int`, *optional*):\n",
" The limit on the number of datasets fetched. Leaving this option\n",
" to `None` fetches all datasets.\n",
" cardData (`bool`, *optional*):\n",
" Whether to grab the metadata for the dataset as well. Can\n",
" contain useful information such as the PapersWithCode ID.\n",
" full (`bool`, *optional*):\n",
" Whether to fetch all dataset data, including the `lastModified`\n",
" and the `cardData`.\n",
" use_auth_token (`bool` or `str`, *optional*):\n",
" Whether to use the `auth_token` provided from the\n",
" `huggingface_hub` cli. If not logged in, a valid `auth_token`\n",
" can be passed in as a string.\n",
"\n",
"Example usage with the `filter` argument:\n",
"\n",
"```python\n",
">>> from huggingface_hub import HfApi\n",
"\n",
">>> api = HfApi()\n",
"\n",
">>> # List all datasets\n",
">>> api.list_datasets()\n",
"\n",
">>> # Get all valid search arguments\n",
">>> args = DatasetSearchArguments()\n",
"\n",
">>> # List only the text classification datasets\n",
">>> api.list_datasets(filter=\"task_categories:text-classification\")\n",
">>> # Using the `DatasetFilter`\n",
">>> filt = DatasetFilter(task_categories=\"text-classification\")\n",
">>> # With `DatasetSearchArguments`\n",
">>> filt = DatasetFilter(task=args.task_categories.text_classification)\n",
">>> api.list_models(filter=filt)\n",
"\n",
">>> # List only the datasets in russian for language modeling\n",
">>> api.list_datasets(\n",
"... filter=(\"languages:ru\", \"task_ids:language-modeling\")\n",
"... )\n",
">>> # Using the `DatasetFilter`\n",
">>> filt = DatasetFilter(languages=\"ru\", task_ids=\"language-modeling\")\n",
">>> # With `DatasetSearchArguments`\n",
">>> filt = DatasetFilter(\n",
"... languages=args.languages.ru,\n",
"... task_ids=args.task_ids.language_modeling,\n",
"... )\n",
">>> api.list_datasets(filter=filt)\n",
"```\n",
"\n",
"Example usage with the `search` argument:\n",
"\n",
"```python\n",
">>> from huggingface_hub import HfApi\n",
"\n",
">>> api = HfApi()\n",
"\n",
">>> # List all datasets with \"text\" in their name\n",
">>> api.list_datasets(search=\"text\")\n",
"\n",
">>> # List all datasets with \"text\" in their name made by google\n",
">>> api.list_datasets(search=\"text\", author=\"google\")\n",
"```\n",
"\u001b[0;31mFile:\u001b[0m ~/miniconda3/envs/raft-leaderboard/lib/python3.8/site-packages/huggingface_hub/hf_api.py\n",
"\u001b[0;31mType:\u001b[0m method\n"
]
}
],
"source": [
"?list_datasets"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "228750aa-6d92-4d26-971f-5248e056f54b",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"5"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(submissions)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "6dc34fa3-be44-4170-8daf-39f87aae5b34",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"benchmark\n",
"type\n",
"submission_name\n"
]
}
],
"source": [
"for k,v in submissions[3].cardData.items():\n",
" print(k)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "f4dd2dbc",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"DatasetInfo: {\n",
"\tid: moshew/my_raft\n",
"\tsha: 534086adc3aec801687316b3fe162e4231ab0a6b\n",
"\tlastModified: 2022-07-16T17:01:04.000Z\n",
"\ttags: ['benchmark:raft']\n",
"\tprivate: False\n",
"\tauthor: moshew\n",
"\tdescription: \n",
"\tcitation: @InProceedings{huggingface:dataset,\n",
"title = {A great new dataset},\n",
"author={huggingface, Inc.\n",
"},\n",
"year={2020}\n",
"}\n",
"\tcardData: {'benchmark': 'raft', 'type': 'prediction', 'submission_name': 'SetFit300'}\n",
"\tsiblings: None\n",
"\t_id: 621ffdd236468d709f183ac3\n",
"\tdisabled: False\n",
"\tgated: auto\n",
"\tgitalyUid: 0d29a8b3b8364fb2d86b3ad56d62ea4aaf13a5cf95884aa0381b966d79b045e1\n",
"\tlikes: 0\n",
"\tdownloads: 0\n",
"}"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"submissions[0]"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "27079f0b",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "raft-leaderboard",
"language": "python",
"name": "python3"
},
"language_info": {
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"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
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}
|