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huggingface-modelhub / huggingface-modelhub.py

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Adding downloads_last_month and library fields

ea3ef1b almost 3 years ago

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	# coding=utf-8
	# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
	#
	# Licensed under the Apache License, Version 2.0 (the "License");
	# you may not use this file except in compliance with the License.
	# You may obtain a copy of the License at
	#
	# http://www.apache.org/licenses/LICENSE-2.0
	#
	# Unless required by applicable law or agreed to in writing, software
	# distributed under the License is distributed on an "AS IS" BASIS,
	# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
	# See the License for the specific language governing permissions and
	# limitations under the License.
	"""Metadata information of all the models available on HuggingFace's modelhub"""


	import ast
	import csv

	import datasets
	# Some readme files on modelhub are large in size
	csv.field_size_limit(100000000)

	_CITATION = """\

	"""

	_DESCRIPTION = """\
	Metadata information of all the models available on HuggingFace's modelhub
	"""

	_HOMEPAGE = "https://huggingface.co/models"

	_LICENSE = ""

	_URL = "huggingface-modelhub.csv"



	class HuggingfaceModelhub(datasets.GeneratorBasedBuilder):
	"""Metadata information of all the models available on HuggingFace's modelhub"""

	VERSION = datasets.Version("1.0.2")

	def _info(self):
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=datasets.Features(
	{
	"modelId": datasets.Value("string"),
	"lastModified": datasets.Value("string"),
	"tags": datasets.features.Sequence(datasets.Value("string")),
	"pipeline_tag": datasets.Value("string"),
	"files": datasets.features.Sequence(datasets.Value("string")),
	"publishedBy": datasets.Value("string"),
	"downloads_last_month": datasets.Value("int32"),
	"library": datasets.Value("string"),
	"modelCard": datasets.Value("large_string"),
	}
	),
	homepage=_HOMEPAGE,
	license=_LICENSE,
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	"""Returns SplitGenerators."""

	data_file = dl_manager.download_and_extract(_URL)
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	gen_kwargs={
	"filepath": data_file,
	},
	),
	]

	def _generate_examples(self, filepath):
	"""Yields examples."""

	with open(filepath, encoding="utf-8") as f:
	reader = csv.reader(f)
	for id_, row in enumerate(reader):
	if id_ == 0:
	continue
	yield id_, {
	"modelId": row[0],
	"lastModified": row[1],
	"tags": ast.literal_eval(row[2]),
	"pipeline_tag": row[3],
	"files": ast.literal_eval(row[4]),
	"publishedBy": row[5],
	"downloads_last_month": float(row[6]) if row[6] else 0,
	"library": row[7],
	"modelCard": row[8]
	}