Spaces:
Sleeping
Sleeping
Commit
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bbba6d5
1
Parent(s):
c65c2a0
draft notebook
Browse files- get_data.ipynb +2648 -0
get_data.ipynb
ADDED
@@ -0,0 +1,2648 @@
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|
1 |
+
{
|
2 |
+
"cells": [
|
3 |
+
{
|
4 |
+
"cell_type": "code",
|
5 |
+
"execution_count": 9,
|
6 |
+
"metadata": {
|
7 |
+
"id": "D1Sg0CCPc5-3"
|
8 |
+
},
|
9 |
+
"outputs": [],
|
10 |
+
"source": [
|
11 |
+
"from huggingface_hub import list_datasets\n",
|
12 |
+
"from toolz import concat\n",
|
13 |
+
"from toolz import groupby\n",
|
14 |
+
"from toolz import valmap\n",
|
15 |
+
"from toolz import frequencies"
|
16 |
+
]
|
17 |
+
},
|
18 |
+
{
|
19 |
+
"cell_type": "markdown",
|
20 |
+
"metadata": {},
|
21 |
+
"source": [
|
22 |
+
"## Grab datasets with `token-classification` task"
|
23 |
+
]
|
24 |
+
},
|
25 |
+
{
|
26 |
+
"cell_type": "code",
|
27 |
+
"execution_count": 3,
|
28 |
+
"metadata": {
|
29 |
+
"id": "sJzsdvsEdDvQ"
|
30 |
+
},
|
31 |
+
"outputs": [],
|
32 |
+
"source": [
|
33 |
+
"datasets = list(list_datasets(filter=\"task_categories:token-classification\"))"
|
34 |
+
]
|
35 |
+
},
|
36 |
+
{
|
37 |
+
"cell_type": "code",
|
38 |
+
"execution_count": 4,
|
39 |
+
"metadata": {
|
40 |
+
"colab": {
|
41 |
+
"base_uri": "https://localhost:8080/"
|
42 |
+
},
|
43 |
+
"id": "IUdw59ftdLc5",
|
44 |
+
"outputId": "21714002-6571-459b-e266-4c4838865d1f"
|
45 |
+
},
|
46 |
+
"outputs": [
|
47 |
+
{
|
48 |
+
"data": {
|
49 |
+
"text/plain": [
|
50 |
+
"DatasetInfo(id='acronym_identification', author=None, sha='c3c245a18bbd57b1682b099e14460eebf154cbdf', last_modified=datetime.datetime(2023, 1, 25, 14, 18, 28, tzinfo=datetime.timezone.utc), private=False, gated=False, disabled=False, downloads=1823, likes=17, paperswithcode_id='acronym-identification', tags=['task_categories:token-classification', 'annotations_creators:expert-generated', 'language_creators:found', 'multilinguality:monolingual', 'size_categories:10K<n<100K', 'source_datasets:original', 'language:en', 'license:mit', 'acronym-identification', 'arxiv:2010.14678', 'region:us'], card_data=None, siblings=None)"
|
51 |
+
]
|
52 |
+
},
|
53 |
+
"execution_count": 4,
|
54 |
+
"metadata": {},
|
55 |
+
"output_type": "execute_result"
|
56 |
+
}
|
57 |
+
],
|
58 |
+
"source": [
|
59 |
+
"datasets[0]"
|
60 |
+
]
|
61 |
+
},
|
62 |
+
{
|
63 |
+
"cell_type": "code",
|
64 |
+
"execution_count": 19,
|
65 |
+
"metadata": {
|
66 |
+
"id": "RfA9LqwYdMaq"
|
67 |
+
},
|
68 |
+
"outputs": [],
|
69 |
+
"source": [
|
70 |
+
"with_language= [dataset for dataset in datasets if \"language\" in [t.split(\":\")[0] for t in dataset.tags]]"
|
71 |
+
]
|
72 |
+
},
|
73 |
+
{
|
74 |
+
"cell_type": "code",
|
75 |
+
"execution_count": 20,
|
76 |
+
"metadata": {
|
77 |
+
"colab": {
|
78 |
+
"base_uri": "https://localhost:8080/"
|
79 |
+
},
|
80 |
+
"id": "MkU8YQMSeoCP",
|
81 |
+
"outputId": "c226628d-0912-4d15-aa97-55aa19041743"
|
82 |
+
},
|
83 |
+
"outputs": [
|
84 |
+
{
|
85 |
+
"data": {
|
86 |
+
"text/plain": [
|
87 |
+
"DatasetInfo(id='acronym_identification', author=None, sha='c3c245a18bbd57b1682b099e14460eebf154cbdf', last_modified=datetime.datetime(2023, 1, 25, 14, 18, 28, tzinfo=datetime.timezone.utc), private=False, gated=False, disabled=False, downloads=1823, likes=17, paperswithcode_id='acronym-identification', tags=['task_categories:token-classification', 'annotations_creators:expert-generated', 'language_creators:found', 'multilinguality:monolingual', 'size_categories:10K<n<100K', 'source_datasets:original', 'language:en', 'license:mit', 'acronym-identification', 'arxiv:2010.14678', 'region:us'], card_data=None, siblings=None)"
|
88 |
+
]
|
89 |
+
},
|
90 |
+
"execution_count": 20,
|
91 |
+
"metadata": {},
|
92 |
+
"output_type": "execute_result"
|
93 |
+
}
|
94 |
+
],
|
95 |
+
"source": [
|
96 |
+
"with_language[0]"
|
97 |
+
]
|
98 |
+
},
|
99 |
+
{
|
100 |
+
"cell_type": "code",
|
101 |
+
"execution_count": 23,
|
102 |
+
"metadata": {
|
103 |
+
"id": "_sZ3Tuvcepbh"
|
104 |
+
},
|
105 |
+
"outputs": [],
|
106 |
+
"source": [
|
107 |
+
"def get_languages(dataset):\n",
|
108 |
+
" tags = list(dataset.tags)\n",
|
109 |
+
" languages = [t for t in tags if t.split(':')[0]=='language']\n",
|
110 |
+
" languages = [language for language in languages if len(language.split(\":\"))==2]\n",
|
111 |
+
" return [t.split(\":\")[1] for t in languages] "
|
112 |
+
]
|
113 |
+
},
|
114 |
+
{
|
115 |
+
"cell_type": "code",
|
116 |
+
"execution_count": 24,
|
117 |
+
"metadata": {
|
118 |
+
"id": "gB_2HynI1PM6"
|
119 |
+
},
|
120 |
+
"outputs": [],
|
121 |
+
"source": [
|
122 |
+
"freqs = frequencies(concat(get_languages(dataset) for dataset in with_language))\n",
|
123 |
+
"freqs = dict(sorted(freqs.items(), key=lambda x: x[1],reverse=True))"
|
124 |
+
]
|
125 |
+
},
|
126 |
+
{
|
127 |
+
"cell_type": "code",
|
128 |
+
"execution_count": null,
|
129 |
+
"metadata": {},
|
130 |
+
"outputs": [],
|
131 |
+
"source": []
|
132 |
+
},
|
133 |
+
{
|
134 |
+
"cell_type": "code",
|
135 |
+
"execution_count": 28,
|
136 |
+
"metadata": {
|
137 |
+
"id": "Fxi6GtSxfg5D"
|
138 |
+
},
|
139 |
+
"outputs": [],
|
140 |
+
"source": [
|
141 |
+
"from huggingface_hub import list_models"
|
142 |
+
]
|
143 |
+
},
|
144 |
+
{
|
145 |
+
"cell_type": "code",
|
146 |
+
"execution_count": 36,
|
147 |
+
"metadata": {
|
148 |
+
"id": "-Hl73tkGgZFQ"
|
149 |
+
},
|
150 |
+
"outputs": [],
|
151 |
+
"source": [
|
152 |
+
"from huggingface_hub import ModelFilter\n",
|
153 |
+
"from tqdm.auto import tqdm"
|
154 |
+
]
|
155 |
+
},
|
156 |
+
{
|
157 |
+
"cell_type": "code",
|
158 |
+
"execution_count": 38,
|
159 |
+
"metadata": {
|
160 |
+
"id": "haD8WcazgWPI"
|
161 |
+
},
|
162 |
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"outputs": [
|
163 |
+
{
|
164 |
+
"name": "stderr",
|
165 |
+
"output_type": "stream",
|
166 |
+
"text": [
|
167 |
+
" 0%| | 0/393 [00:00<?, ?it/s]"
|
168 |
+
]
|
169 |
+
}
|
170 |
+
],
|
171 |
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"source": [
|
172 |
+
"no_model = []\n",
|
173 |
+
"for lang in tqdm(freqs.keys()):\n",
|
174 |
+
" models_for_lang_with_task_token_classification = list(\n",
|
175 |
+
" list_models(filter=ModelFilter(language=lang, task=\"token-classification\"))\n",
|
176 |
+
" )\n",
|
177 |
+
" models_for_lang_any_task = list(list_models(filter=ModelFilter(language=lang)))\n",
|
178 |
+
" if not models_for_lang_with_task_token_classification:\n",
|
179 |
+
" data = {\n",
|
180 |
+
" \"language\": lang,\n",
|
181 |
+
" \"datasets\": freqs[lang],\n",
|
182 |
+
" \"token_classification_models\": len(\n",
|
183 |
+
" models_for_lang_with_task_token_classification\n",
|
184 |
+
" ),\n",
|
185 |
+
" \"all_models\": len(models_for_lang_any_task),\n",
|
186 |
+
" }\n",
|
187 |
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" no_model.append(data)"
|
188 |
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]
|
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"colab": {
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},
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"246"
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