Datasets:
parquet-converter
commited on
Commit
•
2413bc6
1
Parent(s):
209818b
Update parquet files
Browse files- README.md +0 -65
- kpbiomed.py +0 -215
- test.jsonl → large/partial/test/0000.parquet +2 -2
- train_medium.jsonl → large/partial/train/0000.parquet +2 -2
- train_small.jsonl → large/partial/train/0001.parquet +2 -2
- large/partial/train/0002.parquet +3 -0
- large/partial/train/0003.parquet +3 -0
- large/partial/train/0004.parquet +3 -0
- large/partial/train/0005.parquet +3 -0
- large/partial/train/0006.parquet +3 -0
- large/partial/train/0007.parquet +3 -0
- large/partial/train/0008.parquet +3 -0
- large/partial/train/0009.parquet +3 -0
- val.jsonl → large/partial/validation/0000.parquet +2 -2
- train_large.jsonl +0 -3
README.md
DELETED
@@ -1,65 +0,0 @@
|
|
1 |
-
---
|
2 |
-
annotations_creators:
|
3 |
-
- unknown
|
4 |
-
language_creators:
|
5 |
-
- unknown
|
6 |
-
language:
|
7 |
-
- en
|
8 |
-
license:
|
9 |
-
- cc-by-nc-4.0
|
10 |
-
multilinguality:
|
11 |
-
- monolingual
|
12 |
-
task_categories:
|
13 |
-
- text-mining
|
14 |
-
- text-generation
|
15 |
-
task_ids:
|
16 |
-
- keyphrase-generation
|
17 |
-
- keyphrase-extraction
|
18 |
-
size_categories:
|
19 |
-
- 100K<n<1M
|
20 |
-
pretty_name: KP-Biomed
|
21 |
-
---
|
22 |
-
|
23 |
-
# KPBiomed, A Large-Scale Dataset for keyphrase generation
|
24 |
-
## About
|
25 |
-
|
26 |
-
This dataset is made of 5.6 million abstracts with author assigned keyphrases.
|
27 |
-
|
28 |
-
Details about the dataset can be found in the original paper:
|
29 |
-
Maël Houbre, Florian Boudin and Béatrice Daille. 2022. [A Large-Scale Dataset for Biomedical Keyphrase Generation](https://arxiv.org/abs/2211.12124). In Proceedings of the 13th International Workshop on Health Text Mining and Information Analysis (LOUHI 2022).
|
30 |
-
|
31 |
-
|
32 |
-
Reference (author-assigned) keyphrases are also categorized under the PRMU (<u>P</u>resent-<u>R</u>eordered-<u>M</u>ixed-<u>U</u>nseen) scheme as proposed in the following paper:
|
33 |
-
- Florian Boudin and Ygor Gallina. 2021.
|
34 |
-
[Redefining Absent Keyphrases and their Effect on Retrieval Effectiveness](https://aclanthology.org/2021.naacl-main.330/).
|
35 |
-
In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 4185–4193, Online. Association for Computational Linguistics.
|
36 |
-
|
37 |
-
Text pre-processing (tokenization) is carried out using spacy (en_core_web_sm model) with a special rule to avoid splitting words with hyphens (e.g. graph-based is kept as one token). Stemming (Porter's stemmer implementation provided in nltk) is applied before reference keyphrases are matched against the source text.
|
38 |
-
|
39 |
-
## Content
|
40 |
-
|
41 |
-
The details of the dataset are in the table below:
|
42 |
-
|
43 |
-
| Split | # documents | # keyphrases by document (average) | % Present | % Reordered | % Mixed | % Unseen |
|
44 |
-
| :----------- | ----------: | ---------------------------------: | --------: | ----------: | ------: | -------: |
|
45 |
-
| Train small | 500k | 5.24 | 66.31 | 7.16 | 12.60 | 13.93 |
|
46 |
-
| Train medium | 2M | 5.24 | 66.30 | 7.18 | 12.57 | 13.95 |
|
47 |
-
| Train large | 5.6M | 5.23 | 66.32 | 7.18 | 12.55 | 13.95 |
|
48 |
-
| Validation | 20k | 5.25 | 66.44 | 7.07 | 12.45 | 14.05 |
|
49 |
-
| Test | 20k | 5.22 | 66.59 | 7.22 | 12.44 | 13.75 |
|
50 |
-
|
51 |
-
|
52 |
-
|
53 |
-
The following data fields are available:
|
54 |
-
- **id**: unique identifier of the document.
|
55 |
-
- **title**: title of the document.
|
56 |
-
- **abstract**: abstract of the document.
|
57 |
-
- **keyphrases**: list of reference keyphrases.
|
58 |
-
- **mesh terms**: list of indexer assigned MeSH terms if available (around 68% of the articles)
|
59 |
-
- **prmu**: list of <u>P</u>resent-<u>R</u>eordered-<u>M</u>ixed-<u>U</u>nseen categories for reference keyphrases.
|
60 |
-
- **authors**: list of the article's authors
|
61 |
-
- **year**: publication year
|
62 |
-
|
63 |
-
**NB**: The present keyphrases (represented by the "P" label in the PRMU column) are sorted by their apparition order in the text (title + text).
|
64 |
-
|
65 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
kpbiomed.py
DELETED
@@ -1,215 +0,0 @@
|
|
1 |
-
"""KPBiomed benchmark dataset for keyphrase extraction an generation."""
|
2 |
-
|
3 |
-
|
4 |
-
import csv
|
5 |
-
import json
|
6 |
-
import os
|
7 |
-
|
8 |
-
import datasets
|
9 |
-
|
10 |
-
|
11 |
-
# TODO: Add BibTeX citation
|
12 |
-
# Find for instance the citation on arxiv or on the dataset repo/website
|
13 |
-
_CITATION = """\
|
14 |
-
|
15 |
-
"""
|
16 |
-
|
17 |
-
# You can copy an official description
|
18 |
-
_DESCRIPTION = """\
|
19 |
-
KPBiomed benchmark dataset for keyphrase extraction an generation.
|
20 |
-
"""
|
21 |
-
|
22 |
-
# TODO: Add a link to an official homepage for the dataset here
|
23 |
-
_HOMEPAGE = ""
|
24 |
-
|
25 |
-
# TODO: Add the licence for the dataset here if you can find it
|
26 |
-
_LICENSE = "Apache 2.0 License"
|
27 |
-
|
28 |
-
# TODO: Add link to the official dataset URLs here
|
29 |
-
# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
|
30 |
-
# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
|
31 |
-
_URLS = {
|
32 |
-
"train_large": "train_large.jsonl",
|
33 |
-
"train_medium" : "train_medium.jsonl",
|
34 |
-
"train_small" : "train_small.jsonl",
|
35 |
-
"val" : "val.jsonl",
|
36 |
-
"test" : "test.jsonl"
|
37 |
-
|
38 |
-
}
|
39 |
-
|
40 |
-
|
41 |
-
# TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case
|
42 |
-
class KPBiomed(datasets.GeneratorBasedBuilder):
|
43 |
-
"""TODO: Short description of my dataset."""
|
44 |
-
|
45 |
-
VERSION = datasets.Version("0.0.1")
|
46 |
-
|
47 |
-
# This is an example of a dataset with multiple configurations.
|
48 |
-
# If you don't want/need to define several sub-sets in your dataset,
|
49 |
-
# just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.
|
50 |
-
|
51 |
-
# If you need to make complex sub-parts in the datasets with configurable options
|
52 |
-
# You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
|
53 |
-
# BUILDER_CONFIG_CLASS = MyBuilderConfig
|
54 |
-
|
55 |
-
# You will be able to load one or the other configurations in the following list with
|
56 |
-
# data = datasets.load_dataset('my_dataset', 'first_domain')
|
57 |
-
# data = datasets.load_dataset('my_dataset', 'second_domain')
|
58 |
-
BUILDER_CONFIGS = [
|
59 |
-
datasets.BuilderConfig(name="large", version=VERSION, description="This part of my dataset covers the large training data."),
|
60 |
-
datasets.BuilderConfig(name="medium", version=VERSION, description="This part of my dataset covers the medium training data."),
|
61 |
-
datasets.BuilderConfig(name="small", version=VERSION, description="This part of my dataset covers the small training data."),
|
62 |
-
|
63 |
-
]
|
64 |
-
|
65 |
-
DEFAULT_CONFIG_NAME = "small" # It's not mandatory to have a default configuration. Just use one if it make sense.
|
66 |
-
|
67 |
-
def _info(self):
|
68 |
-
# TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
|
69 |
-
#if self.config.name == "small": # This is the name of the configuration selected in BUILDER_CONFIGS above
|
70 |
-
features = datasets.Features(
|
71 |
-
{
|
72 |
-
"id": datasets.Value("string"),
|
73 |
-
"title": datasets.Value("string"),
|
74 |
-
"abstract": datasets.Value("string"),
|
75 |
-
"authors": datasets.Value("string"),
|
76 |
-
"mesh_terms": datasets.features.Sequence(datasets.Value("string")),
|
77 |
-
"year": datasets.Value("string"),
|
78 |
-
"keyphrases": datasets.features.Sequence(datasets.Value("string")),
|
79 |
-
"prmu": datasets.features.Sequence(datasets.Value("string")),
|
80 |
-
}
|
81 |
-
)
|
82 |
-
return datasets.DatasetInfo(
|
83 |
-
# This is the description that will appear on the datasets page.
|
84 |
-
description=_DESCRIPTION,
|
85 |
-
# This defines the different columns of the dataset and their types
|
86 |
-
features=features, # Here we define them above because they are different between the two configurations
|
87 |
-
# If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and
|
88 |
-
# specify them. They'll be used if as_supervised=True in builder.as_dataset.
|
89 |
-
# supervised_keys=("sentence", "label"),
|
90 |
-
# Homepage of the dataset for documentation
|
91 |
-
homepage=_HOMEPAGE,
|
92 |
-
# License for the dataset if available
|
93 |
-
license=_LICENSE,
|
94 |
-
# Citation for the dataset
|
95 |
-
citation=_CITATION,
|
96 |
-
)
|
97 |
-
|
98 |
-
def _split_generators(self, dl_manager):
|
99 |
-
# TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
|
100 |
-
# If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
|
101 |
-
|
102 |
-
# dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS
|
103 |
-
# It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
|
104 |
-
# By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
|
105 |
-
urls = _URLS
|
106 |
-
data_dir = dl_manager.download_and_extract(urls)
|
107 |
-
if self.config.name == "large":
|
108 |
-
return [
|
109 |
-
datasets.SplitGenerator(
|
110 |
-
name=datasets.Split.TRAIN,
|
111 |
-
# These kwargs will be passed to _generate_examples
|
112 |
-
gen_kwargs={
|
113 |
-
"filepath": os.path.join(data_dir["train_large"]),
|
114 |
-
"split": "train",
|
115 |
-
},
|
116 |
-
),
|
117 |
-
datasets.SplitGenerator(
|
118 |
-
name=datasets.Split.TEST,
|
119 |
-
# These kwargs will be passed to _generate_examples
|
120 |
-
gen_kwargs={
|
121 |
-
"filepath": os.path.join(data_dir["test"]),
|
122 |
-
"split": "test",
|
123 |
-
},
|
124 |
-
),
|
125 |
-
|
126 |
-
datasets.SplitGenerator(
|
127 |
-
name=datasets.Split.VALIDATION,
|
128 |
-
# These kwargs will be passed to _generate_examples
|
129 |
-
gen_kwargs={
|
130 |
-
"filepath": os.path.join(data_dir["val"]),
|
131 |
-
"split": "test",
|
132 |
-
},
|
133 |
-
),
|
134 |
-
|
135 |
-
]
|
136 |
-
elif self.config.name == "medium":
|
137 |
-
return [
|
138 |
-
datasets.SplitGenerator(
|
139 |
-
name=datasets.Split.TRAIN,
|
140 |
-
# These kwargs will be passed to _generate_examples
|
141 |
-
gen_kwargs={
|
142 |
-
"filepath": os.path.join(data_dir["train_medium"]),
|
143 |
-
"split": "train",
|
144 |
-
},
|
145 |
-
),
|
146 |
-
datasets.SplitGenerator(
|
147 |
-
name=datasets.Split.TEST,
|
148 |
-
# These kwargs will be passed to _generate_examples
|
149 |
-
gen_kwargs={
|
150 |
-
"filepath": os.path.join(data_dir["test"]),
|
151 |
-
"split": "test",
|
152 |
-
},
|
153 |
-
),
|
154 |
-
|
155 |
-
datasets.SplitGenerator(
|
156 |
-
name=datasets.Split.VALIDATION,
|
157 |
-
# These kwargs will be passed to _generate_examples
|
158 |
-
gen_kwargs={
|
159 |
-
"filepath": os.path.join(data_dir["val"]),
|
160 |
-
"split": "test",
|
161 |
-
},
|
162 |
-
),
|
163 |
-
|
164 |
-
]
|
165 |
-
|
166 |
-
else:
|
167 |
-
return [
|
168 |
-
datasets.SplitGenerator(
|
169 |
-
name=datasets.Split.TRAIN,
|
170 |
-
# These kwargs will be passed to _generate_examples
|
171 |
-
gen_kwargs={
|
172 |
-
"filepath": os.path.join(data_dir["train_small"]),
|
173 |
-
"split": "train",
|
174 |
-
},
|
175 |
-
),
|
176 |
-
datasets.SplitGenerator(
|
177 |
-
name=datasets.Split.TEST,
|
178 |
-
# These kwargs will be passed to _generate_examples
|
179 |
-
gen_kwargs={
|
180 |
-
"filepath": os.path.join(data_dir["test"]),
|
181 |
-
"split": "test",
|
182 |
-
},
|
183 |
-
),
|
184 |
-
|
185 |
-
datasets.SplitGenerator(
|
186 |
-
name=datasets.Split.VALIDATION,
|
187 |
-
# These kwargs will be passed to _generate_examples
|
188 |
-
gen_kwargs={
|
189 |
-
"filepath": os.path.join(data_dir["val"]),
|
190 |
-
"split": "test",
|
191 |
-
},
|
192 |
-
),
|
193 |
-
|
194 |
-
]
|
195 |
-
|
196 |
-
# method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
|
197 |
-
def _generate_examples(self, filepath, split):
|
198 |
-
# TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
|
199 |
-
# The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
|
200 |
-
with open(filepath, encoding="utf-8") as f:
|
201 |
-
for key, row in enumerate(f):
|
202 |
-
data = json.loads(row)
|
203 |
-
# Yields examples as (key, example) tuples
|
204 |
-
yield key, {
|
205 |
-
"id": data["id"],
|
206 |
-
"title": data["title"],
|
207 |
-
"abstract": data["abstract"],
|
208 |
-
"authors" : data["authors"],
|
209 |
-
"mesh_terms" : data["mesh_terms"],
|
210 |
-
"year" : data["year"],
|
211 |
-
"keyphrases": data["keyphrases"],
|
212 |
-
"prmu": data["prmu"],
|
213 |
-
}
|
214 |
-
|
215 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
test.jsonl → large/partial/test/0000.parquet
RENAMED
@@ -1,3 +1,3 @@
|
|
1 |
version https://git-lfs.github.com/spec/v1
|
2 |
-
oid sha256:
|
3 |
-
size
|
|
|
1 |
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:e379243e8b819e5dbbd1346a5116561b15f8f25bc5924d2ed863a5976e2cca6c
|
3 |
+
size 24164643
|
train_medium.jsonl → large/partial/train/0000.parquet
RENAMED
@@ -1,3 +1,3 @@
|
|
1 |
version https://git-lfs.github.com/spec/v1
|
2 |
-
oid sha256:
|
3 |
-
size
|
|
|
1 |
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:721a23e16a9479a4e62706528ba21ef411559cc833dd42a4f409d53a636ce362
|
3 |
+
size 285866168
|
train_small.jsonl → large/partial/train/0001.parquet
RENAMED
@@ -1,3 +1,3 @@
|
|
1 |
version https://git-lfs.github.com/spec/v1
|
2 |
-
oid sha256:
|
3 |
-
size
|
|
|
1 |
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:49c316494e263e2ae0ffe5ece6e552c9d6a24e1287b82e0efe71dfae8af54a81
|
3 |
+
size 285739669
|
large/partial/train/0002.parquet
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:095942e5b16c81fdc8bc3dc9a1e4b51c2a9f71b0b888874aa68d1c536dbd350d
|
3 |
+
size 285905400
|
large/partial/train/0003.parquet
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:c5458bc83ddfcbd1b5e2c2011ebe7325038c6390793e995a93b5ceb52979bbd2
|
3 |
+
size 285712131
|
large/partial/train/0004.parquet
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:849753aeb91ba582d749950392dddcaad4cdbba5d023853ab7fe6a781a430ffa
|
3 |
+
size 285767208
|
large/partial/train/0005.parquet
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:f5a7ac5fb2cd732b6af0689d22c51d1e04e2c26722f63acc28d14586afe5730b
|
3 |
+
size 285746407
|
large/partial/train/0006.parquet
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:00d74ac8e44f6a158f9d5a44e61e5842e4dbd2ad65883ed44f948e6d112073ff
|
3 |
+
size 285906399
|
large/partial/train/0007.parquet
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:b207b716bc87319627b180c75ed2994333e8298b4f0d3b795d4b59a66c1ece21
|
3 |
+
size 285874301
|
large/partial/train/0008.parquet
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:5bd0312bb4efa1850986b982922877bec6793542a1814b32dc3e4b731cac3b6c
|
3 |
+
size 285830147
|
large/partial/train/0009.parquet
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:385b33e09102fc63ce49e23dd71a8f6621cc39c163b6bb021799254f5e8bbc96
|
3 |
+
size 277661246
|
val.jsonl → large/partial/validation/0000.parquet
RENAMED
@@ -1,3 +1,3 @@
|
|
1 |
version https://git-lfs.github.com/spec/v1
|
2 |
-
oid sha256:
|
3 |
-
size
|
|
|
1 |
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:59be6c6f8858b45a6538b7ecf56916352fa787d756d7a231c6d72c3c83c38ddf
|
3 |
+
size 24203935
|
train_large.jsonl
DELETED
@@ -1,3 +0,0 @@
|
|
1 |
-
version https://git-lfs.github.com/spec/v1
|
2 |
-
oid sha256:caed51ed5152871e4513046ed1238eacd6bb6df437e2fca02240a0e1429f1e50
|
3 |
-
size 12333280683
|
|
|
|
|
|
|
|