Update parquet files

.gitignore

@@ -1 +0,0 @@
-.venv/

2018thresh20dev.csv → 2018thresh10corpus/wiki-entity-similarity-train.parquet

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:e80d37c92672e2b2041d47035c737db5c7dc0a9c5938ede28c46cdc25321be5a
-size 37218821
+oid sha256:0e128de93fb002d2888e4f351c897ef997729ff2a8ac51c9dae19008a37390ef
+size 60887054

2018thresh20test.csv → 2018thresh10pairs/wiki-entity-similarity-test.parquet

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:7204244b6dc46350616835b741f925b98f50f6e9b4d5585d7815f226d5afde95
-size 24867451
+oid sha256:6cf19eab5b15d9a1c63f7b25d9eecebce1d8402741f71a94bd55f55b052413d1
+size 31682534

2018thresh10train.csv → 2018thresh10pairs/wiki-entity-similarity-train.parquet

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:6a4c19706eb30b58641b06fa0e5efe53cf545f6bf49e3b5a30e62d96243517c8
-size 255547715
+oid sha256:825b641521a426759efb4269083864daed69516640f489ec269bf88bee61035b
+size 239758776

2018thresh10dev.csv → 2018thresh10pairs/wiki-entity-similarity-validation.parquet

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:6690985884c8b4a58d0377ef2152bb1fbb233355df68d662539da856e697a414
-size 51406267
+oid sha256:fb26a1f4d19dfcba19997a8144370e4b63f62ffa2a6f7b9b1e5a47ef89935f34
+size 47799775

2018thresh20corpus.csv

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:6baf503753198445a388b650edb59f7faef2ecc95c52b0f39edac5d4d40da5cc
-size 136658267

2018thresh10test.csv → 2018thresh20corpus/wiki-entity-similarity-train.parquet

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:87ba47d64b3223a9ceda6c4c414312c4c8fa412c023d63ba8495909206f908fd
-size 34147126
+oid sha256:20f72f54378317b06476a14d34ac069ecdf8e473fa2afe67efa98cf8aa1920d3
+size 42226880

2018thresh20pairs/wiki-entity-similarity-test.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:fc36fa2d5dd6fe009e052e810c1547368e5e44816fb7b737b17bd88abb531693
+size 22626152

2018thresh10corpus.csv → 2018thresh20pairs/wiki-entity-similarity-train.parquet

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:da0986792d65bb2639a77c29ca6e785ffd971edbf49943c4987be68a29bd2330
-size 188776976
+oid sha256:a90b82f23c5b1d694f9f8e9489d23dd3a2bdc409e57bbfca62c12450e5eb16a2
+size 171905597

2018thresh20pairs/wiki-entity-similarity-validation.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:13a726e38e6ac48be2055203535354450a942672aa2a1318508447066124e44e
+size 34176777

2018thresh20train.csv

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:9fcda4ee523a653d09f34c72e2134d56466d145c56a95596468ca7a1f4f9bf9c
-size 185091962

2018thresh5corpus.csv

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:8421a796ff5cf834c0b5ad50afe500ec28f86be825e901ae12a7dcffd580bed9
-size 246919376

2018thresh5corpus/wiki-entity-similarity-train.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7695b0a9f2c35e129c8d7989fc6899217b29bb2549feb37bbbef847ca8df2fc8
+size 84556237

2018thresh5dev.csv

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:abf0f45844c3aa91747be22502d9d91570365aa0d085a1faaec17a04a84028c5
-size 67296490

2018thresh5pairs/wiki-entity-similarity-test.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ff9257327cccecced6039b688dbac68649d960f2a37fcb5065e2697f2c0cd63a
+size 42103893

2018thresh5pairs/wiki-entity-similarity-train.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:cb6a2bc4d3c9e225b83e257b09689c0adfa1232e80e3a91aca890def9b27408c
+size 316754105

2018thresh5pairs/wiki-entity-similarity-validation.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9eaa224b7e7440ff6d2e92b9e74091a6b81a3cf56fbbb3bdf888b9f69dbbb8d8
+size 63317528

2018thresh5test.csv

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:7d9c39063394851308a52c71de4cae38386bfe7e16f73a355221dafe46a74b0f
-size 44759191

2018thresh5train.csv

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:e7b71f64c53d1aea33ec8bd6067b99c766625897904fd7cccdd82d60eac17b5c
-size 334315073

README.md

@@ -1,67 +0,0 @@
----
-annotations_creators:
-- found
-language:
-- en
-language_creators:
-- found
-license:
-- mit
-multilinguality:
-- monolingual
-pretty_name: 'Wiki Entity Similarity
-
-  '
-size_categories:
-- 10M<n<100M
-source_datasets:
-- original
-tags:
-- named entities
-- similarity
-- paraphrasing
-- synonyms
-- wikipedia
-task_categories: []
-task_ids: []
----
-
-# Wiki Entity Similarity
-
-Usage:
-```py
-from datasets import load_dataset
-
-corpus = load_dataset('Exr0n/wiki-entity-similarity', '2018thresh20corpus', split='train')
-assert corpus[0] == {'article': 'A1000 road', 'link_text': 'A1000', 'is_same': 1}
-
-pairs = load_dataset('Exr0n/wiki-entity-similarity', '2018thresh20pairs', split='train')
-assert corpus[0] == {'article': 'Rhinobatos', 'link_text': 'Ehinobatos beurleni', 'is_same': 1}
-assert len(corpus) == 4_793_180
-```
-
-## Corpus (`name=*corpus`)
-
-The corpora in this are generated by aggregating the link text that refers to various articles in context. For instance, if wiki article A refers to article B as C, then C is added to the list of aliases for article B, and the pair (B, C) is included in the dataset.
-
-Following (DPR https://arxiv.org/pdf/2004.04906.pdf), we use the English Wikipedia dump from Dec. 20, 2018 as the source documents for link collection. 
-
-The dataset includes three quality levels, distinguished by the minimum number of inbound links required to include an article in the dataset. This filtering is motivated by the heuristic "better articles have more citations."
-
-| Min. Inbound Links | Number of Articles | Number of Distinct Links |
-|------------|--------------------|--------------------------|
-| 5 | 1,080,073 | 5,787,081 |
-| 10 | 605,775 | 4,407,409 |
-| 20 | 324,949 | 3,195,545 |
-
-## Training Pairs (`name=*pairs`)
-This dataset also includes training pair datasets (with both positive and negative examples) intended for training classifiers. The train/dev/test split is 75/15/10 % of each corpus. 
-
-### Training Data Generation
-The training pairs in this dataset are generated by taking each example from the corpus as a positive example, and creating a new negative example from the article title of the positive example and a random link text from a different article.
-
-The articles featured in each split are disjoint from the other splits, and each split has the same number of positive (semantically the same) and negative (semantically different) examples.
-
-For more details on the dataset motivation, see [the paper](https://arxiv.org/abs/2202.13581). If you use this dataset in your work, please cite it using the ArXiv reference. 
-
-Generation scripts can be found [in the GitHub repo](https://github.com/Exr0nProjects/wiki-entity-similarity).

generate_wes_data.py

@@ -1,86 +0,0 @@
-from datasets import load_dataset
-import pandas as pd
-from nltk.corpus import words
-from nltk import WordNetLemmatizer
-import numpy as np
-from tqdm import tqdm
-
-from collections import defaultdict
-from operator import itemgetter as ig
-from itertools import islice, chain, repeat
-from random import seed, sample, choice, shuffle
-from gc import collect
-
-filter_dict = set(words.words())
-ltize = WordNetLemmatizer().lemmatize
-
-def generate_splits(subset, split=[0.75, 0.15, 0.1]):
-    assert abs(sum(split) - 1.0) < 0.0001
-    # get the data in dictionary form
-    groups = defaultdict(list)
-    ds = load_dataset('Exr0n/wiki-entity-similarity', subset, split='train')
-    ds = list(tqdm(ds, total=len(ds)))
-    for article, link in tqdm(map(ig('article', 'link_text'), ds), total=len(ds)):
-        if (ltize(article.lower()) not in filter_dict) and (ltize(link.lower()) in filter_dict):
-            # print(article, link, 'not quite right!')
-            continue  # remove if link text is a dictionary word but article is not
-        groups[article].append(link)
-    del ds
-
-    # greedily allocate splits
-    order = sorted(groups.keys(), reverse=True, key=lambda e: groups[e])
-    splits = [[] for _ in split]
-    sizes = [0.001] * len(split)    # avoid div zero error
-    for group in order:
-        impoverished = np.argmax([ s - (x/sum(sizes)) for x, s in zip(sizes, split) ])
-        splits[impoverished].append(group)
-        sizes[impoverished] += len(groups[group])
-
-    sizes = [ int(x) for x in sizes ]
-    print('final sizes', sizes, [x/sum(sizes) for x in sizes])
-
-    # generate positive examples
-    ret = [ [[(k, t) for t in groups[k]] for k in keys] for keys in splits ]
-
-    # generate negative examples randomly (TODO: probably a more elegant swapping soln)
-    for i, keys in enumerate(splits):
-        for key in keys:
-            try:
-                got = sample(keys, len(groups[key])+1)  # sample n+1 keys
-                ret[i].append(
-                    [(key, choice(groups[k])) for k in got if k != key] # get a random link title from that key, if it's not the current key
-                    [:len(groups[key])] # ensure we don't have too many
-                )
-            except ValueError:
-                raise ValueError("well frick one group is bigger than all the others combined. try sampling one at a time")
-
-    collect()
-    return [(chain(*s), chain(repeat(1, z), repeat(0, z))) for z, s in zip(sizes, ret)]
-
-
-if __name__ == '__main__':
-    seed(0x326ccc)
-    year = 2018
-    for size in [5, 10, 20]:
-        x = generate_splits(subset=f'{year}thresh' + str(size) + 'corpus')
-
-        for (data, labels), split in zip(x, ['train', 'dev', 'test']):
-            articles, lts = list(zip(*data))
-            df = pd.DataFrame({ 'article': articles, 'link_text': lts, 'is_same': list(labels) })
-            df = df.sample(frac=1).reset_index(drop=True)
-            df.to_csv(f'{year}thresh' + str(size) + split + '.csv', index=False)
-            # print(df.head(30), df.tail(30))
-
-    # tests
-    # for data, labels in x[2:]:
-    #     data = list(data)
-    #     labels = list(labels)
-    #
-    #     assert sum(labels) * 2 == len(labels)
-    #     num = sum(labels)
-    #
-    #     before = [ a for a, _ in data[:num] ]
-    #     after  = [ a for a, _ in data[num:] ]
-    #     assert before == after
-    #
-    #     print(data[num:])

requirements.txt

@@ -1,268 +0,0 @@
-acme==1.21.0
-aiohttp==3.8.1
-aiosignal==1.2.0
-alabaster==0.7.12
-apparmor==3.0.3
-appdirs==1.4.4
-arandr==0.1.10
-argcomplete==1.12.3
-async-timeout==4.0.2
-attrs==21.2.0
-Babel==2.9.1
-beautifulsoup4==4.10.0
-bleach==3.3.0
-blessed==1.17.6
-blis==0.7.4
-Brlapi==0.8.3
-btrfsutil==5.15.1
-CacheControl==0.12.6
-catalogue==2.0.4
-certbot==1.21.0
-certbot-nginx==1.19.0
-certifi==2020.12.5
-cffi==1.15.0
-chardet==4.0.0
-charset-normalizer==2.0.9
-click==7.1.2
-click-completion==0.5.2
-cloudpickle==1.6.0
-colorama==0.4.4
-coloredlogs==15.0.1
-ConfigArgParse==1.5
-configobj==5.1.0.dev0
-contextlib2==0.6.0.post1
-coverage==5.5
-crcmod==1.7
-cryptography==35.0.0
-cycler==0.10.0
-cymem==2.0.5
-datasets==1.17.0
-defusedxml==0.7.1
-dill==0.3.4
-distlib==0.3.3
-distro==1.6.0
-dnspython==2.1.0
-docopt==0.6.2
-docutils==0.17.1
-dotty-dict==1.3.0
-emoji==1.6.1
-en-core-web-lg @ https://github.com/explosion/spacy-models/releases/download/en_core_web_lg-3.1.0/en_core_web_lg-3.1.0-py3-none-any.whl
-file-magic==0.4.0
-filelock==3.4.2
-flake8==3.9.2
-frozenlist==1.2.0
-fsspec==2021.11.1
-future==0.18.2
-gh-md-to-html==1.21.1
-Glances==3.2.1
-greenlet==1.1.0
-gym==0.18.3
-halo==0.0.31
-hid==1.0.4
-hjson==3.0.2
-html5lib==1.1
-httplib2==0.19.1
-huggingface-hub==0.2.1
-humanfriendly==10.0
-idna==2.10
-imagesize==1.3.0
-img2pdf==0.4.1
-importlib-metadata==4.0.1
-importlib-resources==5.2.2
-inquirer==2.7.0
-isc==2.0
-iterfzf==0.5.0.20.0
-jedi==0.17.2
-jeepney==0.6.0
-Jinja2==3.0.1
-joblib==1.0.1
-josepy==1.10.0
-jsonschema==3.2.0
-kaggle==1.5.12
-keyring==23.0.1
-kiwisolver==1.3.1
-lazr.config==2.2.3
-lazr.delegates==2.0.4
-lazr.restfulclient==0.14.2
-lazr.uri==1.0.5
-LibAppArmor==3.0.3
-libarchive-c==3.2
-libfdt==1.6.1
-libvirt-python==7.8.0
-llvmlite==0.36.0
-log-symbols==0.0.14
-loguru==0.5.3
-louis==3.20.0
-lxml==4.6.3
-Markdown==3.3.6
-MarkupSafe==2.0.1
-matplotlib==3.4.2
-mccabe==0.6.1
-meson==0.60.2
-milc==1.4.2
-mock==3.0.5
-more-itertools==8.10.0
-MouseInfo==0.1.3
-msgpack==1.0.2
-multidict==5.2.0
-multiprocess==0.70.12.2
-murmurhash==1.0.5
-mypy-extensions==0.4.3
-neovim==0.3.1
-nftables==0.1
-nltk==3.6.2
-nose==1.3.7
-nose2==0.10.0
-notify-py==0.3.1
-npyscreen==4.10.5
-numba==0.53.1
-numpy==1.20.3
-oauthlib==3.1.1
-ocrmypdf==12.6.0
-openpyscad==0.4.0
-ordered-set==4.0.2
-packaging==20.9
-pacman-mirrors==4.23.1
-pandas==1.3.4
-parsedatetime==2.6
-parso==0.7.1
-pathy==0.6.0
-pbr==5.5.1
-pdfminer.six==20201018
-pendulum==2.1.2
-pep517==0.12.0
-petname==2.0
-pikepdf==3.1.1
-Pillow==8.2.0
-Pit2ya==0.4.2
-pkginfo==1.7.0
-pluggy==0.13.1
-ply==3.11
-preshed==3.0.5
-progress==1.6
-progressbar2==3.55.0
-protobuf==3.17.3
-PTable==0.9.2
-pyarrow==6.0.1
-PyAutoGUI==0.9.52
-pybind11==2.8.1
-pycairo==1.20.1
-pycodestyle==2.7.0
-pycparser==2.21
-pydantic==1.8.2
-pyelftools==0.27
-pyenchant==3.2.1
-pyflakes==2.3.1
-PyGetWindow==0.0.9
-pyglet==1.5.15
-Pygments==2.9.0
-PyGObject==3.42.0
-pylxd==2.3.1
-pymacaroons==0.13.0
-PyMsgBox==1.0.9
-PyMuPDF==1.18.14
-PyNaCl==1.4.0
-pynvim==0.4.3
-pyOpenSSL==21.0.0
-pyparsing==2.4.7
-PyPDF2==1.26.0
-pyperclip==1.8.2
-PyRect==0.1.4
-pyRFC3339==1.1
-pyrsistent==0.18.0
-PyScreeze==0.1.27
-pysha3==1.0.2
-python-apt==0.0.0
-python-dateutil==2.8.1
-python-debian==0.1.42+git20211018
-python-distutils-extra==2.39
-python-editor==1.0.4
-python-jsonrpc-server==0.4.0
-python-language-server==0.36.2
-python-slugify==5.0.2
-python-utils==2.5.6
-python-xlib==0.31
-python3-xlib==0.15
-PyTweening==1.0.3
-pytz==2021.3
-pytzdata==2020.1
-pyusb==1.1.1
-PyYAML==6.0
-ranger-fm==1.9.3
-raven==6.10.0
-readchar==2.0.1
-readme-renderer==29.0
-regex==2021.4.4
-reportlab==3.6.1
-requests==2.26.0
-requests-oauthlib==1.3.0
-requests-toolbelt==0.9.1
-requests-unixsocket==0.2.0
-resolvelib==0.5.5
-retrying==1.3.3
-rfc3986==1.5.0
-scipy==1.7.0
-SecretStorage==3.3.1
-setuptools-scm==6.0.1
-setuptools-scm-git-archive==1.1
-shellescape==3.8.1
-shellingham==1.4.0
-simplejson==3.17.6
-six==1.16.0
-smart-open==5.1.0
-snowballstemmer==2.2.0
-sortedcontainers==2.4.0
-soupsieve==2.2.1
-spacy==3.1.0
-spacy-legacy==3.0.8
-speedtest-cli==2.1.3
-Sphinx==4.3.1
-sphinxcontrib-applehelp==1.0.2
-sphinxcontrib-devhelp==1.0.2
-sphinxcontrib-htmlhelp==2.0.0
-sphinxcontrib-jsmath==1.0.1
-sphinxcontrib-qthelp==1.0.3
-sphinxcontrib-serializinghtml==1.1.5
-spinners==0.0.24
-srsly==2.4.1
-ssoclient==2.1.1
-tabulate==0.8.9
-TBB==0.2
-team==1.0
-termcolor==1.1.0
-text-unidecode==1.3
-thinc==8.0.7
-togglCli==2.4.2
-toml==0.10.2
-tomli==1.2.2
-torch==1.9.1+cu111
-torchaudio==0.9.1
-torchvision==0.10.1+cu111
-tqdm==4.62.3
-twine==3.4.1
-typer==0.3.2
-typing-extensions==3.10.0.2
-ueberzug==18.1.9
-ufw==0.36
-ujson==4.0.2
-urllib3==1.26.7
-uWSGI==2.0.19.1
-validate==5.1.0.dev0
-validate-email==1.3
-wadllib==1.3.4
-wasabi==0.8.2
-wcwidth==0.2.5
-webcolors==1.11.1
-webencodings==0.5.1
-ws4py==0.5.1
-xxhash==2.0.2
-yapf==0.31.0
-yarl==1.7.2
-zipp==3.4.1
-zope.component==5.0.1
-zope.deferredimport==4.3.1
-zope.deprecation==4.4.0
-zope.event==4.5.0
-zope.hookable==5.1.0
-zope.interface==5.4.0
-zope.proxy==4.5.0

wiki-entity-similarity.py

@@ -1,115 +0,0 @@
-import datasets
-
-from dataclasses import dataclass
-import csv
-
-_DESCRIPTION = '''WES: Learning Semantic Similarity from 6M Names for 1M Entities'''
-_CITE = '''\
-@inproceedings{exr0n2022WES
-    author={Exr0n},
-    title={WES: Learning Semantic Similarity from 6M Names for 1M Entities},
-    year={2022}
-}
-'''
-
-_HUGGINGFACE_REPO = "https://huggingface.co/datasets/Exr0n/wiki-entity-similarity/resolve/main/"
-
-@dataclass
-class WikiEntitySimilarityConfig(datasets.BuilderConfig):
-    """BuilderConfig for CSV."""
-    year: int = None
-    type: str = None
-    threshhold: int = None
-    # path: str = None
-
-class WikiEntitySimilarity(datasets.GeneratorBasedBuilder):
-    """WES: Learning semantic similarity from 6M names for 1M entities"""
-    BUILDER_CONFIG_CLASS = WikiEntitySimilarityConfig
-    BUILDER_CONFIGS = [
-        WikiEntitySimilarityConfig(
-            name='2018thresh5corpus',
-            description='raw link corpus (all true): min 5 inbound links, lowest quality',
-            year=2018,
-            type='corpus',
-            threshhold=5,
-            # path="https://huggingface.co/datasets/Exr0n/wiki-entity-similarity/resolve/main/link_synonyms-2018-thresh_5.csv"
-        ),
-        WikiEntitySimilarityConfig(
-            name='2018thresh10corpus',
-            description='raw link corpus (all true): min 10 inbound links, medium quality',
-            year=2018,
-            type='corpus',
-            threshhold=10,
-            # path="https://huggingface.co/datasets/Exr0n/wiki-entity-similarity/resolve/main/link_synonyms-2018-thresh_10.csv"
-        ),
-        WikiEntitySimilarityConfig(
-            name='2018thresh20corpus',
-            description='raw link corpus (all true): min 20 inbound links, high quality',
-            year=2018,
-            type='corpus',
-            threshhold=20,
-            # path="https://huggingface.co/datasets/Exr0n/wiki-entity-similarity/resolve/main/link_synonyms-2018-thresh_20.csv"
-        ),
-        WikiEntitySimilarityConfig(
-            name='2018thresh5pairs',
-            description='training pairs based on min 5 inbound links, lowest quality',
-            year=2018,
-            type='pairs',
-            threshhold=5,
-            # path="https://huggingface.co/datasets/Exr0n/wiki-entity-similarity/resolve/main/2018thresh5"
-        ),
-        WikiEntitySimilarityConfig(
-            name='2018thresh10pairs',
-            description='training pairs based on min 10 inbound links, medium quality',
-            year=2018,
-            type='pairs',
-            threshhold=10,
-            # path="https://huggingface.co/datasets/Exr0n/wiki-entity-similarity/resolve/main/2018thresh10"
-        ),
-        WikiEntitySimilarityConfig(
-            name='2018thresh20pairs',
-            description='training pairs based on min 20 inbound links, high quality',
-            year=2018,
-            type='pairs',
-            threshhold=20,
-            # path="https://huggingface.co/datasets/Exr0n/wiki-entity-similarity/resolve/main/2018thresh20"
-        ),
-    ]
-
-    def _info(self):
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=datasets.Features(
-                {
-                    'article': datasets.Value('string'),
-                    'link_text': datasets.Value('string'),
-                    'is_same': datasets.Value('uint8'),
-                }
-            ),
-            citation=_CITE,
-            homepage="https://github.com/Exr0nProjects/wiki-entity-similarity",
-        )
-
-    def _split_generators(self, dl_manager):
-        path = _HUGGINGFACE_REPO + f"{self.config.year}thresh{self.config.threshhold}"
-        if self.config.type == 'corpus':
-            filepath = dl_manager.download(path + 'corpus.csv')
-            return [ datasets.SplitGenerator(name=datasets.Split.TRAIN,
-                                             gen_kwargs={ 'path': filepath }) ]
-        elif self.config.type == 'pairs':
-            ret = []
-            for n, e in zip(['train', 'dev', 'test'],
-                            [datasets.Split.TRAIN,
-                             datasets.Split.VALIDATION,
-                             datasets.Split.TEST]):
-                fp = dl_manager.download(path + n + '.csv')
-                ret.append( datasets.SplitGenerator(name=e, gen_kwargs={ 'path': fp }) )
-            return ret
-        else:
-            raise ValueError(f"invalid dataset type '{self.config.type}', expected 'corpus' for raw links or 'pairs' for trainable pairs with negative examples")
-
-    def _generate_examples(self, path):
-        with open(path, 'r') as rf:
-            reader = csv.DictReader(rf)
-            for i, row in enumerate(reader):
-                yield i, row