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test_datasets.py
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import copy
import shutil
import pytest
import flair
import flair.datasets
from flair.data import MultiCorpus, Sentence
from flair.datasets import ColumnCorpus
from flair.datasets.sequence_labeling import (
ONTONOTES,
JsonlCorpus,
JsonlDataset,
MultiFileJsonlCorpus,
)
def test_load_imdb_data(tasks_base_path):
# get training, test and dev data
corpus = flair.datasets.ClassificationCorpus(
tasks_base_path / "imdb",
memory_mode="full",
)
assert len(corpus.train) == 5
assert len(corpus.dev) == 5
assert len(corpus.test) == 5
def test_load_imdb_data_streaming(tasks_base_path):
# get training, test and dev data
corpus = flair.datasets.ClassificationCorpus(
tasks_base_path / "imdb",
memory_mode="disk",
)
assert len(corpus.train) == 5
assert len(corpus.dev) == 5
assert len(corpus.test) == 5
def test_load_imdb_data_max_tokens(tasks_base_path):
# get training, test and dev data
corpus = flair.datasets.ClassificationCorpus(tasks_base_path / "imdb", memory_mode="full", truncate_to_max_tokens=3)
assert len(corpus.train[0]) <= 3
assert len(corpus.dev[0]) <= 3
assert len(corpus.test[0]) <= 3
def test_load_imdb_data_streaming_max_tokens(tasks_base_path):
# get training, test and dev data
corpus = flair.datasets.ClassificationCorpus(tasks_base_path / "imdb", memory_mode="full", truncate_to_max_tokens=3)
assert len(corpus.train[0]) <= 3
assert len(corpus.dev[0]) <= 3
assert len(corpus.test[0]) <= 3
def test_load_ag_news_data(tasks_base_path):
# get training, test and dev data
corpus = flair.datasets.ClassificationCorpus(tasks_base_path / "ag_news")
assert len(corpus.train) == 10
assert len(corpus.dev) == 10
assert len(corpus.test) == 10
def test_load_sequence_labeling_data(tasks_base_path):
# get training, test and dev data
corpus = flair.datasets.ColumnCorpus(tasks_base_path / "fashion", column_format={0: "text", 2: "ner"})
assert len(corpus.train) == 6
assert len(corpus.dev) == 1
assert len(corpus.test) == 1
def test_load_sequence_labeling_data_with_boundaries(tasks_base_path):
# get training, test and dev data
corpus = flair.datasets.ColumnCorpus(
tasks_base_path / "trivial" / "trivial_bioes_with_boundaries", column_format={0: "text", 1: "ner"}
)
assert len(corpus.train) == 14
assert len(corpus.dev) == 9
assert len(corpus.test) == 10
# now exclude -DOCSTART- sentences
corpus = flair.datasets.ColumnCorpus(
tasks_base_path / "trivial" / "trivial_bioes_with_boundaries",
column_format={0: "text", 1: "ner"},
banned_sentences=["-DOCSTART-"],
)
assert len(corpus.train) == 12
assert len(corpus.dev) == 8
assert len(corpus.test) == 8
assert len(corpus.train[0].right_context(5)) == 5
# now load whole documents as sentences
corpus = flair.datasets.ColumnCorpus(
tasks_base_path / "trivial" / "trivial_bioes_with_boundaries",
column_format={0: "text", 1: "ner"},
document_separator_token="-DOCSTART-",
documents_as_sentences=True,
)
assert len(corpus.train) == 3
assert len(corpus.dev) == 2
assert len(corpus.test) == 2
assert len(corpus.train[0].right_context(5)) == 0
# ban each boundary but set each sentence to be independent
corpus = flair.datasets.ColumnCorpus(
tasks_base_path / "trivial" / "trivial_bioes_with_boundaries",
column_format={0: "text", 1: "ner"},
banned_sentences=["-DOCSTART-"],
every_sentence_is_independent=True,
)
assert len(corpus.train[0].right_context(5)) == 0
def test_load_sequence_labeling_whitespace_after(tasks_base_path):
# get training, test and dev data
corpus = flair.datasets.ColumnCorpus(
tasks_base_path / "column_with_whitespaces",
column_format={0: "text", 1: "ner", 2: "space-after"},
)
assert len(corpus.train) == 1
assert len(corpus.dev) == 1
assert len(corpus.test) == 1
assert corpus.train[0].to_tokenized_string() == "It is a German - owned firm ."
assert corpus.train[0].to_plain_string() == "It is a German-owned firm."
for token in corpus.train[0]:
assert token.start_position is not None
assert token.end_position is not None
def test_load_column_corpus_options(tasks_base_path):
# get training, test and dev data
corpus = flair.datasets.ColumnCorpus(
tasks_base_path / "column_corpus_options",
column_format={0: "text", 1: "ner"},
column_delimiter="\t",
skip_first_line=True,
)
assert len(corpus.train) == 1
assert len(corpus.dev) == 1
assert len(corpus.test) == 1
assert corpus.train[0].to_tokenized_string() == "This is New Berlin"
def test_load_span_data(tasks_base_path):
# load column dataset with one entry
dataset = flair.datasets.ColumnDataset(
tasks_base_path / "span_labels" / "span_first.txt",
column_name_map={0: "text", 1: "ner"},
)
assert len(dataset) == 1
assert dataset[0][2].text == "RAB"
assert dataset[0][2].get_label("ner").value == "PARTA"
# load column dataset with two entries
dataset = flair.datasets.ColumnDataset(
tasks_base_path / "span_labels" / "span_second.txt",
column_name_map={0: "text", 1: "ner"},
)
assert len(dataset) == 2
assert dataset[1][2].text == "RAB"
assert dataset[1][2].get_label("ner").value == "PARTA"
# load column dataset with three entries
dataset = flair.datasets.ColumnDataset(
tasks_base_path / "span_labels" / "span_third.txt",
column_name_map={0: "text", 1: "ner"},
)
assert len(dataset) == 3
assert dataset[2][2].text == "RAB"
assert dataset[2][2].get_label("ner").value == "PARTA"
def test_load_germeval_data(tasks_base_path):
# get training, test and dev data
corpus = flair.datasets.ColumnCorpus(tasks_base_path / "ner_german_germeval", column_format={0: "text", 2: "ner"})
assert len(corpus.train) == 2
assert len(corpus.dev) == 1
assert len(corpus.test) == 1
def test_load_ud_english_data(tasks_base_path):
# get training, test and dev data
corpus = flair.datasets.UD_ENGLISH(tasks_base_path)
assert len(corpus.train) == 6
assert len(corpus.test) == 4
assert len(corpus.dev) == 2
# check if Token labels are correct
sentence = corpus.train[0]
assert sentence[0].text == "From"
assert sentence[0].get_label("upos").value == "ADP"
assert sentence[1].text == "the"
assert sentence[1].get_label("upos").value == "DET"
def test_load_up_english_data(tasks_base_path):
# get training, test and dev data
corpus = flair.datasets.UP_ENGLISH(tasks_base_path)
assert len(corpus.train) == 4
assert len(corpus.test) == 2
assert len(corpus.dev) == 2
# check if Token labels for frames are correct
sentence = corpus.dev[0]
assert sentence[2].text == "AP"
assert sentence[2].get_label("frame", zero_tag_value="no_label").value == "no_label"
assert sentence[3].text == "comes"
assert sentence[3].get_label("frame").value == "come.03"
def test_load_no_dev_data(tasks_base_path):
# get training, test and dev data
corpus = flair.datasets.ColumnCorpus(tasks_base_path / "fashion_nodev", column_format={0: "text", 2: "ner"})
assert len(corpus.train) == 5
assert len(corpus.dev) == 1
assert len(corpus.test) == 1
def test_load_no_dev_data_explicit(tasks_base_path):
# get training, test and dev data
corpus = flair.datasets.ColumnCorpus(
tasks_base_path / "fashion_nodev",
column_format={0: "text", 2: "ner"},
train_file="train.tsv",
test_file="test.tsv",
)
assert len(corpus.train) == 5
assert len(corpus.dev) == 1
assert len(corpus.test) == 1
def test_multi_corpus(tasks_base_path):
corpus_1 = flair.datasets.ColumnCorpus(tasks_base_path / "ner_german_germeval", column_format={0: "text", 2: "ner"})
corpus_2 = flair.datasets.ColumnCorpus(tasks_base_path / "fashion", column_format={0: "text", 2: "ner"})
# get two corpora as one
corpus = MultiCorpus([corpus_1, corpus_2])
assert len(corpus.train) == 8
assert len(corpus.dev) == 2
assert len(corpus.test) == 2
def test_download_load_data(tasks_base_path):
# get training, test and dev data for full English UD corpus from web
corpus = flair.datasets.UD_ENGLISH()
assert len(corpus.train) == 12544
assert len(corpus.dev) == 2001
assert len(corpus.test) == 2077
# clean up data directory
shutil.rmtree(flair.cache_root / "datasets" / "ud_english")
def _assert_conllu_dataset(dataset):
sent1 = dataset[0]
assert [label.data_point.text for label in sent1.get_labels("ner")] == ["Larry Page", "Sergey Brin", "Google"]
assert [label.value for label in sent1.get_labels("ner")] == ["PER", "PER", "ORG"]
assert [token.get_label("upos").value for token in sent1.tokens] == [
"PROPN",
"PROPN",
"CCONJ",
"PROPN",
"PROPN",
"VERB",
"PROPN",
"PUNCT",
]
assert [token.whitespace_after for token in sent1.tokens] == [
1,
1,
1,
1,
1,
1,
0,
0,
]
ner_spans1 = sent1.get_labels("ner")
assert len(ner_spans1) == 3
upos_spans1 = sent1.get_labels("upos")
assert len(upos_spans1) == 8
rels1 = sent1.get_labels("relation")
assert len(rels1) == 2
assert [token.idx for token in rels1[1].data_point.first] == [7]
assert [token.idx for token in rels1[1].data_point.second] == [4, 5]
sent3 = dataset[2]
ner_labels3 = sent3.get_labels("ner")
assert len(ner_labels3) == 3
upos_labels3 = sent3.get_labels("upos")
assert len(upos_labels3) == 11
rels3 = sent3.get_labels("relation")
assert len(rels3) == 1
assert [token.idx for token in rels3[0].data_point.first] == [6]
assert [token.idx for token in rels3[0].data_point.second] == [1, 2]
def test_load_conllu_corpus(tasks_base_path):
corpus = ColumnCorpus(
tasks_base_path / "conllu",
train_file="train.conllu",
dev_file="train.conllu",
test_file="train.conllu",
in_memory=False,
column_format={1: "text", 2: "upos", 3: "ner", 4: "feats"},
)
assert len(corpus.train) == 4
assert len(corpus.dev) == 4
assert len(corpus.test) == 4
_assert_conllu_dataset(corpus.train)
def test_load_conllu_corpus_in_memory(tasks_base_path):
corpus = ColumnCorpus(
tasks_base_path / "conllu",
train_file="train.conllu",
dev_file="train.conllu",
test_file="train.conllu",
column_format={1: "text", 2: "upos", 3: "ner", 4: "feats"},
in_memory=True,
)
assert len(corpus.train) == 4
assert len(corpus.dev) == 4
assert len(corpus.test) == 4
_assert_conllu_dataset(corpus.train)
def test_load_conllu_plus_corpus(tasks_base_path):
corpus = ColumnCorpus(
tasks_base_path / "conllu",
train_file="train.conllup",
dev_file="train.conllup",
test_file="train.conllup",
column_format={1: "text", 2: "upos", 3: "ner", 4: "feats"},
in_memory=False,
)
assert len(corpus.train) == 5
assert len(corpus.dev) == 5
assert len(corpus.test) == 5
_assert_conllu_dataset(corpus.train)
def test_load_conllu_corpus_plus_in_memory(tasks_base_path):
corpus = ColumnCorpus(
tasks_base_path / "conllu",
train_file="train.conllup",
dev_file="train.conllup",
test_file="train.conllup",
column_format={1: "text", 2: "upos", 3: "ner", 4: "feats"},
in_memory=True,
)
assert len(corpus.train) == 5
assert len(corpus.dev) == 5
assert len(corpus.test) == 5
_assert_conllu_dataset(corpus.train)
def _assert_universal_dependencies_conllu_dataset(dataset):
sent1: Sentence = dataset[0]
assert [token.whitespace_after for token in sent1.tokens] == [
1,
1,
1,
1,
0,
0,
]
assert len(sent1.get_labels("Number")) == 4
assert sent1[1].get_labels("Number")[0].value == "Plur"
assert sent1[1].get_labels("Person")[0].value == "3"
assert sent1[1].get_labels("Tense")[0].value == "Pres"
# assert [token.get_tag("head").value for token in sent1.tokens] == [
# "2",
# "0",
# "4",
# "2",
# "2",
# "2",
# ]
assert [token.get_label("deprel").value for token in sent1.tokens] == [
"nsubj",
"root",
"cc",
"conj",
"obj",
"punct",
]
def test_load_universal_dependencies_conllu_corpus(tasks_base_path):
# This test only covers basic universal dependencies datasets.
# For example, multi-word tokens or the "deps" column sentence annotations are not supported yet.
# Here, we use the default token annotation fields.
corpus = ColumnCorpus(
tasks_base_path / "conllu",
train_file="universal_dependencies.conllu",
dev_file="universal_dependencies.conllu",
test_file="universal_dependencies.conllu",
column_format={
1: "text",
2: "lemma",
3: "upos",
4: "pos",
5: "feats",
6: "head",
7: "deprel",
8: "deps",
9: "misc",
},
)
assert len(corpus.train) == 1
assert len(corpus.dev) == 1
assert len(corpus.test) == 1
_assert_universal_dependencies_conllu_dataset(corpus.train)
@pytest.mark.skip()
def test_hipe_2022_corpus(tasks_base_path):
# This test covers the complete HIPE 2022 dataset.
# https://github.com/hipe-eval/HIPE-2022-data
# Includes variant with document separator, and all versions of the dataset.
# We have manually checked, that these numbers are correct:
hipe_stats = {
"v1.0": {
"ajmc": {
"de": {"sample": {"sents": 119, "docs": 8, "labels": ["date", "loc", "pers", "scope", "work"]}},
"en": {"sample": {"sents": 83, "docs": 5, "labels": ["date", "loc", "pers", "scope", "work"]}},
},
"hipe2020": {
"de": {
"train": {
"sents": 3470 + 2, # 2 sentences with missing EOS marker
"docs": 103,
"labels": ["loc", "org", "pers", "prod", "time"],
},
"dev": {"sents": 1202, "docs": 33, "labels": ["loc", "org", "pers", "prod", "time"]},
},
"en": {"dev": {"sents": 1045, "docs": 80, "labels": ["loc", "org", "pers", "prod", "time"]}},
"fr": {
"train": {"sents": 5743, "docs": 158, "labels": ["loc", "org", "pers", "prod", "time", "comp"]},
"dev": {"sents": 1244, "docs": 43, "labels": ["loc", "org", "pers", "prod", "time"]},
},
},
"letemps": {
"fr": {
"train": {"sents": 14051, "docs": 414, "labels": ["loc", "org", "pers"]},
"dev": {"sents": 1341, "docs": 51, "labels": ["loc", "org", "pers"]},
}
},
"newseye": {
# +1 offset, because of missing EOS marker at EOD
"de": {
"train": {"sents": 23646 + 1, "docs": 11, "labels": ["HumanProd", "LOC", "ORG", "PER"]},
"dev": {"sents": 1110 + 1, "docs": 12, "labels": ["HumanProd", "LOC", "ORG", "PER"]},
"dev2": {"sents": 1541 + 1, "docs": 12, "labels": ["HumanProd", "LOC", "ORG", "PER"]},
},
"fi": {
"train": {"sents": 1141 + 1, "docs": 24, "labels": ["HumanProd", "LOC", "ORG", "PER"]},
"dev": {"sents": 140 + 1, "docs": 24, "labels": ["HumanProd", "LOC", "ORG", "PER"]},
"dev2": {"sents": 104 + 1, "docs": 21, "labels": ["HumanProd", "LOC", "ORG", "PER"]},
},
"fr": {
"train": {"sents": 7106 + 1, "docs": 35, "labels": ["HumanProd", "LOC", "ORG", "PER"]},
"dev": {"sents": 662 + 1, "docs": 35, "labels": ["HumanProd", "LOC", "ORG", "PER"]},
"dev2": {"sents": 1016 + 1, "docs": 35, "labels": ["HumanProd", "LOC", "ORG", "PER"]},
},
"sv": {
"train": {"sents": 1063 + 1, "docs": 21, "labels": ["HumanProd", "LOC", "ORG", "PER"]},
"dev": {"sents": 126 + 1, "docs": 21, "labels": ["HumanProd", "LOC", "ORG", "PER"]},
"dev2": {"sents": 136 + 1, "docs": 21, "labels": ["HumanProd", "LOC", "ORG", "PER"]},
},
},
"sonar": {
"de": {
"dev": {
"sents": 1603 + 10, # 10 sentences with missing EOS marker
"docs": 10,
"labels": ["LOC", "ORG", "PER"],
}
}
},
"topres19th": {
"en": {
"train": {"sents": 5874, "docs": 309, "labels": ["BUILDING", "LOC", "STREET"]},
"dev": {"sents": 646, "docs": 34, "labels": ["BUILDING", "LOC", "STREET"]},
}
},
}
}
hipe_stats["v2.0"] = copy.deepcopy(hipe_stats["v1.0"])
hipe_stats["v2.0"]["ajmc"] = {
"de": {
"train": {
"sents": 1022 + 2, # 2 sentences with missing EOS marker
"docs": 76,
"labels": ["date", "loc", "object", "pers", "scope", "work"],
},
"dev": {"sents": 192, "docs": 14, "labels": ["loc", "object", "pers", "scope", "work"]},
},
"en": {
"train": {
"sents": 1153 + 1, # 1 sentence with missing EOS marker
"docs": 60,
"labels": ["date", "loc", "object", "pers", "scope", "work"],
},
"dev": {
"sents": 251 + 1, # 1 sentence with missing EOS marker
"docs": 14,
"labels": ["date", "loc", "pers", "scope", "work"],
},
},
"fr": {
"train": {
"sents": 893 + 1, # 1 sentence with missing EOS marker
"docs": 72,
"labels": ["date", "loc", "object", "pers", "scope", "work"],
},
"dev": {
"sents": 201 + 1, # 1 sentence with missing EOS marker
"docs": 17,
"labels": ["pers", "scope", "work"],
},
},
}
hipe_stats["v2.0"]["newseye"]["de"] = {
"train": {"sents": 20839 + 1, "docs": 7, "labels": ["HumanProd", "LOC", "ORG", "PER"]} # missing EOD marker
}
hipe_stats["v2.0"]["sonar"] = {
"de": {
"dev": {
"sents": 816 + 10, # 9 sentences with missing EOS marker + missing EOD
"docs": 10,
"labels": ["LOC", "ORG", "PER"],
}
}
}
hipe_stats["v2.1"] = copy.deepcopy(hipe_stats["v2.0"])
hipe_stats["v2.1"]["hipe2020"]["fr"]["train"] = {
"sents": 5743,
"docs": 158,
"labels": ["loc", "org", "pers", "prod", "time"],
}
# Test data for v2.1 release
hipe_stats["v2.1"]["ajmc"]["de"]["test"] = {
"sents": 224,
"docs": 16,
"labels": ["loc", "object", "pers", "scope", "work"],
}
hipe_stats["v2.1"]["ajmc"]["en"]["test"] = {
"sents": 238,
"docs": 13,
"labels": ["date", "loc", "pers", "scope", "work"],
}
hipe_stats["v2.1"]["ajmc"]["fr"]["test"] = {
"sents": 188 + 1, # 1 sentence with missing EOS marker
"docs": 15,
"labels": ["date", "loc", "pers", "scope", "work"],
}
hipe_stats["v2.1"]["hipe2020"]["de"]["test"] = {
"sents": 1215 + 2, # 2 sentences with missing EOS marker
"docs": 49,
"labels": ["loc", "org", "pers", "prod", "time"],
}
hipe_stats["v2.1"]["hipe2020"]["en"]["test"] = {
"sents": 553,
"docs": 46,
"labels": ["loc", "org", "pers", "prod", "time"],
}
hipe_stats["v2.1"]["hipe2020"]["fr"]["test"] = {
"sents": 1462,
"docs": 43,
"labels": ["loc", "org", "pers", "prod", "time"],
}
hipe_stats["v2.1"]["letemps"]["fr"]["test"] = {"sents": 2381, "docs": 51, "labels": ["loc", "org", "pers"]}
hipe_stats["v2.1"]["newseye"]["de"]["test"] = {
"sents": 3336 + 1, # 1 missing EOD marker
"docs": 13,
"labels": ["HumanProd", "LOC", "ORG", "PER"],
}
hipe_stats["v2.1"]["newseye"]["fi"]["test"] = {
"sents": 390 + 1, # 1 missing EOD marker
"docs": 24,
"labels": ["HumanProd", "LOC", "ORG", "PER"],
}
hipe_stats["v2.1"]["newseye"]["fr"]["test"] = {
"sents": 2534 + 1, # 1 missing EOD marker
"docs": 35,
"labels": ["HumanProd", "LOC", "ORG", "PER"],
}
hipe_stats["v2.1"]["newseye"]["sv"]["test"] = {
"sents": 342 + 1, # 1 missing EOD marker
"docs": 21,
"labels": ["HumanProd", "LOC", "ORG", "PER"],
}
hipe_stats["v2.1"]["sonar"]["de"]["test"] = {
"sents": 807 + 8 + 1, # 8 missing EOS marker + missing EOD
"docs": 10,
"labels": ["LOC", "ORG", "PER"],
}
hipe_stats["v2.1"]["topres19th"]["en"]["test"] = {
"sents": 2001,
"docs": 112,
"labels": ["BUILDING", "LOC", "STREET"],
}
def test_hipe_2022(dataset_version="v2.1", add_document_separator=True):
for dataset_name, languages in hipe_stats[dataset_version].items():
for language in languages:
splits = languages[language]
corpus = flair.datasets.NER_HIPE_2022(
version=dataset_version,
dataset_name=dataset_name,
language=language,
dev_split_name="dev",
add_document_separator=add_document_separator,
)
for split_name, stats in splits.items():
split_description = f"{dataset_name}@{dataset_version}/{language}#{split_name}"
current_sents = stats["sents"]
current_docs = stats["docs"]
current_labels = set(stats["labels"])
total_sentences = current_sents + current_docs if add_document_separator else stats["sents"]
if split_name == "train":
assert (
len(corpus.train) == total_sentences
), f"Sentence count mismatch for {split_description}: {len(corpus.train)} vs. {total_sentences}"
gold_labels = set(corpus.make_label_dictionary(label_type="ner").get_items())
assert (
current_labels == gold_labels
), f"Label mismatch for {split_description}: {current_labels} vs. {gold_labels}"
elif split_name in ["dev", "sample"]:
assert (
len(corpus.dev) == total_sentences
), f"Sentence count mismatch for {split_description}: {len(corpus.dev)} vs. {total_sentences}"
corpus._train = corpus._dev
gold_labels = set(corpus.make_label_dictionary(label_type="ner").get_items())
assert (
current_labels == gold_labels
), f"Label mismatch for {split_description}: {current_labels} vs. {gold_labels}"
elif split_name == "dev2":
corpus = flair.datasets.NER_HIPE_2022(
version=dataset_version,
dataset_name=dataset_name,
language=language,
dev_split_name="dev2",
add_document_separator=add_document_separator,
)
corpus._train = corpus._dev
gold_labels = set(corpus.make_label_dictionary(label_type="ner").get_items())
assert (
len(corpus.dev) == total_sentences
), f"Sentence count mismatch for {split_description}: {len(corpus.dev)} vs. {total_sentences}"
assert (
current_labels == gold_labels
), f"Label mismatch for {split_description}: {current_labels} vs. {gold_labels}"
test_hipe_2022(dataset_version="v1.0", add_document_separator=True)
test_hipe_2022(dataset_version="v1.0", add_document_separator=False)
test_hipe_2022(dataset_version="v2.0", add_document_separator=True)
test_hipe_2022(dataset_version="v2.0", add_document_separator=False)
test_hipe_2022(dataset_version="v2.1", add_document_separator=True)
test_hipe_2022(dataset_version="v2.1", add_document_separator=False)
@pytest.mark.skip()
def test_icdar_europeana_corpus(tasks_base_path):
# This test covers the complete ICDAR Europeana corpus:
# https://github.com/stefan-it/historic-domain-adaptation-icdar
gold_stats = {"fr": {"train": 7936, "dev": 992, "test": 992}, "nl": {"train": 5777, "dev": 722, "test": 723}}
def check_number_sentences(reference: int, actual: int, split_name: str):
assert actual == reference, f"Mismatch in number of sentences for {split_name} split"
for language in ["fr", "nl"]:
corpus = flair.datasets.NER_ICDAR_EUROPEANA(language=language)
check_number_sentences(len(corpus.train), gold_stats[language]["train"], "train")
check_number_sentences(len(corpus.dev), gold_stats[language]["dev"], "dev")
check_number_sentences(len(corpus.test), gold_stats[language]["test"], "test")
@pytest.mark.skip()
def test_masakhane_corpus(tasks_base_path):
# This test covers the complete MasakhaNER dataset, including support for v1 and v2.
supported_versions = ["v1", "v2"]
supported_languages = {
"v1": ["amh", "hau", "ibo", "kin", "lug", "luo", "pcm", "swa", "wol", "yor"],
"v2": [
"bam",
"bbj",
"ewe",
"fon",
"hau",
"ibo",
"kin",
"lug",
"mos",
"pcm",
"nya",
"sna",
"swa",
"tsn",
"twi",
"wol",
"xho",
"yor",
"zul",
],
}
masakhane_stats = {
"v1": {
"amh": {"train": 1750, "dev": 250, "test": 500},
"hau": {"train": 1912, "dev": 276, "test": 552},
"ibo": {"train": 2235, "dev": 320, "test": 638},
"kin": {
"train": 2116,
"dev": 302,
"test": 605,
},
"lug": {"train": 1428, "dev": 200, "test": 407},
"luo": {"train": 644, "dev": 92, "test": 186},
"pcm": {"train": 2124, "dev": 306, "test": 600},
"swa": {"train": 2109, "dev": 300, "test": 604},
"wol": {"train": 1871, "dev": 267, "test": 539},
"yor": {"train": 2171, "dev": 305, "test": 645},
},
"v2": {
"bam": {"train": 4462, "dev": 638, "test": 1274},
"bbj": {"train": 3384, "dev": 483, "test": 966},
"ewe": {"train": 3505, "dev": 501, "test": 1001},
"fon": {"train": 4343, "dev": 623, "test": 1228},
"hau": {"train": 5716, "dev": 816, "test": 1633},
"ibo": {"train": 7634, "dev": 1090, "test": 2181},
"kin": {"train": 7825, "dev": 1118, "test": 2235},
"lug": {"train": 4942, "dev": 706, "test": 1412},
"mos": {"train": 4532, "dev": 648, "test": 1294},
"pcm": {"train": 5646, "dev": 806, "test": 1613},
"nya": {"train": 6250, "dev": 893, "test": 1785},
"sna": {"train": 6207, "dev": 887, "test": 1773},
"swa": {"train": 6593, "dev": 942, "test": 1883},
"tsn": {"train": 3489, "dev": 499, "test": 996},
"twi": {"train": 4240, "dev": 605, "test": 1211},
"wol": {"train": 4593, "dev": 656, "test": 1312},
"xho": {"train": 5718, "dev": 817, "test": 1633},
"yor": {"train": 6876, "dev": 983, "test": 1964},
"zul": {"train": 5848, "dev": 836, "test": 1670},
},
}
def check_number_sentences(reference: int, actual: int, split_name: str, language: str, version: str):
assert actual == reference, f"Mismatch in number of sentences for {language}@{version}/{split_name}"
for version in supported_versions:
for language in supported_languages[version]:
corpus = flair.datasets.NER_MASAKHANE(languages=language, version=version)
gold_stats = masakhane_stats[version][language]
check_number_sentences(len(corpus.train), gold_stats["train"], "train", language, version)
check_number_sentences(len(corpus.dev), gold_stats["dev"], "dev", language, version)
check_number_sentences(len(corpus.test), gold_stats["test"], "test", language, version)
@pytest.mark.skip()
def test_nermud_corpus(tasks_base_path):
# This test covers the NERMuD dataset. Official stats can be found here:
# https://github.com/dhfbk/KIND/tree/main/evalita-2023
gold_stats = {
"WN": {"train": 10912, "dev": 2594},
"FIC": {"train": 11423, "dev": 1051},
"ADG": {"train": 5147, "dev": 1122},
}
def check_number_sentences(reference: int, actual: int, split_name: str):
assert actual == reference, f"Mismatch in number of sentences for {split_name} split"
for domain, stats in gold_stats.items():
corpus = flair.datasets.NER_NERMUD(domains=domain)
check_number_sentences(len(corpus.train), stats["train"], "train")
check_number_sentences(len(corpus.dev), stats["dev"], "dev")
@pytest.mark.skip()
def test_german_ler_corpus(tasks_base_path):
corpus = flair.datasets.NER_GERMAN_LEGAL()
# Number of instances per dataset split are taken from https://huggingface.co/datasets/elenanereiss/german-ler
assert len(corpus.train) == 53384, "Mismatch in number of sentences for train split"
assert len(corpus.dev) == 6666, "Mismatch in number of sentences for dev split"
assert len(corpus.test) == 6673, "Mismatch in number of sentences for test split"
@pytest.mark.skip()
def test_masakha_pos_corpus(tasks_base_path):
# This test covers the complete MasakhaPOS dataset.
supported_versions = ["v1"]
supported_languages = {
"v1": [
"bam",
"bbj",
"ewe",
"fon",
"hau",
"ibo",
"kin",
"lug",
"luo",
"mos",
"pcm",
"nya",
"sna",
"swa",
"tsn",
"twi",
"wol",
"xho",
"yor",
"zul",
],
}
masakha_pos_stats = {
"v1": {
"bam": {"train": 775, "dev": 154, "test": 619},
"bbj": {"train": 750, "dev": 149, "test": 599},
"ewe": {"train": 728, "dev": 145, "test": 582},
"fon": {"train": 810, "dev": 161, "test": 646},
"hau": {"train": 753, "dev": 150, "test": 601},
"ibo": {"train": 803, "dev": 160, "test": 642},
"kin": {"train": 757, "dev": 151, "test": 604},
"lug": {"train": 733, "dev": 146, "test": 586},
"luo": {"train": 758, "dev": 151, "test": 606},
"mos": {"train": 757, "dev": 151, "test": 604},
"pcm": {"train": 752, "dev": 150, "test": 600},
"nya": {"train": 728, "dev": 145, "test": 582},
"sna": {"train": 747, "dev": 149, "test": 596},
"swa": {"train": 693, "dev": 138, "test": 553},
"tsn": {"train": 754, "dev": 150, "test": 602},
"twi": {"train": 785, "dev": 157, "test": 628},
"wol": {"train": 782, "dev": 156, "test": 625},
"xho": {"train": 752, "dev": 150, "test": 601},
"yor": {"train": 893, "dev": 178, "test": 713},
"zul": {"train": 753, "dev": 150, "test": 601},
},
}
def check_number_sentences(reference: int, actual: int, split_name: str, language: str, version: str):
assert actual == reference, f"Mismatch in number of sentences for {language}@{version}/{split_name}"
for version in supported_versions:
for language in supported_languages[version]:
corpus = flair.datasets.MASAKHA_POS(languages=language, version=version)
gold_stats = masakha_pos_stats[version][language]
check_number_sentences(len(corpus.train), gold_stats["train"], "train", language, version)
check_number_sentences(len(corpus.dev), gold_stats["dev"], "dev", language, version)
check_number_sentences(len(corpus.test), gold_stats["test"], "test", language, version)
@pytest.mark.skip()
def test_german_mobie(tasks_base_path):
corpus = flair.datasets.NER_GERMAN_MOBIE()
# See MobIE paper (https://aclanthology.org/2021.konvens-1.22/), table 2
ref_sentences = 7_077
ref_tokens = 90_971
actual_sentences = sum(
[1 for sentence in corpus.train + corpus.dev + corpus.test if sentence[0].text != "-DOCSTART-"]
)
actual_tokens = sum(
[len(sentence) for sentence in corpus.train + corpus.dev + corpus.test if sentence[0].text != "-DOCSTART-"]
)
assert ref_sentences == actual_sentences, (
f"Number of parsed sentences ({actual_sentences}) does not match with "
f"reported number of sentences ({ref_sentences})!"
)
assert (
ref_tokens == actual_tokens
), f"Number of parsed tokens ({actual_tokens}) does not match with reported number of tokens ({ref_tokens})!"
@pytest.mark.skip()
def test_bavarian_wiki(tasks_base_path):
corpus = flair.datasets.NER_BAVARIAN_WIKI()
ref_sentences = 3_577
ref_tokens = 75_690
actual_sentences = sum(
[1 for sentence in corpus.train + corpus.dev + corpus.test if sentence[0].text != "-DOCSTART-"]
)
actual_tokens = sum(
[len(sentence) for sentence in corpus.train + corpus.dev + corpus.test if sentence[0].text != "-DOCSTART-"]
)
assert ref_sentences == actual_sentences, (
f"Number of parsed sentences ({actual_sentences}) does not match with "
f"reported number of sentences ({ref_sentences})!"
)
assert (
ref_tokens == actual_tokens
), f"Number of parsed tokens ({actual_tokens}) does not match with reported number of tokens ({ref_tokens})!"
def test_multi_file_jsonl_corpus_should_use_label_type(tasks_base_path):
corpus = MultiFileJsonlCorpus(
train_files=[tasks_base_path / "jsonl/train.jsonl"],
dev_files=[tasks_base_path / "jsonl/testa.jsonl"],
test_files=[tasks_base_path / "jsonl/testb.jsonl"],
label_type="pos",
)
for sentence in corpus.get_all_sentences():
assert sentence.has_label("pos")
assert not sentence.has_label("ner")
def test_jsonl_corpus_should_use_label_type(tasks_base_path):
corpus = JsonlCorpus(tasks_base_path / "jsonl", label_type="pos")
for sentence in corpus.get_all_sentences():
assert sentence.has_label("pos")
assert not sentence.has_label("ner")