naeval

Naeval — comparing quality and performance of NLP systems for Russian language. Naeval is used to evaluate project Natasha components: Razdel, Navec, Slovnet:

Tokenization

See Razdel evalualtion section for more info.

	corpora	syntag	gicrya	rnc
	errors	time	errors	time	errors	time	errors	time
re.findall(\w+\|\d+\|\p+)	4161	0.5	2660	0.5	2277	0.4	7606	0.4
spacy	4388	6.2	2103	5.8	1740	4.1	4057	3.9
nltk.word_tokenize	14245	3.4	60893	3.3	13496	2.7	41485	2.9
mystem	4514	5.0	3153	4.7	2497	3.7	2028	3.9
mosestokenizer	1886	2.1	1330	1.9	1796	1.6	2123	1.7
segtok.word_tokenize	2772	2.3	1288	2.3	1759	1.8	1229	1.8
aatimofeev/spacy_russian_tokenizer	2930	48.7	719	51.1	678	39.5	2681	52.2
koziev/rutokenizer	2627	1.1	1386	1.0	2893	0.8	9411	0.9
razdel.tokenize	1510	2.9	1483	2.8	322	2.0	2124	2.2

Sentence segmentation

	corpora	syntag	gicrya	rnc
	errors	time	errors	time	errors	time	errors	time
re.split([.?!…])	20456	0.9	6576	0.6	10084	0.7	23356	1.0
segtok.split_single	19008	17.8	4422	13.4	159738	1.1	164218	2.8
mosestokenizer	41666	8.9	22082	5.7	12663	6.4	50560	7.4
nltk.sent_tokenize	16420	10.1	4350	5.3	7074	5.6	32534	8.9
deeppavlov/rusenttokenize	10192	10.9	1210	7.9	8910	6.8	21410	7.0
razdel.sentenize	9274	6.1	824	3.9	11414	4.5	10594	7.5

Pretrained embeddings

See Navec evalualtion section for more info.

	type	init, s	get, µs	disk, mb	ram, mb	vocab
ruscorpora_upos_cbow_300_20_2019	w2v	12.1	1.6	220.6	236.1	189K
ruwikiruscorpora_upos_skipgram_300_2_2019	w2v	15.7	1.7	290.0	309.4	248K
tayga_upos_skipgram_300_2_2019	w2v	15.7	1.2	290.7	310.9	249K
tayga_none_fasttextcbow_300_10_2019	fasttext	11.3	14.3	2741.9	2746.9	192K
araneum_none_fasttextcbow_300_5_2018	fasttext	7.8	15.4	2752.1	2754.7	195K
hudlit_12B_500K_300d_100q	navec	1.0	19.9	50.6	95.3	500K
news_1B_250K_300d_100q	navec	0.5	20.3	25.4	47.7	250K

	type	simlex	hj	rt	ae	ae2	lrwc
ruscorpora_upos_cbow_300_20_2019	w2v	0.359	0.685	0.852	0.758	0.896	0.602
ruwikiruscorpora_upos_skipgram_300_2_2019	w2v	0.321	0.723	0.817	0.801	0.860	0.629
tayga_upos_skipgram_300_2_2019	w2v	0.429	0.749	0.871	0.771	0.899	0.639
tayga_none_fasttextcbow_300_10_2019	fasttext	0.369	0.639	0.793	0.682	0.813	0.536
araneum_none_fasttextcbow_300_5_2018	fasttext	0.349	0.671	0.801	0.706	0.793	0.579
hudlit_12B_500K_300d_100q	navec	0.310	0.707	0.842	0.931	0.923	0.604
news_1B_250K_300d_100q	navec	0.230	0.590	0.784	0.866	0.861	0.589

Morphology taggers

	news	wiki	fiction	social	poetry
rupostagger	0.673	0.645	0.661	0.641	0.636
rnnmorph	0.896	0.812	0.890	0.860	0.838
maru	0.894	0.808	0.887	0.861	0.840
udpipe	0.918	0.811	0.957	0.870	0.776
spacy	0.919	0.812	0.938	0.836	0.729
deeppavlov	0.940	0.841	0.944	0.870	0.857
deeppavlov_bert	0.951	0.868	0.964	0.892	0.865

	init, s	disk, mb	ram, mb	speed, it/s
rupostagger	4.8	3	118	48.0
rnnmorph	8.7	10	289	16.6
maru	15.8	44	370	36.4
udpipe	6.9	45	242	56.2
spacy	10.9	89	579	30.6
deeppavlov	4.0	32	10240	90.0 (gpu)
deeppavlov_bert	20.0	1393	8704	85.0 (gpu)

Syntax parser

	news	wiki	fiction	social	poetry
	uas	las	uas	las	uas	las	uas	las	uas	las
udpipe	0.873	0.823	0.622	0.531	0.910	0.876	0.700	0.624	0.625	0.534
spacy	0.876	0.818	0.770	0.665	0.880	0.833	0.757	0.666	0.657	0.544
deeppavlov_bert	0.962	0.910	0.882	0.786	0.963	0.929	0.844	0.761	0.784	0.691

	init, s	disk, mb	ram, mb	speed, it/s
udpipe	6.9	45	242	56.2
spacy	10.9	89	579	31.6
deeppavlov_bert	34.0	1427	8704	75.0 (gpu)

NER

See Slovnet evalualtion section for more info.

	factru	gareev	ne5	bsnlp
f1	PER	LOC	ORG	PER	ORG	PER	LOC	ORG	PER	LOC	ORG
deeppavlov	0.910	0.886	0.742	0.944	0.798	0.942	0.919	0.881	0.866	0.767	0.624
deeppavlov_bert	0.971	0.928	0.825	0.980	0.916	0.997	0.990	0.976	0.954	0.840	0.741
pullenti	0.905	0.814	0.686	0.939	0.639	0.952	0.862	0.683	0.900	0.769	0.566
texterra	0.900	0.800	0.597	0.888	0.561	0.901	0.777	0.594	0.858	0.783	0.548
tomita	0.929			0.921		0.945			0.881
natasha	0.867	0.753	0.297	0.873	0.347	0.852	0.709	0.394	0.836	0.755	0.350
mitie	0.888	0.861	0.532	0.849	0.452	0.753	0.642	0.432	0.736	0.801	0.524

	init, s	disk, mb	ram, mb	speed, articles/s
deeppavlov	5.9	1024	3072	24.3 (gpu)
deeppavlov_bert	34.5	2048	6144	13.1 (gpu)
pullenti	2.9	16	253	6.0
texterra	47.6	193	3379	4.0
tomita	2.0	64	63	29.8
natasha	2.0	1	160	8.8
mitie	28.3	327	261	32.8

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naeval 0.2.0