metadata

license: mit
base_model: xlm-roberta-base
language:
  - multilingual
  - af
  - am
  - ar
  - as
  - az
  - be
  - bg
  - bn
  - br
  - bs
  - ca
  - cs
  - cy
  - da
  - de
  - el
  - en
  - eo
  - es
  - et
  - eu
  - fa
  - fi
  - fr
  - fy
  - ga
  - gd
  - gl
  - gu
  - ha
  - he
  - hi
  - hr
  - hu
  - hy
  - id
  - is
  - it
  - ja
  - jv
  - ka
  - kk
  - km
  - kn
  - ko
  - ku
  - ky
  - la
  - lo
  - lt
  - lv
  - mg
  - mk
  - ml
  - mn
  - mr
  - ms
  - my
  - ne
  - nl
  - 'no'
  - om
  - or
  - pa
  - pl
  - ps
  - pt
  - ro
  - ru
  - sa
  - sd
  - si
  - sk
  - sl
  - so
  - sq
  - sr
  - su
  - sv
  - sw
  - ta
  - te
  - th
  - tl
  - tr
  - ug
  - uk
  - ur
  - uz
  - vi
  - xh
  - yi
  - zh
metrics:
  - f1

⚠️ Warning: An updated version of this model is available here This model is no longer maintained.

Please refer to our Segment any Text paper for more details: https://arxiv.org/abs/2406.16678

xlmr-multilingual-sentence-segmentation

This model is a fine-tuned version of xlm-roberta-base on a corrupted version of the universal dependency datasets. It achieves the following results on the (also corrupted) evaluation set:

Loss: 0.0074
Precision: 0.9664
Recall: 0.9677
F1: 0.9670

Test set performance

Results

All results here are percentage F1:

Opus100 [2]

Who wins most? XLM-RoBERTa: 56, WtPSplit: 12, Spacy (multilingual): 8

	af	am	ar	az	be	bg	bn	ca	cs	cy	da	de	el	en	eo	es	et	eu	fa	fi	fr	fy	ga	gd	gl	gu	ha	he	hi	hu	hy	id	is	it	ja	ka	kk	km	kn	ko	ku	ky	lt	lv	mg	mk	ml	mn	mr	ms	my	ne	nl	pa	pl	ps	pt	ro	ru	si	sk	sl	sq	sr	sv	ta	te	th	tr	uk	ur	uz	vi	xh	yi	zh
Spacy (multilingual)	42.61	6.69	58.52	73.59	34.78	93.74	38.04	88.76	87.70	26.30	90.52	74.15	89.75	89.25	88.77	90.95	87.26	81.20	55.40	93.28	85.77	21.49	60.61	36.83	88.77	5.59	89.39	92.21	53.33	93.26	24.14	90.13	95.38	86.32	0.20	38.24	42.39	0.10	9.66	51.79	27.64	21.77	76.91	77.02	83.60	93.74	39.09	33.23	86.56	87.39	0.10	6.59	93.65	5.26	92.42	2.41	92.07	91.63	75.95	75.91	92.13	93.00	92.96	95.01	93.52	36.97	64.59	21.64	94.05	89.68	29.17	64.99	90.59	64.89	4.14	0.09
WtPSplit	76.90	59.08	68.08	76.42	71.29	93.97	79.76	89.79	89.36	73.21	90.02	80.74	92.80	91.91	92.24	92.11	84.47	87.24	59.97	91.96	88.53	65.84	79.49	83.33	90.31	70.51	82.43	90.58	66.70	93.00	87.14	89.80	94.77	87.43	41.79	91.26	73.25	69.54	68.98	56.21	79.12	83.94	81.33	82.70	89.33	92.87	80.81	73.26	89.20	88.51	65.54	71.33	92.63	64.11	92.72	62.84	91.05	90.91	84.23	80.32	92.30	92.19	90.32	94.76	92.08	63.48	76.49	68.88	93.30	89.60	52.59	77.79	91.29	80.28	75.70	71.64
XLM-RoBERTa (ours)	83.97	41.59	81.56	81.30	85.68	94.34	84.10	91.80	91.23	78.72	92.64	86.73	93.87	94.50	94.57	93.18	90.19	90.28	74.79	94.06	90.46	81.76	84.33	85.62	92.55	67.26	86.61	91.22	72.69	94.53	89.83	92.24	93.78	89.27	41.43	78.39	89.15	36.60	70.51	82.77	58.14	89.41	89.99	88.25	86.82	92.81	86.14	94.73	93.25	92.44	49.39	66.02	93.60	69.22	93.51	61.86	92.84	93.19	89.47	86.24	92.95	93.46	91.79	94.16	93.93	72.74	81.77	74.49	93.17	92.15	62.92	75.65	93.41	84.89	56.85	77.07

Universal Dependencies [3]

Who wins most? XLM-RoBERTa: 24, WtPSplit: 17 Spacy (multilingual): 13

	af	ar	be	bg	bn	ca	cs	cy	da	de	el	en	es	et	eu	fa	fi	fr	ga	gd	gl	he	hi	hu	hy	id	is	it	ja	jv	kk	ko	la	lt	lv	mr	nl	pl	pt	ro	ru	sk	sl	sq	sr	sv	ta	th	tr	uk	ur	vi	zh
Spacy (multilingual)	98.47	80.38	80.27	93.62	51.85	98.95	89.68	98.89	94.96	88.02	94.16	92.20	98.70	93.77	95.79	99.83	92.88	96.33	96.67	63.04	92.37	94.37	0.32	98.45	11.39	98.01	95.41	92.49	0.37	98.03	96.21	99.80	0.09	93.86	98.52	92.13	92.86	97.02	94.91	98.05	84.31	90.26	98.23	100.00	97.84	94.91	66.67	1.95	97.63	94.16	0.37	96.40	0.40
WtPSplit	98.27	83.00	89.28	98.16	99.12	98.52	92.98	99.26	94.56	96.13	96.94	94.73	97.60	94.09	97.24	97.29	94.69	96.71	86.60	72.17	98.87	95.79	96.78	96.08	96.80	98.41	86.39	95.45	95.84	98.18	96.28	99.11	91.43	97.67	96.42	91.84	93.61	95.92	96.13	81.50	86.28	95.57	96.85	99.17	98.45	95.86	97.54	70.26	96.00	92.08	93.79	92.97	97.25
XLM-RoBERTa (ours)	96.81	78.99	91.60	97.89	99.12	95.99	96.05	97.17	96.62	96.29	94.33	94.76	95.73	96.20	97.37	97.49	96.34	95.70	89.78	84.20	95.72	95.95	97.51	96.24	95.62	97.22	92.93	96.88	94.23	96.29	98.40	97.46	96.35	95.82	96.91	95.92	96.27	97.24	95.83	94.63	91.59	95.88	96.43	98.36	96.83	94.95	95.93	89.26	96.52	94.59	96.20	97.31	95.12

Ersatz [4]

Who wins most? XLM-RoBERTa: 10, WtPSplit: 8, Spacy (multilingual): 4

	ar	cs	de	en	es	et	fi	fr	gu	hi	ja	kk	km	lt	lv	pl	ps	ro	ru	ta	tr	zh
Spacy (multilingual)	91.26	96.46	93.89	94.40	97.31	97.15	94.99	96.43	4.44	18.41	0.18	97.11	0.08	93.53	98.73	93.69	94.44	94.87	93.45	68.65	95.39	0.10
WtPSplit	89.45	93.41	95.93	97.16	98.74	95.84	97.10	97.61	90.62	94.87	82.14	95.94	82.89	96.74	97.22	95.16	86.99	97.55	97.82	94.76	93.53	89.02
XLM-RoBERTa (ours)	79.78	96.94	97.02	96.10	97.06	96.80	97.67	96.33	93.73	95.34	77.54	97.28	78.94	96.13	96.45	96.71	92.33	96.24	97.15	95.94	95.76	90.11

German--English code-switching [5]

	de
Spacy (multilingual)	79.55
WtPSplit	77.41
XLM-RoBERTa (ours)	85.78

[1] Where’s the Point? Self-Supervised Multilingual Punctuation-Agnostic Sentence Segmentation (Minixhofer et al., ACL 2023)

[2] Improving Massively Multilingual Neural Machine Translation and Zero-Shot Translation (Zhang et al., ACL 2020)

[3] Universal Dependencies (de Marneffe et al., CL 2021)

[4] A unified approach to sentence segmentation of punctuated text in many languages (Wicks & Post, ACL-IJCNLP 2021)

[5] The Denglisch Corpus of German-English Code-Switching (Osmelak & Wintner, SIGTYP 2023)

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 2e-05
train_batch_size: 64
eval_batch_size: 64
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 5

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1
No log	0.2	100	0.0125	0.9320	0.9487	0.9403
No log	0.4	200	0.0099	0.9547	0.9513	0.9530
No log	0.6	300	0.0092	0.9616	0.9506	0.9561
No log	0.81	400	0.0083	0.9584	0.9618	0.9601
0.0212	1.01	500	0.0082	0.9551	0.9642	0.9596
0.0212	1.21	600	0.0084	0.9630	0.9614	0.9622
0.0212	1.41	700	0.0079	0.9606	0.9648	0.9627
0.0212	1.61	800	0.0077	0.9609	0.9661	0.9635
0.0212	1.81	900	0.0076	0.9623	0.9649	0.9636
0.0067	2.02	1000	0.0077	0.9598	0.9689	0.9643
0.0067	2.22	1100	0.0075	0.9614	0.9680	0.9647
0.0067	2.42	1200	0.0073	0.9626	0.9682	0.9654
0.0067	2.62	1300	0.0075	0.9617	0.9692	0.9654
0.0067	2.82	1400	0.0073	0.9658	0.9648	0.9653
0.0054	3.02	1500	0.0076	0.9656	0.9663	0.9660
0.0054	3.23	1600	0.0073	0.9625	0.9703	0.9664
0.0054	3.43	1700	0.0073	0.9658	0.9659	0.9658
0.0054	3.63	1800	0.0073	0.9626	0.9707	0.9666
0.0054	3.83	1900	0.0073	0.9659	0.9677	0.9668
0.0046	4.03	2000	0.0075	0.9671	0.9659	0.9665
0.0046	4.23	2100	0.0075	0.9654	0.9687	0.9671
0.0046	4.44	2200	0.0075	0.9662	0.9676	0.9669
0.0046	4.64	2300	0.0074	0.9657	0.9684	0.9670
0.0046	4.84	2400	0.0074	0.9664	0.9678	0.9671

Framework versions

Transformers 4.39.1
Pytorch 2.2.1+cu121
Datasets 2.18.0
Tokenizers 0.15.2

Citation

Please consider citing our paper if this model has helped you:

@inproceedings{frohman-etal-2024-segment,
    title = "Segment Any Text: A Universal Approach for Robust, Efficient and Adaptable Sentence Segmentation",
    author={Markus Frohmann and Igor Sterner and Ivan Vulić and Benjamin Minixhofer and Markus Schedl},
    month = nov,
    year = "2024",
    publisher = "Association for Computational Linguistics",
}