File size: 15,085 Bytes
9f3bef1 12250e1 9f3bef1 2509365 12250e1 9f3bef1 12250e1 9f3bef1 12250e1 9f3bef1 12250e1 9f3bef1 12250e1 9f3bef1 12250e1 9f3bef1 12250e1 9f3bef1 12250e1 9f3bef1 12250e1 9f3bef1 0baa6cf |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 |
---
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An updated version of this model is available [here](https://huggingface.co/segment-any-text/sat-12l-sm) This model is no longer maintained.**
**Please refer to our Segment any Text paper for more details: [https://arxiv.org/abs/2406.16678](https://arxiv.org/abs/2406.16678)**
# xlmr-multilingual-sentence-segmentation
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/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](https://aclanthology.org/2023.acl-long.398) (Minixhofer et al., ACL 2023)
[2] [Improving Massively Multilingual Neural Machine Translation and Zero-Shot Translation](https://aclanthology.org/2020.acl-main.148) (Zhang et al., ACL 2020)
[3] [Universal Dependencies](https://aclanthology.org/2021.cl-2.11) (de Marneffe et al., CL 2021)
[4] [A unified approach to sentence segmentation of punctuated text in many languages](https://aclanthology.org/2021.acl-long.309) (Wicks & Post, ACL-IJCNLP 2021)
[5] [The Denglisch Corpus of German-English Code-Switching](https://aclanthology.org/2023.sigtyp-1.5) (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",
}
``` |