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https://github.com/segment-any-text/wtpsplit
Code for Where's the Point? Self-Supervised Multilingual Punctuation-Agnostic Sentence Segmentation
https://github.com/segment-any-text/wtpsplit
deep-learning machine-learning natural-language-processing pretrained-models python sentence-boundary-detection sentence-segmentation sentence-segmenter
Last synced: 19 days ago
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Code for Where's the Point? Self-Supervised Multilingual Punctuation-Agnostic Sentence Segmentation
- Host: GitHub
- URL: https://github.com/segment-any-text/wtpsplit
- Owner: segment-any-text
- License: mit
- Created: 2020-01-31T10:34:17.000Z (almost 5 years ago)
- Default Branch: main
- Last Pushed: 2024-06-25T19:44:51.000Z (5 months ago)
- Last Synced: 2024-06-25T19:50:06.228Z (5 months ago)
- Topics: deep-learning, machine-learning, natural-language-processing, pretrained-models, python, sentence-boundary-detection, sentence-segmentation, sentence-segmenter
- Language: Python
- Homepage: https://aclanthology.org/2023.acl-long.398
- Size: 83 MB
- Stars: 512
- Watchers: 10
- Forks: 34
- Open Issues: 6
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
wtpsplit🪓
Segment any Text - Robustly, Efficiently, Adaptablyâš¡
This repository allows you to segment text into sentences or other semantic units. It implements the models from:
- **SaT** — [Segment Any Text: A Universal Approach for Robust, Efficient and Adaptable Sentence Segmentation](https://arxiv.org/abs/2406.16678) by Markus Frohmann, Igor Sterner, Benjamin Minixhofer, Ivan Vulić and Markus Schedl (**state-of-the-art, encouraged**).
- **WtP** — [Where’s the Point? Self-Supervised Multilingual Punctuation-Agnostic Sentence Segmentation](https://aclanthology.org/2023.acl-long.398/) by Benjamin Minixhofer, Jonas Pfeiffer and Ivan Vulić (*previous version, maintained for reproducibility*).
The namesake WtP is maintained for consistency. Our new followup SaT provides robust, efficient and adaptable sentence segmentation across 85 languages at higher performance and less compute cost. Check out the **state-of-the-art** results in 8 distinct corpora and 85 languages demonstrated in our [Segment any Text paper](https://arxiv.org/abs/2406.16678).
## Installation
```bash
pip install wtpsplit
```
## Usage
```python
from wtpsplit import SaT
sat = SaT("sat-3l")
# optionally run on GPU for better performance
# also supports TPUs via e.g. sat.to("xla:0"), in that case pass `pad_last_batch=True` to sat.split
sat.half().to("cuda")
sat.split("This is a test This is another test.")
# returns ["This is a test ", "This is another test."]
# do this instead of calling sat.split on every text individually for much better performance
sat.split(["This is a test This is another test.", "And some more texts..."])
# returns an iterator yielding lists of sentences for every text
# use our '-sm' models for general sentence segmentation tasks
sat_sm = SaT("sat-3l-sm")
sat_sm.half().to("cuda") # optional, see above
sat_sm.split("this is a test this is another test")
# returns ["this is a test ", "this is another test"]
# use trained lora modules for strong adaptation to language & domain/style
sat_adapted = SaT("sat-3l", style_or_domain="ud", language="en")
sat_adapted.half().to("cuda") # optional, see above
sat_adapted.split("This is a test This is another test.")
# returns ['This is a test ', 'This is another test']
```
## ONNX Support
🚀 You can now enable even faster ONNX inference for `sat` and `sat-sm` models! 🚀
```python
sat = SaT("sat-3l-sm", ort_providers=["CUDAExecutionProvider", "CPUExecutionProvider"])
```
```python
>>> from wtpsplit import SaT
>>> texts = ["This is a sentence. This is another sentence."] * 1000
# PyTorch GPU
>>> model_pytorch = SaT("sat-3l-sm")
>>> model_pytorch.half().to("cuda");
>>> %timeit list(model_pytorch.split(texts))
# 144 ms ± 252 μs per loop (mean ± std. dev. of 7 runs, 10 loops each)
# quite fast already, but...
# onnxruntime GPU
>>> model_ort = SaT("sat-3l-sm", ort_providers=["CUDAExecutionProvider", "CPUExecutionProvider"])
>>> %timeit list(model_ort.split(texts))
# 94.9 ms ± 165 μs per loop (mean ± std. dev. of 7 runs, 10 loops each
# ...this should be ~50% faster! (tested on RTX 3090)
```
If you wish to use LoRA in combination with an ONNX model:
- Run `scripts/export_to_onnx_sat.py` with `use_lora: True` and an appropriate `output_dir: `.
- If you have a local LoRA module, use `lora_path`.
- If you wish to load a LoRA module from the HuggingFace hub, use `style_or_domain` and `language`.
- Load the ONNX model with merged LoRA weights:
`sat = SaT(, onnx_providers=["CUDAExecutionProvider", "CPUExecutionProvider"])`
## Available Models
If you need a general sentence segmentation model, use `-sm` models (e.g., `sat-3l-sm`)
For speed-sensitive applications, we recommend 3-layer models (`sat-3l` and `sat-3l-sm`). They provide a great tradeoff between speed and performance.
The best models are our 12-layer models: `sat-12l` and `sat-12l-sm`.
| Model | English Score | Multilingual Score
|:-----------------------------------------------------------------------|-----:|-----:|
| [sat-1l](https://huggingface.co/segment-any-text/sat-1l) | 88.5 | 84.3
| [sat-1l-sm](https://huggingface.co/segment-any-text/sat-1l-sm) | 88.2 | 87.9
| [sat-3l](https://huggingface.co/segment-any-text/sat-3l) | 93.7 | 89.2
| [sat-3l-lora](https://huggingface.co/segment-any-text/sat-3l/tree/main/loras) | 96.7 | 94.8
| [sat-3l-sm](https://huggingface.co/segment-any-text/sat-3l-sm) | 96.5 | 93.5
| [sat-6l](https://huggingface.co/segment-any-text/sat-6l) | 94.1 | 89.7
| [sat-6l-sm](https://huggingface.co/segment-any-text/sat-6l-sm) | 96.9 | 95.1
| [sat-9l](https://huggingface.co/segment-any-text/sat-9l) | 94.3 | 90.3
| [sat-12l](https://huggingface.co/segment-any-text/sat-12l) | 94.0 | 90.4
| [sat-12l-lora](https://huggingface.co/segment-any-text/sat-12l/tree/main/loras) | 97.3 | 95.9
| [sat-12l-sm](https://huggingface.co/segment-any-text/sat-12l-sm) | 97.4 | 96.0
The scores are macro-average F1 score across all available datasets for "English", and macro-average F1 score across all datasets and languages for "Multilingual". "adapted" means adapation via LoRA; check out the [paper](https://arxiv.org/abs/2406.16678) for details.
For comparison, here the English scores of some other tools:
| Model | English Score
|:-----------------------------------------------------------------------|-----:|
| PySBD | 69.6 |
| SpaCy (sentencizer; monolingual) | 92.9 |
| SpaCy (sentencizer; multilingual) | 91.5 |
| Ersatz | 91.4 |
| Punkt (`nltk.sent_tokenize`) | 92.2 |
| [WtP (3l)](https://huggingface.co/benjamin/wtp-canine-s-3l) | 93.9 |
Note that this library also supports previous [`WtP`](https://arxiv.org/abs/2305.18893) models.
You can use them in essentially the same way as `SaT`models:
```python
from wtpsplit import WtP
wtp = WtP("wtp-bert-mini")
# similar functionality as for SaT models
wtp.split("This is a test This is another test.")
```
For more details on WtP and reproduction details, see the [WtP doc](./README_WTP.md).
## Paragraph Segmentation
Since SaT are trained to predict newline probablity, they can segment text into paragraphs in addition to sentences.
```python
# returns a list of paragraphs, each containing a list of sentences
# adjust the paragraph threshold via the `paragraph_threshold` argument.
sat.split(text, do_paragraph_segmentation=True)
```
## Adaptation
SaT can be domain- and style-adapted via LoRA. We provide trained LoRA modules for Universal Dependencies, OPUS100, Ersatz, and TED (i.e., ASR-style transcribed speecjes) sentence styles in 81 languages for `sat-3l`and `sat-12l`. Additionally, we provide LoRA modules for legal documents (laws and judgements) in 6 languages, code-switching in 4 language pairs, and tweets in 3 languages. For details, we refer to our [paper](https://arxiv.org/abs/2406.16678).
We also provided verse segmentation modules for 16 genres for `sat-12-no-limited-lookahead`.
Load LoRA modules like this:
```python
# requires both lang_code and style_or_domain
# for available ones, check the /loras folder
sat_lora = SaT("sat-3l", style_or_domain="ud", language="en")
sat_lora.split("Hello this is a test But this is different now Now the next one starts looool")
# now for a highly distinct domain
sat_lora_distinct = SaT("sat-12l", style_or_domain="code-switching", language="es-en")
sat_lora_distinct.split("in the morning over there cada vez que yo decÃa algo él me decÃa algo")
```
You can also freely adapt the segmentation threshold, with a higher threshold leading to more conservative segmentation:
```python
sat.split("This is a test This is another test.", threshold=0.4)
# works similarly for lora; but thresholds are higher
sat_lora.split("Hello this is a test But this is different now Now the next one starts looool", threshold=0.7)
```
## Advanced Usage
### Get the newline or sentence boundary probabilities for a text:
```python
# returns newline probabilities (supports batching!)
sat.predict_proba(text)
```
### Load a SaT model in [HuggingFace `transformers`](https://github.com/huggingface/transformers):
```python
# import library to register the custom models
import wtpsplit
from transformers import AutoModelForTokenClassification
model = AutoModelForTokenClassification.from_pretrained("segment-any-text/sat-3l-sm") # or some other model name; see https://huggingface.co/segment-any-text
```
### Adapt to your own corpus via LoRA
Our models can be efficiently adapted via LoRA in a powerful way. Only 10-100 training segmented training sentences should already improve performance considerably. To do so:
Clone the repository and install requirements:
```
git clone https://github.com/segment-any-text/wtpsplit
cd wtpsplit
pip install -r requirements.txt
pip install adapters==0.2.1 --no-dependencies
cd ..
```
Create data in this format:
```python
import torch
torch.save(
{
"language_code": {
"sentence": {
"dummy-dataset": {
"meta": {
"train_data": ["train sentence 1", "train sentence 2"],
},
"data": [
"test sentence 1",
"test sentence 2",
]
}
}
}
},
"dummy-dataset.pth"
)
```
Create/adapt config; provide base model via `model_name_or_path` and training data .pth via `text_path`:
`configs/lora/lora_dummy_config.json`
Train LoRA:
```
python3 wtpsplit/train/train_lora.py configs/lora/lora_dummy_config.json
```
Once training is done, provide your saved module's path to SaT:
```python
sat_lora_adapted = SaT("model-used", lora_path="dummy_lora_path")
sat_lora_adapted.split("Some domains-specific or styled text")
```
Adjust the dataset name, language and model in the above to your needs.
## Reproducing the paper
`configs/` contains the configs for the runs from the paper for base and sm models as well as LoRA modules. Launch training for each of them like this:
```
python3 wtpsplit/train/train.py configs/.json
python3 wtpsplit/train/train_sm.py configs/.json
python3 wtpsplit/train/train_lora.py configs/.json
```
In addition:
- `wtpsplit/data_acquisition` contains the code for obtaining evaluation data and raw text from the mC4 corpus.
- `wtpsplit/evaluation` contains the code for:
- evaluation (i.e. sentence segmentation results) via `intrinsic.py`.
- short-sequence evaluation (i.e. sentence segmentation results for pairs/k-mers of sentences) via `intrinsic_pairwise.py`.
- LLM baseline evaluation (`llm_sentence.py`), legal baseline evaluation (`legal_baselines.py`)
- baseline (PySBD, nltk, etc.) evaluation results in `intrinsic_baselines.py` and `intrinsic_baselines_multi.py`
- Raw results in JSON format are also in `evaluation_results/`
- Statistical significane testing code and results ara in `stat_tests/`
- punctuation annotation experiments in `punct_annotation.py` and `punct_annotation_wtp.py` (WtP only)
- extrinsic evaluation on Machine Translation in `extrinsic.py` (WtP only)
Ensure to install packages from `requirements.txt` beforehand.
## Supported Languages
Table with supported languages
| iso | Name |
|:----|:-----------------------|
| af | Afrikaans |
| am | Amharic |
| ar | Arabic |
| az | Azerbaijani |
| be | Belarusian |
| bg | Bulgarian |
| bn | Bengali |
| ca | Catalan |
| ceb | Cebuano |
| cs | Czech |
| cy | Welsh |
| da | Danish |
| de | German |
| el | Greek |
| en | English |
| eo | Esperanto |
| es | Spanish |
| et | Estonian |
| eu | Basque |
| fa | Persian |
| fi | Finnish |
| fr | French |
| fy | Western Frisian |
| ga | Irish |
| gd | Scottish Gaelic |
| gl | Galician |
| gu | Gujarati |
| ha | Hausa |
| he | Hebrew |
| hi | Hindi |
| hu | Hungarian |
| hy | Armenian |
| id | Indonesian |
| ig | Igbo |
| is | Icelandic |
| it | Italian |
| ja | Japanese |
| jv | Javanese |
| ka | Georgian |
| kk | Kazakh |
| km | Central Khmer |
| kn | Kannada |
| ko | Korean |
| ku | Kurdish |
| ky | Kirghiz |
| la | Latin |
| lt | Lithuanian |
| lv | Latvian |
| mg | Malagasy |
| mk | Macedonian |
| ml | Malayalam |
| mn | Mongolian |
| mr | Marathi |
| ms | Malay |
| mt | Maltese |
| my | Burmese |
| ne | Nepali |
| nl | Dutch |
| no | Norwegian |
| pa | Panjabi |
| pl | Polish |
| ps | Pushto |
| pt | Portuguese |
| ro | Romanian |
| ru | Russian |
| si | Sinhala |
| sk | Slovak |
| sl | Slovenian |
| sq | Albanian |
| sr | Serbian |
| sv | Swedish |
| ta | Tamil |
| te | Telugu |
| tg | Tajik |
| th | Thai |
| tr | Turkish |
| uk | Ukrainian |
| ur | Urdu |
| uz | Uzbek |
| vi | Vietnamese |
| xh | Xhosa |
| yi | Yiddish |
| yo | Yoruba |
| zh | Chinese |
| zu | Zulu |
For details, please see our [Segment any Text paper](https://arxiv.org/abs/2406.16678).
## Citations
For the `SaT` models, please kindly cite our paper:
```
@article{frohmann2024segment,
title={Segment Any Text: A Universal Approach for Robust, Efficient and Adaptable Sentence Segmentation},
author={Frohmann, Markus and Sterner, Igor and Vuli{\'c}, Ivan and Minixhofer, Benjamin and Schedl, Markus},
journal={arXiv preprint arXiv:2406.16678},
year={2024},
doi={10.48550/arXiv.2406.16678},
url={https://doi.org/10.48550/arXiv.2406.16678},
}
```
For the library and the WtP models, please cite:
```
@inproceedings{minixhofer-etal-2023-wheres,
title = "Where{'}s the Point? Self-Supervised Multilingual Punctuation-Agnostic Sentence Segmentation",
author = "Minixhofer, Benjamin and
Pfeiffer, Jonas and
Vuli{\'c}, Ivan",
booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.acl-long.398",
pages = "7215--7235"
}
```
## Acknowledgments
This research was funded in whole or in part by the Austrian Science Fund (FWF): P36413, P33526, and DFH-23, and by the State of Upper Austria and the Federal Ministry of Education, Science, and Research, through grants LIT-2021-YOU-215. In addition, Ivan Vulic and Benjamin Minixhofer have been supported through the Royal Society University Research Fellowship ‘Inclusive and Sustainable Language Technology for a Truly Multilingual World’ (no 221137) awarded to Ivan Vulić. This research has also been supported with Cloud TPUs from Google’s TPU Research Cloud (TRC). This work was also supported by compute credits from a Cohere For AI Research Grant, these grants are designed to support academic partners conducting research with the goal of releasing scientific artifacts and data for good projects. We also thank Simone Teufel for fruitful discussions.
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