POET / README.md

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	---
	tags:
	- Transformers
	- token-classification
	- sequence-tagger-model
	language: fr
	datasets:
	- qanastek/ANTILLES
	widget:
	- text: "George Washington est allé à Washington"
	---

	# POET: A French Extended Part-of-Speech Tagger

	- Corpora: [ANTILLES](https://github.com/qanastek/ANTILLES)
	- Embeddings & Sequence Labelling: [CamemBERT](https://arxiv.org/abs/1911.03894)
	- Number of Epochs: 115

	People Involved

	* [LABRAK Yanis](https://www.linkedin.com/in/yanis-labrak-8a7412145/) (1)
	* [DUFOUR Richard](https://cv.archives-ouvertes.fr/richard-dufour) (2)

	Affiliations

	1. [LIA, NLP team](https://lia.univ-avignon.fr/), Avignon University, Avignon, France.
	2. [LS2N, TALN team](https://www.ls2n.fr/equipe/taln/), Nantes University, Nantes, France.

	## Demo: How to use in HuggingFace Transformers

	Requires [transformers](https://pypi.org/project/transformers/): ```pip install transformers```

	```python
	from transformers import CamembertTokenizer, CamembertForTokenClassification, TokenClassificationPipeline

	tokenizer = CamembertTokenizer.from_pretrained('taln-ls2n/POET')
	model = CamembertForTokenClassification.from_pretrained('taln-ls2n/POET')
	pos = TokenClassificationPipeline(model=model, tokenizer=tokenizer)

	def make_prediction(sentence):
	labels = [l['entity'] for l in pos(sentence)]
	return list(zip(sentence.split(" "), labels))

	res = make_prediction("George Washington est allé à Washington")
	```

	Output:

	![Preview Output](preview.PNG)

	## Training data

	`ANTILLES` is a part-of-speech tagging corpora based on [UD_French-GSD](https://universaldependencies.org/treebanks/fr_gsd/index.html) which was originally created in 2015 and is based on the [universal dependency treebank v2.0](https://github.com/ryanmcd/uni-dep-tb).

	Originally, the corpora consists of 400,399 words (16,341 sentences) and had 17 different classes. Now, after applying our tags augmentation we obtain 60 different classes which add linguistic and semantic information such as the gender, number, mood, person, tense or verb form given in the different CoNLL-03 fields from the original corpora.

	We based our tags on the level of details given by the [LIA_TAGG](http://pageperso.lif.univ-mrs.fr/frederic.bechet/download.html) statistical POS tagger written by [Frédéric Béchet](http://pageperso.lif.univ-mrs.fr/frederic.bechet/index-english.html) in 2001.

	The corpora used for this model is available on [Github](https://github.com/qanastek/ANTILLES) at the [CoNLL-U format](https://universaldependencies.org/format.html).

	Training data are fed to the model as free language and doesn't pass a normalization phase. Thus, it's made the model case and punctuation sensitive.

	## Original Tags

	```plain
	PRON VERB SCONJ ADP CCONJ DET NOUN ADJ AUX ADV PUNCT PROPN NUM SYM PART X INTJ
	```

	## New additional POS tags

	\| Abbreviation \| Description \| Examples \|
	\|:--------:\|:--------:\|:--------:\|
	\| PREP \| Preposition \| de \|
	\| AUX \| Auxiliary Verb \| est \|
	\| ADV \| Adverb \| toujours \|
	\| COSUB \| Subordinating conjunction \| que \|
	\| COCO \| Coordinating Conjunction \| et \|
	\| PART \| Demonstrative particle \| -t \|
	\| PRON \| Pronoun \| qui ce quoi \|
	\| PDEMMS \| Demonstrative Pronoun - Singular Masculine \| ce \|
	\| PDEMMP \| Demonstrative Pronoun - Plural Masculine \| ceux \|
	\| PDEMFS \| Demonstrative Pronoun - Singular Feminine \| cette \|
	\| PDEMFP \| Demonstrative Pronoun - Plural Feminine \| celles \|
	\| PINDMS \| Indefinite Pronoun - Singular Masculine \| tout \|
	\| PINDMP \| Indefinite Pronoun - Plural Masculine \| autres \|
	\| PINDFS \| Indefinite Pronoun - Singular Feminine \| chacune \|
	\| PINDFP \| Indefinite Pronoun - Plural Feminine \| certaines \|
	\| PROPN \| Proper noun \| Houston \|
	\| XFAMIL \| Last name \| Levy \|
	\| NUM \| Numerical Adjective \| trentaine vingtaine \|
	\| DINTMS \| Masculine Numerical Adjective \| un \|
	\| DINTFS \| Feminine Numerical Adjective \| une \|
	\| PPOBJMS \| Pronoun complements of objects - Singular Masculine \| le lui \|
	\| PPOBJMP \| Pronoun complements of objects - Plural Masculine \| eux y \|
	\| PPOBJFS \| Pronoun complements of objects - Singular Feminine \| moi la \|
	\| PPOBJFP \| Pronoun complements of objects - Plural Feminine \| en y \|
	\| PPER1S \| Personal Pronoun First-Person - Singular \| je \|
	\| PPER2S \| Personal Pronoun Second-Person - Singular \| tu \|
	\| PPER3MS \| Personal Pronoun Third-Person - Singular Masculine \| il \|
	\| PPER3MP \| Personal Pronoun Third-Person - Plural Masculine \| ils \|
	\| PPER3FS \| Personal Pronoun Third-Person - Singular Feminine \| elle \|
	\| PPER3FP \| Personal Pronoun Third-Person - Plural Feminine \| elles \|
	\| PREFS \| Reflexive Pronoun First-Person - Singular \| me m' \|
	\| PREF \| Reflexive Pronoun Third-Person - Singular \| se s' \|
	\| PREFP \| Reflexive Pronoun First / Second-Person - Plural \| nous vous \|
	\| VERB \| Verb \| obtient \|
	\| VPPMS \| Past Participle - Singular Masculine \| formulé \|
	\| VPPMP \| Past Participle - Plural Masculine \| classés \|
	\| VPPFS \| Past Participle - Singular Feminine \| appelée \|
	\| VPPFP \| Past Participle - Plural Feminine \| sanctionnées \|
	\| DET \| Determinant \| les l' \|
	\| DETMS \| Determinant - Singular Masculine \| les \|
	\| DETFS \| Determinant - Singular Feminine \| la \|
	\| ADJ \| Adjective \| capable sérieux \|
	\| ADJMS \| Adjective - Singular Masculine \| grand important \|
	\| ADJMP \| Adjective - Plural Masculine \| grands petits \|
	\| ADJFS \| Adjective - Singular Feminine \| française petite \|
	\| ADJFP \| Adjective - Plural Feminine \| légères petites \|
	\| NOUN \| Noun \| temps \|
	\| NMS \| Noun - Singular Masculine \| drapeau \|
	\| NMP \| Noun - Plural Masculine \| journalistes \|
	\| NFS \| Noun - Singular Feminine \| tête \|
	\| NFP \| Noun - Plural Feminine \| ondes \|
	\| PREL \| Relative Pronoun \| qui dont \|
	\| PRELMS \| Relative Pronoun - Singular Masculine \| lequel \|
	\| PRELMP \| Relative Pronoun - Plural Masculine \| lesquels \|
	\| PRELFS \| Relative Pronoun - Singular Feminine \| laquelle \|
	\| PRELFP \| Relative Pronoun - Plural Feminine \| lesquelles \|
	\| INTJ \| Interjection \| merci bref \|
	\| CHIF \| Numbers \| 1979 10 \|
	\| SYM \| Symbol \| € % \|
	\| YPFOR \| Endpoint \| . \|
	\| PUNCT \| Ponctuation \| : , \|
	\| MOTINC \| Unknown words \| Technology Lady \|
	\| X \| Typos & others \| sfeir 3D statu \|

	## Evaluation results

	The test corpora used for this evaluation is available on [Github](https://github.com/qanastek/ANTILLES/blob/main/ANTILLES/test.conllu).

	```plain
	precision recall f1-score support

	ADJ 0.9040 0.8828 0.8933 128
	ADJFP 0.9811 0.9585 0.9697 434
	ADJFS 0.9606 0.9826 0.9715 918
	ADJMP 0.9613 0.9357 0.9483 451
	ADJMS 0.9561 0.9611 0.9586 952
	ADV 0.9870 0.9948 0.9908 1524
	AUX 0.9956 0.9964 0.9960 1124
	CHIF 0.9798 0.9774 0.9786 1239
	COCO 1.0000 0.9989 0.9994 884
	COSUB 0.9939 0.9939 0.9939 328
	DET 0.9972 0.9972 0.9972 2897
	DETFS 0.9990 1.0000 0.9995 1007
	DETMS 1.0000 0.9993 0.9996 1426
	DINTFS 0.9967 0.9902 0.9934 306
	DINTMS 0.9923 0.9948 0.9935 387
	INTJ 0.8000 0.8000 0.8000 5
	MOTINC 0.5049 0.5827 0.5410 266
	NFP 0.9807 0.9675 0.9740 892
	NFS 0.9778 0.9699 0.9738 2588
	NMP 0.9687 0.9495 0.9590 1367
	NMS 0.9759 0.9560 0.9659 3181
	NOUN 0.6164 0.8673 0.7206 113
	NUM 0.6250 0.8333 0.7143 6
	PART 1.0000 0.9375 0.9677 16
	PDEMFP 1.0000 1.0000 1.0000 3
	PDEMFS 1.0000 1.0000 1.0000 89
	PDEMMP 1.0000 1.0000 1.0000 20
	PDEMMS 1.0000 1.0000 1.0000 222
	PINDFP 1.0000 1.0000 1.0000 3
	PINDFS 0.8571 1.0000 0.9231 12
	PINDMP 0.9000 1.0000 0.9474 9
	PINDMS 0.9286 0.9701 0.9489 67
	PINTFS 0.0000 0.0000 0.0000 2
	PPER1S 1.0000 1.0000 1.0000 62
	PPER2S 0.7500 1.0000 0.8571 3
	PPER3FP 1.0000 1.0000 1.0000 9
	PPER3FS 1.0000 1.0000 1.0000 96
	PPER3MP 1.0000 1.0000 1.0000 31
	PPER3MS 1.0000 1.0000 1.0000 377
	PPOBJFP 1.0000 0.7500 0.8571 4
	PPOBJFS 0.9167 0.8919 0.9041 37
	PPOBJMP 0.7500 0.7500 0.7500 12
	PPOBJMS 0.9371 0.9640 0.9504 139
	PREF 1.0000 1.0000 1.0000 332
	PREFP 1.0000 1.0000 1.0000 64
	PREFS 1.0000 1.0000 1.0000 13
	PREL 0.9964 0.9964 0.9964 277
	PRELFP 1.0000 1.0000 1.0000 5
	PRELFS 0.8000 1.0000 0.8889 4
	PRELMP 1.0000 1.0000 1.0000 3
	PRELMS 1.0000 1.0000 1.0000 11
	PREP 0.9971 0.9977 0.9974 6161
	PRON 0.9836 0.9836 0.9836 61
	PROPN 0.9468 0.9503 0.9486 4310
	PUNCT 1.0000 1.0000 1.0000 4019
	SYM 0.9394 0.8158 0.8732 76
	VERB 0.9956 0.9921 0.9938 2273
	VPPFP 0.9145 0.9469 0.9304 113
	VPPFS 0.9562 0.9597 0.9580 273
	VPPMP 0.8827 0.9728 0.9256 147
	VPPMS 0.9778 0.9794 0.9786 630
	VPPRE 0.0000 0.0000 0.0000 1
	X 0.9604 0.9935 0.9766 1073
	XFAMIL 0.9386 0.9113 0.9248 1342
	YPFOR 1.0000 1.0000 1.0000 2750

	accuracy 0.9778 47574
	macro avg 0.9151 0.9285 0.9202 47574
	weighted avg 0.9785 0.9778 0.9780 47574
	```

	## BibTeX Citations

	Please cite the following paper when using this model.

	ANTILLES corpus and POET taggers:

	```latex
	@inproceedings{labrak:hal-03696042,
	TITLE = {{ANTILLES: An Open French Linguistically Enriched Part-of-Speech Corpus}},
	AUTHOR = {Labrak, Yanis and Dufour, Richard},
	URL = {https://hal.archives-ouvertes.fr/hal-03696042},
	BOOKTITLE = {{25th International Conference on Text, Speech and Dialogue (TSD)}},
	ADDRESS = {Brno, Czech Republic},
	PUBLISHER = {{Springer}},
	YEAR = {2022},
	MONTH = Sep,
	KEYWORDS = {Part-of-speech corpus ; POS tagging ; Open tools ; Word embeddings ; Bi-LSTM ; CRF ; Transformers},
	PDF = {https://hal.archives-ouvertes.fr/hal-03696042/file/ANTILLES_A_freNch_linguisTIcaLLy_Enriched_part_of_Speech_corpus.pdf},
	HAL_ID = {hal-03696042},
	HAL_VERSION = {v1},
	}
	```

	UD_French-GSD corpora:

	```latex
	@misc{
	universaldependencies,
	title={UniversalDependencies/UD_French-GSD},
	url={https://github.com/UniversalDependencies/UD_French-GSD}, journal={GitHub},
	author={UniversalDependencies}
	}
	```

	LIA TAGG:

	```latex
	@techreport{LIA_TAGG,
	author = {Frédéric Béchet},
	title = {LIA_TAGG: a statistical POS tagger + syntactic bracketer},
	institution = {Aix-Marseille University & CNRS},
	year = {2001}
	}
	```

	Flair Embeddings:

	```latex
	@inproceedings{akbik2018coling,
	title={Contextual String Embeddings for Sequence Labeling},
	author={Akbik, Alan and Blythe, Duncan and Vollgraf, Roland},
	booktitle = {{COLING} 2018, 27th International Conference on Computational Linguistics},
	pages = {1638--1649},
	year = {2018}
	}
	```

	## Acknowledgment

	This work was financially supported by [Zenidoc](https://zenidoc.fr/) and the [ANR project DIETS](https://anr-diets.univ-avignon.fr) under the contract [ANR-20-CE23-0005](https://anr.fr/en/funded-projects-and-impact/funded-projects/project/funded/project/b2d9d3668f92a3b9fbbf7866072501ef-fd7e69d902/?tx_anrprojects_funded%5Bcontroller%5D=Funded&cHash=cb6d54d24c9e21e0d50fabf46bd56646).