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:

Training data

ANTILLES is a part-of-speech tagging corpora based on UD_French-GSD which was originally created in 2015 and is based on the universal dependency treebank v2.0.

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 statistical POS tagger written by Frédéric Béchet in 2001.

The corpora used for this model is available on Github at the CoNLL-U format.

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

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.

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 and the ANR project DIETS under the contract ANR-20-CE23-0005.