qanastek/pos-french-camembert
POET: A French Extended Part-of-Speech Tagger
People Involved
- LABRAK Yanis (1)
- DUFOUR Richard (2)
Affiliations
- LIA, NLP team, Avignon University, Avignon, France.
- LS2N, TALN team, Nantes University, Nantes, France.
Demo: How to use in HuggingFace Transformers
Requires transformers: ``pip install transformers``
from transformers import CamembertTokenizer, CamembertForTokenClassification, TokenClassificationPipeline
tokenizer = CamembertTokenizer.from_pretrained('qanastek/pos-french-camembert')
model = CamembertForTokenClassification.from_pretrained('qanastek/pos-french-camembert')
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 INTJNew additional POS tags
Evaluation results
The test corpora used for this evaluation is available on Github.
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 47574BibTeX Citations
Please cite the following paper when using this model.
ANTILLES corpus and POET taggers:
@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:
@misc{
universaldependencies,
title={UniversalDependencies/UD_French-GSD},
url={https://github.com/UniversalDependencies/UD_French-GSD}, journal={GitHub},
author={UniversalDependencies}
}LIA TAGG:
@techreport{LIA_TAGG,
author = {Frédéric Béchet},
title = {LIA_TAGG: a statistical POS tagger + syntactic bracketer},
institution = {Aix-Marseille University & CNRS},
year = {2001}
}Flair Embeddings:
@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