metadata
tags:
- flair
- token-classification
- sequence-tagger-model
language: fr
widget:
- text: George Washington est allé à Washington
People Involved
- LABRAK Yanis (1)
- DUFOUR Richard (2)
Affiliations
- LIA, Avignon University, Avignon, France.
- LS2N, Nantes University, Nantes, France.
POET: A French Extended Part-of-Speech Tagger
- Corpus: UD_FRENCH_TREEBANKS
- Model: Flair
- Embeddings: FastText
- Additionnel: LSTM-CRF
- Nombre d'Epochs: 115
Demo: How to use in Flair
Requires Flair: pip install flair
from flair.data import Sentence
from flair.models import SequenceTagger
# Load the model
model = SequenceTagger.load("qanastek/pos-french")
sentence = Sentence("George Washington est allé à Washington")
# predict tags
model.predict(sentence)
# print predicted pos tags
print(sentence.to_tagged_string())
Output:
Training data
UD_FRENCH_GSD_Plus 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 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.
Original Tags
PRON VERB SCONJ ADP CCONJ DET NOUN ADJ AUX ADV PUNCT PROPN NUM SYM PART X INTJ
New 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 | Singular Masculine Demonstrative Pronoun | ce |
| PDEMMP | Plurial Masculine Demonstrative Pronoun | ceux |
| PDEMFS | Singular Feminine Demonstrative Pronoun | cette |
| PDEMFP | Plurial Feminine Demonstrative Pronoun | celles |
| PINDMS | Singular Masculine Indefinite Pronoun | tout |
| PINDMP | Plurial Masculine Indefinite Pronoun | autres |
| PINDFS | Singular Feminine Indefinite Pronoun | chacune |
| PINDFP | Plurial Feminine Indefinite Pronoun | certaines |
| PROPN | Proper noun | houston |
| XFAMIL | Last name | levy |
| NUM | Numerical Adjectives | trentaine vingtaine |
| DINTMS | Masculine Numerical Adjectives | un |
| DINTFS | Feminine Numerical Adjectives | une |
| PPOBJMS | Singular Masculine Pronoun complements of objects | le lui |
| PPOBJMP | Plurial Masculine Pronoun complements of objects | eux y |
| PPOBJFS | Singular Feminine Pronoun complements of objects | moi la |
| PPOBJFP | Plurial Feminine Pronoun complements of objects | en y |
| PPER1S | Personal Pronoun First Person Singular | je |
| PPER2S | Personal Pronoun Second Person Singular | tu |
| PPER3MS | Personal Pronoun Third Person Masculine Singular | il |
| PPER3MP | Personal Pronoun Third Person Masculine Plurial | ils |
| PPER3FS | Personal Pronoun Third Person Feminine Singular | elle |
| PPER3FP | Personal Pronoun Third Person Feminine Plurial | elles |
| PREFS | Reflexive Pronouns First Person of Singular | me m' |
| PREF | Reflexive Pronouns Third Person of Singular | se s' |
| PREFP | Reflexive Pronouns First / Second Person of Plurial | nous vous |
| VERB | Verb | obtient |
| VPPMS | Singular Masculine Participle Past Verb | formulé |
| VPPMP | Plurial Masculine Participle Past Verb | classés |
| VPPFS | Singular Feminine Participle Past Verb | appelée |
| VPPFP | Plurial Feminine Participle Past Verb | sanctionnées |
| DET | Determinant | les l' |
| DETMS | Singular Masculine Determinant | les |
| DETFS | Singular Feminine Determinant | la |
| ADJ | Adjective | capable sérieux |
| ADJMS | Singular Masculine Adjective | grand important |
| ADJMP | Plurial Masculine Adjective | grands petits |
| ADJFS | Singular Feminine Adjective | française petite |
| ADJFP | Plurial Feminine Adjective | légères petites |
| NOUN | Noun | temps |
| NMS | Singular Masculine Noun | drapeau |
| NMP | Plurial Masculine Noun | journalistes |
| NFS | Singular Feminine Noun | tête |
| NFP | Plurial Feminine Noun | ondes |
| PREL | Relative Pronoun | qui dont |
| PRELMS | Singular Masculine Relative Pronoun | lequel |
| PRELMP | Plurial Masculine Relative Pronoun | lesquels |
| PRELFS | Singular Feminine Relative Pronoun | laquelle |
| PRELFP | Plurial Feminine Relative Pronoun | 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
Results:
- F-score (micro): 0.952
- F-score (macro): 0.8644
- Accuracy (incl. no class): 0.952
By class:
precision recall f1-score support
PPER1S 0.9767 1.0000 0.9882 42
VERB 0.9823 0.9537 0.9678 583
COSUB 0.9344 0.8906 0.9120 128
PUNCT 0.9878 0.9688 0.9782 833
PREP 0.9767 0.9879 0.9822 1483
PDEMMS 0.9583 0.9200 0.9388 75
COCO 0.9839 1.0000 0.9919 245
DET 0.9679 0.9814 0.9746 645
NMP 0.9521 0.9115 0.9313 305
ADJMP 0.8352 0.9268 0.8786 82
PREL 0.9324 0.9857 0.9583 70
PREFP 0.9767 0.9545 0.9655 44
AUX 0.9537 0.9859 0.9695 355
ADV 0.9440 0.9365 0.9402 504
VPPMP 0.8667 1.0000 0.9286 26
DINTMS 0.9919 1.0000 0.9959 122
ADJMS 0.9020 0.9057 0.9039 244
NMS 0.9226 0.9336 0.9281 753
NFS 0.9347 0.9714 0.9527 560
YPFOR 0.9806 1.0000 0.9902 353
PINDMS 1.0000 0.9091 0.9524 44
NOUN 0.8400 0.5385 0.6562 39
PROPN 0.8605 0.8278 0.8439 395
DETMS 0.9972 0.9972 0.9972 362
PPER3MS 0.9341 0.9770 0.9551 87
VPPMS 0.8994 0.9682 0.9325 157
DETFS 1.0000 1.0000 1.0000 240
ADJFS 0.9266 0.9011 0.9136 182
ADJFP 0.9726 0.9342 0.9530 76
NFP 0.9463 0.9749 0.9604 199
VPPFS 0.8000 0.9000 0.8471 40
CHIF 0.9543 0.9414 0.9478 222
XFAMIL 0.9346 0.8696 0.9009 115
PPER3MP 0.9474 0.9000 0.9231 20
PPOBJMS 0.8800 0.9362 0.9072 47
PREF 0.8889 0.9231 0.9057 52
PPOBJMP 1.0000 0.6000 0.7500 10
SYM 0.9706 0.8684 0.9167 38
DINTFS 0.9683 1.0000 0.9839 61
PDEMFS 1.0000 0.8966 0.9455 29
PPER3FS 1.0000 0.9444 0.9714 18
VPPFP 0.9500 1.0000 0.9744 19
PRON 0.9200 0.7419 0.8214 31
PPOBJFS 0.8333 0.8333 0.8333 6
PART 0.8000 1.0000 0.8889 4
PPER3FP 1.0000 1.0000 1.0000 2
MOTINC 0.3571 0.3333 0.3448 15
PDEMMP 1.0000 0.6667 0.8000 3
INTJ 0.4000 0.6667 0.5000 6
PREFS 1.0000 0.5000 0.6667 10
ADJ 0.7917 0.8636 0.8261 22
PINDMP 0.0000 0.0000 0.0000 1
PINDFS 1.0000 1.0000 1.0000 1
NUM 1.0000 0.3333 0.5000 3
PPER2S 1.0000 1.0000 1.0000 2
PPOBJFP 1.0000 0.5000 0.6667 2
PDEMFP 1.0000 0.6667 0.8000 3
X 0.0000 0.0000 0.0000 1
PRELMS 1.0000 1.0000 1.0000 2
PINDFP 1.0000 1.0000 1.0000 1
accuracy 0.9520 10019
macro avg 0.8956 0.8521 0.8644 10019
weighted avg 0.9524 0.9520 0.9515 10019
BibTeX Citations
Please cite the following paper when using this model.
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}
}