readme: ---
  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('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:
  
  ![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 F