---
language: fr
license: mit
datasets:
- Sequoia
widget:
- text: Aucun financement politique occulte n'a pu être mis en évidence.
- text: L'excrétion de l'acide zolédronique dans le lait maternel n'est pas connue.
pipeline_tag: token-classification
tags:
- mwe
---

# Multiword expressions recognition.  

A multiword expression (MWE) is a combination of words which exhibits lexical, morphosyntactic, semantic, pragmatic and/or statistical idiosyncrasies (Baldwin and Kim, 2010). The objective of Multiword Expression Recognition (MWER) is to automate the identification of these MWEs.

## Model description

`camembert-mwer` is a model that was fine-tuned from [CamemBERT](https://huggingface.co/camembert/camembert-large) as a token classification task specifically on the [Sequoia](http://deep-sequoia.inria.fr/) dataset for the MWER task.

## How to use

You can use this model directly with a pipeline for token classification:

```python
>>> from transformers import AutoTokenizer, AutoModelForTokenClassification, pipeline
>>> tokenizer = AutoTokenizer.from_pretrained("bvantuan/camembert-mwer")
>>> model = AutoModelForTokenClassification.from_pretrained("bvantuan/camembert-mwer")
>>> mwe_classifier = pipeline('token-classification', model=model, tokenizer=tokenizer)
>>> sentence = "Pour ce premier rendez-vous, l'animateur a pu faire partager sa passion et présenter quelques oeuvres pour mettre en bouche les participants."
>>> mwes = mwe_classifier(sentence)

[{'entity': 'B-MWE',
  'score': 0.99492574,
  'index': 4,
  'word': '▁rendez',
  'start': 15,
  'end': 22},
 {'entity': 'I-MWE',
  'score': 0.9344883,
  'index': 5,
  'word': '-',
  'start': 22,
  'end': 23},
 {'entity': 'I-MWE',
  'score': 0.99398583,
  'index': 6,
  'word': 'vous',
  'start': 23,
  'end': 27},
 {'entity': 'B-VID',
  'score': 0.9827843,
  'index': 22,
  'word': '▁mettre',
  'start': 106,
  'end': 113},
 {'entity': 'I-VID',
  'score': 0.9835186,
  'index': 23,
  'word': '▁en',
  'start': 113,
  'end': 116},
 {'entity': 'I-VID',
  'score': 0.98324823,
  'index': 24,
  'word': '▁bouche',
  'start': 116,
  'end': 123}]

>>> mwe_classifier.group_entities(mwes)

[{'entity_group': 'MWE',
  'score': 0.9744666,
  'word': 'rendez-vous',
  'start': 15,
  'end': 27},
 {'entity_group': 'VID',
  'score': 0.9831837,
  'word': 'mettre en bouche',
  'start': 106,
  'end': 123}]
```

## Training data

The Sequoia dataset is divided into train/dev/test sets:

|               | Sequoia     | train       | dev           | test        |
| :----:        | :---:       |    :----:   |  :---:        | :----:      |
| #sentences    | 3099        | 1955        | 273           |     871        |
| #MWEs         | 3450        | 2170        | 306           |     974        |
| #Unseen MWEs  | _           | _           | 100           |      300       |

This dataset has 6 distinct categories:
* MWE: Non-verbal MWEs (e.g. **à peu près**)
* IRV: Inherently reflexive verb (e.g. **s'occuper**)
* LVC.cause: Causative light-verb construction (e.g. **causer** le **bouleversement**)
* LVC.full: Light-verb construction (e.g. **avoir pour but** de )
* MVC: Multi-verb construction (e.g. **faire remarquer**)
* VID: Verbal idiom (e.g. **voir le jour**)

## Training procedure

### Preprocessing

The employed sequential labeling scheme for this task is the Inside–outside–beginning (IOB2) methodology. 

### Pretraining

The model was trained on train+dev sets with learning rate $3 × 10^{-5}$, batch size 10 and over the course of  15 epochs.

### Evaluation results

On the test set, this model achieves the following results:

<table>
  <tr>
    <td colspan="3">Global MWE-based</td>
    <td colspan="3">Unseen MWE-based</td>
  </tr>
  <tr>
    <td>Precision</td><td>Recall</td><td>F1</td>
    <td>Precision</td><td>Recall</td><td>F1</td>
  </tr>
  <tr>
    <td>83.78</td><td>83.78</td><td>83.78</td>
    <td>57.05</td><td>60.67</td><td>58.80</td>
  </tr>
</table>

### BibTeX entry and citation info

```bibtex
@article{martin2019camembert,
  title={CamemBERT: a tasty French language model},
  author={Martin, Louis and Muller, Benjamin and Su{\'a}rez, Pedro Javier Ortiz and Dupont, Yoann and Romary, Laurent and de La Clergerie, {\'E}ric Villemonte and Seddah, Djam{\'e} and Sagot, Beno{\^\i}t},
  journal={arXiv preprint arXiv:1911.03894},
  year={2019}
}

@article{candito2020french,
  title={A French corpus annotated for multiword expressions and named entities},
  author={Candito, Marie and Constant, Mathieu and Ramisch, Carlos and Savary, Agata and Guillaume, Bruno and Parmentier, Yannick and Cordeiro, Silvio Ricardo},
  journal={Journal of Language Modelling},
  volume={8},
  number={2},
  year={2020},
  publisher={Polska Akademia Nauk. Instytut Podstaw Informatyki PAN}
}

```