frenchnlp 0.2.3

State of the art toolchain for natural language processing in French

French NLP Toolkit

State of the art toolkit for Natural Language Processing in French based on CamemBERT/FlauBERT.

Citation:

@misc{hadoop,
  author={Wang Xiaoou},
  title={frenchnlp: state of the art toolkit for Natural Language Processing in French based on CamemBERT/FlauBERT},
  year={2021},
  howpublished={\url{https://github.com/xiaoouwang/frenchnlp}},
}

Wang Xiaoou. (2021). frenchnlp: state of the art toolkit for Natural Language Processing in French based on CamemBERT/FlauBERT. https://github.com/xiaoouwang/frenchnlp.

• sentence similarity measure

  • For why average pooling/[cls] shouldn't be used to represent sentence, see

  Reimers, Nils, and Iryna Gurevych. “Sentence-BERT: Sentence Embeddings Using Siamese BERT-Networks.” ArXiv:1908.10084 [Cs], August 27, 2019. http://arxiv.org/abs/1908.10084.

  • For use of sentence similarity in real life, see

  Xiaoou Wang, Xingyu Liu, Yimei Yue. “Mesure de similarité textuelle pour l’évaluation automatique de copies d’étudiants.” TALN-RECITAL 2021. Download

• to do, text classification pipelines

How to use the package

from frenchnlp import *
from transformers import AutoTokenizer, AutoModel
import torch

Transformer-based sentence similarity measure (using CamemBERT as example)

Using the [cls] token

compare_compare_cls(model,tokenizer,sentence1,sentence2)

fr_tokenizer = AutoTokenizer.from_pretrained('camembert-base')
fr_model = AutoModel.from_pretrained('camembert-base')

sentences = [
    "J'aime les chats.",
    "Je déteste les chats.",
    "J'adore les chats."
]

for i in range(1,3):
    print(f"similarité sémantique entre\n{sentences[0]}\n{sentences[i]}")
    print(bert_compare_cls(fr_model,fr_tokenizer,sentences[0],sentences[i]))

Output:

similarité sémantique entre
J'aime les chats.
Je déteste les chats.
0.9145417
similarité sémantique entre
J'aime les chats.
J'adore les chats.
0.9809468

Average pooling

compare_bert_average(model,tokenizer,sent1,sent2)

fr_tokenizer = AutoTokenizer.from_pretrained('camembert-base')
fr_model = AutoModel.from_pretrained('camembert-base')

for i in range(1,3):
    print(f"similarité sémantique entre\n{sentences[0]}\n{sentences[i]}")
    print(compare_bert_average(fr_model,fr_tokenizer,sentences[0],sentences[i])

Output:

similarité sémantique entre
J'aime les chats.
Je déteste les chats.
0.9145417
similarité sémantique entre
J'aime les chats.
J'adore les chats.
0.9809468

Using multilingual sentence embeddings

See above for the reference on multilingual sentence embeddings.

compare_sent_transformer(model,sent1,sent2)

from sentence_transformers import SentenceTransformer

sent_model = SentenceTransformer('stsb-xlm-r-multilingual')

for i in range(1,3):
    print(f"similarité sémantique entre\n{sentences[0]}\n{sentences[i]}")
    print(compare_sent_transformer(sent_model,sentences[0],sentences[i])

Output:

similarité sémantique entre
J'aime les chats.
Je déteste les chats.
0.46124768
similarité sémantique entre
J'aime les chats.
J'adore les chats.
0.9557947

License

Codes

frenchnlp is licensed under Apache License 2.0. You can use frenchnlp in your commercial products for free. We would appreciate it if you add a link to frenchnlp on your website.

Models

Unless otherwise specified, all models in frenchnlp are licensed under CC BY-NC-SA 4.0.