readme: ---
  language: fr
  ---
  # Pytorch Fork of [tblard/tf-allocine](https://huggingface.co/tblard/tf-allocine)
  
  A french sentiment analysis model, based on [CamemBERT](https://camembert-model.fr/), and finetuned on a large-scale dataset scraped from [Allociné.fr](http://www.allocine.fr/) user reviews.
  ## Results
  | Validation Accuracy | Validation F1-Score | Test Accuracy | Test F1-Score |
  |--------------------:| -------------------:| -------------:|--------------:|
  |               97.39 |               97.36 |         97.44 |         97.34 |
  The dataset and the evaluation code are available on [this repo](https://github.com/TheophileBlard/french-sentiment-analysis-with-bert).
  ## Usage
  ```python
  from transformers import AutoTokenizer, TFAutoModelForSequenceClassification
  from transformers import pipeline
  tokenizer = AutoTokenizer.from_pretrained("tblard/tf-allocine")
  model = TFAutoModelForSequenceClassification.from_pretrained("tblard/tf-allocine")
  nlp = pipeline('sentiment-analysis', model=model, tokenizer=tokenizer)
  print(nlp("Alad'2 est clairement le meilleur film de l'année 2018.")) # POSITIVE
  print(nlp("Juste whoaaahouuu !")) # POSITIVE
  print(nlp("NUL...A...CHIER ! FIN DE TRANSMISSION.")) # NEGATIVE
  print(nlp("Je m'attendais à mieux de la part de Franck Dubosc !")) # NEGATIVE
  ```
  ## Author
  Théophile Blard – :email: theophile.blard@gmail.com
  If you use this work (code, model or dataset), please cite as:
  > Théophile Blard, French sentiment analysis with BERT, (2020), GitHub repository, <https://github.com/TheophileBlard/french-sentiment-analysis-with-bert>
  