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tblard/tf-allocine tblard/tf-allocine
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pytorch

tf

Contributed by

tblard Théophile Blard
1 model

How to use this model directly from the 🤗/transformers library:

			
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from transformers import AutoTokenizer, TFAutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("tblard/tf-allocine") model = TFAutoModelForSequenceClassification.from_pretrained("tblard/tf-allocine")

tf-allociné

A french sentiment analysis model, based on CamemBERT, and finetuned on a large-scale dataset scraped from Allociné.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.

Usage

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