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pt-tblard-tf-allocine
philschmid/pt-tblard-tf-allocine
pt-tblard-tf-allocine is a text classification model from philschmid. Use it when you need a label for a piece of text. It is set up for transformers.
A french sentiment analysis model, based on CamemBERT, and finetuned on a large-scale dataset scraped from Allociné.fr user reviews. The dataset and the evaluation code are available on this repo.
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At a glance
- Task
- Text Classification
- Library
- transformers
- Model type
- camembert
- Access
- Public
- Created
- Mar 2, 2022
- Updated
- Oct 27, 2021
- SHA
- 842f2b47
Try a prompt
Model card
From the Hugging Face model README
Pytorch Fork of tblard/tf-allocine
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: [email protected] 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
- Task
- Text Classification
- Library
- transformers
- Type
- camembert
- Languages
- fr
- Created
- Mar 2, 2022
- Updated
- Oct 27, 2021