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