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Modèle de détection de 4 sentiments avec FlauBERT (mixed, negative, objective, positive)

Comment l'utiliser ?

from transformers import AutoTokenizer, AutoModelForSequenceClassification
from transformers import pipeline

loaded_tokenizer = AutoTokenizer.from_pretrained('flaubert/flaubert_large_cased')
loaded_model = AutoModelForSequenceClassification.from_pretrained("DemangeJeremy/4-sentiments-with-flaubert")

nlp = pipeline('sentiment-analysis', model=loaded_model, tokenizer=loaded_tokenizer)

print(nlp("Je suis plutôt confiant."))
[{'label': 'OBJECTIVE', 'score': 0.3320835530757904}]

Résultats de l'évaluation du modèle

Epoch Validation Loss Samples Per Second
1 2.219246 49.476000
2 1.883753 47.259000
3 1.747969 44.957000
4 1.695606 43.872000
5 1.641470 45.726000

Citation

Pour toute utilisation de ce modèle, merci d'utiliser cette citation :

Jérémy Demange, Four sentiments with FlauBERT, (2021), Hugging Face repository, https://huggingface.co/DemangeJeremy/4-sentiments-with-flaubert

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