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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