# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("nlptown/flaubert_small_cased_sentiment")
model = AutoModelForSequenceClassification.from_pretrained("nlptown/flaubert_small_cased_sentiment", device_map="auto")flaubert_small_cased_sentiment
This is a flaubert_small_cased model finetuned for sentiment analysis on product reviews in French. It predicts the sentiment of the review, from very_negative (1 star) to very_positive (5 stars).
This model is intended for direct use as a sentiment analysis model for French product reviews, or for further finetuning on related sentiment analysis tasks.
Training data
The training data consists of the French portion of amazon_reviews_multi, supplemented with another 140,000 similar reviews.
Accuracy
The finetuned model was evaluated on the French test set of amazon_reviews_multi.
- Accuracy (exact) is the exact match on the number of stars.
- Accuracy (off-by-1) is the percentage of reviews where the number of stars the model predicts differs by a maximum of 1 from the number given by the human reviewer.
| Language | Accuracy (exact) | Accuracy (off-by-1) |
|---|---|---|
| French | 61.56% | 95.66% |
Contact
NLP Town offers a suite of sentiment models for a wide range of languages, including an improved multilingual model through RapidAPI.
Feel free to contact us for questions, feedback and/or requests for similar models.
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="nlptown/flaubert_small_cased_sentiment")