Instructions to use nlptown/bert-base-multilingual-uncased-sentiment with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nlptown/bert-base-multilingual-uncased-sentiment with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="nlptown/bert-base-multilingual-uncased-sentiment")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("nlptown/bert-base-multilingual-uncased-sentiment") model = AutoModelForSequenceClassification.from_pretrained("nlptown/bert-base-multilingual-uncased-sentiment") - Inference
- Notebooks
- Google Colab
- Kaggle
Label and Score Detailed Information
#20
by JesusChristLovesYou - opened
Hello nlptown!
I would like to have more information about what the 'label' and 'score' more significantly mean in the context of the model.
Is label a percentage of positiveness based on the score between 1 - 5?
Can you give me more info about that, please?