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
Training data
#2
by sparkdoc - opened
I am interested in benchmarking sentiment models, so need to know what data was used to finetune each model. I see that you used 150k reviews for finetuning in English, but it doesn't state if these reviews are taken from common benchmark sets like Amazon or Yelp.