Text Classification
Transformers
TensorBoard
Safetensors
xlm-roberta
sentiment
multilingual
modernbert
sentiment-analysis
product-reviews
place-reviews
text-embeddings-inference
Instructions to use clapAI/roberta-base-multilingual-sentiment with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use clapAI/roberta-base-multilingual-sentiment with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="clapAI/roberta-base-multilingual-sentiment")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("clapAI/roberta-base-multilingual-sentiment") model = AutoModelForSequenceClassification.from_pretrained("clapAI/roberta-base-multilingual-sentiment", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b4a3e55171ea8136fb83352fcfd3e0b80a3efffbd9b0adf17b7842c5eb9705f5
- Size of remote file:
- 6.9 kB
- SHA256:
- 536ce93c201df248528d59f8c971cae23d68a406f45d52f62ee954992c11c233
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