Text Classification
Transformers
TensorBoard
Safetensors
xlm-roberta
sentiment
multilingual
modernbert
sentiment-analysis
product-reviews
place-reviews
text-embeddings-inference
Instructions to use clapAI/roberta-large-multilingual-sentiment with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use clapAI/roberta-large-multilingual-sentiment with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="clapAI/roberta-large-multilingual-sentiment", device_map="auto")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("clapAI/roberta-large-multilingual-sentiment") model = AutoModelForSequenceClassification.from_pretrained("clapAI/roberta-large-multilingual-sentiment", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- dd8a6f763f36737c8a69b9724f64f45e1769cb1e5a41c37ca304fad510513304
- Size of remote file:
- 6.9 kB
- SHA256:
- 1d078669441e57a7cfe65c832f0b18659b83c1e6b580cc43eba28c2b22a4ea6b
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