Feature Extraction
Transformers
PyTorch
Safetensors
xlm-roberta
sentiment-analysis
text-classification
generic
sentiment-classification
multilingual
text-embeddings-inference
Instructions to use numind/NuSentiment-multilingual with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use numind/NuSentiment-multilingual with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="numind/NuSentiment-multilingual")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("numind/NuSentiment-multilingual") model = AutoModel.from_pretrained("numind/NuSentiment-multilingual") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 807de7d637fb334a1b854d9fdf242575ba4d633924fefbd1d0126cf498c37acf
- Size of remote file:
- 1.11 GB
- SHA256:
- e17ee92e4bed233ff01e5bc6421f08d0bdf11d357542722b96b2cddfc90454dc
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