Text Classification
Transformers
PyTorch
ONNX
Safetensors
distilbert
sentiment-analysis
sentiment
synthetic data
multi-class
social-media-analysis
customer-feedback
product-reviews
brand-monitoring
multilingual
🇪🇺
region:eu
text-embeddings-inference
Instructions to use oxygeneDev/sentiment-multilingual with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use oxygeneDev/sentiment-multilingual with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="oxygeneDev/sentiment-multilingual")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("oxygeneDev/sentiment-multilingual") model = AutoModelForSequenceClassification.from_pretrained("oxygeneDev/sentiment-multilingual") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e0ce3fb8c9e3dbb6868a12ebbdd32492d674fbebd59a3cfdbb758d9cbb86863c
- Size of remote file:
- 541 MB
- SHA256:
- ff7b7323d62a0097d99b13b151fce7bf799006e1e911d813e747d4955eed0df5
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