Text Classification
Transformers
PyTorch
ONNX
Safetensors
distilbert
sentiment-analysis
sentiment
synthetic data
multi-class
social-media-analysis
customer-feedback
product-reviews
brand-monitoring
multilingual
๐ช๐บ
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
File size: 129 Bytes
9ce1333 | 1 2 3 4 | version https://git-lfs.github.com/spec/v1
oid sha256:d0a703ae5fa8faeb9b4595394703a0f1fdd7a4f8d8436acf449c8032d939e519
size 3643
|