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 xcixor/sentiment-swahili with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use xcixor/sentiment-swahili with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="xcixor/sentiment-swahili")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("xcixor/sentiment-swahili") model = AutoModelForSequenceClassification.from_pretrained("xcixor/sentiment-swahili") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3a420b4c08a3a8abc21e7f97379a584fa4309caacd0eacfe07d283e3ffeac66e
- Size of remote file:
- 3.64 kB
- SHA256:
- d0a703ae5fa8faeb9b4595394703a0f1fdd7a4f8d8436acf449c8032d939e519
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