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 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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from xcixor/sentiment-swahili: direct link, hf CLI and curl.
- Browser
- Download file 2.92 MB
-
https://huggingface.co/xcixor/sentiment-swahili/resolve/main/tokenizer.json
- Command line
-
hf download hf://xcixor/sentiment-swahili/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/xcixor/sentiment-swahili/resolve/main/tokenizer.json
2.92 MB
File too large to display, you can check the raw version instead.