Instructions to use Edwintos/XLM-R_Base_Swahili_Topic_Classification_Model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Edwintos/XLM-R_Base_Swahili_Topic_Classification_Model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Edwintos/XLM-R_Base_Swahili_Topic_Classification_Model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Edwintos/XLM-R_Base_Swahili_Topic_Classification_Model") model = AutoModelForSequenceClassification.from_pretrained("Edwintos/XLM-R_Base_Swahili_Topic_Classification_Model") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 61e4ab0f099419cf3cef14b2f01c36dca4496fa80a01259f700d136259a5fdd4
- Size of remote file:
- 1.11 GB
- SHA256:
- b653764dff89ab37f2b818f59fe6a8421f20773e299eafff5d20999864044a32
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